Dynamic Changes in Semantic Memory Structure Supports Successful Problem Solving

Each night, we cross a bridge that connects the waking and sleeping worlds. We know very little about this bridge (symbolizing the sleep-onset period), as our passage is brief and leaves only a few fragmented memories behind. Moreover, sleep researchers have largely overlooked this twilight period, certainly owing to its 'in-between' and eeting nature. However, upon closer examination, the sleep-onset period appears as a rich and dynamic time during which our body and mind undergo signi cant changes. Brain activity slows, muscles relax, and reality gradually distorts: dreamlike images begin to dance before the eyelids. In contrast to the research community, many scientists and artists, such as Thomas Edison and Salvador Dali, were fascinated by this rich period, seeing in it great potential, particularly for increasing their creativity. They even devised methods for capturing creative inspirations from this 'genius gap' before they vanished into the limbo of sleep. They would take naps while holding an object that dropped noisily as they dozed o , awakening them just in time to record some of their discoveries/ideas. Is there any truth in this seductive story? In other words, is the sleep-onset period conducive to creativity?

Résumé

Chaque nuit, nous empruntons un pont qui nous emmène du monde éveillé au monde du sommeil. De ce pont (symbolisant la phase d'endormissement), nous ne savons pas grand-chose, et pour cause. Sa traversée est furtive, et ne laisse que peu de souvenirs. De plus, les chercheurs du sommeil se sont désintéressés de cet état à la frontière entre éveil et sommeil, sans doute en raison de sa nature hybride et insaisissable. Pourtant, à y regarder de plus près, la phase d'endormissement apparaît comme une période riche et dynamique au cours de laquelle notre organisme subit d'importants changements. L'activité cérébrale ralentit, les muscles se détendent, et la réalité commence progressivement à se déformer, laissant alors place à des images oniriques qui se mettent à danser derrière les paupières. D'ailleurs, contrairement aux chercheurs, de nombreux scienti ques et artistes, parmi lesquels Thomas Edison et Salvador Dali, étaient fascinés par cette période en laquelle ils voyaient une réelle source d'inspiration. Ces derniers ont même imaginé une méthode pour capturer cet état intermédiaire et ses inspirations créatives avant qu'elles ne disparaissent dans les limbes du sommeil. Pour ce faire, ils réalisaient des siestes en tenant un objet dans leur main a n que ce dernier tombe bruyamment à l'endormissement, les réveillant ainsi à temps pour noter leurs idées/découvertes. Y a-t-il un semblant de vérité dans cette belle histoire ? Autrement dit, la phase d'endormissement est-elle propice à la créativité ? Cette question a constitué le l conducteur de cette thèse. Notre hypothèse principale était que les états hybrides, à la frontière entre éveil et sommeil, favoriseraient la créativité. Nous avons testé cette hypothèse en examinant à la fois un état physiologique dans lequel le sommeil et l'éveil co-existent (la période d'endormissement, plus précisément le premier stade du sommeil, nommé N1), et un trouble du sommeil, la narcolepsie, dans lequel la frontière entre les deux états est encore plus ne qu'à l'accoutumée.

Nous avons tout d'abord démontré l'existence d'une créativité plus élevée chez les patients narcoleptiques, suggérant un lien (indirect) entre un accès privilégié à la phase d'endormissement (causé par la somnolence diurne excessive des narcoleptiques) et le développement d'une créativité accrue au l du temps. Par la suite, nous avons trouvé un lien direct entre le stade N1 et la créativité, puisqu'une seule minute de N1 su sait à tripler la probabilité de découvrir un raccourci caché pour résoudre une tâche par vi

This question will serve as the central theme of this thesis. Our main hypothesis was that hybrid states, at the borderland between wakefulness and sleep, would promote creativity. We tested this hypothesis by examining both a physiological state in which sleep and wakefulness coexist (the sleep-onset period, speci cally the rst sleep stage, named N1) and a sleep disorder, narcolepsy, in which the line between the two vigilance states is even ner than usual.

We rst demonstrated an increased creative potential in patients with narcolepsy, suggesting an (indirect) link between a privileged access to the sleep-onset period (caused by excessive daytime sleepiness) and the gradual development of creativity over time. Second, we found a direct link between the N1 stage and creativity, given that a single minute of N1 was su cient to triple the probability of discovering a hidden shortcut to solve a task compared to a period of wakefulness. Additionally, this bene cial e ect of the N1 stage disappeared when the subjects reached a deeper sleep (N2). We substantiated these results using spectral analyses and discovered an optimal cocktail for creativity (above and beyond sleep stages), consisting of an intermediate level of alpha (a marker of the wake-to-sleep transition) and a low level of delta (which signs sleep depth). We thus unraveled the existence of a 'creative sweet spot' within the sleep-onset period. Hitting this zone requires striking a balance between falling asleep easily and sleeping too deeply. Finally, we investigated the relationship between memory and creativity during sleep onset, using a newly-designed task that allowed us to evaluate these two cognitive functions within a single experimental design. Regrettably, the creative task was too di cult (not enough solvers) to assess the link between memory and creative problem-solving. However, we found that subjects who slept exclusively in N1 exhibited a 10% forgetting of previously encoded individual memory traces, whereas subjects who transitioned to the N2 stage showed less forgetting. Intriguingly, these last two studies both show distinct behavioral e ects between two seemingly close sleep stages (N1 and N2). These parallel ndings may suggest a link between memory processing (and possibly the pruning of irrelevant information) and the N1-induced boost in creativity. But more importantly, they emphasize the importance of distinguishing the N1 and N2 stages in future research, as they appear to have distinct e ects on cognition.

Overall, our ndings indicate that critical cognitive processes occur during sleep onset. Notably, we found that this period constitutes a doorway into creativity, which neuroscientists could target in the future to elucidate the mechanisms underlying creativity. Additionally, it o ers a great opportunity for the general public to summon their creative muse at will (for example, by using Edison's technique during recreational naps). We hope that this thesis will help rehabilitating the sleep-onset period, which has been the 'Great Forgotten' in the history of sleep research. vii rapport à une période d'éveil. De plus, cet e et béné que du stade N1 disparaissait lorsque les sujets atteignaient un sommeil plus profond (N2). Nous avons con rmé ces résultats par des analyses spectrales et découvert un 'cocktail' idéal pour la créativité (audelà des stades de sommeil), composé d'un niveau intermédiaire d'alpha (un marqueur de la transition veille-sommeil) et d'un niveau faible de delta (qui signe la profondeur du sommeil). Nous avons ainsi révélé l'existence d'une zone propice à la créativité au sein de la période d'endormissement. L'atteindre nécessite de réussir à s'endormir aisément, mais pas trop profondément. En n, nous avons essayé d'étudier le lien théorique entre retraitement des souvenirs et créativité, en utilisant une nouvelle tâche conçue pour pouvoir évaluer ces deux fonctions cognitives au sein d'un même protocole expérimental. Malheureusement, la tâche créative s'est révélée être trop di cile (pas assez de solveurs) pour examiner le devenir de traces mnésiques suite à la résolution de problèmes. Néanmoins, nous avons constaté que les sujets qui avaient dormi uniquement en N1 présentaient un taux d'oubli de 10% des traces mnésiques préalablement encodées, alors que les sujets qui étaient passés en N2 montraient moins d'oubli. Étonnamment, ces deux dernières études mettent en lumière l'existence d'e ets comportementaux distincts entre deux stades de sommeil qui semblent pourtant proches (N1 et N2). Ce parallèle suggère un lien entre le retraitement des souvenirs (et potentiellement la suppression des informations non-pertinentes) et l'augmentation de la créativité associée au N1. En outre, ces résultats soulignent la nécessité de di érencier ces deux stades de sommeil à l'avenir, car le N1 et le N2 semblent a ecter la cognition di éremment. Dans l'ensemble, nos résultats montrent que d'importants processus cognitifs ont lieu lors de la phase d'endormissement. Nous avons notamment découvert que cette période constitue une porte d'entrée vers la créativité, que les neuroscienti ques pourraient cibler dans le futur pour élucider les mécanismes sous-tendant la créativité. De plus, cette période facilement accessible à tous o re l'opportunité unique de convoquer notre muse créative au moment désiré (par exemple, en utilisant la technique d'Edison lors de siestes récréatives). Nous espérons que cette thèse contribuera à réhabiliter cet état à la frontière entre éveil et sommeil, qui fut jusqu'à présent, le grand oublié dans l'histoire de la recherche sur le sommeil.

Il y a Delphine bien évidement dont j'ai déjà parlé, mais aussi mon mari (oui, mon mari est aussi mon oncle...) : Jean-Baptiste, également connu sous le nom de Cassandre.

Cher mari, je te remercie pour les shows que tu nous as o erts tous les midis. Grâce à toi, j'ai eu un bel aperçu des consultations, de la vie de médecin, des di érents troubles psychiatriques et de comment les déceler. Tu as clairement un talent de one-man show, et j'ai hâte de venir te voir à ton premier spectacle ! Merci également d'avoir partagé ta culture G, ta passion bayésienne et dunienne, et merci d'être la mémoire de ce service (grâce à toi, même pas besoin de prendre de notes en congrès !). J'espère qu'on continuera cette grande aventure ensemble, en post-doc ! Et comme si une famille ne su sait pas, j'en ai trouvé une deuxième en la personne de Marie-Amélie et de sa petite famille, Ruben et Victor, que j'apprécie beaucoup tous les trois. Merci de m'avoir montré qu'une belle famille pouvait exister et vivre à la manière de Mr. Bojangles (sans les problèmes psychiatriques et la n tragique). Merci Marie-Amélie pour ton amitié qui m'est très précieuse, ton soutien sans faille. Merci également pour 'Madvouspropose' qui m'a permis de découvrir beaucoup de pépites et de pro ter pleinement de tout ce que cette magni que ville de Paris a à nous o rir.

En me mettant à la place des lecteurs ici, je me dis qu'une petite clari cation est nécessaire. Que mes parents soient rassurés, ils n'ont pas raté mon mariage ! Que mes lecteurs soient rassurés : je ne suis pas totalement dépravée ! Tout ceci n'est que blagues privées. Mes lecteurs rassurés, je peux désormais continuer ces remerciements à rallonge. Merci à Matthieu qui m'a beaucoup transmis tant d'un point de vue humain que professionnel. Grâce à lui, j'ai passé un super stage de M2 qui m'a permis de réaliser par la suite cette thèse. Merci pour les moments de grande poésie, de danse, de fête, de chaleur humaine. Merci pour toutes les discussions e ervescentes, pour tes fulgurances, pour les petit-déj avec viennoiseries à 5h du mat', ou encore pour les moments improbables passés à trimballer un lit dans tout Paris ou à s'endormir à la Bellevilloise. Et surtout merci pour tes belles qualités humaines ; grâce à toi, ce stage de M2 fut une grande et belle aventure. Merci à ton mentor, Thomas, dont j'admire le parcours et l'intelligence et qui m'a bien aidée sur deux des projets de cette thèse. Nos chemins se sont légèrement croisés mais j'espère grandement que tu formeras une belle équipe de recherche avec Delphine et qu'on pourra de nouveau travailler ensemble. Merci à Pauline (Popo pour les intimes) qui fut aussi un peu une deuxième maman pour moi : tu passais ton temps à me dire que je devais dormir plus, calmer mon hyperactivité, et prendre davantage soin de moi. Merci au 'Dr. Dudoignon' pour. . . le café ! Non, sérieusement, merci pour ta grande gentillesse, il est rare de trouver quelqu'un qui soit autant à l'écoute des autres et si observateur au point de connaître tous les détails de la vie de chaque personne du labo, c'est impressionnant ! Merci Stéphane (et aussi Benjamin) d'avoir maintenu un degré des plus importants de caféine dans mon sang. Merci aussi de m'avoir jeté des boulettes de papier chaque midi ; grâce à toi, je me sentais mature ! Merci Saba d'avoir toujours été là pour discuter, pour le soutien et la gentillesse. Merci Marie pour le fun et tes instincts maternels. Merci Emma d'avoir commencé cette grande aventure avec moi, c'est toujours plus facile de le faire accompagnée. Merci Basak de m'avoir rejointe sur le projet Memodream : Yes, we conn ! Je remercie également tous les M2 qui ont e ectué leur stage/début de doctorat pendant la durée de ma thèse, j'ai même eu la joie d'en encadrer certains : Alexandrine, Arthur, Aurélien, Céleste, Clothilde, Estefania, Lucy, Nicolas, Pierre, Sony et Yannis.

Merci pour votre aide des plus précieuses à la passation des expériences, la fameuse 'armée de passation'. Grâce à vous, il y a toujours eu une belle ambiance dans le bureau (parfois même trop quand il s'agissait de réussir à bosser !). Vous êtes géniaux, pro tez bien de la suite de vos études. Une mention spéciale également pour Lucy Doherty grâce à laquelle cette thèse est enjolivée par de très belles illustrations. Un grand avenir dans le graphisme t'attend, c'est certain ! En n, je ne peux pas mentionner chaque personne de ce service qui ne cesse d'ailleurs de se renouveler, mais je n'oublie personne. Merci à tous les médecins que je n'ai pas cités plus haut et qui ont tous été adorables avec moi, à tous les internes, attachés, et un grand merci à l'ensemble du personnel soignant qui a essayé de comprendre ce que je faisais alors même que j'empiétais sur leurs plates-bandes. Je les remercie aussi pour leur humanité, j'ai été à leurs côtés lors de la pandémie de la Covid-19 et j'ai pu observer aux premières loges leur dévouement sans faille pour les patients. Chapeau bas. Merci à la mascotte du laboratoire, Serendipity, qui nous porte chance. Merci à tous les participants des di érentes études sans lesquels la recherche ne pourrait avancer. Je remercie également en particulier un de mes professeurs de faculté, M. Keith Dudley, qui a beaucoup cru en moi et m'a poussée à faire les meilleures études possibles, études qui m'ont permises d'en arriver là où je suis aujourd'hui. Merci également à Adam Horowitz et Fanny Déchanet-Platz dont le mémoire de master et le livre L'écrivain, le sommeil, et les rêves ont constitué une bonne source de citations.

Ces dernières, en seulement deux phrases, décrivent avec talent et concision ce que j'aurais mis un paragraphe à écrire, sans grande fulgurance.

En n, cette thèse ne pourrait avoir lieu sans l'acceptation des membres du jury d'y participer et de l'évaluer. Je remercie donc grandement tous les membres du jury, à savoir Joseph De Koninck, Stéphanie Mazza, Mélanie Strauss, Raphaël Vallat et Emmanuelle Volle. Vous constituez la quasi-intégralité du lectorat de cette thèse ; grâce à vous, le temps passé à l'écrire avait donc un sens. Encore un grand merci, c'est un véritable honneur d'avoir un jury d'une telle qualité.

Hors travail, j'ai aussi pu avoir une vie épanouie, chose importante pour venir à bout d'une thèse. Je tiens donc également à remercier mes amis hors boulot, je ne peux malheureusement pas leur dédier à chacun un petit mot, car j'ai déjà écrit des remerciements qui sont très longs. Mais je vais les citer et j'espère qu'ils sauront reconnaître ce qu'ils m'ont apporté de précieux. Les nominés sont : Alfredo, Anthony, Bianca, Charlotte J., Charlotte P., Chloé V., Chloé D., Delphine G., Fabrice, Guilherme, Lucia, Pitchak, Ruy, et Virginie. La grande majorité d'entre eux ne fait pas partie du même domaine professionnel que moi. Pourtant, ils ont toujours montré un réel intérêt pour ce que je faisais, m'ont beaucoup soutenue et m'ont permis de vivre de beaux moments en dehors du travail. Et même si je ne les vois malheureusement plus, je n'oublie pas non plus mes amis de Londres (Ben, Celia, Ezra, Sam, Sharim, Vivien) qui furent importants pour mon parcours. Je tiens également à remercier une personne que je ne connais malheureusement pas personnellement, mais qui m'accompagne, m'o re de beaux moments de parenthèse et dont l'existence a souvent été d'un grand réconfort pour moi, merci Eva Bester.

En n, je terminerais en remerciant toute ma famille sans laquelle rien de tout cela ne serait possible. Je remercie tout particulièrement ma tante, Valérie, pour laquelle j'ai une grande a ection et admiration. Elle eut également son rôle à jouer dans cette thèse : non seulement elle m'a logée, nourrie, blanchie lors des concours d'entrée à l'école doctorale mais elle fut également là pour moi au début de ma thèse alors que j'étais relativement perdue dans Paris, ne connaissant pas grand monde. Elle m'a également o ert grand nombre de moments d'évasion en famille qui sont indispensables pour maintenir un certain équilibre mental. Adolescente, je la voyais (et la vois toujours) comme un modèle à atteindre, et j'imagine que je lui dois en partie mon envie de 'monter à la capitale'.

Je remercie également mon frère, Maxime (également connu sous le nom de pêcheur fou), qui me fait beaucoup rire et qui, malgré nos vies ô combien di érentes, a toujours suivi de près ce que je faisais et a toujours fait preuve d'une grande erté pour sa petite soeur. Un immense merci également à mon grand frère du CLAN qui m'a aiguillée sur le chemin de la recherche, qui a toujours été là pour moi (toujours. . . ) et dont le soutien et la présence sont des plus précieux pour moi. En remerciement, tu trouveras, dans ce manuscrit, quelques hommages colorimétriques et littéraires.

En n, je remercie mes parents qui sont formidables. Ils ont façonné la personne qui écrit les dernières lignes de cette thèse. Papa, tu m'as transmis le sens des responsabilités, le sérieux, l'honneur, 'l'esprit sain dans un corps sain'. Maman, tu m'as appris à être une femme libre et indépendante, à cultiver ma curiosité, et à m'o rir quelques instants de répits essentiels pour venir à bout d'une thèse. Je ne compte plus le nombre de fois où tu m'as dit : 'Allez viens, on va se balader, tu travailleras après'. Il me semble que les deux combinés forment un bon combo de sérieux et de fantaisie pour venir à bout d'une thèse. Je dois également vous remercier tous les deux de m'avoir toujours soutenue dans ce que je voulais faire, et ce même lorsque je réalisais un petit revirement et décidais au dernier moment de ne pas entrer en médecine mais plutôt d'aller à la fac, avec cet objectif de faire de la recherche sur le sommeil. J'y suis désormais, et c'est en très grande partie grâce à vous ; grâce à votre con ance, à votre aide, à votre soutien et à votre amour. Mes remerciements ne sont que décuplés étant donné le contexte sanitaire di cile dans lequel s'est terminée cette thèse. Grâce à vous, j'ai trouvé un cocon dans lequel me réfugier pour travailler lors de ces multiples con nements et périodes de télétravail. Merci in niment pour tout. J'ai ni d'énumérer ces facteurs qui faisaient que cette thèse ne pouvait que bien se passer : une belle supervision, un environnement de travail enrichissant sur un sujet passionnant, de précieux amis, une magni que famille et le tout dans une ville que j'aime énormément. Je pouvais di cilement trouver mieux. Je termine d'ailleurs ces 4 années avec un petit (grand) pincement au coeur mais comme le dirait Delphine : 'Il faut bien couper le cordon ombilical'. Je me conforte tout de même en me disant que j'ai eu la chance de vivre cette grande aventure et j'espère pouvoir tous vous retrouver très vite. L'assoupissement du corps est-il un réveil de facultés inconnues, et nous met-il en relation avec des êtres doués de ces facultés, lesquels ne sont point perceptibles à notre organisme quand la bête le complique, c'est-à-dire quand nous sommes debout, allant et venant en pleine vie terrestre? Les phénomènes du sommeil mettent-ils la partie invisible de l'homme en communication avec la partie invisible de la nature?

List of Abbreviations

Victor Hugo

Part I GENERAL INTRODUCTION

If sleep does not serve an absolutely vital function, then it is the biggest mistake the evolutionary process has ever made.

-Allan Rechtscha en 1

The science of sleep Sleep appears to be a mystery from an evolutionary perspective. Indeed, someone asleep is vulnerable to predators and incapable of performing basic bodily functions (eat, drink, reproduce, and protect siblings). Yet, sleep has endured throughout evolution, and not just in humans.

Indeed, every animal species studied to date exhibits signs of sleep [START_REF] Cirelli | Is Sleep Essential?[END_REF], including those for which sleep seems di cult and risky. Dolphins, for example, must often surface to breath, so they cannot stop swimming to sleep. However, sleep persisted and adapted to this context. In these species, one hemisphere of the brain sleeps while the other half remains awake in order to maintain life-sustaining motion in water (Figure 1.1A), a phenomenon known as 'unihemispheric sleep' [START_REF] Rattenborg | Behavioral, neurophysiological and evolutionary perspectives on unihemispheric sleep[END_REF][START_REF] Rattenborg | Sleep locally, act globally[END_REF].

The science of sleep 6

Mallard ducks can even switch between unihemispheric and bilateral sleep depending on their environment (Figure 1.1B). They turn to bilateral sleep when in ocks, except for guardian birds on the periphery who literally keep an eye out for potential dangers [START_REF] Rattenborg | Half-awake to the risk of predation[END_REF]. Unihemispheric sleep also enables migrant birds to sleep while ying continuously for weeks without landing (Figure 1.1C) [START_REF] Rattenborg | Evidence that birds sleep in mid-ight[END_REF]. and frigatebirds (C). Adapted from [START_REF] Mukhametov | Interhemispheric asymmetry of the electroencephalographic sleep patterns in dolphins[END_REF], [START_REF] Rattenborg | Half-awake to the risk of predation[END_REF], and [START_REF] Rattenborg | Evidence that birds sleep in mid-ight[END_REF].

The fact that such ingenious methods have evolved to preserve sleep indicates that sleep must serve essential functions (for more information, see the box 'The functions of sleep'). Let us now discover the great complexity of this universal and essential process that is sleep.

The functions of sleep

Despite sleep persistence throughout the animal kingdom, the question of why we sleep remains partially unresolved. We are not short of candidates: sleep is thought to be important for mental and physical restoration, growth, energy conservation, brain waste clearance, synaptic homeostasis, immune, weight and emotional regulation, memory consolidation, creativity... (for a review, see [START_REF] Krueger | Sleep function: Toward elucidating an enigma[END_REF]). However, it is easy to confuse something that occurs during sleep with something that could not happen without sleep. To put it another way, is there one (or several) of all the functions listed above that necessitates the speci c environment of sleep? The most obvious function of sleep is energy restoration. One of the most cited theories is the 'Synaptic Homeostasis Hypothesis' (Tononi and Cirelli, 2006;Tononi and Cirelli, 2014), which postulates that the sleep's function is to downscale synaptic strength to an energy-sustainable baseline level, a theory supported by several studies [START_REF] De Vivo | Ultrastructural evidence for synaptic scaling across the wake/sleep cycle[END_REF][START_REF] Diering | Homer1a drives homeostatic scaling-down of excitatory synapses during sleep[END_REF]. Sleep was later linked to metabolic clearance. Notably, it was found to be involved in the removal of neurotoxic waste from the brain, a mechanism previously observed in mice [START_REF] Xie | Sleep Drives Metabolite Clearance from the Adult Brain[END_REF] and, more recently, in humans [START_REF] Eide | Sleep deprivation impairs molecular clearance from the human brain[END_REF]. Besides, impaired sleep was associated with an increased accumulation of β-amyloid in the brain over time [START_REF] Winer | Sleep as a Potential Biomarker of Tau and -Amyloid Burden in the Human Brain[END_REF][START_REF] Winer | Sleep Disturbance Forecasts -Amyloid Accumulation across Subsequent Years[END_REF], a neurotoxic peptide causing Alzheimer's disease [START_REF] Sadigh-Eteghad | Amyloid-Beta: A Crucial Factor in Alzheimer's Disease[END_REF]. The authors thus concluded that sleep quality could serve as a risk and progression marker of the Alzheimer's disease. This hypothesis, however, falls short of explaining the existence of REM sleep. Indeed, REM sleep is marked by intense activity, with a metabolic rate similar to wakefulness. Cognitive researchers, on the other hand, have proposed that sleep serves an active role in memory consolidation, a hypothesis supported by a large body of research (for reviews, see Diekelmann and Born (2010) and Rasch and Born (2013)). And in this case, both stages are supposed to play a role (Rasch and Born, 2013). Therefore, even though it is still debated [START_REF] Siegel | Memory Consolidation Is Similar in Waking and Sleep[END_REF], it has been proposed that memory consolidation could be labeled as 'the' sleep function because it bene ts from the brain being o ine, a process critical for suppressing putative external interference during the consolidation process. Later on, we will have the opportunity to investigate 'the' alleged sleep function.

The de nition of sleep

To begin, let's ask a seemingly trivial question: what is sleep? How can you tell if someone is sleeping? The answer seems easy: simply take a look. If the person of interest remains still with closed eyelids, breathes deeply (sometimes even loudly...), and does not respond, she is most likely sleeping. Actually, we are not that far o the mark, as we have presented two of the three behavioral criteria commonly used 1. The science of sleep 8 to de ne sleep [START_REF] Peigneux | Sleeping brain, learning brain. The role of sleep for memory systems[END_REF]):

• A recognizable, still posture,

• A reduced responsiveness to external stimuli,

• A homeostatic process: sleep deprivation results in a sleep rebound.

This behavioral de nition is useful, especially for studying sleep in small animals, but is somewhat limited, as a resting person may be misidenti ed as sleeping. It also implies that sleep must be disrupted to assess the sleeper's responsiveness. Furthermore, such a de nition condenses an eight-hour time span into a single label: sleep, precluding the study of ner-grained events (such as sleep stages) within it. Thus, this de nition has been supplemented with a neurophysiological one.

Soon after [START_REF] Berger | ber das Elektrenkephalogramm des Menschen[END_REF] invented the electroencephalography (EEG), [START_REF] Loomis | Potential rhythms of the cerebral cortex during sleep[END_REF] discovered that wakefulness and sleep had markedly di erent neuronal patterns.

EEG was then commonly used to monitor sleep, along with additional parameters to di erentiate between periods with similar EEG markers. Precisely, the gold standard for assessing sleep/wake states in laboratories is 'polysomnography' (abbreviated PSG), literally meaning a readout (graph) of sleep (somnus) that is made up of multiple signals (poly). PSG is obtained by combining recordings of at least: 1) cerebral activity (EEG), 2) eye-movements (electroculography, EOG), and 3) muscular tone (electromyography, EMG). Using this method, [START_REF] Aserinsky | Regularly Occurring Periods of Eye Motility, and Concomitant Phenomena, During Sleep[END_REF] discovered periods of sleep during which the eyes move rapidly beneath the lids and are accompanied by brainwaves close to those observed during wakefulness. These periods of ocular movements were interspersed with periods of complete ocular quiescence, during which the cerebral activity slowed down. Each period exhibited a distinct EEG pattern, and [START_REF] Aserinsky | Regularly Occurring Periods of Eye Motility, and Concomitant Phenomena, During Sleep[END_REF] observed that these two phases regularly repeated throughout the night.

Based on these observations, they named these two stages: 'Non-Rapid Eye Movement' (NREM) sleep and 'Rapid Eye Movement' (REM) sleep, which are still the two main types of sleep today. Since then, NREM sleep has been subdivided into three substages labelled Stages 1 (N1) to 3 (N3). A typical night consists of around 5% of N1, 55% of N2, 20% of N3, and 20% of REM sleep.

The physiology of sleep

NREM and REM sleep alternate cyclically every 90 minutes or so. These 'ultradian' cycles repeat approximately 4-6 times per night, but the ratio of NREM to REM sleep within each 90-minute cycle varies throughout the night. The rst half of the night is dominated by deep NREM sleep (N3). However, as the night progresses into the second half, the balance shifts: REM sleep takes over (mostly in young people), with little N3. This well-organized sleep structure is often depicted on a 'hypnogram', which simply depicts the progression of sleep stages over time (Figure 1.2). There is no agreement on why these two stages follow this alternating pattern.

According to one theory, the 'sequential hypothesis' (Giuditta, 2014), this back-and-forth interplay between NREM and REM sleep is important for memory consolidation, with the sleep stages serving complementary roles. As we will see later, in certain sleep disorders including narcolepsy, this normal cycling is disrupted.

The science of sleep 10

The stages composing a sleep cycle are described below (Figure 1.3): • When lying down relaxed, eyes-closed but awake, the EEG signal is dominated by low-amplitude fast rhythms such as alpha oscillations (8)(9)(10)(11)(12). Rapid eye movements and blinking are visible.

• A sleep cycle begins with a short period of NREM sleep Stage 1 (N1). This phase is characterized by: 1) a decrease in alpha rhythm along with a predominant increase in lower EEG frequencies and theta ), 2) a gradual decrease in muscle tone, and 3) slow rolling eye movements (and no blinks) 1 .

• Next is NREM sleep stage 2 (N2), de ned by a predominance of theta background rhythm on the EEG, as well as the presence of K-complexes (sharp, high-voltage biphasic waves lasting more than 0.5s) and spindles (waxing and waning waves in the sigma band, , lasting 0.3-1s). Slow eye movements may be present, although rare, and muscle tone is reduced.

• Then, as the frequency of slow (delta waves, 0.5-4Hz) oscillations increases, NREM sleep stage 3 (also known as slow-wave sleep, abbreviated SWS) appears. These slow oscillations re ect the bimodal activity of underlying cortical neurons, which alternate between an 'on' mode in which they almost all re simultaneously (up state) and an 'o ' mode in which they all stop ring at the same time (down state).

• REM sleep is ending a sleep cycle. The EEG background activity during REM sleep resembles a mixture of both wakefulness and the N1 stage (alpha and theta rhythms). 4-6s trains of speci c theta waves (called saw-tooth waves) are also observed in the EEG, as well as rapid eye movements (isolated, or in bursts), mixed with rarer slow eye movements. Additionally, as Michel [START_REF] Jouvet | Electromyographic correlations of sleep in the chronic decorticate & mesencephalic cat[END_REF] discovered in cats, EMG shows postural muscle atonia (i.e., the absence of muscle tone). This apparent contradiction between a wake-like brain activity and a paralyzed body inspired him to coin the term 'paradoxical sleep'.

• Occasionally throughout the night (approximately every 6 minutes), we experience brief awakenings (alpha and beta rhythms for 15 seconds or more) or 'arousals' (increased EEG rhythm for 3 to 15 seconds), before returning to sleep. Limitations of this classi cation So far, we have seen that sleep physiology is a complex, well-organized structure in which sleep stages alternate, each with its distinct neurophysiological signature. However, under laboratory conditions, we tend to overlook this complexity by assuming that sleep stages are stable and homogeneous, when in reality, they are dynamic and multifaceted. This approximation has to do with how we score sleep. By tradition, we score sleep stages in 30-second epochs 2 . If a 30-second epoch meets the criteria of a speci c sleep stage (e.g., spindles) for more than 15 seconds (>50%), the entire epoch is scored as that stage (e.g., N2 sleep).

But there are ne-grained modulations within this epoch that are not taken into account (e.g., spindles are not present all along or there are other stages' features within this epoch). Perhaps more troublesome, we continue scoring the following epoch of the same stage as long as no other sleep stage's criterion is met, even if the epoch no longer displays all of the stage's criteria. As a result, the rapid eye movements associated with REM sleep are, for example, only present in about 20% of that stage. This observation is not trivial, as each of these micro-physiological events may have a distinct e ect on cognition. As an example, I observed contrasting e ects on cognition (i.e., attention to the external world) depending on whether I considered REM sleep in its entirety or only the onset of its de ning rapid eye movements (for more information, please see the article in Appendix D). In my thesis, I sometimes attempted to address this issue by utilizing shorter time windows and searching for global electrophysiological markers that were independent of sleep stages. In addition to neglecting important modulations within sleep stages, the standard sleep scoring method is exclusively based on electrophysiological criteria (which are themselves reduced to 3 measures: EEG, EOG, and EMG). It does not account for the peculiar mentation that occurs during sleep, especially our dreaming experiences.

Sleep mentation

Given the enigmatic nature of dreams, it is understandable that they are not included in the traditional classi cation of sleep stages. Indeed, despite the work of generations of brave dream researchers, we have only a very fragmented and incomplete picture of them. Excuse the bad pun, but studying dreams is a nightmare for several reasons:

• The dreams that we remember upon awakening are the only recollections of the numerous phenomenological experiences we had during around eight hours of sleep. Moreover, these dream reports are rare, as the average person recalls a dream only once or twice a week [START_REF] Schredl | Gender di erences in dream recall: A meta-analysis[END_REF][START_REF] Schredl | Factors of home dream recall: a structural equation model[END_REF], implying that the majority of them vanish. Even these rare accounts are not necessarily reliable, as they are subjected to many biases, including forgetting, a posteriori reconstruction of missing fragments, censorship, or di culties with verbal description [START_REF] Schwartz | Sleep imaging and the neuro-psychological assessment of dreams[END_REF].

• The sleeper usually has no control over the dream scenario. As a result, in order to collect data on a speci c dream content, experimenters must take a 'shot-inthe-dark' approach, collecting massive amounts of data in the hope of coming across dream reports of interest.

• It is di cult to investigate the neuronal signatures of dreams without a temporal marker for the reported dream of interest on the recordings.

Thus, dream research has reached a methodological impasse: how to study a subjective phenomenon to which we do not have access at the time it occurs?

To circumvent this signi cant obstacle, researchers came up with 'dream-catcher' models for capturing dream fragments and combining them with sleep physiology. The most commonly used paradigm is based on 'serial awakenings' [START_REF] Noreika | Early-night serial awakenings as a new paradigm for studies on NREM dreaming[END_REF]Siclari et al., 2013) and involves frequently awakening subjects during the night to probe their mental content (a tough paradigm for both the sleeper and the experimenter, but as the saying goes, 'no pain, no gain'). This technique allows collecting large amounts of data (while minimizing forgetting by shortening the time interval between dream experience and report) and correlating it with pre-awakening cerebral activity. This method has yielded promising results with regard to dream experiences and their neuronal correlates [START_REF] Chellappa | Cortical activation patterns herald successful dream recall after NREM and REM sleep[END_REF][START_REF] Horikawa | Neural Decoding of Visual Imagery During Sleep[END_REF][START_REF] Scarpelli | Predicting Dream Recall: EEG Activation During NREM Sleep or Shared Mechanisms with Wakefulness?[END_REF]Siclari et al., 2017).

In these studies, the dreaming experience (in NREM and REM sleep) was associated with decreases in low-frequency activity (particularly in posterior cortical regions) [START_REF] Chellappa | Cortical activation patterns herald successful dream recall after NREM and REM sleep[END_REF][START_REF] Esposito | Reduced Alpha power associated with the recall of mentation from Stage 2 and Stage REM sleep[END_REF][START_REF] Scarpelli | Predicting Dream Recall: EEG Activation During NREM Sleep or Shared Mechanisms with Wakefulness?[END_REF]Siclari et al., 2017), but the literature also reports contrasting results (for a summary table, see [START_REF] Ruby | The Neural Correlates of Dreaming Have Not Been Identi ed Yet. Commentary on "The Neural Correlates of Dreaming[END_REF]).

Moreover, these studies are based on fragmented sleep and retrospective reports, with no 'in real-time' access to the dreaming world (and thus no precise dream temporal marker) or control over the dream scenario. Fortunately, there is a model called 'lucid dreaming' (for more information, see the box 'Lucid dreaming'), which allows for both real-time access to dreaming and control over its scenario.

By using models such as lucid dreaming, scientists are progressively answering long-held questions about dreams. Do dreams exist? By signaling a dream through an ocular code in conjunction with sleep parameters, lucid dreaming rst con rmed the existence of dreams that were previously thought to be made up upon awakening by some researchers [START_REF] Dennett | Are Dreams Experiences?[END_REF][START_REF] Malcolm | Dreaming and Skepticism[END_REF]. Scientists reached the same conclusion with another 'dream-catcher' model: 'REM sleep behavior disorder' (RBD). This sleep disorder is characterized by violent behaviors during sleep, potentially leading to self-or other-injury. Indeed, these patients exhibit a partial or complete loss of the muscle atonia normally associated with REM sleep, resulting in motor behaviors. These behaviors are consistent with subsequent reports of sleep mentation [START_REF] Valli | Can observers link dream content to behaviours in rapid eye movement sleep behaviour disorder? A cross-sectional experimental pilot study[END_REF], implying that these patients are acting out their dreams 3 [START_REF] Arnulf | REM sleep behavior disorder: Motor manifestations and pathophysiology[END_REF]. More recently, [START_REF] Horikawa | Neural Decoding of Visual Imagery During Sleep[END_REF] successfully decoded the content of dreams in real-time based on cerebral activity at sleep onset, again con rming the existence of dreams. 3 Click here for an illustrative video and here for another one.

Lucid dreaming

We usually mistake our dreams for reality. It is only when we wake up that we realize that we were dreaming. But there is a rare exception: sometimes, one gains insight into being in a dream while remaining physiologically asleep and immersed in a dream world, a state that is referred to as 'lucid dreaming'.

Lucid dreaming. ©Lucy Doherty

Keith Hearne, Stephen LaBerge and their colleagues were the rst to objectively measure lucid dreaming [START_REF] Hearne | Lucid dreams: An electrophysiological and psychological study[END_REF][START_REF] Laberge | Lucid dreaming: Physiological correlates of consciousness during REM sleep[END_REF]. The method was as strange as the topic: the dreamer was asked to send a message (i.e., performing a series of eye movements) to the waking world (the experimenter) while still asleep! Surprisingly, it worked: the eye signal appeared on EOG traces for the rst time in 1978, alongside polysomnographic evidence of REM sleep and the sleeper's subjective report of lucidity upon awakening.

"Suddenly, out of the jumbled senseless tos and fros of the two eyemovement recording channels, a regular set of large zigzags appeared on the chart. Instantly, I was alert and felt the greatest exhilaration on realizing that I was observing the rst-ever deliberate signals sent from within a dream to the outside. The signals were coming from another world -the world of dreams -and they were as exciting as if they were emanating from some other solar system in space." [START_REF] Hearne | Lucid dreams: An electrophysiological and psychological study[END_REF] Once its existence was established, lucid dreaming became the foundation for a powerful approach to dream research. Lucid dreamers act as 'undercover agents', providing real-time information about the dreaming world to the experimenters. Potent lucid dreamers can even exert control over their dream scenarios, providing researchers with invaluable tools for lifting the veil on dreams. Do all human beings dream? Not only did the RBD model demonstrate that dreams are not the product of imagination, but it also showed that every human being does dream. Indeed, [START_REF] Herlin | Evidence that non-dreamers do dream: a REM sleep behaviour disorder model[END_REF] observed complex, scenic and dreamlike behaviors in RBD patients who had not remembered a dream in at least ten years. Thus, non-recallers would face a problem with dream encoding, not with dream production. To explain the inter-individual di erence in dream-recall frequency, [START_REF] Koulack | Dream recall and dream recall failure: An arousal-retrieval model[END_REF] originally proposed the 'arousal-retrieval' model: the probability of remembering a dream would be higher if an awakening occurred during it. Recent research has provided support for this model by showing that high dream-recallers exhibit an extra 15 minutes of intra-sleep wakefulness compared to low dream-recallers [START_REF] Eichenlaub | Brain reactivity di erentiates subjects with high and low dream recall frequencies during both sleep and wakefulness[END_REF][START_REF] Ruby | Alpha reactivity to rst names di ers in subjects with high and low dream recall frequency[END_REF]. They also found that high dream-recallers had larger responses to sounds during sleep than low dream-recallers [START_REF] Eichenlaub | Brain reactivity di erentiates subjects with high and low dream recall frequencies during both sleep and wakefulness[END_REF], and later presented evidence that this enhanced reactivity was responsible for the observed increase in intra-sleep wakefulness [START_REF] Vallat | Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream Recall[END_REF].

When do we dream? So, dreams do exist, and it appears that everyone dreams. This begs the following question: when do we dream? Do we dream continuously throughout the night or is it limited to speci c sleep periods? The discovery of REM sleep was a watershed moment in the eld of dream research. Due to the intense brain activity and rapid eye movements associated with that stage, researchers naturally intuited that these eye movements corresponded to the visual activity of dreaming, the so-called 'scanning hypothesis' (Arnulf, 2011). Several studies corroborated this hypothesis: when subjects were awakened in REM sleep, more dreams were reported (≈75%) than when subjects were awakened during NREM sleep (≈18%) [START_REF] Aserinsky | Regularly Occurring Periods of Eye Motility, and Concomitant Phenomena, During Sleep[END_REF]Dement and Kleitman, 1957). These studies established the long-held assumption that dreaming occurred exclusively during REM sleep; the few records of NREM sleep dreams were interpreted as remnants of previous REM sleep dreams . However, in 1962, David Foulkes called this premise into question. He carried out a similar experiment, but with a slight shift in paradigm: instead of asking subjects if they were dreaming, he simply asked them to report whatever was going through their minds prior to awakening. With this more expansive de nition, he obtained a higher rate of sleep mentation during NREM sleep (70%) than previously reported, and this was true even when no REM sleep period preceded the reported NREM sleep dream (Foulkes, 1962). From [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF].

Since then, the percentage of NREM sleep dream recall reported in studies has increased (Figure 1.5), with some inconsistencies certainly related to the dreaming de nition used [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF]. Indeed, in the latter studies, any mental content occurring during REM sleep was labeled as a dream, as opposed to the traditional perception of dreams as bizarre, hallucinatory, vivid, and mostly visual.

This disparity in results between early and late studies is signi cant because it indicates a qualitative di erence between REM and NREM sleep dreams, with REM sleep dreams more closely resembling 'real' dreams. Consistent with this notion, some studies indicate that REM sleep dreams are longer [START_REF] Antrobus | REM and NREM sleep reports: Comparison of word frequencies by cognitive classes[END_REF][START_REF] Cavallero | Slow Wave Sleep Dreaming[END_REF][START_REF] Foulkes | Temporal Sequence and Unit Composition in Dream Reports from Di erent Stages of Sleep[END_REF], more emotional, visual, bizarre, and vivid than NREM sleep dreams, which are more thought-like, plausible, and linked with current issues (Foulkes, 1962;[START_REF] Martin | Structural di erences between REM and non-REM dream reports assessed by graph analysis[END_REF][START_REF] Rechtscha En | Reports of mental activity during sleep[END_REF].

Of note, while there may be a confounding circadian factor at work (i.e., both NREM and REM sleep dreams become more complex throughout the night; [START_REF] Casagrande | Variations in Sleep Mentation as a Function of Time of Night[END_REF], [START_REF] Fosse | Thinking and hallucinating: Reciprocal changes in sleep[END_REF], and [START_REF] Wamsley | Circadian and ultradian in uences on dreaming: A dual rhythm model[END_REF]), late-night REM sleep dreams still appear more complex than late-night NREM sleep dreams [START_REF] Wamsley | Circadian and ultradian in uences on dreaming: A dual rhythm model[END_REF]. Certain qualitative changes are also REM-dependent, such as an increase in dreams' emotionality [START_REF] Wamsley | Circadian and ultradian in uences on dreaming: A dual rhythm model[END_REF] and bizarreness [START_REF] Casagrande | Variations in Sleep Mentation as a Function of Time of Night[END_REF].

Precisely, REM sleep dreams are thought to be 'hyper-associative', which means that they connect loosely associated memories: a fragment of one memory trace is associated with another distant one, a process that certainly accounts for their bizarre characteristics (Malinowski and Horton, 2015). This is important to remember because this hyperassociativity is thought to underpin sleep-related creativity, which we will discuss in greater detail in Chapter 5. We thus experience mental content in each sleep stage, but the question of whether we dream continuously remains pending. When subjects do not report a dream, are they having none or have they simply forgotten about them? Advances in our understanding of dream correlates may help us addressing that question by examining whether these correlates are observed continuously throughout the night.

So far, we have seen that sleep is a complex and dynamic process composed of di erent stages (each exhibiting distinct neurophysiological signatures) and accompanied by peculiar mental content, dreams. Dreams are observed in the two main sleep stages, but di er in quality, with REM sleep dreams being closer to the popular view of what a dream is. Beyond these within-sleep di erences in physiological and subjective features, the demarcation between vigilance states themselves is not as clear-cut as originally thought. Sleep has long been regarded as a global, 'all-or-nothing' phenomenon. We would be either awake or asleep. However, a growing body of literature challenges this view, demonstrating that these two states are not mutually exclusive; rather, they appear inextricably linked.

A blurry frontier between wakefulness and sleep

Sleep intrusions into wakefulness We previously described the phenomenon of unihemispheric sleep, in which certain animal species (e.g., dolphins and birds) exhibit a brain state that combines awake and asleep regions, across left and right hemispheres (for a reminder, see Figure 1.1). Interestingly, comparable episodes of local sleep (although restricted to speci c brain regions rather than a single hemisphere) have been observed in awake rodents and in humans [START_REF] Bernardi | Neural and Behavioral Correlates of Extended Training during Sleep Deprivation in Humans: Evidence for Local, Task-Speci c E ects[END_REF][START_REF] Hung | Local experience-dependent changes in the wake EEG after prolonged wakefulness[END_REF][START_REF] Quercia | Local Use-Dependent Sleep in Wakefulness Links Performance Errors to Learning[END_REF][START_REF] Vyazovskiy | Local sleep in awake rats[END_REF]. For instance, [START_REF] Finelli | Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep[END_REF] observed local sleep episodes in sleep-deprived subjects, with theta activity gradually increasing over frontal areas during prolonged wakefulness. These results suggest that local sleep serves a homeostatic role, compensating for sleep deprivation. [START_REF] Vyazovskiy | Local sleep in awake rats[END_REF] observed a similar phenomenon in rats: after an extended period of wakefulness, some cortical neurons brie y went o ine, and these sleep-like episodes were often limited to a single cortical region. During these local sleep episodes, rats were still active and had an 'awake' EEG, but they performed worse in a sugar pellet reaching task [START_REF] Vyazovskiy | Local sleep in awake rats[END_REF].

Therefore, while both the EEG and behavior indicated wakefulness, some local populations of neurons were asleep ('o '), which negatively correlated with rats' performance. Additionally, local sleep was observed not only in sleepdeprived individuals but also in well-rested subjects while performing cognitive tasks (Andrillon et al., 2019;[START_REF] Quercia | Local Use-Dependent Sleep in Wakefulness Links Performance Errors to Learning[END_REF]. Again, these episodes of local sleep during wakefulness were associated with decreased performance, as well as with a subjective experience of mind-wandering or mind-blanking, depending on the region where local sleep occurred (Figure 1.6;Andrillon et al. (2019)). Given that humans spend around half of their lives mind-wandering [START_REF] Killingsworth | A wandering mind is an unhappy mind[END_REF], this result suggests that sleep and wakefulness are more frequently linked than one assumes (for a review, see Siclari and Tononi (2017)). Moreover, I discussed cases of local sleep intrusions during wakefulness, but the opposite is also true, namely wake intrusions into sleep. Wake intrusions into sleep Along with the aforementioned brief awakenings and arousals that occur approximately every 6 minutes throughout the night [START_REF] Bonnet | EEG Arousal Norms by Age[END_REF], there are times when sleep becomes unstable, such as during what are 1. The science of sleep 20 known as 'cyclic alternating pattern' (CAP) [START_REF] Terzano | The cyclic alternating pattern as a physiologic component of normal NREM sleep[END_REF], which are phasic events that occur during NREM sleep. Precisely, CAPs refer to long-lasting (several minutes) periodic activity that consists of two distinct EEG patterns: phase A consists of brief Kcomplexes indicative of a transient arousal that periodically interrupts theta/delta tonic activities, and phase B, which is composed of NREM sleep theta/delta tonic activities.

The authors suggest that CAPs denote a state of sustained sleep-wake instability, oscillating between increased (phase A) and decreased (phase B) arousability. Similarly, [START_REF] Lecci | Coordinated infraslow neural and cardiac oscillations mark fragility and o ine periods in mammalian sleep[END_REF] reported in mice and humans, an alternation within NREM sleep of stable periods composed of numerous spindles (related to memory consolidation) and fragile periods with low spindle activity (associated with increased environmental alertness).

Overall, it appears that the borderland between wakefulness and sleep is porous.

Chapter Summary

We have begun to appreciate sleep's great complexity, from its well-oiled mechanism of cycles in which sleep stages alternate (each with its own electrophysiological signature) up to its peculiar cognition (i.e., dreams). Although complex, this viewpoint remains reductive: sleep stages tend to be far more complex and dynamic than the standard criteria would suggest, and transitions between stages are not that clear. Just as the border between stages appears to be porous, so does the border between wakefulness and sleep. While we used to believe that we were either completely awake or asleep, compelling evidence now indicates that the two states are frequently intertwined. So far, we have focused on sleep intrusions into a globally awake state and vice versa. But there is a genuine 'in-between' zone, where we are neither fully asleep nor fully awake, but rather lost in both worlds: the sleep-onset period. Surprisingly, however, the scienti c community has shown little interest in this hybrid zone. Nonetheless, its richness may have nothing to envy to that of the intensively-studied REM sleep (to which it actually bears a strong resemblance, as we will see). This fascinating 'twilight' zone, which we pass through every night on our way to sleep, will be the subject of the next chapter.

Whereas it is easy to distinguish between the conditions of alertness, or being wide awake, and de nite sleep, the passage from one to the other involved a succession of intermediate states, part wakefulness and part sleep in varying proportions -what is designated in Italian as dormiveglia, or sleep-waking.

-Nathaniel Kleitman

2

The doorway to sleep 29 2.3 Narcolepsy, We covered the great complexity of the sleep symphony, with its interlocking NREM and REM sleep cycles, but to initiate this complex process, we must rst fall asleep. However, in comparison to other sleep stages, our knowledge of the sleep-onset period (and particularly of the rst sleep stage, N1) is extremely limited. I believe that the scienti c community neglected this gateway to sleep for two main reasons : 1) as a brief and transitory period occupying just 5% of our nights, N1 sleep can seem mundane and unworthy of study, and 2) due to its elusive nature, this period is di cult to trace.

Tracing the moment of sleep onset

De ning sleep onset is uneasy; it is not as straightforward as 'the precise moment when we fall asleep'. Indeed, there does not appear to be a clear moment of sleep onset when wakefulness turns o and sleep turns on, like a switch. [START_REF] Davis | Human brain potentials during the onset of sleep[END_REF] even compared the di culty of capturing the onset of sleep to the di culty of 2. The doorway to sleep 22 determining the exact moment of death1 . Instead, the sleep-onset period (SOP) appears as a dynamic process lasting several minutes and characterized by a progressive descent into sleep, passing through several distinct sub-stages [START_REF] Hori | Topographical EEG changes and the hypnagogic experience[END_REF]. Furthermore, this progression is frequently non-linear, with back-and-forth motions between substages, adding to the complexity of tracing the sleep-onset period. This sleep-onset period roughly corresponds to the NREM stage 1 (N1 stage) 2 , which is characterized by a drop in alpha rhythm, an increase in theta waves, a decreased muscle tone, and slow-eye movements.

Certainly the most signi cant contribution to this eld comes from the work of [START_REF] Hori | Topographical EEG changes and the hypnagogic experience[END_REF] who segmented this time window into nine sub-stages (H1-H9) to obtain a more detailed image of the putative sequence of processes occurring during the SOP. He partitioned this period according to EEG criteria (for more details, see Additionally, [START_REF] Hori | Topographical EEG changes and the hypnagogic experience[END_REF] examined whether this novel EEG-based classi cation was associated with behavioral and subjective changes. Notably, they observed a linear increase in reaction times (i.e., pressing a button in response to a tone stimulus) with that of the Hori sub-stages. Similarly, reported subjective sleepiness also increased from 7.2% in stage H1 to 43.7% in stage H9. Consistent with [START_REF] Bonnet | The threshold of sleep: perception of sleep as a function of time asleep and auditory threshold[END_REF], it is worth noting that even when H9 (which corresponds to N2 sleep) was entered, 26.2% of subjects perceived themselves as awake (the remaining 30.1% did not answer the question). Reported dreaming experiences also di ered, with a peak in the middle of the SOP (Figure 2.1B), at stage H5 [START_REF] Hori | Topographical EEG changes and the hypnagogic experience[END_REF] The process of entering sleep thus a ects every single physiological, cognitive, subjective, and behavioral system (Figure 2.2), emphasizing the importance of comprehending that critical period. Could we then use Hori's ne-grained classi cation to identify a precise marker of sleep onset? Ogilvie (2001) reviewed how all parameters tested by researchers evolved with Hori sub-stages. He concluded that only passive behavioral ones captured a clear-cut shift (e.g., the moment a hand-held switch is depressed), presumably re ecting the transition between wakefulness and sleep. In contrast, the changes observed with the other measures (including active behavioral ones such as responding to an auditory tone) were too gradual to determine a speci c timing when sleep begins (Ogilvie, 2001). Passive behavioral measures thus appear to be the best candidate for pinpointing a precise moment of sleep onset (i.e., when pressure on the switch disappears). However, Ogilvie (2001) also noted that, when all of these measures (including passive behavioral ones) are taken simultaneously, they do not seem to align so as to indicate a common moment of sleep onset (Figure 2.2). The observation that not all measures of sleep onset coincide is however made by comparing distinct studies (e.g., no study examined at once if the passive behavioral measure coincided with the remaining ones), and averaging among subjects (Figure 2.2).

Therefore, I believe that the inability to pinpoint a precise time of sleep onset could 2. The doorway to sleep 25 emanate from between-subject variability, a point that has rarely been considered. Indeed, because each subject has a unique sleep onset dynamic and a unique ' ngerprint' sleep structure [START_REF] De Gennaro | An electroencephalographic ngerprint of human sleep[END_REF][START_REF] Finelli | Individual ' ngerprints' in human sleep EEG topography[END_REF], one could expect that every individual has a unique sleep onset threshold as well (e.g., alpha dropout, rst K-complex, rst spindle, etc...). Additionally, even within a single subject, we may anticipate uctuations in his/her sleep-onset dynamic, based on a variety of factors such as tiredness, anxiety, and so on. This within-and between-subject heterogeneity in the sleep-onset dynamic is nicely exempli ed in [START_REF] Prerau | Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics[END_REF]. [START_REF] Prerau | Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics[END_REF] developed a Bayesian model that allows assigning a continuous statistical probability of wakefulness by combining data from multiple modalities, including EEG spectral power, muscle activity, and behavioral responses (i.e., squeezing a ball on each inspiration). This model, as opposed to the standard sleep scoring method, can capture ner granularity within the wake-to-sleep transition. For example, in Figure 2.3 A, the wake probability curve reveals sudden awakenings within the SOP that are not represented on the hypnogram (i.e., smooth N1 stage). Interestingly, the same subject had a di erent SOP phenotype on another night. While the hypnogram shows rapid transitions to the N1 stage, the wake probability curve indicates a high likelihood of wakefulness. This high wake probability derives from the fact that the subject continued to respond correctly long after the alpha power dropped (Figure 2.3 B), and the fact that delta/theta power increased only around 5 minutes after the alpha rhythm dropout, a delay that may explain the persistence of behavioral responses. This observation is congruent with the work of [START_REF] Magnin | Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans[END_REF] who showed that the shift of EEG activity to lower frequencies depends on changes in thalamic activity, which precede changes in cortical activities with a high variability in timing between subjects. This latter information is crucial as it exempli es how failing to account for such between-and within-subject variability might hinder accurate tracking of the sleep-onset period. For example, at stages H3/H4 (alpha dropout), a subject with a signi cant gap between alpha dropout and low-frequencies appearance would continue to respond behaviorally and would certainly consider him/herself awake,

The doorway to sleep

whereas another subject without this EEG desynchronization would exhibit sleep-onset indicators (e.g., cessation of motor responses, dreaming experiences...). When averaging across all participants, researchers may thus inject considerable noise into the data, potentially impeding the identi cation of a point at which all measures coincide to indicate sleep. In fact, such a moment may exist, but it would not be the same for everyone (e.g., H5/6 for subject 1, and H3/4 for subject 2), nor would it be necessarily the same for a given individual from one night to the next. Altogether, these few examples highlight the limitations of our current classi cation of sleep stages, and the importance of considering between-and within-subject variability.

Including a behavioral measure may be crucial when assessing wake-sleep states as it seems sensitive to the delay between alpha dropout and the rise in low EEG frequencies (after all, behavior was the original criterion to de ne sleep before the invention of EEG). Indeed, [START_REF] Prerau | Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics[END_REF] demonstrated how an automated and real-time algorithm combining multiple data sources (including a behavioral task that is synchronized on internal inputs rather than external ones) can provide an invaluable tool for tracking the wake-to-sleep transition. However, because it is based on complex algorithms and on 64-electrode EEG, this great model is di cult to apply in a clinical setting (or at home). Moreover, even though [START_REF] Prerau | Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics[END_REF] used a minimally disruptive behavioral task that did not rely on awakening sounds, it still required the intervention of the sleepers, and may thus interfere with the ongoing process.

Passive behavioral measurement may thus be critical for developing a reliable, individual marker of sleep onset, such as the one intuitively developed by Thomas Edison. Indeed, the story goes that this well-known inventor would have developed such a technique to capture the twilight zone between wakefulness and sleep. As [START_REF] Jr | Relax: Its Good for You[END_REF] reported:

"

Edison used to work very hard in his research -at beta, the faster brain wave frequencies. Then when he would reach a 'sticking point', he would take one of his famous 'cat naps'. He would doze o in his favorite chair, holding steel balls in the palms of his hands. As he would fall asleep, drifting into alpha, his arms would relax and lower, letting the balls fall into pans on the oor. The noise would wake Edison, and very o en he would awaken with an idea to continue with his project. As implied by the story, Thomas Edison attributed many of his discoveries to this technique, which he dubbed his 'genius gap', a topic that we will revisit later.

Salvador Dali developed a similar technique using a key. Below is his description (Dali, 1992) of what he named 'the slumber with a key': " You must resolve the problem of 'sleeping without sleeping', which is the essence of the dialectics of the dream, since it is a repose which walks in equilibrium on the taut and invisible wire which separates sleeping from waking [...] You must seat yourself in a bony armchair [...] Your two hands must hang beyond the arms of the chair [...] Your wrists must be held out in space and must have been previously lubricated with oil of aspic. This is intended to facilitate the benumbing of your hands at the moment when the slumber approaches, inducing the tingling that is produced when one of your members goes to sleep [...] In this posture, you must hold a heavy key [...] Under the key, you will previously have placed a plate upside down on the oor [...] This fugitive moment when you had barely lost consciousness and during which you cannot be assured of having really slept is totally su cient.

"

Surprisingly, however, only a few studies investigated the relationship between passive behavioral measures (i.e., nger muscle relaxation) and EEG activity [START_REF] Blake | Factors in uencing brain potentials during sleep[END_REF][START_REF] Perry | A passive behavioral measure of sleep onset in high-alpha and low-alpha subjects[END_REF][START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF]. Some of them relied on the depression of a hand-held switch [START_REF] Perry | A passive behavioral measure of sleep onset in high-alpha and low-alpha subjects[END_REF][START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF], but, to the best of my knowledge, only one study from the 1930s relied on the drop of an object, like in Edison's story [START_REF] Blake | Factors in uencing brain potentials during sleep[END_REF]. The authors observed that hand-held objects dropped between 0.5 and 25 seconds after the alpha rhythm had disappeared, which would correspond to our current stage N1. However, these ndings should be interpreted with extreme caution because they are based on a single study that lacked clear reports and statistical analyses and was conducted at a time when di erent EEG recording and scoring methods were in use. Moreover, none of these three studies evaluated whether muscle relaxation also coincided with sleep subjective experiences (e.g., dreams). Of note, [START_REF] Horowitz | Dormio: A Targeted Dream Incubation Device[END_REF] also recently designed a device called 'Dormio' that aims at targeting sleep onset by combining several measurements (i.e., heart rate, electrodermal activity, and muscle exion). However, their product has not yet been commercialized.

A passive behavioral technique, such as Edison's, may thus provide an individual speci c marker of sleep onset, allowing to explore the precise mechanisms behind sleep onset. Such a technique would also be useful for waking Otherwise, we would have to randomly wake subjects up during the SOP, which might be too early (before sleep onset) for some subjects or too late (after sleep onset) for others.

But it remains to determine whether Edison's technique could provide such a reliable marker of sleep onset (with a convergence between EEG, subjective, and behavioral measures) that would adapt to anyone, a question that we sought to answer in Study 2.

In summary, we currently lack a reliable, passive marker capable of accurately capturing the precise time of falling asleep when combining behavioral, subjective and neurophysiological measures. As a result, some authors believe that the N1 stage is an intermediate state closer to wakefulness than to genuine sleep. In practice, researchers studying the cognitive functions of sleep either consider the onset of sleep to coincide with the onset of N2 (in which case, N1 data is mixed with wake data), or, on the contrary, mix N1 and N2 data, or simply discard this 'noisy' transitory zone from their analyses. This is unfortunate because this phase is characterized by a dynamic brain activity and a rich phenomenal experience that are strikingly similar to those of the well-known REM sleep stage, and thus may be associated with several interesting, covert cognitive processes.

The N1 stage vs. REM sleep, a striking resemblance

The N1 and REM sleep stages share a striking resemblance both in terms of neurophysiology and subjective experience. It is worth noting here that this resemblance led sleep clinicians in the 1960s to refer to the current N1 stage as 'descending stage 1' (because it occurred at the beginning of sleep, which is often represented on the hypnogram as a state placed beneath wakefulness) and the current REM sleep as 'ascending stage 1' (because it often terminates a sleep cycle and is followed by wakefulness). One of the main contributions of Michel Jouvet was later to show that REM sleep was not a light sleep stage terminating sleep cycles before awakening, as previously thought, but rather a completely di erent brain state, as evidenced, for instance, by muscle paralysis (in comparison to low muscle tone in N1), the presence of REMs bursts (vs. slow eye movements in N1), or EEG di erences (saw-tooth waves in REM sleep vs. vertex sharp waves in N1) (see a review of this epistemology in [START_REF] Debru | Neurophilosophie du rêve[END_REF]). Therefore, REM sleep is unquestionably a deeper stage than the N1 stage. Still, these two stages share many similarities, implying that the N1 stage may be worth studying as well.

Cerebral activity First, the cerebral activity of the N1 stage is composed of alpha/theta EEG rhythms, as in REM sleep, particularly at alpha disappearance [START_REF] Bódizs | Wakefulness-sleep transition: emerging electroencephalographic similarities with the rapid eye movement phase[END_REF]. Additionally, [START_REF] Bódizs | Human parahippocampal activity: non-REM and REM elements in wake-sleep transition[END_REF] observed a rhythmic hippocampal activity in the N1 stage similar to the one observed during REM sleep (with an increase in the 1.5-3Hz frequency band that reached 82% of REM sleep value).

Physiology Sleep onset also exhibits other physiological characteristics of REM sleep.

For example, [START_REF] Tinguely | Non-rapid eye movement sleep with low muscle tone as a marker of rapid eye movement sleep regulation[END_REF] reported a low muscle tone during the wake-to-sleep transition that was comparable to that observed during REM sleep. These episodes occurred more frequently after REM sleep deprivation [START_REF] Werth | Selective REM sleep deprivation during daytime. II. Muscle atonia in non-REM sleep[END_REF] and during periods of increased REM sleep pressure (morning sleep: 60.9% vs. night sleep: 35.5%; [START_REF] Tinguely | Non-rapid eye movement sleep with low muscle tone as a marker of rapid eye movement sleep regulation[END_REF]). These observations led the authors to hypothesize that these episodes of low muscle tone may represent early manifestations of REM sleep. The phasic muscular twitches associated with REM sleep can also be observed during the N1 stage [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF]. Additionally, the slow eye movements normally associated with sleep onset occur during REM sleep as well [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF]. Furthermore, some eye movements during N1 stage may be sharp enough to blur the di erence with REM sleep in some sleepers, even for experienced scorers.

Phenomenological experiences A hallmark of the sleep-onset period also resides in the 'hypnagogic experiences', also known as 'hypnagogia'. Before I describe them in details, I'd like to point out that this terminology is sometimes used to describe the sleep-onset period itself 3 . However, to avoid confusion, I will use the term 'hypnagogia' to refer to the ' eeting, involuntary and spontaneous perceptual experiences that occur during the descent into sleep'. Of note, this phenomenon is distinct (even if debatable, as we will see) from the 'hypnagogic hallucinations' that are observed in normal subjects but more frequently in patients with narcolepsy. Hypnagogogic hallucinations correspond to a clinical symptom used to describe vivid, often frightening, hallucinations (perceptions without any object to perceive) that occur during the sleep-onset period (but also when patients are fully awake). In the case of visual hallucinations, patients have their eyes open and report that the hypnagogic hallucinations superimpose on their actual environment. Besides, patients frequently believe that these experiences are real (e.g., a patient sees a spider on the ceiling and powerlessly waits for it to swoop down on him, Leu-Semenescu et al. ( 2011)).

In contrast, hypnagogic experiences, which [START_REF] Windt | Dreaming: A Conceptual Framework for Philosophy of Mind and Empirical Research (1er édition)[END_REF] aptly named 'oneiragogia' (literally, 'the bridge between wakefulness and dreaming') can be found in up to 70% of the general population [START_REF] Mavromatis | Hypnagogia : the nature and function of the hypnagogic state[END_REF]. Just like dreams, the remaining 30% potentially experience hypnagogia as well, but without recalling them (maybe due to a faster transition to deeper sleep). Local patterns of brain slowing might explain the occurrence of hypnagogia. Indeed, centro-frontal areas 'fall asleep' rst during the wake-to-sleep transition (as indicated by localized slow-wave activity), followed later by sensory posterior brain regions [START_REF] Marzano | How we fall asleep: regional and temporal di erences in electroencephalographic synchronization at sleep onset[END_REF][START_REF] Siclari | Two distinct synchronization processes in the transition to sleep: a high-density electroencephalographic study[END_REF]. A similar desynchronization was observed at the subcortical level, with thalamic deactivation preceding that of the cortex by several minutes at sleep onset [START_REF] Magnin | Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans[END_REF]. This di erential timing in deactivation may also favor the emergence of hypnagogic experiences. As aforementioned (Figure 2.1), the probability of reported hypnagogia increases with Hori sub-stages, reaching a peak of 45.7% at stage 5 (corresponding to the dropout of alpha activity), but hypnagogic experiences can also be observed earlier. Indeed, Nielsen (2017) reported so-called 'micro-dreams' following a single second of alpha drop-out (Figure 2.5).

Hypnagogic photos, Raymond Hains

As with dreams, visual imagery (86% of all hypnagogia) is the most common sensory modality experienced during sleep onset (Hori et al., 1994) (Figure 2.1). Hypnagogic contents are also highly variable and heterogeneous. They range from isolated, eeting imagery (i.e., typically kaleidoscopically changing phenomena such as geometric patterns, shapes, and light ashes) to extremely elaborate, full-edged dreams [START_REF] Mavromatis | Hypnagogia : the nature and function of the hypnagogic state[END_REF][START_REF] Nielsen | Microdream neurophenomenology[END_REF].

Importantly, these hypnagogic experiences frequently equal or exceed REM sleep mentation in frequency and report length [START_REF] Foulkes | Individual di erences in mental activity at sleep onset[END_REF][START_REF] Foulkes | Mental activity at sleep onset[END_REF][START_REF] Vogel | An alternative view of the neurobiology of dreaming[END_REF][START_REF] Vogel | Limited discriminability of REM and sleep onset reports and its psychiatric implications[END_REF]. Moreover, many hypnagogic contents are as bizarre and dreamlike as REM reports [START_REF] Foulkes | Mental activity at sleep onset[END_REF][START_REF] Vogel | An alternative view of the neurobiology of dreaming[END_REF]. Besides, a recent study found that the dreamlike quality of hypnagogia was dependent on REM sleep pressure: the higher the latter, the more dreamlike the mentation at sleep onset (and REM sleep) were [START_REF] Nielsen | Partial REM-sleep deprivation increases the dream-like quality of mentation from REM sleep and sleep onset[END_REF].

Taken together, these ndings indicate a phenomenological proximity between sleep onset and REM sleep. Some authors even hypothesized that these brief waketo-sleep transitions are actually fragmentary episodes of 'covert' REM sleep, and that hypnagogic experiences are nothing else than covert REM sleep dreams [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF]. Thus, the sleep-onset period is accompanied by rich and complex processes.

Studying it may even provide advantages over REM sleep [START_REF] Nielsen | Microdream neurophenomenology[END_REF] because of its ease of access. As an example, it could aid elucidating the neural correlates of dreams (as we will see in the Discussion part).

This permeable border between wakefulness and sleep occasionally blurs completely, resulting in medical conditions in which subjects are often living 'in-between', neither fully awake, nor fully asleep. One of them is narcolepsy. 2.3 Narcolepsy, What is narcolepsy? Narcolepsy is a rare neurological disorder (a ecting approximately 0.02-0.05% of the population; [START_REF] Bassetti | Narcolepsy -clinical spectrum, aetiopathophysiology, diagnosis and treatment[END_REF]), in which the ability to regulate sleep-wake cycles is impaired, resulting in an extremely ne borderland between sleep and wakefulness (for a review of the subject, see [START_REF] Bassetti | Narcolepsy -clinical spectrum, aetiopathophysiology, diagnosis and treatment[END_REF]). This results in inappropriate sleep phenomena occurring during wakefulness (i.e., daytime sleep intrusions) and vice versa (i.e., high sleep fragmentation with multiple awakenings during nighttime sleep). The origin of such sleep-wake regulation failure can be traced back to a critical population of hypothalamic neurons producing a neurotransmitter called the 'hypocretin' (alternatively known as 'orexin'), which is essential for maintaining wakefulness throughout the day. Indeed, once released, this molecule travels throughout the brain and activates wake-promoting areas, which in turn produce wake-promoting neurotransmitters such as acetylcholine, dopamine, serotonin, or noradrenaline (Figure 2.6A). In narcolepsy, patients' immune system misidenti es these hypocretin-producing neurons as foreign and potentially dangerous. As a result, it destroys nearly all of them (90-95%; Figure 2.6B), resulting in a variety of symptoms. Hallucinations As we mentioned before, patients with narcolepsy experience hallucinations during the wake-to-sleep and sleep-to-wake transitions, which are referred to as 'hypnagogic' and 'hypnopompic' hallucinations, respectively. In comparison to normal subjects experiencing hypnagogic hallucinations, patients with narcolepsy have more frequent, holistic, and immersive hypnagogic hallucinations.

Of note, many of the symptoms exposed above in narcolepsy could t into the covert REM sleep theory in healthy subjects. For example, [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF] hypothesized that SOREMs (which are also observed in healthy subjects; [START_REF] Bishop | The frequency of multiple sleep onset REM periods among subjects with no excessive daytime sleepiness[END_REF]) were unmasked instances of covert REM sleep transitions. Unpublished results to date would support this notion by demonstrating a correlation between SOREMs and REM sleep pressure: the number of SOREMs would double in sleepy subjects compared to wellrested subjects [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF]. Early research has also shown that injecting cholinergic drugs into the ventricular system of cats resulted in hallucinatory behavior [START_REF] Sherwood | E ects of Intraventricular Acetylcholine, Cholinesterase, and Related Compounds in Normal and 'Catatonic' Cats[END_REF]. Because the same drugs have been shown to lengthen REM sleep periods, such observations support once again the idea of a close relationship between sleep onset (when some hallucinations occur) and REM sleep. [START_REF] Liddon | Sleep Paralysis and Hypnagogic Hallucinations: Their Relationship to the Nightmare[END_REF] consistently hypothesized that "hypnagogic hallucinations represent aspects of REM periods occurring during the waking state".

To summarize, patients with narcolepsy show sleep intrusions into wakefulness and wakefulness intrusions into sleep (Figure 2.7), as well as symptoms characteristic of a hybrid state between wakefulness and REM sleep (cataplexy, sleep paralysis, hypnagogic hallucinations).

Given the association between intra-sleep wakefulness and dream recall, it is not surprising that patients with narcolepsy have a higher dream recall frequency [START_REF] Schredl | Dreaming in patients with sleep disorders[END_REF]Schredl, 1998) and complexity (Fosse, 2000) when compared to controls. The fact that patients with narcolepsy often oscillate between wakefulness and sleep may also explain why they are more prone to experiencing lucid dreams (in fact, waking up in the middle of the night is a technique for inducing lucid dreaming; [START_REF] Schredl | Inducing lucid dreams: The wake-up-back-to-bed technique in the home setting[END_REF]). Precisely, the majority of patients with narcolepsy (80%) are frequent lucid dreamers (Dodet et al., 2015;Rak et al., 2015), in contrast with the general population in which recurrent lucid dreams are rare (e.g., 1% of a German sample report lucid dreams several times per week; [START_REF] Schredl | Frequency of lucid dreaming in a representative German sample[END_REF]. Narcolepsy is thus an excellent research model for investigating dream neuronal correlates and their impact on cognition, a model that I used in an ongoing project (Appendix A). Indeed, thanks to patients with narcolepsy, it is possible to have an experimental control and a temporal marker of dreams (if the patient is a lucid dreamer), as well as to collect REM sleep episodes in just a few daytime naps in a sleep laboratory and even to perform fMRI sleep studies in a noisy scanner.

Taken together, patients with narcolepsy are frequently in a hybrid state between wakefulness and sleep. They thus have preferential, increased access to sleep, particularly sleep onset/REM sleep and dreams.

Chapter Summary

We have seen that the transition from wakefulness to sleep is not devoid of interest. It is a rich and dynamic period which largely overlaps with the N1 stage. Furthermore, the N1 stage combines features of both wakefulness (e.g., persistence of some behavioral responses) and sleep, and shares several similarities with REM sleep. Is the N1 stage merely an extension of wakefulness, or as [START_REF] Nielsen | A review of mentation in REM and NREM sleep: "covert" REM sleep as a possible reconciliation of two opposing models[END_REF] hypothesized, a covert fragment of REM sleep? Or is the N1 stage a one-ofa-kind state characterized by unique features and processes, as Edison and Dali intuited? We do not really know because this stage has been largely ignored by the scienti c community certainly for the main two reasons stated above. First, we need a reliable marker of sleep onset to study it properly (a gap that we attempted to ll in Study 2). Second, this 1-10 minute stage may be considered anecdotal in comparison to other sleep stages such as N2 and N3 stages, which account for roughly 75% of our nights. Nonetheless, we saw that only a few seconds of alpha dropout was su cient to produce a micro-dream phenomenology. Therefore, given the predominance of such spontaneous thoughts in daily life [START_REF] Killingsworth | A wandering mind is an unhappy mind[END_REF], brief sleep intrusions may occur far more frequently than we might anticipate. This already porous border between wakefulness and sleep (e.g., regular back-and-forth motions between the two vigilance states, hybrid states as in local sleep) is deregulated in some cases, as seen in patients with narcolepsy who oscillate between wakefulness and sleep in an exacerbated manner. Altogether, the wake-to-sleep transition appears to be more prevalent than we assume and far from uninteresting, but our understanding of this promising period (especially the N1 stage) is poor. The main purpose of the present thesis is thus to gain a better understanding of what occurs during that rich and complex N1 stage, as well as its putative impact on our waking cognition. Because we know so little about this period and its functions, I will detail in the next chapter one of the most well-documented functions of sleep, memory, and see how the N1 stage ts in.

The two-stage model of memory consolidation

Numerous articles have demonstrated the importance of sleep for memory (namely, the faculty of the brain by which data is encoded, stored and retrieved when needed). Sleep is especially associated with the second of these memory stages, memory consolidation, because it prevents memories from fading away by transforming newly encoded, labile memories into more stable and long-term representations (Diekelmann and Born, 2010).

The rst study to test this process was conducted in 1924, when [START_REF] Jenkins | Obliviscence during Sleep and Waking[END_REF] observed that sleep helped cement newly learned information, protecting it from being forgotten, when compared to an equal amount of time spent awake. 1 English translation:"However, when nothing remains from a long-dormant past, after the people have died, after the things have been broken and scattered, taste and smell alone, more fragile but more enduring, more insubstantial, more persistent, and more faithful, remain poised for a long time, like souls, remembering, waiting, and hoping, amid the ruins of everything else; and bear un inchingly, in the minuscule and almost impalpable drop of their essence, the vast structure of recollection."

Sleep to remember 39

Numerous studies have now replicated these ndings, demonstrating a bene cial e ect of sleep on declarative (i.e., memory of facts and events) and procedural (i.e., the type of memory responsible for knowing how to do things, like driving) memory on a variety of tasks. Additionally, sleep aids maintaining the ability to form new memories [START_REF] Mander | Wake deterioration and sleep restoration of human learning[END_REF]. But how can the brain acquire new information without erasing prior knowledge? The 'two-stage' model of memory consolidation [START_REF] Buzsáki | Two-stage model of memory trace formation: a role for "noisy" brain states[END_REF][START_REF] Marr | Simple memory: a theory for archicortex[END_REF] provides an elegant solution to this problem. It presumes the existence of two types of memory storage (Figure 3.1):

1. A short-term memory storage, which allows for rapid learning and ensures that information is stored temporarily, 2. A long-term memory storage, which enables the long-term storage of information. [START_REF] Marr | Simple memory: a theory for archicortex[END_REF] proposed that the hippocampus rapidly stores the day's events (shortterm storage) before transferring the information to the cortex (long-term storage) during sleep. This view would reconcile the two functions of sleep. Indeed, transferring memories from short-term to long-term storage not only enables them to be safely stored, but also frees up room in the hippocampus for new memories. These concepts laid the groundwork for contemporary models of memory consolidation [START_REF] Mcclelland | Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory[END_REF][START_REF] Squire | Retrograde amnesia and memory consolidation: a neurobiological perspective[END_REF]. The following section will discuss recent research that supports this model and demonstrates how sleep plays a critical role in it. [START_REF] Wilson | Reactivation of hippocampal ensemble memories during sleep[END_REF] made a signi cant contribution to this eld by establishing a putative mechanism responsible for the transfer of information from the hippocampus to the cortex. They observed in rodents that hippocampal place cells that red together during exploration were likely to re together during subsequent sleep (but not in the sleep period before exploration). Notably, [START_REF] Wilson | Reactivation of hippocampal ensemble memories during sleep[END_REF] observed that such reactivations of a previous wake experience often occurred in conjunction with high-frequency bursts of activity dubbed 'sharp wave ripples' (SWRs) (Figure 3.2A). The authors coined the term 'replay' to refer to this phenomenon and proposed that replay could be the neural mechanism supporting memory consolidation.

Replay, a mechanism for memory consolidation

Subsequent research established a wealth of evidence to support this claim (for reviews, see Ólafsdóttir et al. (2018Ólafsdóttir et al. ( ), O'Neill et al. (2010)), and Rasch and Born (2013)).

First, hippocampal replays (found predominantly during NREM sleep) were linked to improved spatial memory performance in rodents [START_REF] Dupret | The reorganization and reactivation of hippocampal maps predict spatial memory performance[END_REF]. Subsequent research established a causal role for these reactivations in memory consolidation by demonstrating that disrupting SWRs (and presumably replays) during sleep signi cantly impaired subsequent memory [START_REF] Girardeau | Selective suppression of hippocampal ripples impairs spatial memory[END_REF] (Figure 3.2B). These replays were also observed in birds [START_REF] Dave | Song replay during sleep and computational rules for sensorimotor vocal learning[END_REF] and primates [START_REF] Ho Man | Coordinated reactivation of distributed memory traces in primate neocortex[END_REF]. What about human evidence of replay? Due to a scarcity of single-unit electro-physiological studies, there is less evidence for the existence of hippocampal replay in humans. However, advanced methods have been recently used to decode reactivations from brain activity in sleeping humans. For example, one study [START_REF] Schönauer | Decoding material-speci c memory reprocessing during sleep in humans[END_REF] produced encouraging results: they used a multivariate pattern classi cation to decode participants' brain activity during sleep and to determine what types of images they had viewed in a preceding learning session. They decoded patterns of learning-related material during sleep and found a positive correlation between classi cation probability estimates (i.e., memory processing) and later memory performance (only for NREM sleep). Other evidence of reactivation in humans comes from studies using the 'targeted memory reactivation' (TMR) method for experimentally triggering reactivations of previous learning (Oudiette and Paller, 2013). TMR entails exposing sleepers to sensory cues that have previously been associated with a particular memory trace in order to elicit its reactivation. If replay aids in memory consolidation, this technique should improve memory performance. Several studies have shown that this is the case [START_REF] Antony | Cued Memory Reactivation During Sleep In uences Skill Learning[END_REF]Rasch et al., 2007;Rudoy et al., 2009;[START_REF] Schreiner | Boosting Vocabulary Learning by Verbal Cueing During Sleep[END_REF]. However, it was unclear whether this technique actually induced neural replays in humans (but it is the case in rodents; [START_REF] Bendor | Biasing the content of hippocampal replay during sleep[END_REF]). Rasch et al. (2007) exposed participants to an odor (associated with learning) while they slept in an fMRI scanner and found that it activated the hippocampus and cortical regions (Figure 3.2C), indicating a possible replay during SWS (the same was not true for exposure during wakefulness or REM sleep). [START_REF] Lewis | How Targeted Memory Reactivation Promotes the Selective Strengthening of Memories in Sleep[END_REF] and [START_REF] Wilson | Reactivation of hippocampal ensemble memories during sleep[END_REF]. (B) Disrupting replay impairs learning. Adapted from [START_REF] Girardeau | Selective suppression of hippocampal ripples impairs spatial memory[END_REF] and Ólafsdóttir et al. (2018). (C) Odor-cue is associated with improved memory along with the activation of hippocampal and necortical regions. Adapted from Diekelmann and Born (2010) and Rasch et al. (2007). (D) Odor-evoked brain reactivation during sleep promotes memory consolidation. Adapted from [START_REF] Rasch | Let's replay[END_REF] and [START_REF] Shanahan | Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation[END_REF].

This was a rst step, but there was still no evidence of a speci c activity pattern being replayed. Only recently have [START_REF] Shanahan | Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation[END_REF] demonstrated that speci c activity patterns are replayed during human sleep and predict later memory performance. Participants learned the positions of objects (divided into several categories, e.g., animals/tools) while in a scanner. As previously shown, each category elicited a distinct pattern of activity that can be decoded using multi-voxel pattern analysis [START_REF] Norman | Beyond mind-reading: multi-voxel pattern analysis of fMRI data[END_REF]. Participants then learned to associate each category of objects with an odor and were then re-exposed to some odors while sleeping. The next morning, as expected, they had a better memory for the locations of objects associated with the odors delivered during sleep (Figure 3.2D). But crucially, the scents also caused a replay of the brain pattern associated with its corresponding category [START_REF] Shanahan | Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation[END_REF].

Moreover, the amount of overlap between brain patterns during learning and subsequent reactivation during sleep could predict performance gain (Figure 3.2D).

Taken together, these studies indicate that replays of memory traces during sleep serve memory consolidation. According to the standard model of consolidation, these replays would facilitate memory transfer from the hippocampus to the neocortex by stabilizing labile memory traces for long-term storage. This idea presupposes an exchange of information between the hippocampus and the cortex. Is that the case?

The hippocampo-cortical dialogue Studies have shown that learning-related patterns of activity are replayed during sleep not only in the hippocampus, but also in the prefrontal cortex [START_REF] Euston | Fast-Forward Playback of Recent Memory Sequences in Prefrontal Cortex During Sleep[END_REF], and are even coordinated with the hippocampus [START_REF] Peyrache | Replay of rule-learning related neural patterns in the prefrontal cortex during sleep[END_REF]. SWRs are also time-aligned with thalamo-cortical spindles, cortical slow oscillations, and delta waves [START_REF] Battaglia | Hippocampal sharp wave bursts coincide with neocortical "up-state" transitions[END_REF][START_REF] Peyrache | Inhibition recruitment in prefrontal cortex during sleep spindles and gating of hippocampal inputs[END_REF][START_REF] Siapas | Coordinated Interactions between Hippocampal Ripples and Cortical Spindles during Slow-Wave Sleep[END_REF][START_REF] Sirota | Communication between neocortex and hippocampus during sleep in rodents[END_REF]Staresina et al., 2015) (Figure 3.3 A and B). This dialogue may aid in the coordination of the two structures, but its role in memory consolidation was still unknown. Maingret et al. (2016) arti cially enhanced hippocampo-cortical interactions during sleep by electrically stimulating the neocortex when hippocampal SWRs were detected, which induced propagating delta waves and spindles. This protocol resulted in a reinforcement of the coupling between hippocampal

Sleep to remember

SWRs and delta spindles, which in turn resulted in improved memory performance (Figure 3.3C). This study provided evidence for a causal role of the hippocampo-cortical dialogue during sleep in memory consolidation (Maingret et al., 2016), and suggested that the underlying mechanism involves a ne-tuned coordination of SWRs, delta waves, and spindles. Such a temporally ne-tuned coupling would allow information to be transferred from the hippocampus to the cortex (Figure 3.3D). Thus, there is a fairly convincing model of memory consolidation in which sleep is critical, but some gray areas remain. Because the majority of replays are observed in NREM sleep, most e orts have been directed toward understanding the processes that occur during this stage, leaving REM sleep conspicuously absent from this model. And the few studies that have attempted to assess its function have yielded con icting ndings: some show a bene cial e ect of REM sleep on memory [START_REF] Karni | Dependence on REM sleep of overnight improvement of a perceptual skill[END_REF], others a detrimental e ect [START_REF] Rasch | Pharmacological REM sleep suppression paradoxically improves rather than impairs skill memory[END_REF], and still others no e ect at all [START_REF] Genzel | Slow wave sleep and REM sleep awakenings do not a ect sleep dependent memory consolidation[END_REF]. Such di erences may be explained by the type of memory under investigation: the 'dual-process' theory proposes that NREM sleep aids declarative memory whereas REM sleep helps procedural memory [START_REF] Plihal | E ects of early and late nocturnal sleep on declarative and procedural memory[END_REF], but this theory remains debated. Other authors proposed that the sequence NREM-REM sleep (so called 'sequential hypothesis'), is what matters (Giuditta, 2014), rather than a speci c sleep stage. This uncertainty about the role of REM sleep in memory consolidation may be due in part to a fundamental technical issue: REM sleep typically occurs only after a certain amount of NREM sleep (50-90 minutes). This implies that: 1) it is extremely di cult to obtain 'pure' episodes of REM sleep that have not been contaminated by previous NREM sleep, and thus isolate the REM sleep speci c role, and 2) REM sleep is not easily attained by participants in daytime nap studies (even more in a noisy fMRI scanner). In one of my thesis projects, I attempted to close this knowledge gap by following the fate of a memory motor trace from learning to consolidation during sleep and by evaluating the contribution of each sleep stage in this process. To accomplish this, I used a one-of-a-kind model: lucid dreamers with narcolepsy, a model that allows bypassing the technical issues raised above (see the box on lucid dreaming). However, due to the massive amount of data to be analyzed, this is still an ongoing project that can be found in the Appendix A.

N1 is a sleep stage that has received even less attention from researchers investigating sleep-dependent memory consolidation than REM sleep. Indeed, because animal models used in sleep research lack an analogue of the NREM sleep subdivision classically described in humans (Lacroix et al., 2018), they do not give information about the signi cance of each NREM stage (especially the N1 stage) in the memory process. Moreover, the literature reveals that replays are not always associated with sleep. Indeed, some studies (both in rodents and humans) reported replays right after encoding, during 'awake rest' (for a review, see [START_REF] Tambini | Awake Reactivation of Prior Experiences Consolidates Memories and Biases Cognition[END_REF]), and some observed a positive correlation between these awake replays and subsequent memory performance [START_REF] Buch | Consolidation of human skill linked to waking hippocampo-neocortical replay[END_REF]Oudiette et al., 2013). Given the porous boundary between wake rest and the N1 stage, and the rarity of EEG recordings to check for sleep intrusions during wake, it is possible that some of these awake replays actually occurred during bouts of N1 (or during local sleep). If this is the case, it would suggest that N1 plays a role in sleep-dependent memory processing. We investigated this possibility in Study 3. Besides, as we will see below, the rare human studies conducted on the topic point to a possible link between the N1 stage and memory processing.

Memory processing at sleep onset

If we push further Giuditta (2014)'s idea that memory consolidation requires the succession of NREM and REM sleep, one could imagine a unique role for the N1 stage in this process, for example, as a period during which recent experiences are reviewed and important ones are tagged for subsequent consolidation. In support of this idea, the hippocampus, a key brain region for memory consolidation as we have just seen, exhibits increased activity in the late N1 period compared to wakefulness (Picchioni et al., 2008).

Moreover, as aforementioned in chapter 2, N1 is characterized by hypnagogia, which are vivid, dreamlike mentation. These hypnagogic experiences have been shown to incorporate daytime events [START_REF] Solomonova | Di erent temporal patterns of memory incorporations into dreams for laboratory and virtual reality experiences: relation to dreamed locus of control[END_REF]Stickgold et al., 2000;Wamsley et al., 2010a;[START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF]. For example, some subjects reported hypnagogic experiences related to the video game Tetris after playing it (Stickgold et al., 2000).

"

I was just seeing Tetris shapes oating around in my head like they would in the game, falling down, sort of putting them together in my mind.

"

Interestingly, the same research group reported that hypnagogic content was later found in NREM and REM sleep dream contents within the same night of sleep (Wamsley and Stickgold, 2019). Therefore, the dreamlike experiences observed at sleep onset could re ect the rst step of a memory processing that continues during subsequent sleep stages. Indeed, even if still debated, several lines of evidence suggest that dreaming re ects memory processing (for more information, see the box titled 'Dreaming and memory consolidation').

Dreaming and memory processing

Does dream experience re ect the reactivation and possible consolidation of speci c mnemonic content in the sleeping brain? Several lines of evidence point to a positive conclusion [START_REF] Wamsley | Dreaming and O ine Memory Consolidation[END_REF].

• First, there is a continuity in subjective experiences between day and night, so that wake experiences are incorporated into dreams (Fosse et al., 2003;[START_REF] Wamsley | Dreaming and O ine Memory Consolidation[END_REF]. Such an observation suggests that dream contents re ect the re-processing of newly encoded material that is happening during sleep.

• Second, dream phenomenology corresponds to the proposed memory functions of NREM and REM sleep. Indeed, NREM sleep is predominantly associated with hippocampus-dependent declarative memory consolidation, and subjective reports from NREM sleep are more likely to contain episodic memory sources. REM sleep, on the other hand, is predominantly associated with the processing of emotional memories [START_REF] Groch | The role of REM sleep in the processing of emotional memories: evidence from behavior and event-related potentials[END_REF], and REM sleep dream experiences are particularly vivid and emotional.

• Third, there is evidence that dream experiences are linked to improved memory. For example, [START_REF] Fiss | The mnemonic function of dreaming[END_REF] showed that after reading a short story, participants who reported story-related dreams had better memories of the text. Similarly, De Koninck et al. (1990) demonstrated that people who incorporated words from a newly learned language into their dreams learned better the foreign language. [START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF] more recently showed that dreaming of a virtual maze task was associated with a ten-fold increase in performance when compared to participants who reported no related dreams or who thought about the task while awake. They later replicated these ndings after a full night of sleep (Wamsley and Stickgold, 2019).

• Finally, there is some evidence that memory replay is not only associated with neural reactivation but also with concomitant subjective experience. For instance, [START_REF] Wamsley | The expression of trace conditioning during non-REM sleep and its relation to subjective experience[END_REF] showed that replaying fear-conditioned cues had an impact on the emotional valence of dreams.

In summary, some evidence suggests that dreams re ect sleep-dependent memory processing. However, the link between dreaming and memory consolidation is still speculative, as contradictory ndings have been reported in the literature as well (for a summary table of the pros and cons, see Plailly et al. (2019)).

Despite evidence suggesting a link between subjective experiences at sleep onset and memory, almost no research has been conducted on the link between the N1 stage and memory processing. One study (Lahl et al., 2008) found that a 6-min nap improved word-recall compared to wakefulness, but the authors did not isolate the N1 stage from deeper sleep stages, preventing us from drawing a rm conclusion about the role of each stage in the observed memory bene t. Their results also contradict previous research, which reported an amnesia of materials encoded in the few minutes before sleep onset (Wyatt et al., 1994;Wyatt et al., 1997). However, in their paradigm, subjects were presented with stimuli while falling asleep, calling into question the source of this memory de cit: did sleep onset interfere with the consolidation or the encoding of those stimuli due to lower executive functions? To this day, the question remains unanswered. Our goal in Study 3 was to assess the fate of newly encoded memories following a brief period of the N1 stage.

Chapter Summary

Sleep is essential for consolidating newly acquired memories. This function is thought to rely on the replay of memory traces, allowing labile memories to be transferred from the hippocampus to the neocortex for long-term memory storage. Some elements suggest that memory processing begins at sleep onset, but the literature on the subject is almost non-existent. We attempted to ll this gap with Study 3. We will see in the next chapters that sleep not only cements newly acquired memories, but also remodels and integrates them into our vast memory storage. This remodeling process can boost creative problem-solving [START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]Llewellyn, 2016), which is presumed to rely on the formation of new associations and ideas through the reorganization of existing knowledge [START_REF] Kenett | A Semantic Network Cartography of the Creative Mind[END_REF][START_REF] Mednick | The associative basis of the creative process[END_REF][START_REF] Schilling | A 'Small-World' Network Model of Cognitive Insight[END_REF]. And here comes the second cognitive function of interest in this thesis: creativity, which is inextricably linked to the rst, memory. However, before delving deeper into the relationship between sleep and creativity, a little digression is required to de ne creativity.

Creativity involves the power to originate, to break away from existing ways of looking at things, to move freely in the realm of the imagination, to create and re-create worlds fully in one's mind, while supervising all this with a critical inner eye.

-Oliver Sacks

4

What is creativity? Creativity is at the heart of daily life activities and is one of humanity's most de ning characteristics. All innovations that have drastically changed our way of life, from the telephone, the internet, spaceships, to works of art, would not exist without creativity. Besides, creativity is no longer exclusively associated with artists and inventors; it has risen to become one of the most sought-after soft skills for companies in the twenty-rst century. Nonetheless, we (researchers and the general public) have a limited understanding of what creativity is or how it works. The very idea of creativity is vague, with no conclusive consensus in the literature as to what constitutes creativity1 . This 'de nition problem' of creativity is highlighted in a review done by [START_REF] Plucker | Why isn't creativity more important to educational psychologists? Potentials, pitfalls, and future directions in creativity research[END_REF], which shows that only 38% of 90 studies on creativity provided an actual de nition of the term 'creativity'.

To put it another way, 62% of these studies did not de ne what they were working on. Is there, however, a satisfactory de nition of creativity? What is the common denominator among all examples considered to be creative? What conditions are necessary and su cient for creativity? We will attempt to address these questions in this chapter.

The de nition of creativity

There have been numerous attempts to de ne creativity. For example, the researcher Bill [START_REF] Lucas | Creative teaching, teaching creativity and creative learning[END_REF] de ned it as "a state of mind in which all our intelligences are working together", while the psychologist E. Paul [START_REF] Torrance | Scienti c views of creativity and factors a ecting its growth[END_REF] proposed that it is "the process of sensing problems or gaps in information, then identifying the di culties and seeking solutions through trial and error or through forming hypotheses". The author Ken [START_REF] Robinson | Out of our minds: learning to be creative[END_REF] o ered a more contemporary de nition of creativity as "imaginative processes with outcomes that are original and of value". These examples show that part of the reason for this diversity of de nitions is that creativity can be viewed as a personality trait (who we are), a process (what we do) or a product (what we make), a multifactorial aspect of creativity that adds to the di culty of studying it.

Besides, I am con dent that if I polled my hundreds of readers (one can dream!), each one would give me a di erent de nition of creativity. Some would nd that a particular work of art is creative, while others would say it is not. And I'm talking about art on purpose. Indeed, when it comes to creativity, art is often the rst thing that comes to mind. Most people believe that a musician is more creative than a corporate executive. Yet, a musician may be excellent at following his chords (a virtuoso) but incapable of improvising a piece of music, whereas the corporate executive may have to nd novel solutions to his company's everyday problems. Therefore, just as the simple equation 'REM sleep = dreams' is false, so is the equation 'creativity = art', as we will see. To begin, let's draw some examples of creative achievements to help us identify the indicators of creativity.

Creative examples To demonstrate the importance of creativity, let us begin with an extreme case in which creativity literally saved lives. We are in April 1970, and the Apollo 13 spacecraft has just launched with the mission of reaching the Moon. However, before the crew arrived at their destination, an explosion damaged the aircraft, forcing them to abandon their command module and to crowd into the small lunar carrier. The amount of carbon dioxide produced by the astronauts exceeded the capacity of the lithium hydroxide lters in the lunar module. Astronauts become hypoxic as they lose breathable air. They would pass out if a quick solution was not found.

Photograph of the mailbox

They had one option: swap the lunar module lters for the command module lters. Yet, both had di erent shapes. Their survival literally depended on putting a square peg into a round hole, using only the pieces available on the board. They did so by creating the so-called 'mailbox', out of cardstock, plastic bag, and tape, and successfully kept carbon dioxide levels low during their return trip to Earth. As a rst hint as to how creativity will be de ned, here are [START_REF] King | Apollo 13 creativity: in-the-box innovation[END_REF]'s words on this event: "The team generated novel and workable solutions to problems they had neither encountered nor imagined before". Let us take another example, Charles Darwin's theory of evolution. This theory did not appear out of nowhere; Darwin embarked on a long, worldwide voyage around the world aboard the ship Beagles. His numerous observations led him to develop his theory of evolution based on the mechanism of natural selection: traits that help organisms survive and reproduce become more prevalent in a population over time.

Darwin therefore combined the data he had into a novel and coherent theory. Let us now conclude with an artistic example from a renowned poet, André Breton. Breton sought to explore the unconscious mind's creative powers in order to "resolve the previously contradictory conditions of dream and reality into an absolute reality, a super-reality" (Breton, 1924). To accomplish this, he founded a novel cultural movement known as 'surrealism' associated with several art-making methods, such as the 'surrealism automatism' that consists of suppressing conscious control over the making process, or the 'exquisite corpse' (cadavre exquis, in French), which promotes the creation of bizarre, dreamlike graphisms. Something is considered creative if it is both original and appropriate (i.e., adapted to a given environment). This illustration was inspired by [START_REF] Abraham | The Neuroscience of Creativity[END_REF].

The rst one is fairly obvious: each of the preceding examples involves the generation of a novel, original idea in some way. Quantitatively, an original idea is statistically rare. In fact, originality is the main de ning attribute of creativity [START_REF] Runco | The Standard De nition of Creativity[END_REF]), but it is not the only one. Indeed, novelty alone does not make something creative2 , it must be accompanied by a second component: appropriateness. The mailbox was original, but more importantly, it was functional and adapted to the context (i.e., the need to reduce the carbon dioxide level). Appropriateness thus refers to the value or adequacy of the response in terms of how meaningful or suitable it is in a given context. With these two elements, we now have the common de nition of creativity (Sternberg and Lubart, 1999): " Creativity is the ability to produce work that is both novel (i.e., original, unexpected) and appropriate (i.e., useful, adaptive given the task constraints).

"

Although the scienti c community broadly agrees that originality and appropriateness are the two de ning factors of creativity, some argue that additional factors should be considered in order to capture the full complexity of creativity. Among them is the factor of 'surprise' (i.e., non-obvious) [START_REF] Boden | The Creative Mind : Myths and Mechanisms[END_REF], which is di cult to distinguish from originality. [START_REF] Mackinnon | The Identi cation of Creativity[END_REF] proposed an alternative factor, the 'realization' of the idea. According to him, "true creativity involves a sustaining of the original insight, an evaluation and elaboration of it, a developing of it to the full" [START_REF] Mackinnon | The Identi cation of Creativity[END_REF]. In other words, one may speak of true creativity only when an idea has been instantiated, allowing it to be evaluated and appreciated. This factor highlights the importance of di erentiating between creative potential and creative achievement. In this manuscript, we will stick to the aforementioned de nition of creativity, which is the most widely accepted and allows us to consider creative potential as well.

The magnitude of creativity One nal point to mull over when de ning creativity is the degree of creativity of a given idea/product. Kaufman and A Beghetto (2009) developed the 4C model of creativity, which has four levels of creative magnitude:

1. Mini-c, or personal creativity, is particularly relevant to children because it concerns "novel and personally meaningful interpretation of experiences, actions, and events" (Beghetto and Kaufman, 2007) that are not considered creative by others (e.g., a child's drawing hidden in a drawer that no one nds creative).

2. Little-c: everyday "creative actions in which the non-expert may participate" and that are recognized as creative by at least some individuals (e.g., the drawing has been framed and is now displayed in the family's living room).

3. Pro-c: "the developmental and e ortful progression beyond Little-c that has not yet attained Big-C status".

Big-C: eminent creative contributions.

This distinction is important since the productions of an eminent Big-C creator, such as Darwin or Breton, cannot be compared to a child's drawing, even if both meet the criteria of creativity. But personal, Little-c creativity should not be disregarded, especially in laboratories studying cognition. Indeed, the central question is not so much why there are geniuses (although this is an interesting question), but rather what mechanisms promote creativity in the average person. Therefore, this thesis will focus primarily on the Little-c magnitude of creativity. Now that we have clari ed what the term 'creativity' will mean throughout the manuscript, we can move on to the next hurdle: how to measure creativity? 4.2 Approaches to measuring creativity

Questionnaires

Not only is creativity di cult to de ne, it is also very di cult to evaluate. There are numerous approaches to measuring creativity. Self-report questionnaires are used to inquire about personality traits related to creativity (e.g., openness to experience, neuroticism: tendency towards self-doubt, etc...) as well as accomplishments in creative domains, such as the Creative Achievement Questionnaire (CAQ) developed by Carson et al. (2005). The CAQ assesses creative achievement across 10 domains of creativity (e.g., music, scienti c discovery, culinary arts). The subjects assign a score between 0 to 7 to each domain. In the music domain, for example, the subjects score 0 if they have 'no training or recognized talent in this area', 1 if they play 'one or more musical instruments pro ciently', up to 7 if their 'compositions have been critiqued in a national publication'. In Study 1, we used both types of questionnaires. Although useful and simple to use, these questionnaires have limitations because they are based on subjective reports that are prone to biases. For example, subjects may overestimate their level of creativity to portray a positive image of themselves, or on the contrary, they may underestimate it if they are predisposed to believe, for example, that creativity is reserved for artists. Furthermore, a subject may have a high creative potential but not use it for creative achievement, for instance, due to a lack of con dence or time. For these reasons, this approach is often combined with objective measures of creativity in order to obtain a complete picture (as we did in Study 1).

Objective measures

A quick look at the literature on creativity shows that studies have employed a wide variety of tasks to measure creativity. I will present a non-exhaustive list of them (mostly restricted to sleep studies given that it is the primary subject of this thesis).

Since there is no de nitive consensus (to my knowledge) on the classi cation of creative measures, I divided them into 3 main categories.

Network exploration

As we will cover in more depth soon, creativity is thought to rely on the structure of our memory network and our ability to move from one concept to another in order to generate new ideas and solve problems [START_REF] Kenett | A Semantic Network Cartography of the Creative Mind[END_REF][START_REF] Mednick | The associative basis of the creative process[END_REF]. Several tasks have been developed to evaluate this network exploration ability, which can be subdivided into two main modes of creative thinking, divergent thinking and convergent thinking (mirroring the originality and appropriateness components of creativity).

Divergent-exploratory thinking is clearly the backbone of creativity assessment.

It assesses a person's ability to generate as many solutions as possible based on a given stimulus, much like a search engine (Lubart et al., 2011). The dominant test in this eld is the Torrance Tests of creative thinking [START_REF] Torrance | Predictive validity of the Torrance Tests of Creative Thinking[END_REF] that encompasses a great variety of divergent-thinking tasks. Amongst them is the 'Alternate Uses Task' (AUT), in which participants are asked to come up with as many di erent uses for common objects as they can. For example, a cardboard box could be used as storage, but also as a soundbox, a desk, a bird feeder by lling it with seeds, or as fuel to heat a space. Originality (novelty), exibility (category switches), uency (number of di erent uses), and elaboration (descriptiveness) are the evaluation criteria.

Convergent-integrative thinking Most studies, however, focus solely on this aspect of creativity, ignoring its polar opposite, the 'convergent-integrative thinking'. Here, subjects must integrate several elements into a coherent and original synthesis (the 'useful, adaptive' part of the de nition of creativity). The Remote Associates Test (RAT) developed by [START_REF] Mednick | The associative basis of the creative process[END_REF], is one of the most widely used task for testing convergent thinking. Participants in the RAT are given three words (e.g., cookie, heart, and sixteen) and asked to nd a fourth that connects all three (i.e., sweet; cookies are sweet, sweet sixteen, and sweetheart). Since then, several homologous variants of this task have been developed. [START_REF] Bowden | Normative data for 144 compound remote associate problems[END_REF] created the Compound Remote Associates (CRA) to alleviate the di culties in comparing RATs due to the variety of solution strategies (e.g., synonyms, associations, compounds, etc...). The solution word in the CRA problem is always related to the triad words in the same way, namely by forming a compound word (e.g., the solution to the triad Age/Mile/Sand is Stone, as it forms the compounds Stoneage, Milestone, and Sandstone). Later, [START_REF] Bendetowicz | Brain morphometry predicts individual creative potential and the ability to combine remote ideas[END_REF] developed the Combination Association Task, in which they varied the semantic distance between the cue words and the solution word to assess the participants' ability to combine word associates as a function of their semantic distance. In all cases, what is considered creative here is not the nal solution per se, but the capacity to form a novel connection between distant concepts. Of note, this task could also be considered to have a divergent component, given that the rst solution that comes to mind is often incorrect, prompting the subjects to seek alternative solutions. In the same vein, [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF] developed the associational breadth task: subjects are given words and must respond with the rst three words that come to mind as being meaningfully associated.

Interestingly, there is a test battery known as 'The Evaluation of Creative Potential' (EPoC) that assesses these two key modes of creative thinking in two di erent domains of expression (graphic and verbal) using both abstract and concrete stimuli (Lubart et al., 2011). Divergent-exploratory tasks require participants to generate as many ideas as possible based on a single stimulus, such as inventing as many di erent endings to one story as possible (verbal) or generating as many drawings as possible incorporating one shape (graphic). The convergent-integrative tests involve combining multiple elements to form a unique production, such as inventing an entire story with three main imposed characters (verbal) or creating one original drawing that incorporates a set of imposed forms (graphic). Thus, in contrast to early creativity research, which was mainly centered on a single measure of creative potential (mostly divergent thinking), the EPoC test allows for the study of creativity's multidimensionality (Barbot et al., 2016). We thus decided to use this battery test in conjunction with the previously mentioned self-reported questionnaires in Study 1.

What is creativity? 56

Gist abstraction

The second task category could fall under the previous convergent group. However, I have opted to separate them because the tasks that I will present here do not need accessing and exploring our long-term memory store, but rather require identifying a gist within novel memory traces. Precisely, in these tasks that we used in Studies 2 and 3, subjects are given a strategy for completing a task, but there is another, much easier and faster way to do it that relies on a hidden regularity. For example, in Study 2, we used the Number Reduction Task (NRT; [START_REF] Thurstone | Factorial studies of intelligence[END_REF] , which has been extensively used in the literature [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF][START_REF] Debarnot | Sleep does not facilitate insight in older adults[END_REF]Wagner et al., 2004;[START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF]. In the NRT, participants are given 8digit strings of 1, 4 and 9 numbers and must determine the string's nal solution.

They begin with the rst two digits and then use their response along with the next digit to determine the next response and so on until they reach the end of the string and report the nal solution. There are two rules: if the two digits are the same, they have to report that number (e.g., 4-4, response: 4), but if they are di erent, they have to report the remaining number (e.g., 1-4, response: 9). Not mentioned to the subjects, there is a hidden regularity in each string, such that the second response is always the nal solution (Figure 4.2). Finding the hidden rule thus requires identifying a common pattern (i.e., the 'gist') among all mnesic traces. But can it be classi ed as a creative task? Returning to our earlier de nition of creativity, we can see that the NRT satis es the requirements. Indeed, discovering the hidden regularity of the NRT o ers subjects a solving strategy that is both new (i.e., not the one speci ed in the instructions) and e cient (i.e., they solve the task much more easily and faster with it).

4. What is creativity? 57

Problem-solving

Finally, another way to measure creativity is to test participants' ability to solve problems.

In contrast to previous tasks, problem-solving tasks would require more than 'simply' associating ideas. Subjects would need to overcome the typical implicit mental assumptions elicited by these problems (which bias the search in a wrong direction), and reorganize their mental representation of the problem to broaden the space of possible solutions. The 9 dots problem.

Upper panel: classical unsuccessful attempt, and lower panel: example of one solution.

The most cited example is the nine dots problem, which may actually have inspired the metaphor to 'think outside the box' (i.e., think unconventionally). This problem requires participants to connect all of the 9 dots using no more than 4 continuous straight lines. The di culty stems from people's natural tendency to form an imaginary square with the dots, which forces them to draw the lines within this boundary (Figure 4.3). Thus, resolving this problem requires literally 'thinking outside the box' (the originality component of the de nition of creativity) and tracing lines outside of this implicit square (Figure 4.3). But other types of problems have also been utilized, such as riddles [START_REF] Durso | Graph-Theoretic Con rmation of Restructuring during Insight[END_REF] or video games [START_REF] Beijamini | After Being Challenged by a Video Game Problem, Sleep Increases the Chance to Solve It[END_REF][START_REF] Beijamini | Sleep Facilitates Problem Solving With No Additional Gain Through Targeted Memory Reactivation[END_REF]. In their video game, subjects control a character, and their goal is to bring it to a nal position by moving objects around.

Then, to complete the nal level, they must apply previously learned strategies but in a completely di erent way (i.e., picking up the character itself instead of objects).

Interestingly, almost all of these problems are also known as 'insight problems', referring to the fact that participants solve them in a Eureka moment (i.e., the solution suddenly comes to mind), which leads us to the following section. Indeed, now that we have established what creativity is and how to measure it, the natural follow-up question is: how does creativity emerge? 4.3 How does creativity emerge?

The Eureka phenomenon

Attempting to answer the question of how creative emerges is, once again, a daunting task. The di culty stems primarily from the ethereal nature of creativity: we appear to have no control over it, no speci c recipe for forcing creative ideas. Instead, they seemingly appear out of nowhere in these so-called 'Eureka' moments, sudden epiphanies most famously illustrated by Newton's falling apple, which would have given birth to the concept of gravity. Consistently, artists and scientists frequently report not knowing how they came up with new ideas, sometimes even claiming that someone uttered the solution to them. Besides, during Greco-Roman times, creativity was regarded as a divine trait over which humans had no control. It was purely mythic inspiration provided by gods, as illustrated in the excerpt below from Plato's Ion: "

The lyric poets are not in their senses when they make these lovely lyric poems. No, when once they launch into harmony and rhythm, they are seized with the Bacchic transport, and are possessed... it is not they who utter these precious revelations while their mind is not within them, but... it is God himself who speaks, and through them becomes articulate to us.

"

As previously stated, the scienti c community refers to this phenomenon as 'insight'.

It refers to the sudden discovery of a solution to a problem in an 'all-or-none fashion', without e ort or the ability to articulate the mental steps that led to the solution, and is usually accompanied by a positive surprise [START_REF] Kounios | The cognitive neuroscience of insight[END_REF]. Of note, it is distinct from the clinical term insight that refers to the awareness of one's own psychiatric symptoms. There is a wealth of literature on this phenomenon, particularly on the neural signature preceding the moment of Eureka (for a review, see [START_REF] Kounios | The cognitive neuroscience of insight[END_REF]), but this is beyond the scope of this manuscript. Here, we will focus on the process that may result in this phenomenon rather than on the phenomenon itself.

Crucially, we must bear in mind that creativity is not the only process in which we have poor introspection. Many processes (e.g., perception, decision-making, learning) occur unconsciously; this prevents our mind from being inundated with information.

Accordingly, our understanding of creativity has shifted from an occult mystery to an 'introspective mystery' [START_REF] Boden | Creativity as a Neuroscienti c Mystery[END_REF]. Creativity is now viewed as a process that can be acquired by anyone, rather than a gift bestowed by a muse on the fortunate.

The associative basis of the creative process

"

There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations inde nitely; but they are the same old pieces of coloured glass that have been in use through all the ages.

Mark Twain "

The contemporary view is that creativity does not originate ex nihilo but rather builds on what already exists, on our experiences. This concept of 'combinational creativity' [START_REF] Boden | Creativity as a Neuroscienti c Mystery[END_REF][START_REF] Boden | The Creative Mind : Myths and Mechanisms[END_REF][START_REF] Mednick | The associative basis of the creative process[END_REF] postulates that novel ideas emerge from combinations, permutations and tweaking of existing ideas. Simply put, creative ideas would involve the generation of unfamiliar combinations of familiar ideas (a lmmaker Kirby Ferguson made a series on the topic if you want to have a look 3 ).

The memory network In theoretical frameworks, like a tree branching out (Figure 4.4A), accumulated knowledge is represented as a semantic network [START_REF] Collins | Retrieval time from semantic memory[END_REF][START_REF] Steyvers | The large-scale structure of semantic networks: statistical analyses and a model of semantic growth[END_REF]. Precisely, each item in memory is represented by nodes that are connected to one another via connections, so that closely related concepts are directly linked. The retrieval of knowledge from such a network is supposedly based on a process known as 'spreading activation' [START_REF] Anderson | A spreading activation theory of memory[END_REF][START_REF] Collins | A spreading-activation theory of semantic processing[END_REF], in which the activation of one node spreads to neighboring nodes by virtue of the semantic closeness of their associative links (forged by experience). This explains why activating an item like 'white' results in the fast retrieval of strongly semantically-related concepts like 'snow' or 'black' (Figure 4.4B).

"

The di culty lies, not in the new ideas, but in escaping from the old ones, which ramify into every corner of our mind.

John Maynard Keynes "

Following this line, it appears natural that someone who lacks the necessary elements in his/her repertoire will be unable to come up with creative ideas. However, if the necessary knowledge is acquired, what mechanism may be responsible for sudden bursts of creative sparks within a given individual network?

Most theories agree that, to generate novel ideas, one needs to break free from stereotypical associations and access more remote associations in his memory network.

However, it is important to remember that creativity is about more than coming up with new ideas; it is also about integrating a bunch of inputs into a coherent picture (the convergent part of creativity, which is most evident in problem-solving tasks). A simple network exploration would not be su cient in this case; it would also require network reorganization. Besides, [START_REF] Mednick | The associative basis of the creative process[END_REF] de nes the creative process as "the forming of associative elements into new combinations which either meet speci ed requirements or are in some way useful". More recently, a theoretical work proposed precise mechanisms for insight problem-solving [START_REF] Schilling | A 'Small-World' Network Model of Cognitive Insight[END_REF]. [START_REF] Schilling | A 'Small-World' Network Model of Cognitive Insight[END_REF] posited that insight would emerge when nodes/edges in the semantic network are created or modi ed, resulting in the creation of a network 'shortcut'. This shortcut would allow for a decrease in path length between some nodes, a change in the individual's representation of the problem, and/or the formation of a cascade of new connections, ultimately leading to the solution of the problem.

Creative problem-solving and network reorganization However, researchers rarely provide direct evidence of this reorganization (certainly owing to a lack of quantitative measures to evaluate it); rather, they infer that when people solve a problem, they reorganize it. To prove that reorganization is really the mechanism yielding creative ideas would require testing how a given subject's network evolves as he/she works on a creative problem and whether quantitative changes in the network organization can be related to creative sparks (a pre/post paradigm). There are now emerging network science tools to directly reconstruct individual semantic networks from their semantic associations collected in a behavioral task, such as the relatedjudgment task. Here, participants are asked to rate the semantic relatedness of pairs of words, which then represent the edges of the network while the words represent the nodes. Only one study, to my knowledge, used such a method to explore the possible connection between problem-solving and network restructuring [START_REF] Durso | Graph-Theoretic Con rmation of Restructuring during Insight[END_REF]. In their study, participants were given a story and had to come up with an explanation to make sense of it. Here is the story: "A man walks into a bar and asks for a glass of water. The bartender points a shotgun at the man. The man says 'Thank you', and walks out."

Successfully solving this riddle rst requires rephrasing the vague question 'How to make sense of this story?' as 'Why did the sight of the shotgun replace the man's need for a glass of water?'. Participants were given 2 hours to attempt to solve this puzzle. Then, based on their answers, subjects were divided into solvers who discovered the riddle solution (i.e., because the guy had hiccups, which could be remedied by either water or being surprised by the sight of the gun) and non-solvers who did not. Subjects then had to rate the relatedness of word pairs so that researchers could construct semantic graphs that would be a proxy of participants' mental representation of the problem. The words presented in the relatedness judgment task could be classi ed into three categories: those that appeared in the puzzle (e.g., bar, glass of water), those that were relevant to the solution (e.g., surprise, remedy), and those that were relevant to the context of the story but not to the solutions (e.g., objects found in a bar such as TV, pretzels).

As expected, [START_REF] Durso | Graph-Theoretic Con rmation of Restructuring during Insight[END_REF] demonstrated that solvers' graphs di ered from non-solvers' graphs (the correlation between the two graphs was essentially zero), with a shift in the graph's central node from 'bartender' (in non-solvers) to 'relieved' (in solvers; Figure 4.5). Additionally, crucial pairs of words for the solution were only found in the solvers' graph, where all hiccough remedies (paper bag, surprise, glass of water) were connected to the remedy node. Only in the solvers' graphs were the concepts 'surprise' and 'remedy', as well as 'thank you ' and 'relieved',connected (Figure 4.5). To ascertain whether the process of arriving at the solution was important to the solvers' network structure, they conducted a control in which they directly gave the solution to the subjects. In this case, subjects' semantic network was not the same as the solvers' graph, indicating that attempting to solve the puzzle was necessary for network restructuring. Progressive insightrelated network reorganization. Adapted from [START_REF] Durso | Graph-Theoretic Con rmation of Restructuring during Insight[END_REF].

Still, one might wonder whether it was the restructuring that led to the solution or the other way around (the inevitable chicken and egg conundrum...). To try to answer this question, the authors tracked the evolution of the participants' graphs from the beginning (before they heard the story, 'Naive' condition), through the story-telling ('Story'), and nally to the solution ('Solved'), passing through two time periods before solving ('Solved-1', 'Solved-2'). They observed a progressive restructuring of participants' network (at the group level) up to the point of solution, implying that restructuring was indeed important in creative problemsolving (Figure 4.6). Surprisingly, this study was overlooked by researchers and, to my knowledge, these ndings have not been replicated so far.

Altogether, this mechanism of memory network exploration and recon guration could explain how a creative idea or a solution to a problem can emerge (state), but also why some people are more creative than others (trait).

Trait vs. state Indeed, according to the concept of combinatorial creativity, people with a broader range of knowledge and experience would have more concepts to draw on than those with narrower foundations, and thus would be more creative. Consistently, openness to experience -'the drive for cognitive exploration of inner and outer experience' -is the single strongest and most consistent trait to predict creative achievement among all the personality traits tested [START_REF] Kaufman | Openness to Experience and Intellect Di erentially Predict Creative Achievement in the Arts and Sciences[END_REF]. Besides, recent studies highlight di erences in the memory networks of high and low creative individuals [START_REF] Kenett | Flexibility of thought in high creative individuals represented by percolation analysis[END_REF][START_REF] Kenett | Investigating the structure of semantic networks in low and high creative persons[END_REF]. [START_REF] Kenett | Investigating the structure of semantic networks in low and high creative persons[END_REF], for example, demonstrated that the semantic network of highly creative individuals is less spread out, less modular (the network is made up of less sub-communities), and more connected (Figure 4.7). The higher small worldness network found in high creative people would thus allow for better network connectivity, facilitating the connection of remote associations. Recently, [START_REF] Benedek | Revisiting Mednick's Model on Creativity-Related Di erences in Associative Hierarchies. Evidence for a Common Path to Uncommon Thought[END_REF] also tested [START_REF] Mednick | The associative basis of the creative process[END_REF]'s model, according to which the network associative hierarchy of low creative individuals would be steep (i.e., with a few stereotypical, strong associations: the word table would only activate the word chair), in contrast to a at one in high creative people (i.e., a less biased, rigid associative network; the word table is more likely to activate remote concepts such as leg or food). However, [START_REF] Benedek | Revisiting Mednick's Model on Creativity-Related Di erences in Associative Hierarchies. Evidence for a Common Path to Uncommon Thought[END_REF] discovered no di erence in associative hierarchy between low and high creative people, but creative people had higher associative uency and more uncommon responses. Their results indicate that high and low creative people have the same general network structure and follow the same common to uncommon association path, but they do so at a much faster speed, allowing them to retrieve more distant associations earlier. [START_REF] Benedek | Revisiting Mednick's Model on Creativity-Related Di erences in Associative Hierarchies. Evidence for a Common Path to Uncommon Thought[END_REF] concluded that "Creativity may not be related to a special organization of associative memory, but rather to a more e ective way of accessing its contents". Why so? Do these subjects bene t from peculiar conditions that enable rapid access to far-o concepts? Knowing them would provide essential tools for mastering creative inspiration. These are all questions that we will try to answer in the next section.

Incubation, the key to unlocking creativity

When reviewing artists' anecdotes about the birth of their ideas (see the box entitled 'Anecdotes: creative incubators'), one thing always comes up: ideas frequently emerge out of nowhere during periods of diverted attention, when our minds are o wandering elsewhere, when we zone out.

Anecdotes: creative incubators

• "In the shower, with the hot water coming down, you've left the real world behind, and very frequently things open up for you. " Woody Allen

• "How vain it is to sit down to write when you have not stood up to live! Methinks a that the moment my legs begin to move, my thoughts begin to ow. " Henry Davis Thoreau

• Charles Darwin took daily strolls and dubbed it 'his thinking path'.

• "All truly great thoughts are conceived by walking". Friedrich Nietzsche

• "Solvitur ambulando" (Latin for: 'It is solved by walking'). Diogene

• Ludwig Wittgenstein coined the concept of the 'Three Bs' for unleashing creative power; 3Bs for bed, bath, and bus, all activities during which daydreaming is favored. b a Old English to say 'I think'. b I'd like to express my gratitude to Scott Barry Kaufman, a researcher on creativity, who has been a great source of inspiration for me. The majority of the anecdotes in this box come from his book Wired to Create.

A similar concept is outlined in the canonical 'four-stage model' of creativity, which was developed by the British psychologist Graham Wallas in his book The Art of Thought.

Based on his observations of eminent creators, [START_REF] Wallas | The Art of Thought[END_REF] proposed that the creative process follows a four-step pattern:

1. The preparation phase during which the creator painstakingly searches for a solution to a problem (or a new idea).

2. Then comes the incubation phase, a period of rest/distraction during which the creator sets aside the problem and allows the unconscious to take over, engaging in what Einstein referred to as 'combinatory play'.

3. After the incubation phase comes the illumination, which corresponds to the sudden and conscious discovery of a solution to the problem, the aforementioned phenomenon of 'insight'.

4. The veri cation phase is associated with conscious deliberation in order to evaluate and nalize the details of the solution.

The Wallas' 4-stage model. 5 5 Illustration from the website Hilmylab.

What is creativity?

67

A second model, Geneplore (a contraction of the words Generate and Explore), similarly postulates a rst phase during which ideas are generated and then a second phase during which these ideas are explored and evaluated for their utility and suitability. This cycle would repeat until a satisfactory solution is reached [START_REF] Finke | Creative cognition: Theory, research, and applications[END_REF]. In line with this model, Colin [START_REF] Martindale | Creativity, primordial cognition, and personality[END_REF] developed his 'conceptual-primordial cognition continuum', in which states of mind range from 'ordinary, wakeful, reality-oriented, rational cognition' to primordial cognition, which includes 'several types of fantasy and reveries up to dreaming'. Accordingly, the relationship between this continuum and creativity can be viewed as an inverted-U curve (Figure 4.8), with intermediate levels of disinhibition (such as in reveries) being associated with increased creativity [START_REF] Abraham | Is there an inverted-U relationship between creativity and psychopathology[END_REF]. Remember this curve because we will encounter a similar one later on.

As we can see, regardless of the model, they all include a stage of idea generation, facilitated by diverted attention, followed by a stage of veri cation. Several studies support these theoretical frameworks by showing that incubation has a positive impact on creativity (for reviews, see [START_REF] Ritter | Creativity-the unconscious foundations of the incubation period[END_REF] and [START_REF] Sio | Does incubation enhance problem solving? A meta-analytic review[END_REF]). In the next section, we will try to elucidate the neural correlates of these relevant processes.

Neuroscience of creativity

The Default Mode Network (DMN), which was discovered accidentally, is now regarded as one of the most important brain networks 6 , particularly in the eld of creativity. This brain network, also known as the 'looking in' or 'imagination network' (for more information on the brain regions it encompasses, see Figure 4.9), is active whenever a 6 Long live serendipity! person is not focused on the external world, but rather is engaged in spontaneous, internally-directed thoughts [START_REF] Christo | Mind-wandering as spontaneous thought: a dynamic framework[END_REF], such as recalling the past and imagining the future [START_REF] Andrews-Hanna | Evidence for the Default Network's Role in Spontaneous Cognition[END_REF]. Its opposite network is the Central Executive Network (CEN) or 'looking out' network, which is active whenever a person is focused on a speci c task, on the external world. A third brain network, the Salience Network, is also important. It basically determines where attention is focused (inside or outside). For example, if you are in a dull diner, your salience network will pass the baton to the DMN, and your thoughts will drift away. Then, all of a sudden, somebody calls your name, the salience network switches from the DMN to the CEN activation, and you are now focused on what the person is telling you. What happens when someone attempts to generate new ideas? Scientists recorded brain activity while professional jazz pianists were improvising [START_REF] Limb | Neural Substrates of Spontaneous Musical Performance: An fMRI Study of Jazz Improvisation[END_REF]. In comparison to playing an over-learned musical sequence, improvisation was associated with an increased activation of the DMN (particularly the medial prefrontal cortex), as well as a deactivation of the dorso-lateral prefrontal cortex (dlPFC), a CEN core region. The same team later replicated these ndings during lyrical improvisation [START_REF] Liu | Neural Correlates of Lyrical Improvisation: An fMRI Study of Freestyle Rap[END_REF] and poetry composition [START_REF] Liu | Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process[END_REF], both of which were associated with a decrease in the dlPFC (cognitive control). Such a reduction in top-down control would aid in escaping the dominant, obvious ideas, therefore allowing for further exploration into our associative web in search of non-obvious ideas. This stepwise network exploration certainly explains why, in divergent-thinking tasks, ideas become more creative with time [START_REF] Beaty | Why do ideas get more creative across time? An executive interpretation of the serial order e ect in divergent thinking tasks[END_REF].

However, the picture is not so simple. Most of the aforementioned studies only asked subjects to come up with new ideas, but it is important to remember that creativity is not solely about novelty, but also about appropriateness. 4.5 The ideal cocktail for creative sparks [START_REF] Ellamil | Evaluative and generative modes of thought during the creative process[END_REF] developed a novel paradigm, closer to real-life creative problems, for evaluating the two fundamental components of the creative process, namely idea generation and evaluation. They speci cally asked participants to design book covers in an fMRI scanner, while alternating between the generation and the evaluation of their ideas. As expected, idea generation required the recruitment of the medial temporal lobe regions (i.e., the DMN), but creative evaluation required the joint activation of regions from both the central executive and default mode networks. This last nding is surprising given that these two networks are normally anti-correlated (i.e., when one is active, the other is silent; [START_REF] Fox | The human brain is intrinsically organized into dynamic, anticorrelated functional networks[END_REF] and [START_REF] Fox | The Global Signal and Observed Anticorrelated Resting State Brain Networks[END_REF]). Based on this unexpected observation, the authors concluded: "What creative individuals may share in common is a heightened ability to engage in contradictory modes of thought [. . . ], such as deliberate and spontaneous processing".

Interaction process-trait Follow-up studies con rmed this conclusion. For example, [START_REF] Takeuchi | Failing to deactivate: the association between brain activity during a working memory task and creativity[END_REF] observed participants' brain activity while they performed an e ortful working memory task. They found that the more creative the participants were, the less their precuneus (a part of the DMN) was deactivated. Given that this region is normally silent when one is focused on a task, the fact that it remains active in creative individuals emphasizes the notion that "an inability to suppress seemingly unnecessary cognitive activity may actually help creative subjects in associating two ideas represented in di erent networks" [START_REF] Takeuchi | Failing to deactivate: the association between brain activity during a working memory task and creativity[END_REF].

With these ndings in mind, it is easy to anticipate why creative individuals see things di erently than others: they may frequently be in an 'in-between' zone (this should ring a bell...), with an inside and outside focus at the same time. Interestingly, the aforementioned personality trait 'openness to experience' associated with creativity, is scored on items like 'I see beauty in things that others don't' or 'I am a daydreamer'.

Thus, creative people would be more frequently in a state of daydreaming, often looking for new associations and paying attention to details that others may dismiss as unimportant. This idea was beautifully expressed by Edgar Allan Poe: " They who dream by day are cognizant of many things which escape those who only dream by night. In their gray visions they obtain glimpses of eternity, and thrill, in waking, to nd that they have been upon the verge of the great secret.

"

If you have made it this far, rst of all, thank you and congratulations for your bravery. Second, you should notice a pattern emerging: an 'in-between' state, such as the one found at sleep onset and in narcolepsy, may be an ideal cocktail for creative sparks. . .

Chapter Summary

Creativity is a multifaceted concept entailing two de ning components: originality and suitability. The exploration and reorganization of our memory network would enable the formation of novel and uncommon associations, and thus the birth of creative ideas. This process would explain both why some individuals are more creative than others (e.g., more knowledge, more connected networks) and how ideas emerge in a given individual (regardless of his/her creative trait). The latter creative process would be facilitated by disinhibition and attentional shifts during an incubation period. Sleep, and particularly sleep onset, could be an ideal incubation period for creative inspiration, as we have seen that highly creative individuals maintain a balance between inner and outer attention, a kind of 'inbetween' state that we similarly experience when we fall asleep. As such, I invite you to read the last main chapter of this Introduction, titled 'Sleeping on it, awakening creativity'.

It is a common experience that a problem di cult at night is resolved in the morning after the committee of sleep has worked on it.

-John Steinbeck

5

Sleeping on it, awakening creativity Numerous anecdotes attest to the positive e ect of sleep on creativity. For example, Saint-Pol-Roux, a French poet, used to hang a sign on his bedroom door that read: "Do not disturb: Poet at work." (Breton, 1924). This view of sleep as a creative muse has become folk wisdom, to the point where it is frequently advised that when confronted with a di cult problem, one should "Sleep on it". In a questionnaire examining the occurrence of sleep-related insights into real-life problems in the general population [START_REF] Perdomo | Sleep Fosters Insight Into Real-Life Problems[END_REF], the vast majority of participants (80%) reported having experienced this phenomenon at least once in their lives (40% regularly), and attributed these 'Eureka' moments to the sleep mentation. How does it work?

How does sleep boost creativity?

As we saw in the previous chapter, a period of incubation where attention is diverted away from the problem at hand would be bene cial to creativity. Sleep is, by de nition, a period of incubation that could be particularly useful for creative problem-solving for two main reasons. First, sleep is characterized (at least partially) by deactivation of the dorsolateral prefrontal cortex (dlPFC) in conjunction with activation of the default mode network, particularly during REM sleep (as later shown in detail). This cocktail of brain activities could promote the emergence of a more exible cognition, allowing for the exploration of directions that are not taken by an awake mind. Furthermore, sleep not only cements memory traces, but also qualitatively modi es them [START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]. Given that creativity emerges from the recombination of knowledge, as discussed in chapter 4, such a memory reorganization during sleep may be important for creative insights. This sleep-dependent memory restructuring is often described in 3 steps [START_REF] Landmann | The reorganisation of memory during sleep[END_REF][START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]: 1) sleep abstracts the gist from newly encoded memory traces (schema formation), 2) integrates this knowledge into pre-existing schema (schema integration), and 3) deconstructs these schema (schema disintegration) to create new associations. In the following subsections, we will examine the evidence for each of these steps before discussing their relevance.

Artist at work. ©Lucy Doherty

Sleep to generalize

First, sleep (preferably NREM sleep) would aid in the formation of new schema and the ability to generalize across multiple items. Indeed, a great deal of research shows that sleep facilitates the abstraction of commonalities, or 'gist' [START_REF] Batterink | Sleep facilitates learning a new linguistic rule[END_REF][START_REF] Durrant | Overnight consolidation aids the transfer of statistical knowledge from the medial temporal lobe to the striatum[END_REF][START_REF] Durrant | Sleep-dependent consolidation of statistical learning[END_REF][START_REF] Ellenbogen | Human relational memory requires time and sleep[END_REF][START_REF] Gómez | Naps Promote Abstraction in Language-Learning Infants[END_REF][START_REF] Lau | Relational Memory: A Daytime Nap Facilitates the Abstraction of General Concepts[END_REF][START_REF] Lerner | Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules[END_REF][START_REF] Payne | The Role of Sleep in False Memory Formation[END_REF]. For example, [START_REF] Durrant | Sleep-dependent consolidation of statistical learning[END_REF] exposed participants to a series of tones that shared a probabilistically determined sequential structure, and tested for recognition with new sequences that followed the same statistical pattern. They showed that participants who slept (during a nap or a full night of sleep) performed better on the recognition task than subjects who remained awake during the same time period. Additionally, [START_REF] Durrant | Sleep-dependent consolidation of statistical learning[END_REF] reported a positive correlation between task performance improvement and the amount of SWS.

This study thus indicates that sleep (especially SWS here) facilitates the abstraction of statistical patterns.

Gist abstraction and creativity

This process of gist abstraction is not trivial as it can favor the convergent aspect of creativity (i.e., nding a solution to a problem, a coherent synthesis of a problem at hand). Besides, one seminal study (Wagner et al., 2004) showed that 'sleep inspires insight', supposedly by promoting the identi cation of the overall architecture of interwoven information. In their study, they used the aforementioned 'Number Reduction Task' (NRT, [START_REF] Thurstone | Factorial studies of intelligence[END_REF]). As a reminder, participants are given a tedious and time-consuming way of solving mathematical problems for which an undisclosed simpler method exists (i.e., their second computed answer is always the nal solution). Subjects who ended up discovering this rule were consequently much faster, as they reported the answer immediately after the second response was processed. The remaining 'non-solver' subjects were then subdivided into two groups: full night of sleep and wakefulness.

Following retesting, the authors observed that more than twice as many subjects gained insight into this hidden regularity after sleep, compared to wakefulness (Figure 5.1). Importantly, contrary to popular belief, this 'Eureka' moment did not occur immediately after awakening; it took approximately 135 additional trials before subjects gained insight. Thus, rather than generating insight, sleep appears to increase the likelihood of experiencing it, possibly by identifying and strengthening relevant associative links among recently encoded task memories (although the authors did not discuss this possibility and why the Eureka moment was delayed). 2004) also included two control groups. In the rst, subjects remained awake during the day rather than at night ('wake-day' vs. 'wake-night'). In that case, the authors observed a comparable proportion of insight regardless of the wake group (Figure 5.1), indicating that the initial wake group's lower percentage of insight was not due to higher sleep pressure (i.e., following sleep deprivation) or to circadian factors. The authors also examined the converse and showed that increased alertness did not promote insight: subjects who completed the task after sleeping (or 'after wake'; Figure 5.1) without a Pre phase did not show any improvement (Wagner et al., 2004).

They concluded: "Control conditions excluded the idea that this e ect was due to nonspeci c e ects of sleep deprivation, circadian rhythm, or proactive in uences of sleep on subsequent capabilities of problem solving or divergent thinking" (Wagner et al., 2004).

The same team then attempted to replicate this muse e ect of sleep and identify its mechanisms [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF][START_REF] Yordanova | Increased alpha (8-12 Hz) activity during slow wave sleep as a marker for the transition from implicit knowledge to explicit insight[END_REF][START_REF] Yordanova | Di erential associations of early-and late-night sleep with functional brain states promoting insight to abstract task regularity[END_REF]. To do so, they used a split-night paradigm with two groups: an early-night group rich in N3/SWS (i.e., with a learning session at 10 p.m. followed by a 3h nap during the rst half of the night), and a late-night group rich in REM sleep (i.e., with a learning session at 2.30 a.m. followed by a 3h nap during the second half of the night). 2008) also investigated whether prior implicit knowledge of the NRT hidden regularity was required for post-sleep explicit discovery of the rule. If a subject had faster response times for predictable responses (R5-R7) due to the mirror structure, than for unpredictable responses (R2-R4), the subject was considered to exhibit a pre-sleep implicit knowledge. They showed that increased alpha activity during SWS was associated with a higher probability of transforming pre-implicit knowledge into explicit knowledge [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF][START_REF] Yordanova | Increased alpha (8-12 Hz) activity during slow wave sleep as a marker for the transition from implicit knowledge to explicit insight[END_REF]. Later, [START_REF] Debarnot | Sleep does not facilitate insight in older adults[END_REF] reported a similar proportion of insight (50%) as in Wagner et al. (2004) in young adults after a whole night of sleep (even though they did not have a wake control group). Of note, they also reported that this percentage of insight was signi cantly greater than that observed in elderly adults (be it after sleep or wake). This result suggests that memory processing is associated with insight (a topic that we will address below), given that age-related alterations in the prefrontal cortex and hippocampus (two key regions for memory, as previously covered) are frequent [START_REF] Giovanello | Reduced Speci city of Hippocampal and Posterior Ventrolateral Prefrontal Activity during Relational Retrieval in Normal Aging[END_REF].

Altogether, the contradictory ndings exposed above on the NRT leave us uncertain as to what was responsible for the initial ndings of a sleep-dependent boost of insight (Wagner et al., 2004), a point that we aimed at clarifying in Study 2.

The mechanism behind gist abstraction [START_REF] Lewis | Overlapping memory replay during sleep builds cognitive schemata[END_REF] developed the 'information overlap to abstract' (iOtA) framework as a mechanism for explaining how sleep abstracts patterns underlying new memories (Figure 5.3). The iOtA postulates that when overlapping memories are replayed close together in time, areas of overlap are strengthened via Hebbian plasticity. Together with memory degradation (e.g., downscaling), this strengthening would enable "abstracted networks of connected neural nodes to gradually develop into a more detailed or accurate representation of the relations between various experiences, eventually producing what we call a schema" [START_REF] Lewis | Overlapping memory replay during sleep builds cognitive schemata[END_REF]. They use birthday parties as an example. By replaying memories of multiple birthday parties, you may eventually extract the gist that they all involve balloons, cakes, and gifts. Interestingly, a growing body of evidence indicates that replays are not only the temporally compressed reinstantiation, during rest, of sequential neural activity observed during prior active wakefulness. Indeed, replays encompass a wide range of contents, and some of them could help gist abstraction. For instance, [START_REF] Foster | Reverse replay of behavioural sequences in hippocampal place cells during the awake state[END_REF] observed that neuronal replays can occur in a reversal temporal sequence to that observed during behavior, a phenomenon known as 'reverse replay'. Combining forward and reverse replays may, for example, enable the detection of a palindromic structure, such as the 5. Sleeping on it, awakening creativity 78 one present in the NRT. Multiple experiences can also be reactivated concurrently during sleep [START_REF] Kudrimoti | Reactivation of hippocampal cell assemblies: e ects of behavioral state, experience, and EEG dynamics[END_REF]. And even replays of shortcut trajectories that had never been previously experienced have been documented [START_REF] Gupta | Hippocampal Replay Is Not a Simple Function of Experience[END_REF]. This type of shortcut replays is consistent with a role of reactivation in elaborating predictions for future actions through the association of disparate experiences into a coherent, novel representation. Last, unlike highly accurate awake replays (i.e., precise recapitulation of original experiences), sleep replays are more 'noisy' [START_REF] Karlsson | Awake replay of remote experiences in the hippocampus[END_REF], a lower delity that has been theorized to serve memory generalization [START_REF] Roumis | Hippocampal sharp-wave ripples in waking and sleeping states[END_REF].

Altogether, between-memory trace reactivations may enable the extraction of common patterns (gist, Figure 5.4). However, this relationship remains to be proven, given that both processes are rarely studied concurrently in human studies, a gap that Study 3 sought to ll. One study, however, showed that reactivation of a creative task (i.e., AUT) via TMR during sleep bene ted creativity [START_REF] Ritter | Good morning creativity: task reactivation during sleep enhances bene cial e ect of sleep on creative performance[END_REF]. Recently, another study provided similar ndings: playing auditory cues associated with unsolved puzzles led to a 55% improvement after sleep [START_REF] Sanders | Targeted Memory Reactivation During Sleep Improves Next-Day Problem Solving[END_REF].

Sleep to integrate

Second, sleep would be involved in the process of integrating these novel memories into the pre-existing memory stockpile (schema integration; [START_REF] Lewis | Overlapping memory replay during sleep builds cognitive schemata[END_REF] and [START_REF] Tamminen | Sleep Spindle Activity is Associated with the Integration of New Memories and Existing Knowledge[END_REF]). Typically, studies in this eld [START_REF] Gaskell | Lexical competition and the acquisition of novel words[END_REF] ask participants to learn new words (e.g., cathedruke) that, when supposedly integrated into the mental lexicon, should compete with similar-sounding words (e.g., cathedral). Using this task, [START_REF] Dumay | Sleep-Associated Changes in the Mental Representation of Spoken Words[END_REF] observed such phonological competition following sleep (but not after wakefulness), suggesting that sleep aids in the lexical integration of novel words into their neocortical phonological neighborhood.

Later, [START_REF] Tamminen | The Role of Sleep Spindles and Slow-Wave Activity in Integrating New Information in Semantic Memory[END_REF] and [START_REF] Tamminen | Sleep Spindle Activity is Associated with the Integration of New Memories and Existing Knowledge[END_REF] showed that this integrative process is associated with spindle activity, which may support the idea that this process is occurring concurrently with replays, as sleep spindles are temporally correlated with the occurrence of hippocampal ripples [START_REF] Sirota | Communication between neocortex and hippocampus during sleep in rodents[END_REF].

Sleep to reorganize

Last, sleep (particularly REM sleep), is thought to aid in schema deconstruction by reorganizing associative memories in a way that fosters creativity. Cai et al. (2009) conducted one pivotal experiment in this eld that deserves to be described in detail. Participants in their study had to complete two tasks: the aforementioned RAT and word analogies, in which subjects had to ll in a missing word to complete an analogy (e.g., fast is to slow, what hard is to e. . . , with the solution being 'easy'). Crucially, and unbeknownst to them, half of the analogy answers were solutions to the RATs given during the Post session. After completing these two tasks, participants were randomly assigned to either a nap group (they could sleep for a maximum of 90 minutes) or a quiet rest group (listening to classical music). The former nap group naturally subdivided into a REM group (i.e., participants who spent an average of 14 minutes in REM sleep) and a NREM group (participants who had very few, if any, REM sleep).

Following this break, participants were re-tested on various RATs that fell into three categories: 1) repeated RATs (old RATs that they attempted to solve during the Pre session), 2) primed RATs (novel RATs that were primed in the Pre session by the analogy task), and 3) novel RATs that they had never seen before. And here is a summary of their key ndings:

• Participants, regardless of the group, were better at solving repeated RATs in the Post session, implying that incubation alone increases creative associations (independently of sleep).

• Only the REM sleep subjects displayed a signi cant improvement on the primed RATs. Neither memory retention nor total sleep duration were found to be associated with this improvement in REM sleep. REM sleep thus promotes the use of prior information for creative problem-solving.

• None of the conditions (including REM sleep) improved creative problem-solving when participants were not previously exposed to the problems (novel RATs). Thus, this study suggests that creative problem-solving requires prior exposure to problem-related information.

Overall, the bene ts of REM sleep were not in improving memory or general mental agility, but in favoring the formation of associations between existing ideas, the aforementioned 'combinatorial creativity'. The hypothesis is as follows: while the brain is in a 'dormant' state, there is a spread of activation from previously-activated nodes to related nodes of the network [START_REF] Yaniv | Activation and metacognition of inaccessible stored information: potential bases for incubation e ects in problem solving[END_REF]. REM sleep would be the optimal period for activation spreading (reaching more distal nodes), and such increased spread of activation across the network of individual memory traces would favor the formation of more distant, atypical associations, while skipping the most immediate, common associations. This hypothesis is supported by Stickgold et al. (1999) in another seminal paper. To assess the level of semantic spreading, the authors used a semantic priming task.

This task is based on a well-known phenomenon known as 'semantic priming' [START_REF] Meyer | Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations[END_REF][START_REF] Neely | Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention[END_REF]. In sum, subjects recognize a word (e.g., sleep) faster when it is preceded by a semantically strong-related prime word (e.g., dream)

5. Sleeping on it, awakening creativity 81 than when it is preceded by a weakly-related word (e.g., lullaby), as well as faster with a weakly-related word compared to a semantically-unrelated word (e.g., bread) (Figure 5.5 A). This priming e ect is thought to re ect the organization of a semantic network: because two semantically-related words are represented by two close nodes in the network, the activation of one node spreads quickly to the other, in contrast to more distant words. Thus, Stickgold et al. (1999) used this task to investigate the e ect of sleep stages on associative link strength changes. Precisely, subjects were presented with a rst 'prime' word, followed by a second 'target' word (or pseudo, non-existing word). They had to indicate whether the target word was either a word or a non-word by pressing the corresponding key as quickly as possible. Crucially, the semantic relationships between prime-target pairs of words varied, ranging from no relationship, weak relationship, all the way up to a strong relationship.

Participants completed this task prior to taking a nap (Pre), during which they were awakened from either NREM or REM sleep, and then did the task again immediately after awakening (Post). The underlying hypothesis was that the neurochemical state of the brain would be partially the same as it was during any sleep stages they were in before awakening (i.e., 'state carryover'). And here is what they found: as expected, subjects were faster on strongly-related priming than on weakly-related priming following an awake period (control), and the same was true for NREM sleep. But surprisingly, when participants were awakened from REM sleep, they responded faster to weak primes than to strong primes (Figure 5.5 B). This reversal observed after REM sleep suggests that the automatic spread of activation (which is thought to underpin semantic priming) is modi ed during REM sleep in a way that favors distal associations. Of note, in the Pre condition performed immediately before subjects retired for the night, subjects responded equally well to weakly-and strongly-related priming (Figure 5.5 B), suggesting the existence of a distinct, hybrid cognition at sleep onset (as we will discuss later). Consistently with Stickgold et al. (1999)'s ndings, Walker et al. (2002b) later showed that participants awakened from REM sleep solved 32% more anagrams than subjects awakened from NREM sleep. Stickgold et al. (1999). Of note, the higher the value, the greater the priming e ect, as priming is expressed as the di erence between RT unrelated and RT related (e.g., 600-560 = 40ms).

It is worth noting here that the premise of these studies was that the observed e ects re ected the cognition in place during REM sleep due to state carryover. However, to my knowledge, no direct evidence was o ered to support that claim. Besides, most studies report no di erence in cognitive performance between NREM and REM sleep awakenings, only a general sleep inertia (regardless of sleep stages) when compared to wakefulness [START_REF] Jewett | Time course of sleep inertia dissipation in human performance and alertness[END_REF][START_REF] Occhionero | Time Course of Sleep Inertia Dissipation in Memory Tasks[END_REF]. Moreover, [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF] observed a similar role of REM sleep in facilitating semantic network spreading, but only when awakening occurred immediately after REM sleep, and the e ect vanished if REM sleep was followed by NREM sleep. The authors interpreted their ndings in light of sleep stage carryover, but any remnant of REM sleep had certainly already dissipated by that point, given that the test was not conducted immediately following awakenings (as in Stickgold et al. (1999) and [START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF]), but 30 minutes later. Therefore, one could alternatively argue that either NREM sleep 'overwrites' what happens during REM sleep or that subjects need to wake up in a creative state (no matter what happens afterwards). This topic will be covered in greater depth in the Discussion part, but it is worth mentioning here. In any case, these studies reveal that REM sleep is associated with a more exible, uid cognition, with the brain biased toward seeking the most non-obvious, distant associations possible rather than generating the most likely ones, a process that may foster creative ideas.

The REM sleep fertile components The unique pattern of neurochemistry and neurophysiology that takes place during REM sleep may support its creative function (for a review, see [START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]). The high cortical levels of acetylcholine in REM sleep (even higher than during wakefulness) could allow synapses to be remodeled (older synapses would be degraded while novel synapses would form), as acetylcholine is known to be critical for plasticity [START_REF] Rasmusson | The role of acetylcholine in cortical synaptic plasticity[END_REF]. REM sleep is also characterized by the presence of ponto-geniculo-occipital waves, which massively activate several cortical regions, possibly favoring the spread of activation. Moreover, as previously stated, creativity is frequently associated with a reduction in cognitive control, allowing for the exploration of novel, non-obvious directions not taken while awake. The deactivation of the dorso-lateral prefrontal cortex (dlPFC) during REM sleep may allow the mind to roam freely by removing its brake (the reasoning part of the brain). Indeed, the prefrontal cortex functions as a conductor, orchestrating and inhibiting possibilities that are deemed rationally inappropriate. This prefrontal deactivation, in conjunction with the activation of the default mode network, may create ideal conditions for exploration/divergent thinking.

Altogether, these REM sleep components would o er an ideal environment for the formation of novel, unexpected connections within existing cortically-encoded knowledge. These features could also account for the strangeness and hyper-associative nature of REM sleep dreams (Malinowski and Horton, 2015) that have been associated with creativity (for a summary, see the box entitled 'Creativity in dreams'). As Stickgold et al. (1999) put it:

"

Nowhere is such enhanced association more evident than during dreaming, an automatic process in which images showing only the weakest relationships with one another are strung together to produce bizarre and incongruous dream narratives.

"

Creativity in dreams

Federico Fellini, The Book of Dreams This is to illustrate the immense complexity and fantasy inherent in dreams.

The link between creativity and dreaming has been a topic of intense speculation, largely based on anecdotal reports of artistic and scienti c discoveries made while dreaming, including Mendeleev's periodic table, the Beatles' song 'Yesterday', Otto Loewi's Nobel Prize-winning discovery of the neurotransmitter, and many more. Some evidence suggests that dreaming and creativity do indeed have a close relationship.

Dreams are not accurate replays of past events First, the vast majority of dreams are not exact copies of the learning experience. For example, Fosse et al. (2003) observed that while 65% of dream reports contained elements of waking experiences, only 1.4% of them were exact replicas of waking experiences. Similarly, the few studies that found a positive relationship between task-related dreaming and performance improvement report that task-related dream content was fragmented and mixed with older memories (Wamsley et al., 2010a;Wamsley and Stickgold, 2019). Dreams would thus re ect the sleep-dependent integration of novel traces into established cortical memory networks. As previously discussed, this process may, in turn, promote creativity. Is there evidence to support this?

To test empirically whether dreams inspire creative sparks, Barrett (1993) asked participants to 'incubate' a problem in their dreams (by thinking about it before falling asleep). Half of the subjects recalled a dream which they felt was related to the problem, and judged that 35% of these dream reports contained a solution to the problem (independent scorers converged to similar results). As aforementioned, 40% of a large population sample also reported regularly experiencing sleep-related insights, which they primarily attributed to remembered sleep mentation [START_REF] Perdomo | Sleep Fosters Insight Into Real-Life Problems[END_REF]. Additionally, both dream recall frequency (Schredl, 1995;Schredl, 2000a;Schredl and Erlacher, 2007) and complexity (Sladeczek and Domino, 1985) correlate with higher creativity. Finally, new (but unpublished) ndings may establish a direct link between dreaming and creativity [START_REF] Horowitz | Dormio: A Targeted Dream Incubation Device[END_REF]. Indeed, [START_REF] Horowitz | Dormio: A Targeted Dream Incubation Device[END_REF] found that dreaming about a given word was associated with an increase in creativity related to that content (this result will be discussed in more depth later).

Pending questions on REM sleep and creativity Overall, these studies point to REM sleep as being a fertile environment for expanding a richer network of associations to draw on in the future. However, many questions remain pending.

First, it is unclear whether REM sleep merely allows for the retrieval of more distant concept without disturbing the network structure, or whether this far-fetched retrieval results in memory network rearrangement and dismantlement, as most researchers assume. Moreover, in addition to being scarce in comparison to the plethora of papers on NREM sleep and memory consolidation [START_REF] Klinzing | Mechanisms of systems memory consolidation during sleep[END_REF], the studies evoked thus far have focused primarily on the role of REM sleep on associative tasks and on a short time-scale (i.e., just after awakenings, relying on sleep carryover). This raises at least two main questions. First, can we observe an impact of REM sleep on creativity using more 'ecological' tasks, such as those requiring story invention? And perhaps even more importantly, does the observed REM sleep-related e ect on creativity last and possibly accumulate over time? This is a question worth investigating, especially when pursuing a long-term goal of increasing creativity. However, to my knowledge, the impact of REM sleep on a longer time scale has remained unknown, which is understandable given the methodological challenges inherent to investigating such a question. Indeed, how can the role of REM sleep over such a long timescale be examined in laboratory conditions? We tried to ll this gap in Study 1 by resorting to a unique population that has a longstanding privileged access to REM sleep and dreams: patients with narcolepsy (for a reminder, see section 2.3).

A sequential model

Altogether, the studies discussed above converge on the idea that sleep qualitatively modi es our memories, which may facilitate creative problem-solving in a variety of ways: by extracting the gist from multiple memory traces (i.e., the convergent part of creativity), by making inferences/predictions based on this gist abstraction, or by favoring the exploration of non-obvious directions (which could potentially lead to a network reorganization). Di erent stages have been linked with these various processes, which [START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF] Pending questions on sleep and creativity Although elegant, this theory falls short of explaining a portion of the literature. Why did [START_REF] Beijamini | Sleep Facilitates Problem Solving With No Additional Gain Through Targeted Memory Reactivation[END_REF] nd a correlation between SWS quantity and performance on a task that does not rely on gist abstraction, a problem-solving task that should be favored by REM sleep according to [START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]? Why did [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF] fail to observe a clear boost of insight in the SWS-rich nap compared to the REM sleep-rich nap using a gist abstraction task (i.e., the NRT)? To add to the confusion, even within the same task category, the studies that report a bene cial e ect of a speci c sleep stage on creative problem-solving are sometimes not replicated. For instance, Cai et al. (2009) showed a speci c e ect of REM sleep on enhancement of primed RATs, but the same team later reported that REM sleep was a negative predictor of performance on these same primed RATs [START_REF] Whitehurst | Autonomic activity during sleep predicts memory consolidation in humans[END_REF]. Similarly, the e ect reported by Stickgold et al. (1999) was not replicated by two follow-up studies (Kol et al., 2003a;[START_REF] Kol | The time course of sleep inertia in a sematic priming paradigm[END_REF]. Additionally, this sequential view of NREM and REM sleep as serving distinct, complementary functions in memory reprocessing might be too simplistic. First, this concept of reorganization remains abstract, given that it is rarely measured. Second, one could imagine that both gist abstraction and network reorganization occur concurrently. Indeed, the integration of new information into a network necessarily modi es it, and it seems counter-intuitive and ine cient for the integrative process to begin randomly before deconstructing the network and reorganizing it 'properly'. Consistently, we will see in the next part that access to and reorganization of the memory network may occur far earlier than we believe, during the sleep-onset period.

Overall, there are many unanswered questions, suggesting that we are still a long way from guring out how to enhance the creative power of sleep. These mechanisms, as well as the role of each sleep stage in a variety of creative tasks, await clari cation in the future. Besides, while the role of sleep in creative problem-solving is frequently mentioned in the literature, supporting evidence for this role is surprisingly scarce when compared to other cognitive functions like memory consolidation. As a scienti c community, we require additional research to con rm the putative role of sleep in creativity, which was the primary goal of the present thesis. Indeed, the Study 1 aimed at testing the long-term impact of REM sleep on more ecological creative tasks, while the Studies 2 and 3 aimed at shedding light on another sleep stage that has been overlooked thus far, despite the existence of numerous arguments in favor of a role of N1 in creativity, as we will see.

Sleep onset, a creative zone?

Many elements plead in favor for a role of sleep onset in creativity.

Anecdotal accounts To begin, anecdotes about the muse e ect of sleep are often ascribed to REM sleep, when in fact, some may be attributed to the sleep-onset period.

Take, for example, one of the most frequently cited stories: August Kekulé's discovery of the hexagonal structure of the benzene molecule. Here are his exact words: " I was sitting writing at my text book, but the work did not progress; my thoughts were else where. I turned my chair to the re and dozed. Again the atoms were gamboling before my eyes. This time the smaller groups kept modestly in the background. My mental eye, rendered more acute by repeated visions of this kind, could now distinguish larger structures, of manifold conformation: long rows, sometimes more closely tted together; all turning and twisting in snakelike motion. But look! What was that? One of the snakes had seized hold of its own tail, and the form whirled mockingly before my eyes. As if by a ash of lightning I awoke; and this time also I spent the rest of the night in working out the consequences of the hypothesis. Let us learn to dream, gentlemen, then perhaps we shall nd the truth ... but let us beware of publishing our dreams before they have been put to the proof by the waking understanding.

"

When F. Kekulé had this vision, he was most likely snoozing, in the sleep-onset period. Consistently, some artists and scientists, including Thomas Edison, Albert Einstein, and Salvador Dali, suspected that this 'twilight' zone between wakefulness and sleep could be a 'creative sweet spot'. As mentioned earlier, Dali and Edison even developed a method for capturing this eeting moment (they would hold an object in their hands so that it would fall at sleep onset, waking them up just in time to capture insightful impressions). Thomas Edison attributed many of his discoveries to this technique, which he dubbed his 'genius gap'. Is there any truth to this seductive story that has been widely reported in the media? Until now, scientists have not addressed this question. The goal of Study 2 was to close this gap by investigating whether the N1 stage could be responsible for sudden ashes of insight, as the story of Thomas Edison implies. Memory and hypnagogia As we reviewed in section 3.3, sleep onset is also probably involved in memory processing, a process that appears interlinked with creativity. Indeed, some studies show that recent wake experiences are incorporated into sleep-onset imagery (Stickgold et al., 2000;Wamsley et al., 2010a;[START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF]. But more importantly, to form associations between disparate memories by recombining separate, temporally distant but semantically close memory traces. This cross-episode integration of temporally distal but semantically proximal memories is reminiscent of the integrative encoding theory (i.e., overlapping items from separate events are bridged across into a unique representation; Shohamy and Wagner (2008)). Similar 5. Sleeping on it, awakening creativity 90 reports of a combinatorial process in hypnagogia can be traced back to the 1980s, when Mavromatis wrote in his thesis: "There are numerous reports of hypnagogic experiences which contain unrecognizable images whose parts are, nonetheless, recognized as belonging to past perceptual or imaginative experiences, being 'like' something already experienced but with added dimensions" [START_REF] Mavromatis | Hypnagogia : the nature and function of the hypnagogic state[END_REF].

This combinatorial process potentially facilitates the abstraction of commonalities between memory traces, which in turn may promote creativity. Moreover, it highlights the possibility of o line memory processing at sleep onset that goes beyond memory consolidation. Indeed, such recombination of memories during hypnagogia suggests that sleepers already have access to the long-term memory network at sleep onset, and that reorganization occurs not only within newly-encoded memory traces, but rather between them and our existing memory repertoire. Wamsley and Stickgold (2019) also showed that hypnagogic contents are later incorporated into NREM and REM sleep dreams of the same night (Wamsley and Stickgold, 2019), suggesting that hypnagogia initiates a process that continues during subsequent sleep stages. Moreover, Wamsley et al. (2010a) found that direct reports of task-related hypnagogia are higher at the beginning of sleep onset and decrease with sleep duration, transforming into more abstract representations (combination of novel and ancient semantically related memories). These studies suggest that sleep onset reprocesses novel memory traces and rapidly integrates and recombines them with our memory network. Studying hypnagogia could thus bring interesting insights into the mechanisms underlying the initial steps of sleep-related memory consolidation and network integration.

An hybrid state Another argument in favor of the N1 stage playing a role in creativity is its resemblance to REM sleep, the creative sleep stage par excellence (for a detailed description of this resemblance, see section 2.2). The N1 stage may even outperform REM sleep at igniting creative sparks. Indeed, one of the main distinctions between the two is that the N1 stage combines some features of REM sleep and wakefulness, potentially creating an ideal cocktail for the emergence of insightful solutions (for a review, see [START_REF] Benedek | Spontaneous and controlled processes in creative cognition[END_REF]). Indeed, as previously stated, the de nition of 5. Sleeping on it, awakening creativity 91 creativity highlights two crucial components: 1) the ability to generate novel ideas, and 2) the capacity to evaluate their suitability in a given context. F. Kekulé illustrated this two-sided nature of creativity when he stated: Indeed, sleep onset is a period during which metacognitive control (i.e., the ability to monitor our thoughts) and cognitive control (i.e., the ability to direct our thoughts) gradually decline, but not necessarily simultaneously. Therefore, it appears that there are times during the sleep-onset period when metacognition and cognitive control are decoupled, allowing the sleepers to freely watch their mind wander. In fact, subjects report increased uidity in the association of ideas and disinhibition during hypnagogia while remaining aware of the outside world, sometimes even to the point of holding a conversation [START_REF] Mavromatis | Hypnagogia : the nature and function of the hypnagogic state[END_REF]. As Horowitz (2019) wrote: " Hypnagogia is a middle ground, an experiential in-between. It is a space where we step outside ourselves and begin to dream yet maintain capacity for mental surveillance, a doorway through which we bring the world into the dream and the dream into the world.

"

Consistently, some studies report a reduction in the anti-correlation between the DMN and the attention and executive control networks during light sleep [START_REF] Larson-Prior | Modulation of the brain's functional network architecture in the transition from wake to sleep[END_REF][START_REF] Picchioni | Sleep and the functional connectome[END_REF][START_REF] Sämann | Development of the brain's default mode network from wakefulness to slow wave sleep[END_REF], as well as during sleep o set (Vallat et al., 2019), which could lend credence to the notion that these transitory states are ideal for creativity. A pivotal study Finally, a recent study [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF] 2004), who demonstrated that sleep promotes insight (for a refresher, see that part). But this time, after being exposed to the NRT, subjects were only allowed 10 minutes of 'awake quiescence' (i.e., a quiet rest spent in a dimly lit room with reduced sensory stimulation). Following this resting period, the number of participants who discovered the hidden rule more than doubled, when compared to participants who had an 'active rest' (i.e., performing an engaging perceptual task). The proportion of participants gaining insight after the quiescence period was strikingly similar to the one observed after a full night of sleep (Wagner et al., 2004). This nding is particularly 5. Sleeping on it, awakening creativity 93 interesting because N1 intrusions are likely to occur in such quiet rest conditions [START_REF] Horikawa | Neural Decoding of Visual Imagery During Sleep[END_REF]Tagliazucchi and Laufs, 2014a). For example, [START_REF] Tagliazucchi | Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep[END_REF] discovered that a third of subjects transitioned to sleep within 3 minutes (inside an fMRI scanner) after analyzing 1.147 resting-state fMRI data sets. Similarly, [START_REF] Horikawa | Neural Decoding of Visual Imagery During Sleep[END_REF] found that subjects reached the N1 stage in only 6 minutes (inside a fMRI scanner). N1 intrusions could thus be responsible for the 'insightbooster' e ect observed in [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF]. However, because the authors did not record the participants' vigilance level with polysomnography, the question remains open and awaits investigation.

Despite these indications that N1 may play a role in creativity, no study has been conducted to test it. To my knowledge, the sole study on the relationship between the sleep-onset period and creativity comes from unpublished results presented in Horowitz (2019)'s master thesis, from Robert Stickgold's team. They used a newly-developed device to capture sleep onset, the Dormio glove. The authors aimed at performing 'dream incubation' (Horowitz, 2019), which consists of giving a target word (e.g., tree) to participants shortly before they fall asleep and evaluating its incorporation into hypnagogia by awakening them at sleep onset and probing their mental content (Figure 5.10). 2020) demonstrated that this technique was e ective at integrating the target word into hypnagogic dreams, as ≈67% of mentation reports included references to the word 'tree', a higher percentage than reported without sleep priming (3%). More importantly, they then used the target word (e.g., tree) presented during sleep as material for post-sleep creative tasks (there was no creative task prior to sleep). For example, subjects were asked to generate as many alternative uses as possible (i.e., AUT) for the word 'tree'. Their preliminary results (unpublished data) suggest that dream incubation of a target word results in higher creativity (e.g., higher semantic distance between the word tree and the uses generated) compared to a period without dream incubation (Figure 5.11). Importantly, they also appeared to nd a gain in creativity after presumed N1 (measured by Dormio) without dream incubation, as compared to a similar period spent awake (Figure 5.11). However, the authors did not con rm that the Dormio device accurately detected sleep onset using polysomnograhic recordings. Thus, it is possible: 1) that certain mental reports did not occur at sleep onset, and 2) that the increased creativity was not related to sleep onset. To my knowledge, they have only conducted a pilot study with coupled PSG-Dormio (Horowitz, 2019), but their results are encouraging. Indeed, the Dormio seems to accurately detect the N1 stage and wake up the participant at that time, and not sooner or later (Figure 5.12). Therefore, these preliminary, unpublished ndings may also point to a link between N1 and creativity. Taken together, despite numerous arguments in favor of the N1 stage playing a role in memory and creativity, the literature on the subject is almost nil, a gap that we sought to ll in Studies 2 and 3.

Horowitz et al. (

Chapter Summary

Sleep appears to be an ideal environment for the generation of creative ideas. Sleep is thought to reprocess and reshape our memories in a exible environment (i.e., diminished executive control), allowing for the emergence of new associations and gist. One particular stage of sleep, REM sleep, is thought to be preferentially associated with increased creativity. However, some questions remain. Does the advantage of REM sleep apply to more ecological, creative tasks? Is there a longterm e ect on creativity of accumulating REM sleep periods? We attempted to address these questions in Study 1 by examining a unique population, patients with narcolepsy. Additionally, we saw that the doorway to sleep may represent another 'creative sweet spot', perhaps even more so than REM sleep, because it combines features of both REM sleep and wakefulness, a hybrid state that may be optimal for generating and evaluating creative ideas. Nonetheless, the putative link between sleep onset and creativity has not been empirically addressed until now. We sought to ll this gap in Study 2. Last, most theories rely on the idea that creativity emerges following memory reorganization. However, studies rarely investigate the fate of memory traces in terms of both creativity and memory consolidation, a question we addressed in Study 3. As we near the end of this Introduction, I invite you to read the following chapter for a summary of my thesis objectives.

The most fertile region seems to be the marshy shore, the borderland between sleep and full awakening, where the matrices of disciplined thought are already operating, but have not yet su ciently hardened to obstruct the dreamlike uidity of the imagination.

-Arthur Koestler

6

Summary of the thesis

State of the art

In the present thesis, we aim to address some unanswered questions in the literature on sleep and creativity:

• Is there a long-term impact of sleep (particularly REM sleep) on creativity?

• Is the sleep-onset period a creative sweet zone as well?

• What is the relationship between creativity and memory processing?

Main objectives 6.2.1 Study 1 : Evaluate the creative potential in narcolepsy

As previously stated (see section 2.3), patients with narcolepsy exhibit excessive daytime sleepiness: they constantly cycle back and forth into sleep (including rapid transitions 6. Summary of the thesis 97 from wake to REM sleep, SOREMPs) with repeated naps and more fragmented sleep.

They also exhibit symptoms indicative of a hybrid state between wakefulness and REM sleep, including cataplexy, sleep paralysis, hypnagogic hallucinations, REM sleep behavior disorder, and lucid dreaming. They are strong lucid dreamers and exhibit a higher dream recall frequency and complexity than controls (Fosse, 2000). In short, subjects with narcolepsy have life-long privileged access to the N1 stage and REM sleep, two sleep stages that appear to be optimal for the development of creativity. Moreover, because they exhibit these symptoms for an extended period of time, they provide a unique opportunity to experimentally investigate the long-term e ects of these stages on creativity.

The main goals of Study 1 were:

• To determine whether patients with narcolepsy possess greater creative abilities than healthy subjects,

• To ascertain whether creativity scores correlate with narcolepsy symptoms. The main goals of Study 2 were:

• To determine whether holding an object while resting may provide a reliable marker of the N1 stage and an e ective 'hypnagogia-catcher'

• To gure out whether the sleep-onset period inspires insight

• To investigate the impact of hypnagogia on insight 6.2.3 Study 3: Evaluate the impact of the N1 stage on both memory and creativity

The majority of research on sleep-related cognitive functions focuses on either memory or creativity, but rarely on both at once. As a result, the hypothesized relation between creativity and memory that is central to most theoretical frameworks remains elusive.

The primary challenge is to identify a task capable of evaluating the two within a single experimental design. Thus, in Study 3, we created a novel behavioral task to examine how recent memories are transformed during the sleep-onset period and to determine the fate of individual memory traces both in terms of memory processing and creativity.

For example, with this task, we aimed at testing whether discovering a hidden rule (the gist) results in the forgetting of the memory traces per se, as Lewis and Durrant (2011)'s theory predicts (pruning out of redundant information).

The main goals of Study 3 were:

• To complement the ndings of Study 2 on the role of the N1 stage in insight, using a di erent task

• To assess whether memory processing starts at sleep onset

• To examine the relationship between memory and insight

Taken together, these studies can help to shed light on this forgotten land that is sleep onset, as well as advance our understanding of how creative insights emerge in the brain.

Part II

EXPERIMENTAL RESULTS

1

Increased creative thinking in narcolepsy

1.1 Digest

State of the art

The literature suggests a link between one particular sleep stage (REM sleep) and creative problem-solving (Cai et al., 2009;[START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF]Stickgold et al., 1999;[START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF]. According to theoretical frameworks, REM sleep provides an ideal environment for spreading activation of a memory trace across cortical regions, which would aid in escaping impasses and retrieving distant, novel ideas. However, it is important to remember that this hypothetical link between REM sleep and creativity is based on the same few studies that we keep citing. Furthermore, all of the aforementioned studies focused exclusively on the e ect of REM sleep on associative tasks and on a short time scale (directly after a break containing REM sleep, mostly relying on state carryover). This raises two main questions: can we observe an impact of REM sleep on more 'ecological' creative tasks, such as story invention?

And, perhaps more importantly, does the observed REM-related e ect on creativity persist over time; that is, can we bridge the trait and process levels? This is a question worth investigating, especially when pursuing a long-term goal of increasing creativity.

However, to the best of my knowledge, the impact of REM sleep on a longer time scale 1. Increased creative thinking in narcolepsy 101 has remained unknown, which is understandable given the methodological challenges inherent in assessing the long-term impact of a variable in laboratory conditions.

Here, we addressed this issue by calling upon a unique population: patients with narcolepsy. These patients exhibit excessive daytime sleepiness, resulting in frequent daytime naps that are often accompanied by rapid transitions into REM sleep. They also show symptoms of a dissociation between REM sleep and wakefulness (e.g., cataplexy, sleep paralysis). Patients with narcolepsy are also dream masters: compared to the general population, they exhibit higher dream recall (Fosse, 2000) and lucid dreaming frequencies (Dodet et al., 2015;Rak et al., 2015), two factors that are positively correlated with creativity (Blagrove and Hartnell, 2000;[START_REF] Schredl | The e ect of dreams on waking life[END_REF]Schredl and Erlacher, 2007;Zink and Pietrowksy, 2013). In short, patients with narcolepsy have privileged, repeated access to sleep (particularly REM sleep and sleep onset) and dreams.

Objectives For all of these reasons, we hypothesized that patients with narcolepsy would have developed a higher creative potential over time. Such an hypothesis would be consistent with anecdotal reports of patients with narcolepsy using naps to solve problems. However, one might alternatively expect a decrease in creativity as a result of increased tiredness or some narcolepsy-related cognitive disorders (such as attentional or memory impairments).

Methods To address this question, we used two questionnaires on creative pro les and achievements to compare the creative abilities of patients with narcolepsy (N = 185) with matched controls (N = 126). We also combined these measures with an objective battery test of creative potential (N = 30 for each group) that evaluates the two key modes of creative thinking (divergent-exploratory thinking and convergent-integrative thinking) in two domains of expression (graphic and verbal).

Increased creative thinking in narcolepsy 102

Results Patients with narcolepsy scored higher than controls on the two questionnaires. They also outperformed control subjects on the objective test of creative performance. Additionally, most symptoms of narcolepsy were positively correlated (to a certain extent) with creativity scores on the questionnaire assessing creative pro les.

Conclusion

These ndings demonstrate an increased creative potential in narcolepsy, suggesting a link between privileged access to sleep (particularly REM sleep/sleep onset)

and the long-term development of creativity.

Paper

Increased creative thinking in narcolepsy Ce ´lia Lacaux,1,2 Charlotte Izabelle, 1,2 Giulio Santantonio, 3,4 Laure De Ville `le, 1,2 Johanna Frain, 1,2 Todd Lubart, 5 Fabio Pizza, 3,4 Giuseppe Plazzi, 3,4 Isabelle Arnulf

Introduction

The poet Saint-Pol-Roux used to hang on his bedroom door every night a sign that read 'Do not disturb: Poet at work' (Breton, 1924). Giving credit to his empiric conception of sleep as a creative muse, Wagner et al. (2004) demonstrated that sleep helps gain insight (i.e. the sudden discovery of a solution). Among the sleep stages, rapid eye doi:10.1093/brain/awz137 BRAIN 2019: 142;[START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF]-1999[START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF] movement (REM) sleep is most frequently associated with dreaming (Nir and Tononi, 2010). The link between creativity and dreaming has been a topic of intense speculation (Stickgold and Walker, 2004), mainly based on anecdotal reports of artistic and scientific discoveries made while dreaming, such as the periodic table of Mendeleev [START_REF] Strathern | Mendeleyev's dream: the quest for the elements[END_REF]. Notably, both dream recall frequency (Schredl, 1995(Schredl, , 2000;;Schredl and Erlacher, 2007) and complexity (Sladeczek and Domino, 1985) have been correlated with higher creativity. Plus, participants asked to 'incubate' a problem in their dreams frequently dreamed a solution useful to solving their problem (Barrett, 1993). Theoretical accounts propose that, instead of purely consolidating newly existing memories-a process that takes place during non-REM sleep via neuronal replays (O'Neill et al., 2010; Born and Wilhelm, 2012)-REM sleep would allow these mnesic traces to mingle and associate with already-stored memories in novel and abstract ways (Stickgold et al., 2001). REM sleep dreaming could be seen as a process that detects elements of similar meanings in past events and fuses them together to reflect specific experiential patterns (Llewellyn, 2016). Consistent with this theory, only 1-2% of dream reports are faithful copies of previous experiences (Fosse et al., 2003). Most of them rather hyper-associate different memories (Malinowski and Horton, 2015), resulting in fictional bizarre memories. In line with this assumption, a few studies demonstrated that REM sleep might favour spreading activation of a memory trace within cortical regions. This would lead to a reorganization of associative networks and an expansion of the problem space, which would help forming non-obvious associations and reaching new solutions (Cai et al., 2009). In a seminal work, Stickgold et al. (1999) showed that participants awakened from REM sleep (versus non-REM sleep) were faster at recognizing target words when they were preceded by a weakly related prime (e.g. thief-wrong) than a strongly related prime (e.g. hot-cold). Moreover, REM sleep had a beneficial role on anagram solving (Walker et al., 2002) and on creative problem solving that required the integration of loosely associated information (Cai et al., 2009). Neuroimaging data suggest that functional connectivity of higher order brain associative areas during REM sleep favours associations between distant memories (Chow et al., 2013). Beyond these studies, the literature linking REM sleep (and their accompanying dreams) and creativity remains sparse. Moreover, most studies are based on simple associative tests but do not directly evaluate creativity per se as defined by Sternberg and Lubart (1999), i.e. the ability to produce work that is both novel (original, unexpected) and appropriate (useful, adaptive concerning task constraints).

It is unlikely that creativity per se is instantaneously achieved in a nap. Rather, the development of creativity probably stretches over years, including numerous sleep periods. If this is true, how can the role of REM sleep over such a long timescale be examined? Our solution was to call upon REM sleep and dream experts: subjects with narcolepsy. Subjects with narcolepsy experience excessive daytime sleepiness often accompanied by direct transitions from wakefulness to REM sleep, named sleep onset in REM periods (SOREMs) (American Academy of Sleep Medicine, 2014). They have repeated naps (often containing REM sleep), in contrast with healthy controls who have only occasional naps and rarely reach REM sleep during daytime. They present symptoms of a dissociated REM sleep, including cataplexy (sudden loss of muscular tone with clear consciousness), sleep paralysis, hypnagogic hallucinations, REM sleep behaviour disorder and lucid dreaming. Indeed, subjects with narcolepsy are more potent lucid dreamers than controls, meaning that they are more often conscious of dreaming when dreaming in REM sleep than controls (Dodet et al., 2015;Rak et al., 2015). They also have a higher dream recall frequency and complexity than normal controls (Fosse, 2000) and a more fragmented REM sleep. Consequently, subjects with narcolepsy exhibit a privileged access to sleep (particularly REM sleep) and dreams. Interestingly, lucid dreaming has been positively linked with creativity (Blagrove and Hartnell, 2000;Zink and Pietrowksy, 2013), as confirmed by anecdotal reports in our subjects with narcolepsy who used naps to solve real-life problems. For all these reasons, subjects with narcolepsy might have developed higher creative abilities. However, given their massive level of tiredness, one may expect that their cognitive abilities (including creativity) could be impaired. We postulated that they would score higher on creativity tests compared to controls. To test this assumption, we compared the creative abilities in subjects with narcolepsy versus matched controls using selfadministered questionnaires and a formal test of creative potential.

Materials and methods

Subjects

The study was performed in two National Reference Centres for narcolepsy, in the Pitie ´-Salpe ˆtrie `re University Hospital in Paris (France) and in the University Hospital of Bologna (Italy). Over the course of 6 months, all consecutive adult subjects with narcolepsy visiting the centres for routine yearly follow-up were invited to complete two self-administered questionnaires on creativity. A subgroup (unselected) had formal tests of creativity during a day spent in the hospital in the French centre. Subjects had to meet the international criteria for narcolepsy (American Academy of Sleep, 2014), including excessive daytime sleepiness occurring daily for more than 3 months, a mean sleep latency lower than 8 min on the multiple sleep latency test, two or more sleep-onset REM periods and no other causes for these findings (including acute and chronic sleep deprivation, shift work disorder, depression, sleep apnoea syndrome, circadian sleep disorders, and recent withdrawal of an antidepressant, all reasons that had been carefully excluded in the two reference centres). They were classified as suffering from narcolepsy type 1 if they had Creativity in narcolepsy BRAIN 2019: 142;[START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF]-1999[START_REF] Buzsáki | Two-stage model of memory trace formation: a role for "noisy" brain states[END_REF] frank cataplexies and/or were hypocretin-1 deficient (as measured in the CSF), and from narcolepsy type 2 if it was not the case. Most subjects with narcolepsy had a brain MRI at time of diagnosis, which was normal, as expected in narcolepsy. No brain MRI was performed in controls. The same questionnaires were given to healthy controls recruited by word of mouth and matched for age, sex and laterality with the narcolepsy groups in each country. Healthy controls had normal daytime sleepiness as assessed by the Epworth Sleepiness Scale [START_REF] Johns | A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale[END_REF], reported no history of sleep disorders and had no anxiety or depressive symptoms, as assessed using the Hospital Anxiety and Depression Scale [START_REF] Zigmond | The hospital anxiety and depression scale[END_REF]. Thirty healthy control subjects (included in the general control group), matched for age, sex, laterality and education, performed the formal test of creativity in France.

The protocol was approved by both local ethical committees, and all participants signed an informed consent form. No participant was paid for the study, except the healthy controls who received a monetary compensation of 20e for completing the formal test of creativity. Subjects with narcolepsy performed the formal test when present in the sleep laboratory for a follow-up clinical examination and were not paid for their participation.

Demographic and clinical measures

In addition to the collection of information on age, sex, laterality and education level (Schneider, 2013), the participants completed at the same time as the creativity tests, a questionnaire reporting the presence of cataplexy, hypnagogic hallucinations, sleep paralysis, enacted dream behaviour evocative of clinical REM sleep behaviour disorder [START_REF] Postuma | A single-question screen for rapid eye movement sleep behavior disorder: a multicenter validation study[END_REF] and lucid dreaming. They completed the Epworth Sleepiness Scale and the Hospital Anxiety and Depression Rating scale. The treating physician reported the results of the multiple sleep latency test at the time of diagnosis and the current treatment.

Creativity questionnaires

Participants completed two questionnaires of creativity, including the Test of Creative Profile (TCP, [START_REF] Sellier | Test of Creative thinking -Torrance[END_REF] and the Creative Achievement Questionnaire (CAQ, Carson et al., 2005). The TCP contains 57 yes/no questions, examining three different types of creativity, including 'Innovative', 'Imaginative' and 'Researcher' profiles. An Innovative person tries to change situations into better ones and typically scores positively on the sentence: 'In general, when you want to achieve something, you only rely on yourself: no one is able to solve your personal problems'. Subjects with an Imaginative profile are not directly concerned by down-to-earth problems but are more those with a poetic soul who score typically positively on the question: 'At one point or another of your existence, you were taken by the itch to write (or to create a work of art), if only for your personal pleasure'. The Researcher profile generally consists of scientists or inventors guided by a specific subject. Contrary to Innovative persons, they have no ideas to propose on every topic but only on a long-term mature one, and contrary to the Imaginative profile, they remain down-to-earth, their work being subjected to the evaluation of a rigorous scientific method and experimentation. Each positive answer for each profile was summed and normalized on a 100-point scale to enable comparisons between the three profiles.

The CAQ (Carson et al., 2005;[START_REF] Silvia | Assessing creativity with self-report scales: a review and empirical evaluation[END_REF] is a reliable self-reported assessment of creative achievement across 10 domains (visual arts, music, dance, architectural design, creative writing, humour, inventions, scientific discovery, theatre/ film and culinary arts). Each domain is scored by the subject between 0 and 7, including 0 ('I have no training or recognized talent in this area'), 1 ('I have taken lessons in this area'), and six additional scores for ascending achievement (e.g. 'I have won a national prize in the fields of science or medicine'). For the scores selected, subjects indicated how many times each achievement was earned. The summation of all scores resulted in the total creative achievement score. By asking about significant, observable accomplishments, the scores at the CAQ are inherently skewed (Beghetto and Kaufman, 2007;Kaufman and Beghetto, 2009), aiming to capture those rare individuals with eminent creativity or with professional expertise in a creative area.

Formal test of creativity

The Evaluation of Potential Creativity (EPoC) test battery is an objective test evaluating creative abilities (Lubart et al., 2011). It contains eight subtests that assess the two key modes of creative thinking, including divergent-exploratory thinking (i.e. finding the greatest number of solutions based on a given stimulus) and convergent-integrative thinking (i.e. integrating several elements into a coherent synthesis) on two different domains of expression (graphic and verbal). With its ability to capture the multidimensionality of creativity, this test contrasts markedly with the past decades of research on creativity, which was mainly based on single indicators of creative potential, mainly divergent thinking tasks (Barbot et al., 2016).

In divergent-exploratory tasks, participants were asked to generate as many ideas as possible based on a single stimulus, such as inventing as many different endings of one story (verbal) or generating as many different drawings as possible incorporating one shape (graphic). The convergent-integrative tests consist of incorporating multiple elements to form a unique production, such as inventing an entire story that incorporates three main imposed characters (verbal) or producing one original drawing that incorporates a set of forms (either abstract or concrete). For details on the timeline and on the eight tasks of this test, refer to the Supplementary material and Supplementary Fig. 1). Participants were asked to name their productions and to offer original solutions (different from what others may propose). They had a time constraint of 10-15 min depending on the task. The full EPoC test lasted 2 h 30 min in total (which included a 30-min break during which 6 of 30 subjects per group slept).

Two independent scorers (C.L., D.O.) blind to the group (control or narcolepsy) scored the data following established guidelines (Lubart et al., 2011), and met later to discuss points of disagreement to converge to a consensual score. The EPoC test was originally designed for children, and there are no existing norms for adults with a large age range. For each of the eight tests, all productions from the 60 participants were mixed and then scored for creativity relative to each other. Among them, the measures for two tasks of verbal divergent thinking were removed from the analysis in five subjects (two controls and three subjects with narcolepsy) because of a misunderstanding of the instruction. For the divergent-graphic tasks (both abstract and concrete), the raw score corresponded to the fluency (i.e. the number of drawings produced). Fluency has been highly correlated with originality (r = 0.80) in various studies [START_REF] Mouchiroud | Children's original thinking: an empirical examination of alternative measures derived from divergent thinking tasks[END_REF]. If two subjects obtained the same score, the originality of their stories/drawings was further taken into account to disentangle them. Originality scores were judged relatively to the participants' performance. For the divergentverbal tasks, three factors were taken into account, including the fluency (i.e. the number of beginnings or endings of the story imagined), the elaboration of the story (i.e. the number of words) and the originality. There was an excellent interscorer agreement for scoring the divergent-exploratory dimension (Cohen's J = 0.923).

The convergent tasks (both graphic and verbal) were initially scored following the manual guidelines (Lubart et al., 2011). However, because of inter-rater agreement issues, we built a 5factor model encompassing the main criteria of creativity and scored productions using this rubric to reduce subjectivity. The five factors included (in decreasing order of priority) the originality of the production (judged relative to one another), the integration of the elements altogether, the coherence of the story/drawing (i.e. digressions from the main storyline), the respect of the constraint (i.e. number of elements used), and the elaboration of the production (i.e. details). The first criterion (originality) was weighted by 5, the second by 4 and so on to obtain a final score of creativity. These weights were attributed based on the instructions given to the subjects ('integrate these elements into a single original and coherent production'), and coupled with the objective criteria of creativity (Lubart et al., 2011). There was a good inter-scorer agreement for scoring the convergent-integrative based on the 5-factor model (r = 0.715, P 5 0.0001).

As recommended in the EPoC manual (Lubart et al., 2011), the raw scores of the eight tests were then transformed into standard scores on a 7-point Likert scale that fit a Gaussian distribution amongst participants. Thus, 4.75% of the productions obtained a score of 1, 11.1% a score of 2, 21.2% a score of 3, 25.9% a score of 4, 21.2% a score of 5, 11.1% a score of 6, and 4.75% a score of 7. Importantly, raw scores highly correlated with standard scores for each of the four divergent tests (r = 0.97-0.98, P 5 0.001, Supplementary material and Supplementary Table 4). Therefore, this transformation allows comparison of scores amongst different creative dimensions without modifying or discarding information. Examples of ratings for the graphic divergent and convergent tests are provided in Fig. 1.

Statistical analysis

Chi-squared tests were used to test relationships between categorical variables. Linear mixed model analyses were performed using R (R Core Team, 2017) and the package lme4 to test the relationship between independent variables of interest (i.e. narcolepsy) and creativity. Subjects were entered as a random effect to take variability between subjects into account. Student's t-tests were performed as a parametric measure of the difference between two means when required and for small sample sizes (530), and Z-tests were performed for large sample sizes (430). Spearman correlations were used as a non-parametric measure of the correlations between the measures of creativity (EPoC test versus questionnaires), and Pearson correlations were used to compare parametric measures (scores on the creativity questionnaires) with continuous measures of symptoms (e.g. sleep latency, anxiety and depressive symptoms). Inter-test Spearman correlations and principal component analysis with varimax rotation were additionally conducted with the eight variables measured by the EPoC test to determine the appropriate number of factors to be retained in our model. Interjudge agreement was evaluated with the Cohen's J-test.

Results

Characteristics of the samples

A total of 185 subjects with narcolepsy were included (71% with narcolepsy type 1 and 29% with narcolepsy type 2), including 118 subjects in Paris (aged 37.0 AE 15.3, 59% female) and 67 subjects (aged 34.8 AE 17.0, 52% female) in Bologna. As indicated in Table 1, the 126 healthy controls were matched for age, gender, and laterality with the subjects from the narcolepsy group. The education level was lower on average in the narcolepsy group than in the control group. As expected by the disorder, subjects with narcolepsy had more severe daytime sleepiness, more frequent sleep and depressive symptoms, and took stimulants and anti-cataplectic drugs (including antidepressants and sodium oxybate) more frequently than controls (Table 1). They did not differ on anxiety symptoms. The subgroups of 30 subjects with narcolepsy and 30 healthy controls who performed the EPoC test did not differ in age, gender, laterality and education level, or anxiety symptoms. Subjects with narcolepsy had, on average, higher levels of daytime sleepiness, more frequent sleep and depressive symptoms and took stimulants and anti-cataplectic drugs more frequently than controls (Supplementary material and Supplementary Table 1).

Self-reported assessments of creativity in subjects with narcolepsy and controls

The subjects with narcolepsy scored higher than controls on the total score of the TCP [mean = 58.9 in narcolepsy versus 55.1 in control, z(308) = À3.32; P = 0.001] and on all three different creative profiles: Innovative [z(308) = À2.06; P = 0.039], Imaginative [z(308) = À2.79; P = 0.005] and Researcher [z(308) = À2.8; P = 0.005] (Fig. 2A). Similarly, subjects with narcolepsy had higher scores on the CAQ [mean = 10.4 in narcolepsy versus 6.4 in control, z(308) = À1.99; P = 0.047] (Fig. 2B). Of note, the medians were not different between the two groups (median = 4 for both groups), as expected by the skewed nature of the CAQ. When looking separately at the two cohorts (Supplementary Fig. 2), French subjects with narcolepsy scored higher than controls both on the TCP and on the CAQ. When restricting analysis to type 1 narcolepsy only, subjects with narcolepsy also scored higher than controls on both questionnaires [TCP: z(143) = À3.19, P = 0.001; CAQ: z(143) = À 2.12; P = 0.034]. Italian subjects with narcolepsy scored higher than controls on the TCP but not on the CAQ. As the CAQ is designed to provide highly skewed data and to give the highest weight to the fewest individuals with higher levels of achievement (the outliers) to capture the so-called 'Big-C' creativity that is by definition uncommon (Beghetto and Kaufman, 2007;Kaufman and Beghetto, 2009), we performed a Z-test rather than a non-parametric test that would eliminate our data of interest. The difference between subjects with narcolepsy and controls was mostly driven by these outliers (score on the CAQ 4 28, i.e. maximum score for each domain), which represented 6.5% of subjects with narcolepsy and 3.2% of controls (P = 0.30; Fig. 2B).

Associations between self-reported creativity and sleep characteristics

In the narcolepsy group, subjects with hallucinations, sleep paralysis, clinical REM sleep behaviour disorder and lucid dreaming had higher scores on the TCP (Table 2). Lucid dreaming was associated with higher scores on the Imaginative profile [F(1,306) = 8.40; P = 0.004] and tended to be associated with the Innovative profile [F(1,306) = 3.214; P = 0.074] but not with the Researcher profile [F(1,306) = 2.052; P = 0.15]. In contrast, cataplexy did not influence creativity, as measured with the total score on the TCP. Higher sleepiness and depression scores correlated (to a mild degree) with higher scores on the TCP (Table 3). There was no difference or correlation between the score on the CAQ and symptoms of narcolepsy and sleep measures (Tables 2 and3). The use of stimulants and anti-cataplectic drugs (including venlafaxin and sodium oxybate) had no impact on the scores of the TCP and of the CAQ (Table 2 andSupplementary Table 2 Figure 1 Templates and examples of scored results for the divergent and convergent graphic tasks on the EPoC. Using a given template (left), the subjects had to draw as many drawings as possible by using this shape for divergent-graphic tasks (abstract or concrete). For the convergent-graphic tasks, they had to use at least four of the presented items (abstract or concrete) into a single original production. Examples of drawings judged as non-creative (middle) because participants frequently made these particular drawings (divergent) or because there were only reproductions of the elements without any sense to them (convergent, scored 1 on a 7-point scale). Examples of drawings judged as creative (right) because the subjects never/rarely made them (divergent) or because they integrated all elements into coherent, original storybased drawings (convergent, scored 5 and 6, respectively, on a 7-point scale). Templates are protected by copyright@Hogrefe.

for detailed comparisons between each drug prescribed for narcolepsy and scores on the questionnaires).

As our two groups differed in terms of educational level, we additionally evaluated the influence of educational level on both scores. There was no significant correlation between educational level and the total score of the TCP (r = 0.40; P = 0.49), but a higher educational level was associated with higher scores on the CAQ (r = 0.22; P = 0.0001).

Objective creative production: analysis of principal factors

Before comparing objective performance between narcolepsy and control groups, we evaluated the correlations between the eight subtests of the EPoC to verify whether they were independent and to offer a view of its internal structure. Creativity in narcolepsy BRAIN 2019: 142;[START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF]-1999| 1993 A principal component analysis with varimax rotation was conducted with these eight variables measured by the EPoC test to determine the appropriate number of factors to retain. The analysis yielded two factors that explained 58.7% of the variance for the entire set of variables (Fig. 3A). The first factor (comprising the graphic and verbal divergent tasks) explained 38.6% of the variance and was labelled 'Divergent'. The second factor (comprising the graphic and verbal convergent tasks) explained 20.1% of the variance and was labelled 'Convergent'. For interpretation of the two factors, the varimax orthogonal rotation showed a grouping of items similar to that of the cluster analysis, where the first factor was Divergent and the second factor was Convergent. Consequently, this twofactorial model (Divergent and Convergent creative thinking) will be used to present the results.

Objective assessment of creative production in subjects with narcolepsy versus healthy controls

Based on the principal component analysis, we thus combined all scores from the four divergent tests (i.e. Divergent) and from the four convergent tests (i.e. Convergent). Linear mixed model analyses (with subjects entered as a random effect) revealed that subjects with narcolepsy obtained an overall score at the EPoC test significantly higher than controls [t(57.88) = 2.7; P = 0.009; Fig. 3B]. When looking separately at both modes of creative thinking, we found that the narcolepsy group scored higher than the control group on the Convergent mode of creative thinking [t(58) = 2.7; P = 0.008; Fig. 3C] and tended to score higher on the Divergent dimension [t(57.97) = 1.7; P = 0.09; Fig. 3D]. Examples of performance in the graphical divergent and convergent dimensions of this test can be found in Fig. 1 and results for the four main creative dimensions are displayed in Supplementary Fig. 3. Of note, the Divergent dimension had an incomplete sample, as 5 of 60 subjects had to be removed from the analysis on the verbal divergence creativity because of a misunderstanding of the instructions. This likely explains why a stronger effect was observed for the convergent rather than the divergent mode of creative thinking. The homogeneity of performance within subjects was additionally assessed by calculating the prevalence of performance heterogeneity, which represents the percentage of subjects with more than 3 points (two times the standard deviation, with standard deviation = 1.46) of difference in the scores between two tests in each dimension. The mean percentage of heterogeneity in the four subtests was 1.67% for both convergent and divergent creative thinking. Thus, the subjects had consistent performance along the eight tests of the EPoC test, meaning that a high score in one test predicted a high score for all the other tests, whether verbal or graphical. In addition, the five factors (originality, integration, coherence, constraint and elaboration) used to generate the score of convergence were separately analysed (Supplementary Fig. 4A). Subjects with narcolepsy scored higher on the factors of originality, elaboration and with a trend on the integration factor (P = 0.08). There were also good between-factors correlations (Supplementary Fig. 4B), including with the 'coherence' factor suggesting that it does assess a part of creativity (although not a prominent one). No difference between both groups was observed for the 'constraint' factor and it did not correlate well with the remaining factors. This was expected as this variable only took into account whether subjects fully respected the instructions, which most subjects did. Nonetheless, this penalty factor was necessary in the analyses as the respect of the instructions is part of the definition of creativity we based our work on. Additionally, these subscales correlated well with the scores at the questionnaires of creativity (Supplementary Fig. 4B). Overall, these analyses supported the validity of our multifactorial model in evaluating convergent creative thinking. They also demonstrated that subjects with narcolepsy did not sacrifice integration and elaboration for originality. On the contrary, they created both more original and more integrated productions, including many details.

Associations between creative performance and sleep characteristics

In the narcolepsy group, no sleep symptoms (including lucid dreaming) and sleep latency measures influenced creative performance (Supplementary Table 3). Importantly, subjects with narcolepsy type 1 and type 2 had similar creative performance [F(1,57) = 2.48; P = 0.12]. A higher educational level predicted higher creative performance (r = 0.45; P = 0.003). Gender [t(58) = 0.38; P = 0.71] and laterality [F(3, 56) = 1.40; P = 0.25] had no impact on creative performance, whereas older age tended to be associated with lower performance (r = À0.236, P = 0.07).

Correlation between questionnaires and objective tests of creativity

As shown in Fig. 3E, higher scores on the TCP correlated with higher convergent (r = 0.27; P = 0.036) but not with divergent thinking scores (r = À0.02; P = 0.87). Higher scores on the CAQ correlated with higher convergent (r = 0.46; P = 0.0002) and divergent (r = 0.26; P = 0.04) thinking scores. The scores on these two questionnaires correlated with each other (r = 0.32; P = 0.012).

Discussion

The main result here is that subjects with narcolepsy have a higher creative potential than controls, as assessed using creativity profile and achievement questionnaires in a large sample (including subjects from two different countries) and by testing objective creative performance. The high creative potential in narcolepsy extended to all modes of creative thinking analysed here, except for Divergent creative thinking (but P = 0.09). Indeed, subjects with narcolepsy scored higher on average and in all the different creative profiles (Imaginative, Innovative and Researcher) assessed by the TCP and were consistent in their performances on the subtests (divergent, convergent, verbal, graphic, abstract, concrete) of the EPoC test. The study was not restricted to narcolepsy type 1 (which is caused by hypocretin-1 deficiency) but included also subjects with narcolepsy type 2 (a less stable SOREM phenotype with yet unknown pathophysiology). Of interest, the creativity was similarly increased in each type of narcolepsy, suggesting that a complete hypocretin-1 deficiency is not necessary per se to increase creativity. Rather, other factors, such as lucid dreaming (which is as frequent in narcolepsy type 1 and type 2, Dodet et al., 2015) may explain the creative effect.

Starting with disadvantages, subjects with narcolepsy scored higher than controls

These results are strengthened by the fact that the subjects with narcolepsy who completed the self-administered questionnaires had a lower educational level than controls, a bias that should be detrimental to their creative scores, because lower educational level was correlated with lower creativity scores in the CAQ and in the EPoC test. They were also more tired than controls, constantly fighting the urge to sleep, and some might have attention deficits [START_REF] Naumann | Cognitive deficits in narcolepsy[END_REF], although attention was not tested here. Because of these disadvantages, one could have predicted impaired high-level cognitive skills (including creativity) in subjects with narcolepsy. In contrast, subjects with narcolepsy scored higher than controls on most creative dimensions. Moreover, a higher sleepiness propensity (measured by the Epworth sleepiness scale, but not by the mean sleep latency test) mildly correlated with a higher creativity score on the TCP. One may wonder whether the frequent sleep bouts that subjects with narcolepsy perform in passive conditions offer them the opportunity to mind wander and to incubate complex problems.

With this higher creative potential, subjects with narcolepsy scored higher than controls on the CAQ, but only a minority took advantage of this higher creative potential to generate professional creative products (i.e. creative achievement), be it a novel, musical composition, or a sculpture. This limited achievement despite a higher creative potential may have several causes, including difficulties in implementing novel ideas because of sleepiness, tiredness, apathy, emotional blunting (as a way to avoid cataplexy), difficulty in running a business, and lack of support. This study offers clinicians the opportunity to inform their patients of this hidden creative potential and encourage them to exploit their full potential.

One may now imagine that stimulants prescribed for narcolepsy (including modafinil, methylphenidate and pitolisant) could increase creative thinking, as they increase attention and are often regarded as cognitive enhancers [START_REF] Turner | Cognitive enhancing effects of modafinil in healthy volunteers[END_REF]. However, modafinil has rather negative consequences on creativity, as healthy high creative subjects have lower scores on divergent and on convergent creativity when taking modafinil compared to placebo [START_REF] Mu ¨ller | Effects of modafinil on non-verbal cognition, task enjoyment and creative thinking in healthy volunteers[END_REF]Mohamed, 2014). Thus, the stimulants in narcolepsy could be detrimental to their creativity. In addition, subjects with narcolepsy scored similarly on the creative tests, whether they were or not treated with stimulants, suggesting that the stimulants did not play a major role here. Moreover, the anti-cataplectic drugs (and especially sodium oxybate) had a rather negative effect on creativity as assessed with the questionnaires (Supplementary Table 2). Sodium oxybate decreases the dream recall frequency (Rak et al., 2015), which may result in a decreased creative potential. In any case, the higher creative potential observed in subjects with narcolepsy cannot be attributed to their treatments, which have either no or a deleterious impact on creativity.

The role of dissociated REM sleep in creativity

This higher creative potential in narcolepsy is consistent with our initial prediction of the specific role of REM sleep in creativity. Indeed, subjects with narcolepsy have privileged access to REM sleep and to dreams. They often fall asleep directly into REM sleep, a phenomenon that occurs rarely in controls. Many symptoms (cataplexy, sleep paralysis, hypnagogic hallucinations, lucid dreaming and REM sleep behaviour disorder) indicate that the normal boundary between wakefulness and REM sleep is blurred in narcolepsy. Of interest, these symptoms (except cataplexy) were associated with higher scores on the TCP in the narcolepsy group. Notably, 43% of the 185 subjects with narcolepsy were frequent lucid dreamers according to the questionnaire, compared to 3% of the 126 normal controls, supporting further, with a larger sample in the present study, the higher percentage of lucid dreamers previously reported in three cohorts of subjects with narcolepsy (Dodet et al., 2015;Rak et al., 2015;[START_REF] Oudiette | REM sleep respiratory behaviours match mental content in narcoleptic lucid dreamers[END_REF]. Here, in accordance with previous studies in normal subjects (Blagrove and Hartnell, 2000;Zink and Pietrowksy, 2013), lucid dreaming had a positive impact 1996 | BRAIN 2019: 142;[START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF]-1999 C. Lacaux et al. C. Lacaux et al. per se (in the whole sample) on subjective measures of creativity. Lucid dreaming was not associated with higher objective performances in the EPoC, but this may be due to a smaller sample. Whether lucid dreaming is the cause or consequence of creativity remains an open question (Zink and Pietrowksy, 2013). Similar to previous reports in healthy lucid dreamers (Scha ¨dlich and [START_REF] Scha ¨dlich | Applications of lucid dreams: An online study[END_REF], some of our subjects reported using lucid dreams to come up with novel and creative ideas and to solve problems. This observation would suggest the direction of causation going from lucid dreaming to creativity. Moreover, subjects with narcolepsy recall their dreams much more (three to four times more often) than normal subjects do (Rak et al., 2015). This ability may explain their higher creative potential, as in healthy subjects (Schredl and Erlacher, 2007), high dream recall frequency predicts a high frequency of creative dreams (i.e. dreams that stimulate waking-life creativity). Being able to access and remember one's dreams could provide a higher pool of ideas from which drawing creative inspirations. This is particularly relevant to the hypothesized importance of REM sleep in the increased creativity observed in subjects with narcolepsy, as REM sleep dreams often contain unusual, bizarre elements, compared to non-REM dreams that display more common replay of mundane experiences (Foulkes, 1962). These two processes (increased dream recall frequency and unusual dreams) precisely correspond to the two criteria (i.e. fluency and originality) used for evaluating divergentexploratory creative thinking. Conversely, the hyper-association of distinct elements from different memories found in dreams (Malinowski and Horton, 2015) resembles that of convergent-integrative thinking, when different elements need to be combined to form a single original production. The ability to recall these hyper-associated dreams may thus constitute a template from which to learn how to use these 'building blocks' in an original way. This high creative potential should naturally result in self-reported higher creative profiles and accomplishments, which is what we observed with the two questionnaires of creativity. These results can also be discussed in the framework of a recent neuronal network model [START_REF] Hobson | Waking and dreaming consciousness: Neurobiological and functional considerations[END_REF], which views REM sleep and dreams as an opportunity for optimizing a generative model that predicts the world by minimizing redundancy or complexity. To be able to generate a great diversity of scenarios that the person will encounter during waking, the sleeping brain would need to rehearse fictional scenarios and to explore new possibilities that could be experienced during wakefulness. This idea is reminiscent of the relationship between dreams and creativity, as the latter is precisely defined as the ability to explore new possibilities within a constrained context.

Limitations

This study has several limitations. Creativity was assessed using a subset of psychometric measurements (57 questions about creativity, 10 domains of achievement, plus eight formal creativity tests). Whereas one may want to extend the evaluation of achievement in domains such as economy or administration, as well as formally test creativity in three-dimension creations, we chose to use here validated questionnaires and tests for this first study in narcolepsy. Potential confounding factors such as social or cultural influences were not directly examined. However, our two groups are unlikely to differ on these variables as most cases of narcolepsy are sporadic [START_REF] Nishino | Clinical and neurobiological aspects of narcolepsy[END_REF] and indeed there is no indication in the literature and in our clinical experience that subjects with narcolepsy belong to a specific social or cultural environment. Subjects with narcolepsy also had, on average, a lower educational level than controls, which should be a disadvantage for creativity. In contrast, it is more likely that their sleep characteristics (their most notable difference with controls) are responsible for our findings. This hypothesis is supported by the positive correlations between patients' symptoms and scores of the TCP. To directly assess this assumption, longitudinal studies are needed to further examine within-subject relationships between disease onset and creativity. Moreover, many patients with narcolepsy (68% for the questionnaires and 72% for the EPoC test) were treated with stimulants at the time of the creativity tests. However, their creativity scores did not differ from those of untreated patients. It would be interesting to formally test the creative performances in a large group of untreated patients. Also, we did not replicate the finding of a higher score on the CAQ in the Italian cohort. This may be attributed to the smaller sample size and to the fact that they were on average younger than the French cohort, thus reducing the likelihood of attaining professional expertise in a creative field. Additionally, the narcolepsy group did not score significantly higher than controls on the divergent mode of creative thinking, but a trend was present (P = 0.09) and would likely reach significance with more statistical power (five subjects were removed from the analyses). Moreover, the results from this dimension highly correlated with both convergent creative thinking and the CAQ, and performance of subjects across all the creative dimensions of the EPoC test were homogeneous. We did not assess the role of REM sleep in creativity directly because we decided to study process acquired over the long term, which could not be evaluated by comparison of a performance before versus after a period of REM sleep. Another limitation of this study may concern the choice of creativity questionnaires. Indeed, the TCP is less often used in this research field. However, we selected it as a way to differentiate creative profiles. The CAQ provides skewed data but simultaneously provides the opportunity to evaluate concrete creative achievement and capture eminent creativity. That being said, both questionnaires correlated with the objective measures of creative performance, further supporting their reliability as measures of creativity.

Creativity in narcolepsy BRAIN 2019: 142;[START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF]-1999

Conclusion

We highlight here that subjects with narcolepsy show higher creative thinking than controls. Sleepiness and dissociated states of REM sleep and wakefulness have a positive impact on the creative profile. We suggest that this higher performance may be due to more frequent opportunities to incubate and associate ideas during sleep (and especially during REM sleep) and to remember them upon awakening. These positive results could be used in clinical settings to encourage subjects with narcolepsy to exploit their full creative potential, thus providing a silver lining when facing the difficulties associated with this disastrous sleep disorder. to a problem). They found that more than twice as many subjects gained insight into a hidden rule after a full night of sleep (≈60%) compared to a night spent awake (≈23%). Crucially, using the same paradigm, [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF] recently reported a strikingly similar gain of insight after only 10 minutes of awake 'quiescence'. The authors concluded that a brief period of awake rest was su cient to induce insight.

Supplementary Tables

However, brief sleep intrusions were very likely to occur in such quiet rest conditions [START_REF] Horikawa | Neural Decoding of Visual Imagery During Sleep[END_REF]Tagliazucchi and Laufs, 2014a). Were they responsible for this 'insight-booster' e ect? The question remains pending because the authors did not record (with polysomnography) the participants' state of vigilance during the resting period. Nonetheless, based on the existing literature, such a hypothesis is plausible.

Sleep onset is commonly referred to as the rst stage of sleep (N1), a stage that is electrophysiologically (mixed alpha/theta rhythms) and phenomenologically similar to REM sleep. Indeed, the N1 stage is characterized by hypnagogia, which are dreamlike experiences that occur during the descent into sleep and are frequently compared to REM dreams [START_REF] Foulkes | Mental activity at sleep onset[END_REF][START_REF] Vogel | An alternative view of the neurobiology of dreaming[END_REF]. Because REM sleep holds a central place in the literature on sleep and creativity, N1 could likewise play a role. Besides, we reported in Study 1 an increased creative potential in patients with narcolepsy [START_REF] Lacaux | Increased creative thinking in narcolepsy[END_REF], a sleep population that passes through this sleep-onset period several times per day. A subsequent study further identi ed hypnagogic hallucinations as a key modulator of creativity in narcolepsy [START_REF] D'anselmo | Creativity in Narcolepsy Type 1: The Role of Dissociated REM Sleep Manifestations[END_REF]. Altogether, several arguments support the idea that the N1 stage may contribute to creativity, but, to my knowledge, this hypothesis has never been tested.

Objectives

We thus decided to assess whether the N1 stage fosters insight, with the initial hypothesis being that pure N1 would be su cient to promote insight to a level comparable to Wagner et al. (2004) and [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF].

Methods We reused the Number Reduction Task from Wagner et al. (2004) and [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF]. Subjects (N = 103) were given mathematical problems without knowing that a hidden rule allowed them to be solved almost instantly. They completed this task before and after a 20-minute break in which they could rest while holding an object in their hands. Such a procedure was inspired by the famous inventor Thomas Edison.

The story goes that he would drift o to sleep while holding an object in his hand, so that it would noisily fall when his muscles relaxed, waking him up just in time to catch insightful impressions. We thus tested Edison's intuition that there is a brief window in the sleep-onset period that is conducive to creativity. Throughout the break, participants' vigilance state was monitored using video-polysomnography.

Sleep onset is a creative sweet spot 125

Results We discovered that after only around one minute of N1, most participants gained insight into the hidden rule, with a proportion of 'Eureka' three times higher than that of subjects who remained awake (83% vs. 31%). We also found that this bene cial e ect vanished when subjects reached deeper sleep, N2 (only 14% of solvers in the N2 group). We also con rmed these results (above and beyond sleep stages) using spectral analyses performed on the entire resting period: a medium level of alpha and a low level of delta was the optimal cocktail for insight. Finally, we showed that Edison's technique was an e ective method for capturing subtle modulations in the sleep-onset dynamic as well as a good 'hypnagogia-catcher'. well as to summon our creative muse at will.

Conclusion

Paper

The following article has been o cially accepted for publication in Science Advances on October 14 th , 2021.

INTRODUCTION

A few empirical studies have shown that sleep helps extract statistical regularities (1-3), solve problems (4,5), or reorganize associative memories in a way that promotes creativity (6)(7)(8).

Although the role of sleep in creative problem-solving is oft-cited in the sleep literature (9,10),

supporting evidence is surprisingly scarce when compared to other cognitive functions like memory consolidation (11). Most existing research has focused on the role of Rapid Eye Movement (REM) sleep and slow-wave sleep in creative problem-solving, which would respectively foster distant semantic associations and gist abstraction (10). Contrary to other sleep stages, the first stage of non-REM sleep (N1) has received little attention, and its cognitive role is largely unknown. Yet, a recent study showed that 10-min of "awake quiescence" (i.e., a quiet rest spent in a dimly lit room with reduced sensory stimulation) more than doubled the number of subjects who discovered a hidden rule compared to 10-min of active wake (12). Such conditions are likely to foster brief intrusions into N1 (13,14), but this possibility was not investigated.

Nonetheless, we believe that N1 presents an ideal cocktail for creativity. Indeed, creative cognition is supposed to rely on a dynamic interplay between brain networks involved in spontaneous thinking (default mode network) and cognitive control, which respectively support creative idea generation and evaluation (15). Neuroimaging studies of the sleep onset period have shown that N1 precisely engages these networks instrumental to creativity (16,17). In addition, N1 is accompanied by involuntary, spontaneous, dream-like perceptual experiences (18,19) that incorporate recent wake experiences (20,21) in a creative way by bounding them with loosely associated memories (22). Such hypnagogic experiences could be considered as an exacerbated version of awake spontaneous thoughts (e.g., mind-wandering) (23,24), and similarly foster the generation of novel ideas (25). In line with this hypothesis, we recently reported an increased creative potential in patients with narcolepsy (26), a population with frequent transitions toward sleep during the day. A subsequent study further identified hypnagogic hallucinations as a key modulator of creativity in narcolepsy (27). On the other hand, thalamic deactivation in N1 often precedes that of the cortex by several minutes (28), suggesting that executive abilities are not completely abolished during this stage. Consistently, subjects are sometimes capable of producing behavioral responses in N1 ( 29) and often surprisingly report that they were awake when awakened from N1 (30,31). These observations support the view that N1 is a hybrid, "semi-lucid" state where individuals start to be decoupled from their environment and can therefore freely watch their mind wander while maintaining their logical ability to identify creative sparks (32).

Here, we tested whether a brief period of N1 fosters creative insight, defined herein as the sudden discovery of a solution to a problem (1). We used the Number Reduction Task (NRT) (33), which has been shown to reliably capture an incubation-related gain of insight following a full night of sleep (1) or 10-min of wake quiescence (12). At each trial, subjects (N = 103) were given strings of 8 digits and instructed to reach a final digit solution as quickly as possible. To do so, they were informed that applying two simple rules in a stepwise manner would lead to the solution. Unbeknownst to them, a hidden rule permitted them to shortcut the series of operations and to obtain the solution much faster (see Fig. 1A for more details). After a short training, all participants completed two blocks of 30 trials each (Pre phase, Fig. 1A). Participants who gained insight into the hidden rule at that early phase (15.53%, 16/103) were excluded from subsequent analyses. Then, participants had a 20-min break (Break phase) during which they were asked to relax in a semi-reclined position with their eyes closed. Participants were instructed to hold an object in their right hand and, if the object were to fall, to report out loud their stream of thoughts just before the fall (see Figs. 1B andC for an illustration).

RESULTS

We scored each participant's break using standard sleep scoring criteria (34), and divided participants into 3 groups based on their vigilance state during the break (see demographic and sleep parameters in Supplementary Table S1): the "Wake" group (subjects who stayed awake for the entire break duration, N = 49), the "N1" group (subjects with at least one 30-second

epoch

A single minute of N1 inspires insight

We found a significant effect of the group (Wake vs. N1 vs. N2) on the percentage of participants who discovered the hidden rule after the break (Fisher's Test, p < 0.001, Fig. 2A).

This effect was driven by the percentage of insight in the N1 group, which was 2.7 times higher (83.33%, 20/24 subjects) than in the Wake group (30.61%, 15/49; comparison N1 vs Wake:

Fisher's Test, p < 0.001) and 5.8 times higher than in the N2 group (14.29%, 2/14, comparison with N1 vs N2: Fisher's Test p < 0.001). The percentage of individuals gaining insight was similar in the Wake and N2 groups (Fisher's Test, p = 0.32). Of note, only 3 out of the 16 participants who found the rule before the break (excluded from the previous and remaining analyses) slept in N1 (without any signs of N2), strongly suggesting that there is no a priori relationship between a specific sleep/wake trajectory during the nap and general insight abilities.

A delayed Eureka moment

Subjects explicitly reported insight into the hidden rule at the end of the experiment and insight was confirmed by a steep decrease in solving time after the Eureka moment (Fig. 2B, mean Before = 13.07 ± 3.71 secs, After = 3.81 ± 2.46, Wilcoxon signed-rank, z = -6.33 , p < 0.001), and accompanied by an increase of accuracy (mean Before = 91.15 ± 6.73 %, After = 96.86 ± 3.71%, Wilcoxon signed-rank, z = 5.05, p < 0.001) for solvers only (see Supplementary Fig. S1 for details on accuracy and speed-accuracy trade-off). The "Eureka" moment was calculated by an algorithm (see Methods) that automatically detected abrupt changes in solving times across all Post trials. The same algorithm was used in non-solvers for comparison with solvers and to confirm the absence of insight (see Fig. 2D for an illustrative example in one solver and one nonsolver). Importantly, this Eureka moment did not occur immediately following the resting period, but rather after 94 trials on average, regardless of the group (mean Wake = 82.67, N1 = 101.55, N2 = 106.50, Kruskal-Wallis: χ 2 (2) = 0.64, p = 0.73, Fig. 2C), a result consistent with previous studies using the NRT (1,12). Of note, we did not find evidence of an implicit knowledge of the rule in solvers before Eureka (see Supplementary Fig. S2). Altogether, these results demonstrate that an incubation period containing a brief period of N1 has a dramatic effect on insight, but that this beneficial effect vanishes if participants reach a deeper state of sleep (N2). Importantly, control analyses suggest that the higher proportion of insight observed in the N1 group is not related to confounding factors, as all groups were indistinguishable in terms of initial performance, subjective vigilance, or motivation level (see Supplementary Table S1). Participants in the N2 group were slightly slower in the post-break

Psychomotor Vigilance Test (PVT) than the other groups (Supplementary Table S1). However, performance on the first NRT block of the Post phase was similar in the three groups (mean solving time: Wake = 10.6s; N1 = 11.92s; N2 = 11.29s, Kruskal-Wallis: p = 0.41), suggesting that any behavioral trace of sleep inertia quickly vanished after the PVT. This finding, along with the fact that the average length of the Post phase was approximately one hour (leaving plenty of time to discover the rule after the dissipation of sleep inertia), strongly suggests that the lower rate of insight in the N2 group cannot be explained by sleep inertia.

Neurophysiology of the sweet spot

Here, the twilight zone between wakefulness and sleep was lumped together as N1, following the standard approach in sleep research. However, sleep onset is a complex, dynamic process (35,36), potentially encompassing multiple transitions between different sub-stages (31), each with subtle variations in physiological activity (e.g., alpha/theta, muscle relaxation). To better understand the critical factors for boosting insight, we calculated the power spectra for the entirety of the resting period and assessed whether it could predict subsequent Eureka moments in all subjects. These analyses revealed that the power in the delta ) and alpha (9-9.8Hz) bands were both predictive of insight (Fig. 3A). Precisely, we found a negative, quadratic effect of alpha power on insight (Figs. 3A andD), meaning that subjects with intermediary levels of alpha power had the higher probability of insight (Fig. 3E). Furthermore, we observed a negative, linear effect of delta power on insight (Figs. 3A andB), meaning that subjects with highest levels of delta power had the lowest probability of insight (Fig. 3C). Crucially, the effect of delta and alpha power was assessed above and beyond each participant's group (Wake, N1 or N2) and could therefore explain why some subjects who remained awake still discovered the hidden rule (i.e., wake solvers). Indeed, post-hoc analyses in the Wake group show that wake solvers differed from their peers (i.e., wake non-solvers) in that they had significantly lower levels of squared alpha power (unpaired Wilcoxon rank test, W = 355, p = 0.008) with a trend towards lower levels of delta power (W = 316, p = 0.08). Overall, these results point to the existence of a creative sweet spot within the sleep onset period, as well as the reasons why some participants failed to reach it. Indeed, reaching it requires a trade-off between the ability to fall asleep and attain N1 (which is favored by an intermediate level of alpha), but without too much sleep pressure to avoid transitioning into deeper sleep (associated with a low level of alpha and a high level of delta power).

To

A reliable marker of sleep onset

Finally, we aimed to experimentally verify Edison's intuition that holding an object while napping is propitious to capture creative sparks (i.e., the object would drop and awaken the subject at the precise moment of the creative sweet spot). We first tested whether participants dropped the object when asleep. This does not appear to be entirely the case: of the 63 drops, 26 (41.27%) occurred after N1. However, when we examine the wake-to-sleep transition taking into account micro-sleep episodes (MSE), which are not considered in the standard sleep scoring method over 30s-long windows (see Methods), this proportion rises to 77.78% (N = 49/63), with the bottle dropping after approximately 1 minute of accumulated micro-sleep episodes, Supplementary Table S1). The bottle drop thus appears more as a marker of the accumulation of micro-sleep episodes rather than of consolidated N1. We further tested the ability of Edison's method to capture sleep onset by performing spectral analyses restricted to the period around the bottle's drop ([-50, -10]s). We observed a clear power increase in the delta band 24 seconds before the drops (significant cluster: [-23.8, -0.2]s, pcl uster<0.0001, Fig. 4A), an increase that was superior to the one that would be observed by chance (randomly generated time drops, see Methods). This result was consistent regardless of the group, even in subjects who dropped the bottle in the absence of any micro-sleep episodes (Fig. 4B). Of note, this delta increase was congruent with subjective reports as most subjects (81.48%) reported that they had been drifting

to sleep when the bottle dropped. Interestingly, Edison's technique also acted as an efficient "hypnagogia-catcher" (see some examples in Supplementary Materials) since the amount of collected hypnagogic experiences when the object dropped (61.70%, with 34.48% of those being task-related) was significantly higher than when it did not drop (22.50% collected at the end of the nap, χ 2 = 13.50, p < 0.001), as well as higher than the percentage of hypnagogic experiences collected after awakenings from MSE in an additional control experiment (see the Methods for more information on that control experiment; mean cumulated time of MSE before awakenings = 1.10 min ± 1.08; 44/137 = 32.12% of awakenings with reported hypnagogia; χ 2 = 12.80, p<0.001; Fig. 4C). However, neither hypnagogic experiences in general (χ2 = 0.14, p = 0.71) nor task-related hypnagogia (χ2 = 0.05, p = 0.83) had an effect on insight. Altogether, electrophysiological, behavioral and subjective measures all point to the conclusion that Edison's technique permits detecting fine-grained shifts towards sleep even in subjects who would normally be considered awake. Is this technique useful for fostering insight in addition to detecting sleep onset? We believe that it does to some extent: it does not aid in precisely capturing the creative sweet spot, but it does help in keeping subjects who have reached it in that zone. Indeed, we demonstrated that the bottle is sensitive to the accumulation of delta power (Figs. 4A andB), the same frequency that we discovered is detrimental for insight (Fig. 3). This observation, combined with the fact that the bottle always woke the subjects up (100%), suggests that it interrupted the build-up of delta and prevented subjects from reaching deeper sleep, which was deleterious for insight. Consistently, only 5 subjects of the 33 (15%) who fell asleep and dropped the bottle showed evidence of N2 before the first bottle's drop. Furthermore, permutation analyses show that the proportion of sleep after the bottle's drop is lower than would be expected by chance if the bottle fell at random times (Fig. 4D).

DISCUSSION

Taken together, our results demonstrate that the discovery of a hidden rule is 2.7 times more likely after spending only one minute of N1 during an incubation period, compared to a similar period of quiet rest including only wakefulness. This facilitating role of N1 in insight disappeared when participants reached deeper sleep. Spectral analyses substantiate these findings and unravel a 'creative sweet spot' consisting of a medium level of alpha and a low level of delta. This creative sweet spot largely overlaps with the standard N1 stage, but not always, as it was also identified in wake solvers who never entered N1.

Consequently, methods that wake people up during N1 (such as a recently developed wearable electronic device called Dormio, (37)) have the potential to act as a creativity-booster. Here, we showed that Edison's technique seems effective at keeping subjects in the creative sweet spot by preventing deeper sleep from supplanting it. Further work, for example using a control experiment without the bottle, would be necessary to draw definite conclusions on the effectiveness of Edison's technique in fostering insight. Yet, because it does not require any material besides an everyday object, Edison's technique can be applied by anyone eager to summon their creative muse, either at home or in workplaces. As our study evidenced neural signatures capturing the creativity sweet spot, closed-loop brain-computer interface (BCI) methods could also be developed to help participants reach and stay within the creative sweet spot in order to boost creativity with a high level of precision. Furthermore, more research is needed to determine whether our findings are generalizable to other types of creative tasks (e.g., when the problem to solve is explicit).

We also provided empirical evidence that Edison's technique can be used to detect subtle changes in the wake-to-sleep transition that are not captured by the traditional scoring method.

Indeed, previous studies (35,36) have highlighted the difficulty of pinpointing a specific moment of sleep onset at which all measures coincide, be it electrophysiological (e.g., alpha drop, theta/delta increase, loss of muscle tone), behavioral (e.g., cessation of responses), or subjective (e.g., hypnagogic experiences, feeling of having slept when woken up). Edison's technique could thus help in better characterizing the fleeting moment of sleep onset by capturing each person's specific sleep onset dynamics. Such a method could be particularly interesting in people who have a bad perception of their vigilance states (e.g., in individuals with insomnia). In addition, we showed that Edison's technique appears optimal for collecting large amounts of hypnagogic experiences, facilitating the study of their electrophysiological signatures and specific roles in various cognitive processes (e.g., in memory).

Our study leaves open some intriguing questions for future research. For example, it is unclear why the Eureka moment occurred after around 94 trials of practicing again the task upon awakening. While this finding is fully consistent with other studies using the NRT (1,12), it contrasts with the popular view (and Edison's story) in which a solution to a problem seemingly arises as soon as we wake up. This delayed Eureka occurred roughly 30 min after awakening and could thus correspond to the time for sleep inertia to vanish (typically less than 30min, (38)).

This hypothesis is, however, not satisfactory because we did not observe any difference in the time of the Eureka moment between Wake, N1, and N2 subjects (which should increase along with an increase in sleep inertia). Furthermore, we did not find any evidence that participants used this delay as a verification stage (e.g., checking the accuracy of the hidden rule as a new strategy, which should be reflected as a slowing in solving times in the trials immediately before the Eureka moment). An alternative hypothesis is that N1 favors implicit gist abstraction by restructuring task-related memories and stores the hidden rule in a 'strategy-store'; then, the use of this new strategy would be determined by a second implicit process occurring when confronted again with the task during wakefulness (e.g., accumulation of evidence until a threshold determining when it is optimal to stop exploiting the original strategy and exploring whether another, more efficient strategy is available).

This explanation is, however, speculative, and the suppressing effect of N2 on insight remains surprising in this framework. One possibility is that N2 episodes were too brief to allow the full sleep-dependent memory process to unfold. By interrupting spontaneous memory reactivations of task material, we potentially destabilized them, making them more labile and susceptible to forgetting (39). Alternatively, it could be that N1 puts participants in the creative mindset for insight and what would matter in that case would be the last sleep stage before awakening.

Another open question is whether hypnagogic experiences played a role in inspiring insight. We did not find any evidence in favour of this hypothesis in our study. However, it is possible that some participants did not report any task-related mental experiences because they simply forgot having them or that we failed to recognize some far-fetched associations with the task due to the bizarreness of hypnagogic content (22). Finally, while our findings strongly support the fact that N1-related processes favor insight, it is still unclear whether some factors or individual differences facilitated the 'N1 trajectory' in the first place. Further research is needed to determine the optimal conditions for hitting the creative sweet spot and whether it depends on a specific profile of individual sleep onset dynamics.

Despite its importance in our daily lives, the neural and cognitive mechanisms underlying creative problem-solving remain poorly understood. By identifying sleep onset as a key period for inspiring insight, our study provides a well-identified, short window to focus on when investigating the neural mechanisms of creative problem-solving. Overall, we hope that our work will precipitate further research on this "twilight" zone, which has been largely neglected by scientists thus far. 12), but we adapted the task so that it runs on Matlab (Psychtoolbox). Participants were presented with 8-digit strings, composed of 3 possible numbers (1, 4 and 9). They were told that they had to find the final solution to each string. This could be done by transforming the string into a response string, through a sequential application of two rules:

MATERIALS AND METHODS

Participants

• The "same" rule: if two successive digits are the same, the response is this digit (e.g., 4-4, response: 4)

• The "different" rule: If they are different, the result is the remaining third digit (e.g., 1-4, response: 9).

To come up with the final solution, participants had to apply these rules in a stepwise manner, starting by comparing the first two digits of the string then using their first response together with the next digit to determine the second response and so on until the end of the string. They had to press Enter to validate the final solution. Participants had no time limit, but they were told that they had to find the solution as quickly as possible. To keep them motivated, participants received feedback on whether their response was correct after each trial.

Importantly, we did not mention to the subjects that a hidden regularity determined all the strings. All responses' strings had the same internal structure (ABCDDCB; A, B and C being one of the three digits 1, 4 or 9): the last three responses mirrored the preceding three responses, meaning that the second response in each trial was always the final solution. Thus, gaining insight into this regularity would dramatically decrease response time because participants could abruptly shortcut their responses by pressing Enter immediately after entering the second answer.

Of note, strings varied from trial to trial, thus gaining insight into the rule could not originate from the mere repetition of the same finger sequence in all trials. Correctness and solving time were recorded for each trial.

Insight was defined as a:

• Steep decrease in the response time for correct trials

• Explicit report of the hidden regularity during the post-task questionnaire Experimental Procedure -The protocol was subdivided into 4 main phases.

1.

Training. Before starting the session, participants had to perform two correct strings on paper with the experimenter to ensure that they correctly understood the task. They then started the computerised task with the Training phase composed of 10 trials (that did not contain the hidden rule).

2. Pre-Phase. Following Training, they had to complete 2 blocks of 30 trials each.

Break.

All participants had a 20-min break in a dark room without sensory stimulation.

They were installed in a semi-reclined position in a chair, with their eyes closed and legs on a footrest. They had to hold an object in their right hand, with their hand carefully placed outside the armrest (see Fig. 1B). Participants were simply told to relax or sleep if desired. If the object fell (the sound of which awakened them), they were instructed to describe out loud what was going through their mind before it fell. We told them that their mental content could be of any kind: thoughts, images, reveries, dreams... Once done, they were told to pick up the object and repeat this procedure until the end of the break. Participants were constantly monitored with an infrared camera to ensure that they followed the instructions appropriately.

At the end of the rest period, we asked participants to describe their mental content during the break and state whether they thought about the task, were awake or drowsy, and whether holding the object was tiring and/or preventing them from falling asleep. This design allowed us to naturally subdivide our subjects into three main groups:

• Wake group (subjects who did not sleep): N = 49

• N1 group (subjects who entered N1, without any signs of other sleep stages): N = 24

• N2 group (subjects who attained the second stage N2): N = 14 At the end of the experiment, we verified their knowledge of the rule and asked whether they were used to solve enigmas.

Object -We tested many objects (spoon, small steel spheres, stress balls, etc.) before finding the right one. We determined that the ideal object needed to meet the following criteria: makes a noise when falling, be light in weight to avoid cramped arms, be slippery to facilitate the fall, and be large enough so that the fist cannot close on it (preventing the object from falling). The object that we chose for this experiment was a 14.5cm tall, 5.5cm diameter, light (55 g) drinking cup/bottle (see Fig. 1B), which succeeded in capturing sleep onset in a pilot study. This object is cheap (around 3€) and anyone who wishes to buy one can ask us for the fabric. Two scoring methods were applied:

• The standard sleep scoring guidelines from the American Academy of Sleep Medicine (AASM) (34). Any subject with at least one epoch of N1 was assigned to the N1 group, unless signs of N2 (spindles, K-complexes) were present (in that case, the subject was put in the N2 group). By definition, no participant from the Wake group had more than 15s of continuous sleep (standard criterion to score an epoch in sleep), but they could have shorter episodes of sleep.

• A micro-sleep scoring method, like the one recently developed by Hertig-Godeschalk et al. (2020) (42). EEG was scored continuously to identify micro-sleep episodes (MSE), which were defined here as any short window of at least 3 seconds of theta activity with alpha loss. This method was used to detect micro-sleep episodes that are not captured by the standard sleep scoring method, providing an in-depth analysis of the wake-to-sleep transition zone which is the focus of the present paper.

The Eureka moment -To detect sudden changes in solving time (taken here as a marker of insight), we used an algorithm provided by Matlab named "findchangepts", which detects abrupt changes in signals. For a vector x with N elements, the "findchangepts" function partitions x into two regions, x(1:changepoint-1) and x(changepoint:N), which minimizes the sum of the residual (squared) error of each region from its local mean. The same algorithm was used for both solvers and non-solvers. By definition, the algorithm always finds a point in which solving time decreases the most, even if the 'drop' is small or corresponds to a random, not sustainable deviation in reaction time in one trial, which was typically the case in non-solvers.

Hypnagogic experiences -We compared the proportion of hypnagogic experiences collected right after the bottle drop and at the end of the rest period (for participants who did not drop the bottle). Here, a mental report was considered as a hypnagogic experience only if it was a "fleeting, involuntary, spontaneous, perceptual, and bizarre mental content" (some examples are reported in Supplementary Data, see text and video). Of note, if we had used a broader definition (i.e., any reported mentation), the percentage of reported hypnagogic experiences at drop would have been 100%, rather than 63.64% with our more conservative definition (see Results section). To account for the time factor in hypnagogic recall, we also compared the amount of reported hypnagogic experiences at the bottle's drop with the one obtained in an additional control experiment, in which subjects took a 30-minute break in a dark bedroom; they were regularly awakened by a sound and asked to describe their mental content. We then calculated the percentage of reported hypnagogia when the awakening sound occurred after micro-sleep episodes to be in comparable conditions to the current study.

EEG spectral analyses -We computed the power spectrum of EEG electrode O1 in two different ways. First, we extracted the power spectrum over contiguous epochs of 30s

(corresponding to the epochs used for sleep scoring). We used Welch's method to compute the Power Spectra Density (PSD) between 1 and 30Hz using windows of 6s with 50% overlap and a frequency resolution of 0.2Hz. The power over each 6-s window was averaged for each 30s epoch. Epochs which exceeded an absolute amplitude of 150µV were excluded from this analysis. We then applied the FOOOF toolbox to compute a smoothed version of the power spectrum (43). The corresponding power spectra were finally averaged across the entire Break.

Second, we computed the time-resolved power spectrum around bottle drops and random time drops (-50s to 10s around these times). Windows which exceeded an absolute amplitude of 750µV before a drop were excluded from this analysis (7/46 recordings). We used a multi-taper time-frequency decomposition with a single Hanning taper over 6s long windows. Power was extracted between 1 and 30Hz with a 0.2Hz resolution. This operation was performed for each bottle drop in each recording (if any) and the corresponding power was averaged across drops where a recording contained more than one. For comparison, we also generated random time drops using a uniform distribution between the beginning and end of the Break. A total of 100 random times were drawn for each recording with at least one drop, and power was averaged across these draws.

Statistical analysis -Inter-judges' agreement was evaluated with the Cohen's Κ test. Fisher's and Chi-squared tests were used in the analysis of contingency tables. Kruskal-Wallis tests were used for ordinal variables. Non-parametric statistics were applied (Mann-Whitney U Test for independent samples, Wilcoxon signed-rank for paired samples and Kruskal-Wallis for more than two groups) when variables could not be approximated to the normal distribution (Shapiro-Wilk Test). All tests were two-tailed and a probability level of less than 0.05 was considered significant. All computations were performed using Matlab 2018b. For the analysis of the power spectra (Fig. 3A), we applied two-way ANOVAs at each frequency bin with two betweensubject predictors: sleep (whether participants fell asleep during the break or not) and insight (whether participants found the solution after the Break or not). The power (predicted variable)

was either z-scored (across recordings) at each frequency bin to investigate linear effects of the predictors, or z-scored and squared to examine quadratic effects. To correct for multiple comparisons, we used a cluster permutation approach (43) with a cluster alpha set at 0.1 (used to define the candidate clusters), a Monte-Carlo alpha set at 0.05 (used to determine the significance of the clusters) and 1000 permutations. A minimal size of 5 consecutive frequency bins was applied for significant clusters. This stringent approach is designed to control for Type-1 errors (False Positives) and resulted in two clusters with narrow frequency bands. While this method allows to conclude on the existence of statistically significant differences, it does not permit the absolute demarcation of these effects (44). Thus, our findings do not affirm that the effects are restricted to the narrow bands identified by the cluster permutation. In addition, only the effect of insight (linear and quadratic) is shown in Fig. 3A. Power was then averaged for each of the clusters obtained and a two-way ANOVA, like the ones described above, was applied to the z-scored and squared z-scored power (Figs. 3B andD).

We also fitted logistic regression models to attempt to predict insight with a set of variables of interest (Sleep Group, z-scored Delta Power, z-scored squared Delta Power, z-scored Alpha Power, z-scored squared Alpha Power) and covariates (Bottle-Drop, Epworth Score, mean post PVT reaction times, and percentage of microsleep). We fitted models with increasing levels of complexity as follows: S2.

SUPPLEMENTARY TEXT

Examples of hypnagogic reports

• "I saw geometric shapes, like the old screensavers on computers where there was a square that became a triangle, then a round. Something like that, blurry, with colours."

• "I saw lots of numbers in black with little triangles. At first, they were far away, and then they came closer. Then, it was calmer, the sea, with little boats in the sand. And then I saw the strings of my guitar and I played piano with the keyboard."

• "I was in wheat fields."

• "At one point, I saw a horse in the hospital. There was also a man who was doing the same experiment as me, who was much older and who had a kind of plastic helmet on his face."

• "I was hearing a sound that said 1,4, 9."

• "I had the feeling of being at the water's edge, no wind, there were airy sounds, as in a temperate forest in summer."

• "I was inside the Colosseum in Rome when it was still in its prime. I also saw concentric shapes that were shrinking and certain figures that I called functions that I was trying to identify. One that really struck me was what I called the oscillator that turned one figure into another and then back to the original one."

Supplementary Video. Examples of hypnagogic experiences reported by participants.

Participants reported their mental content either directly after dropping the bottle and at the end of the break.

Transition

In Study 2, we demonstrated that sleep onset is a creative sweet spot: one minute of N1 drastically increased the percentage of subjects who discovered a hidden regularity within a task. The role of sleep in abstracting commonality has been supposed to rely on replaying memory traces, identifying patterns within them, and reorganizing the memory network accordingly. However, this hypothesis has been rarely tested, as studies typically focus on either creative problem-solving or memory, rather than on a combination of the two cognitive processes. In the following Study 3, we designed a new task to concurrently track the fate of individual memory traces in terms of memory consolidation and creative problemsolving.

Digest

State of the art In Study 2, we showed that the N1 stage was su cient to inspire insight at a level comparable, if not higher, to a full night of sleep. Which mechanism may be responsible for this surge of inspiration at sleep onset? [START_REF] Milivojevic | Insight Recon gures Hippocampal-Prefrontal Memories[END_REF] demonstrated that identifying a link between information results in a reorganization of the memory network (useless information is suppressed while novel, gist representations are created). This study is, however, unique in that most studies assess either memory or creative problem-solving. Additionally, there is a substantial literature on the bene cial e ect of sleep on memory (for reviews, see Diekelmann and Born (2010), Paller et al. (2021), and Rasch and Born (2013)), but the majority of studies focused on the consolidated stages of NREM sleep (N2 and N3), omitting the rst stage (N1).

Nonetheless, numerous factors point to memory processing beginning at sleep onset. N1 is associated with an increased hippocampal activity (Picchioni et al., 2008), and dreamlike experiences that incorporate recent experiences (Stickgold et al., 2000;Wamsley et al., 2010a;Wamsley and Stickgold, 2019;[START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF]. Moreover, these memory-related fragments in hypnagogic experiences are later found in NREM and REM sleep dreams of the same night (Wamsley and Stickgold, 2019). The N1 stage may thus play a role in reviewing recent events and tagging relevant memory traces for consolidation in subsequent NREM sleep. Yet, the literature on sleep onset and memory is almost nil, and the few studies that have been conducted on the topic have yielded inconsistent results. One study (Lahl et al., 2008) showed that a 6-min nap improved memory (but did not distinguish between N1 and N2 stages), whereas Wyatt et al. (1994) and Wyatt et al. (1997) reported a sleep onset amnesia for the materials encoded in the few minutes preceding sleep onset.

Objectives The purpose of this study was thus to address this knowledge gap by determining whether sleep onset plays a role in memory processing, and, if so, how this might relate to creative problem-solving. Our initial hypothesis was that sleep onset would bene t creative problem-solving by extracting the gist common to all memory traces (replicating our ndings from Study 2) and impact subsequent memory performance by suppressing redundant information (the individual memory traces).

Methods Using a newly-developed behavioral task, we examined how recent experiences are transformed at sleep onset. Subjects (N=63) completed a memory task (i.e., memorize the locations of objects) and had their memory tested before and after a 30-minute rest period that included either wakefulness, N1, or N2. Importantly, they were also explicitly instructed to nd a hidden rule that allows knowing any object's position without the need to memorize it.

Memory loss at sleep onset 163

Results We found no e ect of group on insight (but the overall number of solvers was low). However, when we examined the data from the remaining 52 non-solvers, we discovered that memory recall was lower (10% forgetting of the pre-encoded material) after a resting period including N1, compared to subjects who continued into N2

(3.46% of forgetting). Moreover, we observed a negative correlation between the ratio of alpha/theta power (a marker of the transition towards sleep) and the level of forgetting in sleepers.

Conclusion

We showed that memory processing takes places during N1, certainly by promoting forgetting.

Paper

This paper is currently in the stage of preparation for submission. It should be noted that the following paper focuses exclusively on the memory part. Concerning the insight part, we intend to conduct additional control experiments prior to writing a paper in order to better understand what might be causing the observed discrepancies between

Studies 2 and 3. The results of the insight part, as well as the percentage of solvers in each group, can be found at the end of the paper, in Supplementary.

INTRODUCTION

"There is a deeply cut cave, a hollow mountain, near the Cimmerian country, the house and sanctuary of drowsy Sleep. [...] There still silence dwells. But out of the stony depths flows Lethe's stream [of oblivion], whose waves, sliding over the loose pebbles, with their murmur, induce drowsiness."

The Metamorphoses, Ovid -Book XI, The House of Sleep

Numerous human studies have demonstrated that sleep improves or stabilizes memory in a variety of tasks, including perceptual, associative, spatial, motor, and emotional learning [START_REF] Born | Sleep to remember[END_REF]Diekelmann & Born, 2010;Paller et al., 2021;Rasch & Born, 2013). A growing body of research on animals supports the notion that this sleep-dependent memory consolidation is enabled by a hippocampo-neocortical dialogue orchestrated by slow oscillations during NREM sleep (Maingret et al., 2016;Ólafsdóttir et al., 2018). Importantly, sleep does not blindly consolidate all memories formed during the day, but rather selectively consolidates those that are expected to be of future relevance (Oudiette et al., 2013;Saletin et al., 2011;[START_REF] Stickgold | Sleep-dependent memory triage: Evolving generalization through selective processing[END_REF]. How the sleeping brain performs this 'memory triage' remains mysterious [START_REF] Stickgold | Sleep-dependent memory triage: Evolving generalization through selective processing[END_REF]. One could imagine that the first sleep stage, known as NREM sleep stage 1 (N1), could contribute to this process by reviewing recent experiences and either deleting the memory traces deemed irrelevant (if the tagging process occurred pre-sleep), or directly tagging the memory traces considered important for consolidation in subsequent sleep stages. However, because animal models used in sleep research lack an equivalent to the classically described NREM sleep subdivision in humans (Lacroix et al., 2018), they cannot provide information about the role of each NREM sleep stage, particularly N1, in memory. On the other hand, cognitive researchers have paid little attention to the N1 stage, possibly due to its fleeting nature, leaving us in the dark about its potential role in memory processing.

Several factors suggest the involvement of sleep onset in memory processing. First, the hippocampus, a key brain region for memory consolidation, exhibits increased activity in the late N1 period compared to wakefulness (Picchioni et al., 2008). Second, N1 is associated with vivid, dreamlike experiences that often integrate daytime events (Stickgold et al., 2000;Wamsley, Perry, et al., 2010;[START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF]Wamsley & Stickgold, 2019), and several studies showed a positive correlation between dreaming about a task and subsequent memory performance (De Koninck et al., 1990;Plailly et al., 2019;[START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF]Wamsley & Stickgold, 2019). Moreover, memory-related experiences that are incorporated into sleep onset mentation are later found in the contents of NREM and REM sleep dreams within the same night of sleep (Wamsley & Stickgold, 2019). Such findings would imply that the same memories are processed sequentially across the successive sleep stages (reminiscent of the 'sequential hypothesis', Giuditta, 2014), supporting the aforementioned idea that N1 acts as an initiating stage, tagging memories for consolidation during subsequent NREM sleep [START_REF] Stickgold | How Do I Remember? Let Me Count the Ways[END_REF]. This notion is also substantiated by Stenstrom et al. (2012) who found that hypnagogic experiences connect fragments from distal memory sources that share semantic similarity, suggesting that a hippocampo-dependent integrative process (Shohamy & Wagner, 2008) occurs during this stage (i.e., the blending of overlapping past events into an integrated memory representation). Recently, we confirmed the role of N1 in such a gist abstraction process, as we found that spending on average one minute in N1 tripled the chance of discovering a hidden regularity within memory traces (83%), compared to wakefulness (30%) or deeper sleep (N2: 14%) [START_REF] Lacaux | Sleep onset is a creative sweet spot[END_REF]. Our study thus adds support to the notion that the N1 stage is involved in memory triage, identifying a common pattern among several memory traces.

However, beyond these findings, the literature on the sleep-onset period and memory remains sparse and inconclusive. One study showed that a 6-min nap was sufficient to improve wordrecall compared to a similar period of wakefulness (Lahl et al., 2008). However, those naps included both N1 and N2 stages, thus preventing us from drawing a definitive conclusion about the respective contribution of each NREM sleep stage to the observed memory benefit. In contrast, older studies reported evidence that sleep onset was associated with retrograde amnesia of the materials encoded in the few minutes prior to sleep onset (Wyatt et al., 1994(Wyatt et al., , 1997)). In those studies, word pairs were presented to participants as they were falling asleep, so it is unclear whether sleep onset interfered with the consolidation or encoding of those stimuli.

Here, we aimed at better understanding whether the twilight zone between wakefulness and sleep contributes to memory consolidation. To do so, we compared how a 30-minute resting period that included either only wakefulness, N1, or N1+N2 impacted the fate of memories that had been successfully encoded prior to the resting period. Task -Subjects had to learn the precise location of 48 visual stimuli on an 8x6 squared grid background displayed on a 34.4-cm computer screen. Locations were randomly determined for each stimulus and each participant at the beginning of the session. Visual stimuli were pictures representing a variety of objects or animals and had a dimension of 156x156 pixels (corresponding to 4.13x4.13 cm). Subjects were instructed to reposition the pictures as precisely as possible to maximize their bonus monetary reward (maximum reward per picture = 10 cents). Each object's precise location was indicated by a black-and-white square in the center of the object. The Psychtoolbox [START_REF] Brainard | The Psychophysics Toolbox[END_REF][START_REF] Kleiner | What's new in Psychtoolbox-3[END_REF] was used to program the task in Matlab R2018b. We computed two measures of accuracy for each object:

METHODS

1) a continuous, precise measure consisting of the geometric distance between the position given by the participants and the correct position, and 2) a binary correct vs. incorrect measure.

An object was considered correct if the participant placed it within 5.4 cm of its original location (corresponding to the diagonal of a square unit within the grid, see Figure 1 for an illustration of the correct perimeter). This threshold was explicitly stated to participants and was the same as in Oudiette et al. (2013) and Rudoy et al. (2009).

Experimental design -Participants performed this spatial memory task (for details on the task, see Figure 1) before and after a 30-min resting period while monitored via polysomnography.

The protocol was divided into four main phases (summarized in Figure 1):

Learning phase -Participants first went through a Learning phase in which they had to memorize the precise location of 48 stimuli. First, the stimuli appeared one by one at the correct location, and each stimulus was repeated twice in a random order (passive viewing). The stimuli then appeared at a wrong location, and subjects had to drag them to the correct location using a computer mouse (active learning phase). They received feedback on their performance during this phase to ensure proper encoding. A picture was considered 'encoded' if the subject placed it correctly twice in a row (see the Task section for details on how accuracy was measured). The entire learning phase (passive viewing + active learning) was divided into three blocks of 16 stimuli, separated by a short break. It stopped when participants encoded the pictures' positions well enough (i.e., 80% of correct stimuli). After completing the learning phase, participants were given a short, 3-min break before proceeding to the next phase (Pre).

Phase 2 (Pre) -Participants were tested on their memory for the 48 pictures (MemoryTest Pre). During the test, each object (one at a time) appeared in a wrong position and participants had to drag the object to its correct location using a computer mouse, like in the learning phase, but without feedback this time. Of note, unbeknownst to the participants, the starting position of an object was not determined randomly, but rather according to a hidden rule: it was always located on a fixed-length diagonal to the final/correct object's location. This paradigm was originally designed to also assess the impact of N1 on insight (i.e., the sudden discovery of a solution to a problem, here a hidden rule that allowed predicting the position of any object).

But in this study, only participants who did not find the rule (N=52) were considered, and the part on insight will be the subject of a separate paper.

Phase 3 (Break) -After the first memory test, participants were given a 30-minute resting period. They were instructed to lie on a bed in a dark room, eyes closed, and to rest and sleep as they pleased. Every 6 minutes (plus a jitter randomly chosen from 0 to 30 seconds), we played a sound to wake the participants up. After each awakening, participants were instructed to tell out loud what was going through their mind before the alarm (e.g., thoughts, images, reveries, dreams…). This procedure aimed first at minimizing the probability that participants would fall into deeper sleep stages and thus maximizing the probability of obtaining resting periods with N1 as the only sleep stage. Second, it sought to assess the impact of spontaneous mental contents on memory processing. According to this design, we split participants into three distinct groups based on their vigilance state during the break (see demographic and sleep parameters in Table 1): a 'Wake group' (subjects who stayed awake during the whole break; N = 16), a N1 group (subjects who reached N1 without any signs of deeper sleep stages; N = 15), and a N2 group (subjects who reached N2 stage; N = 21, of which two directly transitioned from wakefulness to N2 and therefore without any N1). Critically, all experimental conditions were identical between these three groups.

Phase 4 (Post) -After the resting period, participants were tested again on the memory task (MemoryTest Post), using the same procedure as for the Pre test.

To measure potential differences in vigilance levels between groups, participants also performed a 3-min Psychomotor Vigilance Test (PVT) at the beginning of Phases 2 (Pre) and 4 (Post). The PVT consists of monitoring the appearance of a stimulus on a screen and responding to each appearance as quickly as possible [START_REF] Grant | 3-minute smartphone-based and tablet-based psychomotor vigilance tests for the assessment of reduced alertness due to sleep deprivation[END_REF].

Mental content -Each mental content reported during the resting period was labelled as a dream or thought, with each being further classified as task-related or not. Given that we were studying the wake-to-sleep transition, we opted for a more conservative definition of dream than is commonly used (i.e., any mental content during sleep). Here, a mental report was only deemed as an oneiric experience if it was 'involuntary, spontaneous, perceptual, and bizarre' (i.e., unusual, non-thought like). EEG spectral analyses -A spectral decomposition of the pre-processed EEG signal was conducted on the entire resting period for the occipital electrode (O1). Epochs with an absolute amplitude above 500 µV were discarded from this analysis. Welch's method was used to estimate power spectral density (PSD) for two frequency bands (Alpha: 8-12Hz and Theta: 4-7Hz) using a 6-s sliding-window with 50% overlap and a frequency resolution of 0.2Hz. The power over each 6-s window was averaged for each 30-s epoch. Finally, the corresponding power spectra were averaged across the whole Break duration. A constant of 3 was added to the power values to guarantee that they were all greater than zero, before calculating an alpha/theta ratio for each subject. Of note, the reported findings are similar for constant values superior or equal to 3. We also computed a normalized version of the alpha/theta ratio by zscoring the individual values within each group (Wake, N1, and N2).

Sleep monitoring -

Statistical analyses.

As our aim was to investigate whether sleep-dependent memory consolidation starts as early as N1, we restricted our behavioral analyses to items that were sufficiently encoded before sleep. Accordingly, we excluded data from pictures incorrectly remembered (i.e., pictures that were positioned above the distance threshold of 5.4cm) during the Pre test (mean = 7.40 ± 5.11 items excluded). Fisher tests were used to test relationships between categorical variables. Kruskal-Wallis tests (or One-Way ANOVAs for normally distributed data) were conducted to test the impact of the group (Wake, N1, or N2) on performance. When appropriate, additional post-hoc comparisons with Tukey-Kramer correction for multiple comparisons were performed. Wilcoxon signed-rank tests were used to compare two paired non-parametric variables, and Mann-Whitney for independent samples. In the case of non-significant results, Bayesian statistics were computed using JASP (JASP Team, 2019) with a prior distribution following a Cauchy distribution with a default scale rate of 0.707. A Bayes Factor BF01 typically above 3 (i.e., the null hypothesis is three times more likely than the alternative hypothesis) provides supportive evidence for the null hypothesis.

Correlations between the alpha/theta ratio and the memory performance were performed using Pearson's correlation (and correlation plotting using the gramm toolbox; [START_REF] Morel | Gramm: Grammar of graphics plotting in Matlab[END_REF]. The

Cohen's Κ test was used to evaluate inter-judge agreement. All tests were two-tailed and a probability level of less than 0.05 was considered significant. All computations were performed using Matlab 2018b.

RESULTS

Participants performed a memory task before and after a 30-min break during which they were allowed to sleep (see Figure 1 and Methods for details on the experimental task and timeline).

Depending on their vigilance state during the resting period, participants were subdivided into three groups (Wake, N1, and N2). Participants' demographic and sleep parameters are provided in (Schneider, 2013). Exact p-values are directly reported (ANOVA or Kruskal-Wallis tests when appropriate; ordinal data or violations of normality were assessed with the Shapiro-Wilk test, and the Chi-squared test for comparisons of proportions). When appropriate, post-hoc comparisons have been computed with Tukey-Kramer correction for multiple comparisons, and we reported significant differences between Wake and N1/N2 (*), N2 and Wake/N1 (#), and between all groups ($).

N1 is associated with forgetting

We found that the change in recall accuracy (geometric distance) after the resting period significantly differed as a function of the group (Wake, N1, N2) (Figure 2A, χ 2 (2) = 6.82, p = 0.03, Kruskal-Wallis). The pre-post difference was significantly smaller than 0 (forgetting) for all groups (p <0.001, Wilcoxon signed-rank), but this difference was larger for N1 subjects, who overall tended to place the objects further away from their correct location after the nap than before (mean Δ distance pre-post: Wake = -0.53 ± 0.42 cm , N1 = -0.83 ± 0.89 cm, N2 = -0.27 ± 0.36 cm, pvalue between N1 and N2 = 0.055; post-hoc comparisons with Tukey-Kramer correction for multiple comparisons). Results obtained with the binary, rather than continuous, estimate of recall were concordant, as N1 subjects tended to have a lower overall accuracy than Wake and N2 subjects (Figure 2B, mean Wake = -1.04%, N1 = -3.61%, N2 = + 0.10%, p = 0.088, Kruskal-Wallis test).

Importantly, the overall decrease in performance observed in the N1 group was not due to baseline differences in memory abilities, as all groups performed equally well during the Pre phase both in terms of average distance from the correct location (p = 0.17, Kruskal-Wallis)

and of number of correct objects (p = 0.48, see Table 1). Furthermore, control analyses indicate that the difference observed in the N1 group was not related to other confounding factors (e.g., sleep inertia), as all groups were matched in terms of vigilance, or educational level (see Table 1). Of note, we did not have enough individuals who reported unambiguous task-related oneiric experiences (5 subjects out of 52: 9.62%) to perform statistical analyses on them. This low percentage is likely due to the prevalence of the task stimuli (e.g., animals, objects) in daily life, making it difficult to determine whether a report including an item is directly related to the task. However, we had sufficient data to assess the impact of reported dreams in general or task-related thoughts on performance. We found no difference in forgetting levels between subjects who reported oneiric experiences and those who did not (mean forgetting with dream reports = 6.09 ± 4.54, without = 6.29 ± 8.61, p = 0.23, Wilcoxon rank sum test), nor between subjects who thought about the task during the resting period and those who did not (mean with = 6.08 ± 5.94, without = 6.53 ± 6.90, p = 0.94, Wilcoxon rank sum test).

Memory loss following N1 concerns a subset of items

We then decided to investigate further whether this decrease in performance associated with N1 was due to a general decrease in precision for all objects or to forgetting of a subset of items. To do so, we classified the 48 objects into 4 categories: 1) 'Always Good' (correct objects in both the Pre and Post phases), 2) 'Gained' (false at Pre, correct at Post), 3) 'Always Bad' (false in both the Pre and Post phases), and 4) 'Lost' (correct at Pre, false at Post). Only the Lost objects category differed between groups (χ2(2) = 7.70, p = 0.02, Kruskal-Wallis test;

see Supplementary Figure S1). Post-hoc comparisons showed that there was a significantly higher number of Lost objects (on average, 3.6 objects) following a resting period containing the N1 stage, as compared with one containing N2 (N1 vs. N2, p = 0.02; Wake vs. N1, p = 0.66; Wake vs. N2, p = 0.17; post-hoc comparisons with Tukey correction). This corresponded to a 10% forgetting rate (� 𝑁𝑁𝑁𝑁 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑁𝑁𝑁𝑁 𝐶𝐶𝐿𝐿𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐿𝐿 𝑃𝑃𝐶𝐶𝐶𝐶 � × 100), Figure 2C).

Additionally, we attempted to determine what these lost items had in common. Wyatt et al. (1994) reported a sleep onset-related amnesia of stimuli presented just prior to sleep onset (3 minutes before), with no deficit for stimuli presented earlier. Here, we found no difference in the timing of items' presentation relative to sleep onset between those that remained correct and those that were Lost during the active learning phase (3 possible blocks; mean presentation block Always-Good = 1.92, Lost = 2.06, p = 0.13) or during the Pre phase (48 stimuli; mean presentation order Always-good = 23.74, Lost = 25.49, p = 0.18, Wilcoxon-signed rank).

Instead, we found that the Lost objects were the ones that participants had the most difficulty encoding (i.e., they were repeated more times during the Learning phase before encoding than the Always-good objects: Figure 2D; mean Lost = 2.46 repetitions, mean Always-good = 2.12; z = -2.74, p = 0.0061, Wilcoxon signed-rank test; no between-group difference in the number of repetitions before encoding of these Lost and Always-good objects: p = 0.41 and p = 0.75 respectively). Individual data is also depicted by circles alongside their boxplot. Kruskal-Wallis tests were performed for comparisons between all three groups; when appropriate, post-hoc comparisons with Tukey correction have been computed. One-sample Wilcoxon signed-rank was used for comparison with zero level, and two-samples for comparisons between two paired samples: Always Good and Lost. Stars are used alone to report the p-values obtained when comparing data to zero, stars with a line for between-group differences, and stars above a square bracket for post-hoc comparisons. *** p<0.001; **, p<0.01; *, p<0.05, ns, for non-significant differences.

Figure 2 -Memory loss at sleep onset. (A) Delta (pre-post) distance and (B) delta (post-pre) accuracy (percentage of correct objects) for each group (Wake, N1 and N2). Negative values thus indicate forgetting for both measures. The delta distance was computed on the items that were correct during the Pre test (items sufficiently encoded to potentially benefit from memory consolidation during the break). (C)

Neurophysiology of memory loss

We then performed spectral analyses to test whether a deeper N1 stage (as commonly illustrated by the alpha/theta ratio) would be associated with more forgetting. We first confirmed expected between-group differences in power spectral profiles (Figure 3A) and alpha/theta ratios (a common marker of sleepiness, Figure 3B), both of which indicated a gradual increase in sleep depth between Wake, N1, and N2 subjects (alpha power and alpha/theta ratio significantly differed between groups; mean alpha/theta ratio for Wake = 1.42, N1 = 1.21, N2 = 1.03; F(2,49) = 21.12, p<0.001, one-way ANOVA). Second and more importantly, we then normalized (zscored) the alpha/theta ratios and forgetting rates within each group and quantified the correlation between these variables. We observed a significant negative correlation between the alpha/theta ratio and the forgetting rate in all sleepers (including both the N1 and N2 groups; These EEG results complement our behavioral results by demonstrating that variations in the alpha/theta levels influence the extent of memory loss within a single stage, such as the N1 stage.

DISCUSSION

Countless studies have shown a positive relationship between sleep and memory in recent decades [START_REF] Born | Sleep to remember[END_REF]Diekelmann & Born, 2010;Rasch & Born, 2013). The role of the NREM sleep stages 2 and 3, and, to a lesser extent, of REM sleep, have been extensively studied, but previous work has left aside the putative impact of the first stage of NREM sleep (N1) on memory processing. Instead, most studies either conflate this period with the following NREM sleep stages or with wakefulness, potentially because of the hybrid and fleeting nature of the sleep-onset period. We overcame this issue by devising an experimental paradigm allowing us to isolate N1 specific role in the fate of recently encoded memories. We found that a resting period including only N1 was associated with a lower memory recall (average of 10% memory loss of previously encoded items), compared to a resting period including both N1 and N2 stages. Of note, this memory loss was driven by the items that subjects struggled the most to encode during learning. We extended this finding with spectral analyses, which showed a negative correlation between the ratio of alpha/theta power (a marker of sleepiness) and the forgetting rate for all sleepers (both N1 and N2 groups combined). The somewhat counterintuitive observation that this correlation extended to the N2 group (associated with less forgetting) suggests that additional mechanisms (e.g., spindles, slow oscillations), correlated with sleepiness (i.e., the alpha/theta ratio), operate during the N2 stage to counteract the forgetting effect.

Our finding that N1 is associated with more forgetting is consistent with the results of two previous studies (Wyatt et al., 1994(Wyatt et al., , 1997)), which reported a decrease in recall of stimuli presented a few minutes prior to sleep onset. Yet, their paradigm differed from the present one in that subjects were presented with stimuli while falling asleep, whereas in our study, participants had already encoded memories before reaching N1. Therefore, one may question whether the observed memory deficit in those 1990s studies was related to an active role of the sleep-onset period in forgetting or to a reduction in cognitive abilities (e.g. decreased encoding abilities) associated with the vigilance decline accompanying sleep onset (Ogilvie, 2001). The Wyatt results may thus reflect a deficiency in encoding, whereas our study shows the forgetting of correctly encoded items. Of note, the between-group differences in memory performance that we observed in our study are unlikely to be due to sleep inertia at awakening, as all groups had comparable vigilance levels at the beginning of the Post phase (see Table 1), and we did not find a similar memory loss in N2 participants (who are more likely to experience sleep inertia than N1 subjects). Overall, our findings suggest that N1 specifically promotes the forgetting of recently encoded memories, particularly the ones that were encoded with the greatest difficulty.

At first glance, our results appear to contradict those of another study (Lahl et al., 2008), which reported an enhanced retention of word lists following a 6-min ultra-short nap. In that study, however, the short nap was long enough to include N2 episodes, and the authors lumped together both NREM sleep stages. This distinction with our paradigm is crucial since we found that N1 and N2 have different effects on memory. While N1 was associated with a memory impairment, subjects who continued into deeper sleep (N2) showed less forgetting. However, in contrast with an extensive literature indicating a beneficial role of N2 in memory, we did not observe any difference in memory performance between the Wake and N2 groups. One could argue that the periods of N2 in our study were both shallower and shorter than those observed during longer naps. Indeed, although our participants were allowed to be asleep for a maximal duration of 30 minutes, they spent only a few minutes in N2 (and not necessarily continuously), as they were repeatedly awakened during their naps (every 6 minutes). Thus, it is possible that more consolidated sleep with a higher density of spindles and slow oscillations is required to observe memory benefits [START_REF] Cairney | Memory Consolidation Is Linked to Spindle-Mediated Information Processing during Sleep[END_REF][START_REF] Marshall | Boosting slow oscillations during sleep potentiates memory[END_REF][START_REF] Mednick | The Critical Role of Sleep Spindles in Hippocampal-Dependent Memory: A Pharmacology Study[END_REF][START_REF] Ngo | Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory[END_REF].

Of note, the N2-related memory consolidation typically observed in the literature is often characterized by less forgetting in the N2 compared to the Wake group. In 'wake rest' conditions, however, sleep intrusions (either locally or into the N1 stage; Andrillon et al., 2019;Tagliazucchi & Laufs, 2014) are likely. For example, Tagliazucchi & Laufs (2014) demonstrated that a third of participants fell asleep within 3 minutes of a resting state inside an fMRI scanner. Such a mixture of different vigilance states (wakefulness and brief episodes of sleep) could account for some inconsistencies in the literature, as well as partly for our failure to replicate the widely observed N2-related consolidation effect. Indeed, brief transitions into N1 in 'wake' groups could lower the average memory performance within these groups, in turn explaining a difference between the Wake and N2 groups. In line with this idea, Brokaw et al. (2016) reported that some subjects in a 'wake rest' condition spent a few minutes in N1 and performed worse than fully awake subjects, a result consistent with our observation of a forgetting effect in the N1 group. Thus, since N1 appears to differ in its effect on memory from both wakefulness and N2, studies would benefit from systematically monitoring vigilance states to ascertain the precise mechanisms underlying a given memory effect.

What role does N1 play in sleep-related memory processing? Based on our findings, we see two possibilities. The first one is that, in accordance with the synaptic downscaling hypothesis (Tononi & Cirelli, 2006, 2014), N1 actively suppresses items with a weaker synaptic weight (the Lost objects in our study). Such erasure of information could be necessary to make room in the synaptic network for subsequent memory consolidation in N2. Besides, forgetting is viewed as an essential function of sleep for efficient learning, complementary to memory consolidation (Feld & Born, 2017;[START_REF] Hardt | Decay happens: The role of active forgetting in memory[END_REF]Poe, 2017). Interestingly, this function was originally hypothesized to be associated with another closely related sleep stage, REM sleep [START_REF] Crick | The function of dream sleep[END_REF], a theory that has recently received strong support [START_REF] Izawa | REM sleepactive MCH neurons are involved in forgetting hippocampus-dependent memories[END_REF][START_REF] Li | REM sleep selectively prunes and maintains new synapses in development and learning[END_REF]. These latest studies provide a putative mechanism underlying forgetting via the pruning of certain synapses. Our results suggest that such a process may occur as early as the N1 stage. Furthermore, our findings are consistent with [START_REF] Feld | The Limited Capacity of Sleep-Dependent Memory Consolidation[END_REF], who showed a sleep-dependent forgetting effect when the amount of encoded information was large. The authors hypothesized that large-scale data encoding results in overlapping hippocampal representations [START_REF] Feld | The Limited Capacity of Sleep-Dependent Memory Consolidation[END_REF], which, when replayed during sleep, would result in the extraction and consolidation of the gist (overlap between memory traces) accompanied by the pruning out of the memory traces after global synaptic renormalization. This process could occur during N1 and account for the observed forgetting of a few items. Indeed, although the number of objects to memorize in our study was smaller (N = 48) than in [START_REF] Feld | The Limited Capacity of Sleep-Dependent Memory Consolidation[END_REF], there were potential overlaps in the memory representations of a few items for two main reasons. First, participants already had built-in, strong associations between specific items (e.g., pan and saucepan). Second, the memory task prompted the formation of additional connections between objects (e.g., the pan is above the saucepan). If one hypothesizes that N1 has a role in gist abstraction (e.g., kitchen objects are positioned in the upper-left corner of the screen), such a process could result in a destabilization of individual memory traces (i.e., the loss of precise object positions). This hypothesis may also explain why the Lost objects were preferentially the ones that participants had the most difficulty encoding, precisely because their representations would overlap, rendering their distinction and encoding more difficult.

An alternative hypothesis would be that N1 instead initiates memory reprocessing by tagging the memory traces most in need of being reinforced (the weaker ones) by subsequent sleep stages (Creery et al., 2015;Drosopoulos et al., 2007;Schapiro et al., 2018;[START_REF] Stickgold | How Do I Remember? Let Me Count the Ways[END_REF].

In that case, by preventing deeper sleep from supplanting N1, we would have aborted this process, resulting in the forgetting of these peculiar items. Further studies would be needed to disentangle between these hypotheses, for example by using multiple short naps of different natures with memory tests between them (e.g., testing whether a second nap including N2 rescues memories that are forgotten after a first nap including only N1). Whichever hypothesis turns out to be correct, the present findings indicate that some kind of memory processing occurs at sleep onset and highlight two different patterns between two seemingly proximal sleep stages, N1 and N2. Interestingly, these findings parallel those we recently observed about creativity [START_REF] Lacaux | Sleep onset is a creative sweet spot[END_REF]. Indeed, we discovered that N1 was associated with a boost of insight (i.e., the sudden discovery of a hidden regularity in the task), but this benefit vanished if subjects reached N2. This analogy suggests that 1) memory and creativity are intertwined, and 2) it is important to consider N1 and N2 stages separately in future studies.

Our study has some limitations. The proportion of participants in each group is low and our results need to be confirmed in future research with larger sample sizes. We also did not have enough subjects who reported unambiguous task-related dreams to evaluate their impact on subsequent performance. Whether task-related dreaming directly impacts memory performance (as implied by some studies; [START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF]Wamsley & Stickgold, 2019) or is merely an epiphenomenal reflection of ongoing memory reprocessing remains an open question for future research.

In conclusion, by using a novel design that separates N1 from the remaining stages of NREM sleep, we discovered that the N1 stage plays a role in memory processing, certainly by promoting forgetting. We hope that our work will launch further investigations to corroborate such results and better understand the mechanisms at work during N1 and its role in determining the fate of our memories.

Supplementary information

Task and experimental protocol

Explicit mention of the existence of a hidden rule Unmentioned in the article, participants were actually assigned a second job in addition to the memory task: to discover a hidden rule that enables predicting the position of any object (be it learned or novel). They were informed explicitly of this goal at the end of the learning phase (to ensure that it did not interfere with proper encoding) and told that their monetary reward would be doubled if they found it to boost their motivation. Pre phase After completing the MemoryTest Pre, subjects entered a 5-minute Re-exionPhase Pre, during which they were instructed to consider what might be the rule and to write down all of their hypotheses (i.e., a good way to track what directions they considered and evaluate putative xations). We then tested their knowledge of the rule using the RuleTest Pre. Here, participants were asked to apply the rule to replace new objects (not encountered during the learning phase) at the right place.

If they were unaware of the rule, they had to guess using their intuition. Each trial provided participants with vague feedback (e.g., 'cold', 'warm'), allowing them to test their hypotheses about the rule. Additionally, participants were frequently asked to state

Memory loss at sleep onset 195

explicitly what they believed the rule was. They also had a button that they could press at any point during the experiment to inform us of a 'Eureka' moment regarding the rule.

Post phase Then, they had a 20-minute break (for more information, see the Methods section of the paper), followed by a Post phase in which participants were retested on their memory for both the old (as indicated in the paper) and new objects to assess their knowledge of the rule (RulePost test). Additionally, because knowing the rule by de nition allows participants to place any object in its proper location, those who discovered it after the resting period may score arti cially high on the MemoryPost test, which in this case would not re ect memory consolidation per se. To palliate this issue, we implemented a second memory test, the MemoryNoRule test, at the conclusion of the Post phase. The participants were evaluated again for their recall of the old objects, but this time, all objects appeared in the center of the screen, preventing them from using the rule. Indeed, regardless of the chosen direction, any item would be o -screen from this position if they applied the rule. This additional test was thus designed to disentangle the e ects of memory consolidation from the strategic use of the rule, allowing us to determine whether discovering the rule (the gist), for example, resulted in the pruning of the memory traces. Ideas and images exist in the twilight realms of consciousness, that shadowy half-being, that state of nascent existence in the twilight of imagination and just on the vestibule of consciousness, a con uence of our recollections through which we establish a center, as it were, a sort of nucleus in [this] reservoir of the soul.

-Samuel Coleridge 1

Summary of ndings

In this thesis, we attempted to approach two elusive phenomena, creative inspiration and the sleep-onset period, and investigated how the two might interact. We hypothesized that hybrid states at the borderland between wakefulness and sleep would promote creativity. We tested our hypothesis by examining both a physiological state in which sleep and wakefulness are commonly intertwined (the N1 stage) and a sleep disorder, narcolepsy, in which the line between the two states is even ner.

Patients with narcolepsy constantly oscillate between wakefulness and sleep throughout the day and are dreams' maestros (i.e., they exhibit a high dream recall frequency as well as frequent lucid dreams). Additionally, they frequently mention during medical consultations that they exploit such privileged access to sleep and dreaming to solve problems and summon their creative muse. In our rst study, we thus tested whether these narcolepsy-speci c characteristics contributed to the long-term development of superior creative abilities. We found that patients with narcolepsy possessed not only above-average creativity but also that this higher creative potential was positively correlated to some extent with symptoms of a hybrid state between wakefulness and sleep, including hypnagogic hallucinations, sleep paralysis, and lucid dreaming.

Summary of ndings 199

Along with providing a silver lining for this disabling disorder, these ndings suggest a possible (albeit indirect) link between hybrid states and creativity (Study 1).

We thus decided to take it a step further and peer directly into a physiological hybrid state that everyone experiences, the sleep-onset period. We stood on the shoulders of giants and drew our inspiration from Thomas Edison, who had long suspected that this eeting moment favored creativity. According to legend, he used to take catnaps while holding spheres in his hands. As he dozed o , the spheres fell, arousing him just in time to capture insightful impressions. In our second study, we empirically tested this enticing story. We discovered that Edison's method was e ective for capturing hypnagogia and detecting subtle changes in the wake-to-sleep transition (that are missed by standard sleep scoring methods). Even more importantly, we also demonstrated that catnaps do indeed trigger ashes of insight. After only one minute of the N1 stage, the vast majority of subjects experienced an 'Eureka' moment, a proportion comparable, if not higher, to that reported after a full night of sleep (Wagner et al., 2004). Crucially, this insight-booster e ect vanished when participants reached deeper sleep (N2), highlighting two diametrically opposed e ects between two seemingly proximal sleep stages. We also con rmed these results using spectral analyses (above and beyond the sleep stages): the optimal cocktail for insight contains a medium level of alpha (a marker of the wake-to-sleep transition) and a low level of delta (an indicator of sleep depth).

We thus unraveled the existence of a creative sweet spot within the sleeponset period. Precisely, this creative sweet spot consists of a transitory state between full wakefulness and consolidated sleep that roughly corresponds to the N1 stage (as classically de ned), but not always, as spectral analyses revealed that wake solvers (who never entered N1) had the same cocktail as N1 solvers. Hitting that gray zone thus requires individuals to strike a balance between falling asleep easily and sleeping too deeply.

Summary of ndings 200

Finally, we attempted to delve deeper into the mechanisms underlying such creative inspiration. Based on the literature, we predicted that creative inspiration would emerge as a result of sleep-dependent memory reprocessing [START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]. Thus, in our third study, we examined how recent memory traces are transformed during the sleep-onset period. To do so, we developed a novel behavioral task that allows assessing the destiny of individual memory traces both in terms of pure memory consolidation and insight (i.e., the extraction of a rule common to all memory traces). We observed a memory loss of previously encoded items in subjects who slept in N1, while participants who reached N2 showed less forgetting. We also found a negative correlation between the ratio of alpha/theta power (a marker of sleep onset) and forgetting in all sleepers (N1 and N2 combined). These two distinct patterns between the N1 and N2 stages intriguingly mirrored those of Study 2, in which a few additional minutes of N2 seemed to cancel out the e ect of N1 alone. However, we could not test whether the memory loss in the N1 group was related to insight because the number of solvers was too small. Altogether, our results indicate that critical cognitive processes occur right at the wake-to-sleep transition (for a schematic summary of the ndings, see Figure 1.1).

Before discussing these ndings in detail, I will outline the main limitations of each study in the following section. The limitations in Study 1 are mainly twofold. The rst is related to how creativity was measured. Indeed, one could argue that narrowing a broad concept such as creativity to a few measurable psychometric entities is problematic. In practice, it seems an impossible feat to apprehend the whole phenomenon of creativity in a single study.

Numerous measures of creativity can be found in the literature, and they are often administered in isolation (e.g., one divergent task). In Study 1, we tried our best to tackle the multifaceted nature of creativity by combining several measures: two questionnaires (one on personality traits and another on creative achievement across 10 domains) administered to two populations from di erent cultural backgrounds (French and Italian), as well as an objective test (the EPoC) that incorporates both convergent and divergent measures using a variety of stimuli (graphic, verbal, abstract, and concrete).

Still, we acknowledge that we certainly missed some aspects of creativity, such as the in uence of environmental factors on creativity.

Our questionnaire selection has some limits as well. First, the Test of Creative Pro les (TCP) is not a validated questionnaire in the literature. We did, however, nd signi cant correlations between the questionnaire's scores and EPoC ratings, which lends credence to its validity. Second, the Creative Achievement Questionnaire (CAQ) is designed to generate skewed data distributions, since it seeks to evaluate the Pro-/Big-c of creativity (i.e., the few eminent creators in the general population). This questionnaire was important in highlighting the disconnect between creative potential and achievement in narcolepsy (i.e., only a small proportion of them used their increased creative potential to develop professional creative expertise). However, one could argue that we did not assess 'real-world', or Little-c creativity. While we could have supplemented our measures with a CAQ adaptation designed to evaluate Little-c creativity [START_REF] Diedrich | Assessment of Real-Life Creativity: The Inventory of Creative Activities and Achievements (ICAA)[END_REF], one could argue that the EPoC is already a test measuring Little-C creativity.

Summary of ndings 202

Second, we interpreted our ndings of a higher creative potential in patients with narcolepsy as indirect evidence for a link between privileged access to (REM) sleep and creativity. However, we lack causal evidence to support the existence of such a link. Furthermore, we did not conduct polysomnographic recordings as our primary hypothesis was that repeated privileged access to sleep would favor the long-term development of creativity. A pre/post paradigm was thus inappropriate for testing this hypothesis. Additionally, because sleep variables can vary from one night to the next, simply recording one night would have certainly revealed nothing (at best, probably false-positive correlations) about the factors that contributed to the longterm development of creativity. Alternatively, one could also argue that we did not measure trait-like di erences, but rather state-like di erences based on sleep variations on the day of the experiment.

A possible way to address this question would be to conduct annual longitudinal studies in patients with narcolepsy, collecting sleep and creativity data. Apart from being a tremendous amount of work, such an enterprise may fail due to the di culty in evaluating the impact of disease duration on creativity scores for two primary reasons.

First, we can only collect data on patients' ages at the time of diagnosis, which often di er from their ages at the onset of the disease, as delayed diagnosis of narcolepsy is quite common. Indeed, the mean time interval between the onset of symptoms and disease diagnosis ranges between 8 and 20 years, with signi cant inter-study and inter-subject variability [START_REF] Thorpy | Delayed diagnosis of narcolepsy: characterization and impact[END_REF]. Second, narcolepsy symptoms typically manifest early [START_REF] Dauvilliers | Age at onset of narcolepsy in two large populations of patients in France and Quebec[END_REF], and patients with a late onset are rare, making it di cult to compare creativity as a function of disease duration without having participants' age as a strong confounding variable (as creativity abilities are usually negatively correlated with age, at least in healthy subjects; [START_REF] Leon | Novel associative processing and aging: e ect on creative production[END_REF] and [START_REF] Massimiliano | The e ects of age on divergent thinking and creative objects production: a cross-sectional study[END_REF]).

Study 2: Sleep onset is a creative sweet spot I believe that the Study 2 is the most robust of the three studies presented here, and you will shortly notice that I centered the Discussion part on it. Still, as with any study, it is not perfect. First, the N2 group's sample size is limited, and large sample sizes are 1. Summary of ndings 203 needed in future research to validate (or refute) the observed negative e ect of N2 on insight. Nonetheless, the fact that we found a negative e ect of the delta power on insight when all subjects were considered together (N = 87), independently of sleep stages, gives weight to the robustness and replicability of this result. Second, despite the implementation of a control for subjects' self-reported general level of problem-solving expertise, we did not include a proper test of creativity to verify that the subjects who fell asleep (and did not enter N2) were not inherently more creative than the others.

However, as we stated in the Study 2, we believe that this inter-individual hypothesis is unlikely. Indeed, if subjects following an N1 trajectory possessed higher general insight abilities, we would expect that pre-solvers (i.e., individuals who found the hidden rule before the break) would also have a greater proclivity to reach the N1 stage during the break. But this was not the case, as only a minority of them slept in N1 (without any signs of N2). Moreover, on questionnaires, N1 subjects reported a similar mean sleep latency to wake and N2 subjects, as well as a comparable degree of problem-solving expertise. Finally, we used a task (the NRT) that relies solely on newly encoded memory traces, rather than on individuals' memory networks (which we saw can in uence creative levels). Therefore, this task choice should help mitigate the e ect of subjects' baseline creativity on observed performance.

Yet, we agree that a test of creativity would be necessary to de nitively rule out this hypothesis. To be honest, we considered it, but encountered an issue: at what point in the experimental design could we include this control without jeopardizing our research? Indeed, the whole study is predicated on participants being unaware of the existence of a hidden regularity within the task. We worked hard to keep it that way, learning by heart the instructions and perfecting our 'poker face'. And we did well, as most participants believed that we were investigating the impact of sleep on motivation.

However, performing a creativity task during the Pre phase could have ruined all of our e orts by alerting participants to the fact that we were studying creativity. On the other hand, if we added the control at the end of the experiment and found no di erence between the N1 stage and wake/N2 groups, the problem would be solved.

But, if N1 subjects scored higher than the others, we would be unable to interpret this 1. Summary of ndings 204 result. Indeed, one could infer that N1 individuals were originally more creative and that the N1 stage had nothing to do with the insight-boost e ect. Or... It does, and a few minutes in the N1 stage would be su cient to boost creativity in the new task we would have administered. Since nearly all of the N1 group is composed of solvers, and nearly all of the N2 group is composed of non-solvers, we would not have been able to disentangle these two hypotheses.

The nal main limitation of this study concerns the exact role of the bottle in insight.

We stated in the article that we think the bottle assisted subjects in remaining in the creative sweet spot by preventing deeper sleep from supplanting it. We provided several elements to substantiate this assertion. In short, the bottle was sensitive to delta power accumulation, and each time it fell, it awakened the subject. Combined, these two observations support the notion that the bottle reduced the likelihood of reaching deeper sleep, but con rmation would require a control condition without the bottle.

Study 3: Memory loss at sleep onset

The main limitation of Study 3 is unquestionably the small sample size. Regrettably, due to the pandemic crisis, this study had to be stopped prematurely, leaving us with relatively low statistical power and making it di cult to draw strong conclusions from its results. The second main limitation is related to the choice of the task. We decided to develop our own since most studies do not allow us to assess the fate of a particular memory trace both in terms of memory and insight, and how the two may interact with one another. The issue with developing anything new is that we have no previous studies to refer to and must therefore navigate by sight (e.g., on the level of task di culty). Thus, despite signi cant time spent developing and piloting the task, it seems that the task still requires adjustments, such as ensuring that the conditions for gaining insight are optimal, as will be discussed shortly. Besides, in addition to its contribution to the memory part, this research also proved instructive on the creativity part. Indeed, it helped in the re nement of hypotheses regarding which factors in the experimental design are optimal (or, conversely, detrimental) for observing a sleep-dependent e ect on creativity. I will discuss these factors in the following chapter.

2

Setting the optimal conditions but none in Study 3. How can these con icting results be explained? In the following chapter, I will examine various factors that may in uence the probability of observing an e ect of sleep on creativity, and discuss each one in light of the literature.

The experimental task

First, one of the primary distinctions between the two paradigms was the task selection.

This may explain the contradictory ndings, as prior research indicates that tasks seem to have a signi cant impact on whether sleep boosts creativity.

Task category

Sleep appears to globally bene t tasks relying on gist abstraction, such as the NRT [START_REF] Debarnot | Sleep does not facilitate insight in older adults[END_REF][START_REF] Lerner | Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules[END_REF]Wagner et al., 2004), as well as tasks 2. Setting the optimal conditions 207 relying on 'network exploration', such as the semantic priming task (Stickgold et al., 1999) or the RAT/CRA (Cai et al., 2009;[START_REF] Carlsson | E ects of a Daytime Nap on Primed and Repeated Remote Associates Tests and Relations with Divergent Creativity[END_REF][START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF][START_REF] Whitehurst | Autonomic activity during sleep predicts memory consolidation in humans[END_REF][START_REF] Zander | Sleep increases explicit solutions and reduces intuitive judgments of semantic coherence[END_REF]. On the other hand, results are less clear-cut for problem-solving tasks (the third category in my classi cation). [START_REF] Beijamini | After Being Challenged by a Video Game Problem, Sleep Increases the Chance to Solve It[END_REF] and [START_REF] Beijamini | Sleep Facilitates Problem Solving With No Additional Gain Through Targeted Memory Reactivation[END_REF] reported signi cant improvements in their game after sleep (62-86% of solvers post-sleep), whereas a few studies using more 'classical' problemsolving tasks showed no bene t from sleep or even incubation [START_REF] Brodt | Incubation, not sleep, aids problem-solving[END_REF][START_REF] Hołda | Ill-De ned Problem Solving Does Not Bene t From Daytime Napping[END_REF][START_REF] Schönauer | Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks[END_REF]. [START_REF] Schönauer | Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks[END_REF]

Setting the optimal conditions 208

However, this observation does not o er a satisfactory explanation for the disparity between Studies 2 and 3, given that both may be regarded as belonging to the category of gist abstraction in which sleep is said to have a positive impact [START_REF] Lerner | Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules[END_REF]. Both tasks, however, had a di erent type of hidden regularity. This di erence is important in light of a recent review in which the authors noted that "the explicit detection of hidden rules is more likely to happen when the rules are of a temporal nature (i.e., event A at time t predicts a later event B) than when they are non-temporal" [START_REF] Lerner | Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules[END_REF]. This observation of sleep preferentially promoting the identi cation of temporal regularities could be congruent with the current ndings. Indeed, we observed a bene cial e ect of the N1 stage on creativity in Study 2 when using a task with a temporal regularity, but found no e ect in Study 3 when using a newly developed behavioral task with a regularity that could be considered spatial (i.e., an object's initial position was always located in a xed-length diagonal to its nal position).

However, the de nition of 'temporal regularity' is ambiguous, and one could alternatively argue that the regularity in Study 3 was also temporal, given that the starting position A could predict a later event B (the nal position). Second, a deeper examination of [START_REF] Lerner | Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules[END_REF]'s review reveals that this observation applies only to studies with an explicit e ect (rule discovery) and is based on a comparison of two unequal sample sizes (Figure 2.

2).

As a result, the idea that di erent types of regularities could account for our contradictory results is not entirely satisfactory.

Setting the optimal conditions 209

Awareness

Another distinction between Studies 2 and 3 was whether participants were explicitly informed of the existence of a rule, which could explain the disparities between the two experiments. Indeed, knowing that there is a rule to be discovered (as in Study 3) may increase the chance of xation and reaching an impasse, in turn lowering the likelihood of nding it. No NRT studies tried modulating this factor (certainly because the task would become too easy), but the literature on another regularity task, the Serial Reaction Time Task, provides some insight into that issue. Indeed, [START_REF] Drosopoulos | Sleep and awareness about presence of regularity speed the transition from implicit to explicit knowledge[END_REF] manipulated task awareness during pre-sleep training and found that both sleep and awareness of the presence of a regularity increased the chance of nding the pattern. These ndings imply that 1) our negative result in Study 3 may not be due to that factor of awareness, and 2) our results in Study 2 may be generalizable to explicit tasks.

The latter prediction is encouraging for the debate over whether the results of Study 2 can be extended to more ecological, natural problem-solving situations in which individuals are aware that they have a problem to address. Besides, some consider that insight involves overcoming an impasse by restructuring the problem representation, a process that necessitates the explicit representation of a currently unreachable problem solution [START_REF] Knoblich | Constraint relaxation and chunk decomposition in insight problem solving[END_REF][START_REF] Öllinger | The dynamics of search, impasse, and representational change provide a coherent explanation of di culty in the nine-dot problem[END_REF]. One goal of Study 3 was to test the impact of sleep in this scenario. However, we made the error of changing too many parameters at once (compared to Study 2), which prevented us from determining which component(s) is (are) to blame for the lack of insight e ect observed in Study 3. We want to perform control experiments to distinguish between these factors, which I will describe in depth later (see the box entitled 'Who is the culprit?').

So far, neither the di erence in task regularity nor the di erence in regularity awareness adequately accounts for the inconsistencies between Studies 2 and 3. There is, however, another task dimension that may be relevant here: task di culty.

Setting the optimal conditions 210

Task di culty

In our Study 2, 15.53% of participants found the rule in the Pre phase, and 31% of participants were able to gain insight after an incubation period (devoid of any sleep).

By contrast, the task in Study 3 was much more di cult, with just 6.35% of participants discovering the hidden rule at Pre and ≈12% in the Post phase (whatever the group).

Sleep may thus only bene t tasks of moderate di culty, not too di cult but not too easy either. In fact, this latter component might also account for the discrepancy in results between Studies 2 and 3. Indeed, in Study 2, subjects had to compute a massive number of fastidious and lengthy trials (330 in all), whereas in Study 3, subjects only had to memorize and replace 48 objects. The fact that the memory test in Study 3 was easier and faster to complete than the NRT in Study 2 may have reduced the chance of searching for and nding an alternative strategy to solve the task in Study 3, as the bene t may not have outweighed the mental cost (i.e., the memory strategy was already e ective). In that regard, one can query whether our newly developed task in Study 3 quali es as a creative task. Does it t the criterion of e ectiveness from the de nition of creativity (see section 4.1) ? I think it does for two main reasons: 1) if they knew the rule, subjects would gain in precision (hence in monetary reward) and could also use it to replace any object (learned or novel), thereby again maximizing the reward, and 2) the instructions made it clear that subjects would receive an extra monetary reward for discovering the rule. This idea of task di culty as an important factor in the sleep-creativity equation is also supported by the literature. Indeed, the few studies that reported no bene t from sleep or even incubation [START_REF] Brodt | Incubation, not sleep, aids problem-solving[END_REF][START_REF] Hołda | Ill-De ned Problem Solving Does Not Bene t From Daytime Napping[END_REF][START_REF] Schönauer | Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks[END_REF] are based on di cult tasks. For example, in [START_REF] Schönauer | Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks[END_REF], most problems had a low solving rate at baseline (around 10%), while [START_REF] Hołda | Ill-De ned Problem Solving Does Not Bene t From Daytime Napping[END_REF] found no bene cial e ect of sleep on an 'ill-de ned' problem involving a complex and ambiguous crime riddle. This could explain the aforementioned failure of classical problem-solving tasks to demonstrate an in uence of sleep on their resolution. In that case, the sleeping brain would not be incapable of 'reorganizing' a problem's mental representation, but rather 2. Setting the optimal conditions 211 could do so only when the task is not too di cult. This observation naturally prompts the following question: What elements contribute to the di culty of one task over another? One key factor may be what information has been preencoded, which is our next building block. Indeed, we can see that no matter what precise mechanism is at work, it all involves memory reprocessing (whether it is the reprocessing of new memory traces for gist abstraction, the exploration, or recon guration of memory networks), implying that material content must be preencoded in order for sleep to operate on it.

Pre-task exposure is necessary

Several studies show the relevance of pre-encoding relevant material before sleep by comparing performance on never-before-presented stimuli to performance on preencoded stimuli. To do so, they either remove the Pre phase or present just a part of the stimuli during the Pre phase, leaving the rest for the Post phase. All of the studies that included this control group found that sleep had no bene cial impact on new stimuli (see Figure 4.4). Precisely, in most studies, there is at least an incubation e ect on repeated items, but no e ect on new stimuli regardless of group (see the summary Figure 4.4). The lack of a positive impact of incubation on novel stimuli, regardless of condition, parallels our results from Study 3, implying that our participants certainly did not encode the crucial information (such as the object's starting location) during the Pre phase to identify the concealed pattern. This hypothesis would be consistent with post-questionnaire surveys in which subjects stated that they did not pay attention to the objects' starting position as they saw it as irrelevant information.

Similarly, participants were free to move the items in any direction, which reduced the odds of identifying the hidden path (i.e., a diagonal) if they took various trajectories.

Therefore, if the starting position (or the hidden trajectory) is not encoded, sleep will replay incomplete mnesic traces devoid of the essential information, possibly explaining why regularity detection did not occur in Study 3. We intend to undertake follow-up experiments to disentangle all of the various causes of why Study 2 succeeded where Study 3 failed (for more details, see the Figure 2.3 for an illustrative summary and 2. Setting the optimal conditions 212 the box 'Who is the culprit?'). In the end, these experiments (Figure 2.3) should shed light on why a seemingly simple hidden regularity (i.e., the object's nal position was always in a xed-length diagonal to its starting position) was so di cult to detect.

Moreover, to end on a positive note, this task appears to be a perfect candidate for studying xation mechanisms, as the majority of our subjects remained xated on a single hypothesis (e.g., one square corresponds to one object's color) despite being able to verify that their hypothesis was incorrect.

Who is the culprit?

Follow-up experiments are planned to understand why participants in our newlydesigned behavioral task failed to discover the hidden rule. Here is a list of the controls we would like to implement:

• Awareness: Subjects are not informed about the existence of a hidden regularity (as in Study 2).

• Task di culty:

-Reduce stimuli complexity by replacing objects with simple patterns to limit the directions the subjects can take, in turn lowering the probability of xation/impasse. Indeed, the stimuli that we used possessed many characteristics, such as colors and categories, allowing for the elaboration of a plethora of scenarios and hypotheses (e.g., all kitchen objects are in that square, see Figure 2.3).

-Reduce the number of possible motions by forcing players to move square-by-square using arrows (like in a chess game) in order to increase the chance that they will repeat the same, correct pattern.

-Increase the di culty of the memory task by adding extra items to memorize, hence increasing the need to nd an alternative way of performing the task.

• Pre-encoding of relevant material: Draw subjects' attention to the starting position by adding eye-catching information (such as emoticons) on starting positions. If subjects do not notice them, we could instead place a xation cross over the initial location and ask subjects to concentrate on it for a certain amount of time before moving the items. Alternatively, we could substitute this rule with one that does not rely on the starting position, such as the nal position of one stimulus predicting the nal position of the next. The notion that pre-task exposure is necessary to observe a sleep-dependent e ect on a task raises the question of whether we would have observed a positive e ect of the N1 stage in Study 2 in the absence of a Pre phase before the resting phase. Although we did not test this hypothesis, we anticipate, based on the literature, that N1 participants in this scenario would show a comparable proportion of insight to the Pre group. Indeed, this is precisely what Wagner et al. (2004) observed when they controlled for this factor: the sleep group (without a Pre phase) had a similar proportion of insight as in the wake or Pre groups. These results indicate that the sleep-related e ect is a result of reprocessing previously-acquired knowledge, rather than a passive, restorative e ect of sleep (e.g., increased alertness or cognitive functions).

Setting the optimal conditions 214

What kind of information must be pre-encoded? We reviewed that material content must be pre-encoded to observe a sleep e ect, and how a failure to encode critical information may explain why we did not nd any e ect on insight in Study 3.

That being said, there is some uncertainty about exactly what information must be given prior to sleep reprocessing. Indeed, Cai et al. (2009) demonstrated that REM sleep had a bene cial e ect only on RATs for which the solution had previously been primed (i.e.,'primed' RATs), but had no additional e ect on RATs that subjects initially attempted to solve in the Pre phase (i.e., 'repeated' RATs), a result that they replicated in a follow-up study [START_REF] Whitehurst | Autonomic activity during sleep predicts memory consolidation in humans[END_REF]. They concluded that "REM sleep facilitates the use of prior information for creative problem solving" (Cai et al., 2009). However, to me, this statement contradicts the lack of REM sleep bene t on repeated items, since one may presume that the subjects were also given all of the required information to work on and identify the common link (assuming it exists in their memory network, of course). Does this imply that sleep is only helpful when the solution is contained within the pre-encoded memory traces (whether it is a hidden regularity or the solution of the RATs)?

This idea could explain the lack of a sleep boost in studies using classical problems (i.e., the encoded mnesic traces do not directly contain the solution; [START_REF] Brodt | Incubation, not sleep, aids problem-solving[END_REF] and [START_REF] Schönauer | Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks[END_REF]). Consistently, from what I understand, in the third study that did not report an impact of sleep on problem-solving [START_REF] Hołda | Ill-De ned Problem Solving Does Not Bene t From Daytime Napping[END_REF], the material content pre-encoded by participants varied according to the video clips they chose to watch prior to the break (that in turn contained or did not contain relevant information for solving the riddle). On the other hand, some studies reported signi cant improvements after sleep on another problem-solving task, a game [START_REF] Beijamini | After Being Challenged by a Video Game Problem, Sleep Increases the Chance to Solve It[END_REF][START_REF] Beijamini | Sleep Facilitates Problem Solving With No Additional Gain Through Targeted Memory Reactivation[END_REF]. Interestingly, the solution of the game was a strategy they had previously used (a sort of analogy transfer), whereas, in the other classical problems, the subjects had to nd a strategy they certainly had never encountered before. This is an important notion, since sleep-dependent failure to solve classical problems would not be due to an inability to reorganize a problem's mental 2. Setting the optimal conditions 215 representation, but rather to a lack of the required knowledge to reorganize the problem. One way to test this idea is to give analogy problems (and their solutions, if necessary) before the classical problems to ensure that the subjects have the solution strategy stored, and then see if sleep facilitates the transfer of elements from the earlier problems to the given problems. Yet, even if it is a theory worth exploring in the future, we must remain cautious since two additional papers [START_REF] Carlsson | E ects of a Daytime Nap on Primed and Repeated Remote Associates Tests and Relations with Divergent Creativity[END_REF][START_REF] Zander | Sleep increases explicit solutions and reduces intuitive judgments of semantic coherence[END_REF] did report a speci c positive e ect of sleep (as opposed to wake) on repeated RATs, whereas another paper did not report any bene cial e ect of any group on repeated CRAs, the counterpart of RATs [START_REF] Landmann | Sleep Strengthens but does Not Reorganize Memory Traces in a Verbal Creativity Task[END_REF].

We need more research, but at least most studies agree that sleep does not improve performance on new stimuli and that material content must be preencoded to observe a bene cial e ect of sleep on creativity, a critical element that could account for the disparities between Studies 2 and 3. Then, in addition to what information is provided to the participants, another factor that may be important here is how they encode it, which brings me to the next factor, individual di erences.

Individual di erences and abilities

Countless individual di erences could modulate the impact of sleep on creativity. I will describe two of them that we encountered in our research. First, the baseline degree of creativity varies across people and can in uence measured performance. I already discussed this topic in the section 1.1, where I stated that one limitation of Study 2 was the lack of a baseline creativity control across participants to ensure that the N1 group was not incidentally more creative than the others. Second, rather than subjects being inherently more creative, it is conceivable that how they mentally represent the task during the Pre phase a ects sleep's e ect on it (a point that does not invalidate the importance of sleep in gaining insight). Consistently, [START_REF] Darsaud | Neural precursors of delayed insight[END_REF] observed that the brain activity of solvers and non-solvers on the NRT already di ered during training. The authors next showed that during the night, solvers' response patterns were transformed with increased activations in the ventral medial prefrontal cortex, a region associated with declarative memory consolidation (this same region showed an increased activation later, right before insight). How the subjects encode the task might thus in uence the impact of sleep on this task. The authors speculated that individuals' learning strategies may di er, with some favoring pure memory consolidation over gist abstraction. Similarly, [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF] investigated whether prior implicit knowledge of the NRT hidden regularity was required for post-sleep explicit discovery of the rule. If a subject had faster response times for predictable responses (R5-R7, due to the mirror structure), than for unpredictable responses (R2-R4), he/she was considered to exhibit a pre-sleep implicit knowledge [START_REF] Verleger | Insights into sleep's role for insight: Studies with the number reduction task[END_REF]. The authors showed that SWS was associated with a higher probability of transforming pre-implicit knowledge into explicit knowledge. Our design of Study 2 did not allow for the application of such a measure because more than half of our subjects made calculations in their heads before entering the nal result. In our study, we opted to implement another implicit task at the end of the Post phase, in which subjects were exposed to a mix of trials following and breaking the rule (this test was not included in the paper). We observed implicit knowledge in non-solvers to some extent, even if it was not present in all subjects, with no group di erence (Figure 2.4). Such a result raises the question of whether this implicit knowledge was already present before the break (the N1 stage enabling its explicit conversion) or whether it built up regardless of what happened before, due to the large number of trials. If so, more trials might be used to determine whether all subjects would eventually nd the rule. However, we believe that it is improbable because people had already completed a large number of trials (330).

Setting the optimal conditions 217

In general, individual di erences are important to take into consideration, yet we have little, if any, control over them. To me, the aforementioned research raises the question of whether some individuals will ever experience insight, regardless of the circumstances. For instance, in Study 2, we had four subjects in the N1 group who did not gain insight. Is it because they did not have the precise 'cocktail for creativity' or are these subjects doomed to never experience insight? This is an interesting yet di cult question to answer, and it is far beyond the scope of this manuscript, but it is worth mentioning.

Delay between the pre and break phases

The time delay between pre and break could also in uence sleep-dependent impact on creativity. Indeed, [START_REF] Barry | A neural network account of memory replay and knowledge consolidation[END_REF] showed that recent memories are preferentially consolidated. Increasing the duration between task exposure and the nap would thus increase the number of new waking experiences that might compete and interfere with the memory traces from the task, in turn lowering the likelihood of them being processed and yielding insight. Due to lab constraints, most experimental designs (including our Studies 2 and 3) include a break either right after the learning phase or only a few hours later, preventing us from testing the impact of longer delays (e.g., days) on the sleep e ect. But, the no-delay vs. short-delay (a few hours) paradigms used in the studies I focused on (see Figure 4.4) do not seem to impact post-sleep creativity di erently. This observation, however, contrasts with a recent study, which showed that problem-solving was favored when subjects directly slept on a problem, as opposed to a delayed condition between the pre and break phases [START_REF] Demasi | Nap timing makes a di erence: Sleeping sooner rather than later after learning improves infants' locomotor problem solving[END_REF]. Therefore, more research is needed to determine whether a delay between the pre phase and the break may represent a crucial factor in the sleep-creativity equation. Setting the optimal conditions. A schematic representation of the various factors that may in uence sleep's e ect on creativity. The color green symbolizes a positive impact, the color orange, a mild e ect, the color red, no e ect, and the color gray denotes an unknown or inconclusive e ect. Of note, the professions' colors were randomly chosen and were just intended to illustrate inter-individual di erences.

Chapter Summary -Setting up the optimal conditions

We reviewed the factors that could explain why sleep di erently impacted insight abilities in Studies 2 and 3. They may be summarized as follows: 1) the nature of the task and its di culty level, 2) what information is preencoded (prior to the resting period), and 3) how the participants encode this information during the pre phase. These factors are important to take into account for anyone interested in setting up optimal experimental conditions for studying the e ect of sleep on creativity. Moreover, because we only have a fragmented picture of each factor's impact and how they may interact with one another, future research may bene t from considering as many of these factors as possible, with the aim of controlling for awareness, task type and di culty, learning strategies, and so on. Knowing this, we tried modulating some of these parameters in Study 3 as opposed to Study 2, but we have been too greedy and changed too many parameters at once (task choice and di culty, awareness...). The lack of an insight-booster e ect in Study 3, however, exempli es how various factors may substantially alter sleep impact, despite the fact that the resting period was relatively similar in both studies. Next, the natural followup question is: Once the aforementioned optimal conditions have been set up, what are the key ingredients for boosting creativity during the resting phase? Our ndings provide some answers, as we will see in the next chapter.

Combien de fois (des centaines, des milliers de fois) la nuit m'a donné des solutions de problèmes [...] Je dirais presque qu'un matin sur deux ou trois, je trouve ainsi en moi une réponse, déposée à je ne sais quel moment de la nuit1 .

-Henri Michaux

3

Ingredients for boosting creativity

Sleep outweighs incubation

As we reviewed in the Introduction (subsection 4.3.4), most studies report that an incubation period (i.e., a period of time during which a problem is set aside prior to further attempts to solve it; [START_REF] Segal | Incubation in Insight Problem Solving[END_REF]) is bene cial for creativity. Thus, one could reasonably question whether our ndings (particularly from Study 2) re ect the e ect of any incubation period rather than the impact of the N1 stage per se. Following this line, the N1 insight-booster e ect would result from the subjects' inability to think about the task, whereas wake subjects would have had plenty of time to mull over it. Yet, this hypothesis is implausible for several reasons. First, it would imply that a comparable e ect on insight would be observed with a wake group whose attention was focused on another task. This is precisely what [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF] tested with the same paradigm, and they found no bene t on insight in such conditions. Second, we found no relationship between task-thinking and insight (see Study 2). Third, we observed a trend toward an incubation impact on insight in the wake group (18% of pre solvers vs. 30.61% post solvers in the wake group, p = 0.051), but that e ect remained signi cantly smaller than the proportion of insight observed in the N1 group. Last, we observed a deleterious e ect of N2 on insight, despite the fact that this period, like N1, could be considered as an incubation period. The insight-booster e ect observed in Study 2 (as well as the memory loss observed in Study 3) is thus due to N1 per se rather than to an aspeci c e ect of incubation. Therefore, we showed that sleep has an insight-boosting e ect that goes beyond a simple incubation period. This result is consistent with the literature on sleep and creativity (see the summary Figure 4.4). Of note, nding the ideal wake control for evaluating the incubation e ect is a tricky task: how to ensure that subjects' attention is directed away from the task? One common method is to provide subjects with another cognitive task to perform during the resting period, which would hopefully divert their attention away from the initial creative task.

However, this strategy is not optimal because, in the end, the groups that are compared drastically di er in what they did during the resting period. In this sense, we believe that our control group is 'cleaner' given that, except for the sleep intrusions, our participants were in the exact same conditions in every group (for both Studies 2 and 3).

Once we have demonstrated that sleep appears to play an additional role in creativity compared to incubation alone, the next question that arises is the following: What are the critical elements of sleep that contribute to this e ect?

Sleep duration

The rst factor that comes to mind is sleep duration, with the intuitive notion that the more sleep one gets, the more creative one must be. The current ndings, however, challenge this idea, since 20-min naps with ultra-short sleep duration (1 minute of N1 on average) were su cient to inspire insight (and to observe an impact on memory as well). memory processing and in the extraction of regularities remains an open and challenging question to address. Indeed, the fact that no association was observed in Study 2 does not imply that it does not exist, as we face the inevitable production vs. recall question: a person not reporting having dreamed about something does not imply that he/she did not, since he/she may have forgotten about it.

Moreover, even if we collect hypnagogic reports, we still have to determine which ones incorporate task material. The answer seems easy: any task-related report.

Unfortunately, because of hypnagogia's potential hyper-associative nature (as reported in REM dreams; Malinowski and Horton (2015)), this selection criterion may reject some task-related hypnagogia that are too far-fetched for our waking consciousness to notice.

In contrast with the present results, a few studies (Wamsley and Stickgold, 2019;[START_REF] Wamsley | Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation[END_REF] found a link between task-related dreaming and subsequent performance (but not on creative tasks). Yet, they do not establish a causal role for dreaming, only a correlation. Indeed, since their task was presented prior to sleep, we cannot tell if dreaming had an active role in boosting performance or if it was just an epiphenomenal re ection of ongoing task reprocessing. To my knowledge, the sole putative evidence for a direct role of dreams may come from unpublished results presented in Horowitz (2019)'s master's thesis. They observed an increased creative performance on material content that they had previously integrated into hypnagogic content (but not presented prior to sleep), compared to a sleep period without dream incubation (see Figure 5.11 and here for a reminder of the experimental protocol and results). Importantly, the authors also appeared to nd a gain in creativity following presumed N1 (as measured by Dormio, not PSG) without dream incubation, as opposed to a similar period spent awake (Figure 5.11), but we await publication.

To summarize, we did not nd a link between task-related hypnagogia and creativity, and the literature on the topic is also inconclusive. Let's then move on to the last putative main ingredient of the sleep-creativity equation: sleep stages.

Sleep stages

Given the dichotomous conception of sleep, which is usually split into NREM and REM sleep, most studies have naturally tried to identify which of the two stages (if any) is most associated with the impact of sleep on creativity. Unfortunately, again, a cursory examination of the literature prevents us from reaching a de nitive conclusion on this subject. Some studies show that REM sleep has a positive impact (Cai et al., 2009;Stickgold et al., 1999;[START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF], while others show that it is NREM sleep (particularly SWS; [START_REF] Beijamini | Sleep Facilitates Problem Solving With No Additional Gain Through Targeted Memory Reactivation[END_REF] and [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF]), and still others show no di erence between the two, only a general sleep advantage. Here, we reported a signi cant impact on insight of an overlooked sleep stage, the NREM sleep stage 1 (N1). This nding is consistent with [START_REF] Drago | Cyclic alternating pattern in sleep and its relationship to creativity[END_REF], who found positive correlations between N1 and some measures of creativity, such as uency and exibility, as well as with the yet-unpublished results in Horowitz (2019)'s master thesis, in which he reported a bene cial e ect of sleep onset on creative tasks (for more details, see here). Of note, some authors also found a positive impact of the N1 stage on creativity [START_REF] Schönauer | Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks[END_REF], but did not mention or discuss it in the main text.

Altogether, we showed that sleep stages appear to in uence subsequent creativity. Notably, we discovered a role for the N1 stage in creativity, a nding that raises several questions about the interpretation of the current results and those previously published.

For example, is the greater creativity observed in narcolepsy in Study 1 truly attributable to privileged access to REM sleep, as we initially hypothesized? And is the N1 stage necessary and su cient to explain the divergent ndings found in the NRT literature?

In the following subsections, we will thus continue to investigate the impact of the sleep stage factor on creativity by addressing these two discussion points.

What is responsible for the increased creativity in narcolepsy?

We initially interpreted our ndings in Study 1 of increased creativity in narcolepsy as a result of privileged access to REM sleep. However, this view may be challenged in light of the N1 insight-booster e ect found in Study 2. Indeed, patients with narcolepsy not only have a privileged access to REM sleep but also more generally to the sleep-onset period, which they pass through many times during the day, as they constantly oscillate between wakefulness and sleep. Thus, one may wonder: Is the increased creativity in patients with narcolepsy due to REM sleep or to the N1 stage?

The N1 stage is not a mere copy of REM sleep To answer this question, we must rst discuss whether the N1 stage and REM sleep are one and the same stage (as Nielsen (2000) theorized with his 'covert REM sleep' hypothesis), because if they are, the question is irrelevant. In the Introduction (section 2.2), I underlined the mysterious resemblance that exists between both, which does not imply that I regard them as interchangeable.

By emphasizing this similarity, I was just attempting to persuade readers that N1 was worth investigating; a psychological tactic for bringing the light of REM sleep on the N1 stage. In the end, I believe that this debate over whether the N1 stage is synonymous with REM sleep is just a matter of semantics. Even if we considered REM sleep and the N1 stage to be the same sleep stage, we would need to coin a new term to account for the dissimilarities between them (e.g., light REM sleep for the N1 stage and deep REM sleep for REM sleep, alike light NREM sleep for N2 and deep NREM sleep for N3). Indeed, the N1 stage is unquestionably a lighter stage than REM sleep, as shown by the persistence of behavioral responses, the cerebral activity that exhibits a greater degree of 'hybridity' between wakefulness and sleep than REM sleep, or by mental content (even if comparable, REM dreams are generally more complex and longer). Thus, one might argue (and that would still be debatable) that REM sleep is a consolidated version of the N1 stage, but this would imply that the two are not equivalent.

The question of which sleep stage is responsible for the increased creativity in narcolepsy is thus still relevant, although the line between REM sleep and the N1 stage is even thinner in patients with narcolepsy, given their frequent rapid transitions from wakefulness to REM sleep (i.e., in that case, the N1 stage and REM sleep share a similar sleep depth). Could privileged access to the N1 stage then explain the high creative potential seen in narcolepsy?

The N1 stage vs. REM sleep To address this point, we would need to rst ascertain if the N1 stage has an impact on other creative tasks, given that the tasks we used to measure creativity in narcolepsy (Study 1) did not rely on gist abstraction (the task category utilized in Study 2). A straightforward rst step that I would like to take is to re-use Stickgold et al. (1999)'s semantic priming task (for more information on it, see here) to compare cognition following N1 vs. REM sleep awakenings. If N1 is crucial for creativity, we would predict a similar (or greater) level of exible cognition in the N1 group compared to the REM sleep group. so, whether N1's impact is superior, equal, or inferior to that of REM sleep. We may expect that the N1 stage would be associated with a higher boost of creativity than REM sleep, given that N1 is a lighter stage than REM sleep (typically with a lower delta power, which we found was detrimental to creativity in Study 2), but this remains an open question.

Long-term development? Second, we also interpreted narcoleptics' higher creative potential in terms of a long-term development of creativity (i.e., creativity becomes a trait in these patients as a result of accumulated periods of daytime REM sleep).

This interpretation was based on the fact that in Study 1, our creative tests were either a questionnaire evaluating creative pro les/achievements over a lifetime or new creative tasks without the opportunity for patients to sleep on them (no nap). However, given the results from Study 2 that brief sleep intrusions can inspire insight, we could alternatively argue that increased creativity in narcolepsy (at least on the EPoC test) is the result of brief intrusions into the sleep-onset period on the day of the experiment. These sleep intrusions would allow them to either directly 'sleep on the task' (if they occurred during the creative assessment) or to be in an overall 'in-between' state if sleep intrusions occurred before the creative assessment and still pervaded wakefulness (e.g., sleep remnants). Alternatively, the higher creative potential found in patients with narcolepsy may be explained by a general tendency to be in a 'hybrid' state. Such a hybrid state would not necessarily imply the presence of sleep intrusions per se (e.g., not enough sleep to score a full epoch in N1). Consistent with this idea, several studies report EEG di erences in patients with narcolepsy during wakefulness, with an increased power in lower frequencies and a decreased alpha power [START_REF] Saletu | EEG-tomographic studies with LORETA on vigilance di erences between narcolepsy patients and controls and subsequent double-blind, placebo-controlled studies with moda nil[END_REF][START_REF] Saletu | EEG-mapping di erences between narcolepsy patients and controls and subsequent double-blind, placebo-controlled studies with moda nil[END_REF][START_REF] Smit | Diurnal spectral EEG uctuations in narcoleptic patients during rest and reaction time tasks[END_REF]. Crucially, [START_REF] Smit | Diurnal spectral EEG uctuations in narcoleptic patients during rest and reaction time tasks[END_REF] demonstrated that patients with narcolepsy had a lower alpha power throughout the day that remained stable over time, implying that the lower alpha is not related to an homeostasis process but rather re ects a genuinely 'in-between' state observed in narcolepsy, certainly caused by state instability1 .

Interestingly, this 'in-between' state closely resembles the ideal cocktail for creativity found in Study 2 (intermediate level of alpha, low level of delta), which explained insight independently of sleep stages. This nding suggests that the main component for eliciting insight may not be N1 per se, but a transitory state between full-wake and consolidated sleep (that roughly corresponds to the N1 stage but not always).

Given that this transitory state appears to be naturally present in narcolepsy (or at least signi cantly more prevalent than in the general population), it could explain their enhanced creativity. This idea that their creativity is state-dependent rather than trait-dependent may also be supported by our observation that stimulants (i.e., moda nil) and anti-cataplectic (i.e., sodium oxybate) medications had a rather negative e ect (p = 0.08 and 0.07, respectively) on creative achievements (CAQ). If their creative potential developed over years and became a trait, then it should not decrease with treatment (which is generally prescribed after years of symptoms due to a delayed diagnosis of narcolepsy; [START_REF] Thorpy | Delayed diagnosis of narcolepsy: characterization and impact[END_REF]). On the contrary, if creativity was dependent on short sleep intrusions and/or a hybrid state on the day of the creative assessment, treatments, by mitigating them, should lower creativity, as observed to some extent in Study 1. We remain however cautious in this interpretation, as this observation of a rather deleterious e ect of treatments on creativity was made on inter-subject comparisons, rather than intra-subject (i.e., creativity before and after treatment). However, research on healthy subjects supports this idea, since they often report a decrease in creativity after psychostimulant uptake (i.e., moda nil) (Mohamed, 2014;[START_REF] Müller | E ects of moda nil on non-verbal cognition, task enjoyment and creative thinking in healthy volunteers[END_REF]. Therefore, patients' elevated creativity could be state-dependent rather than trait-dependent (contrarily to what we initially hypothesized). Both options, however, are not mutually exclusive; longitudinal studies and/or sleep recordings would be required to further investigate this topic (as already discussed here). Additionally, we could reconstruct the semantic memory network of patients with narcolepsy and compare it to that of control subjects. If their network structure is, for example, more connected than that of controls (as observed in high vs. low creative individuals; [START_REF] Kenett | Investigating the structure of semantic networks in low and high creative persons[END_REF]), it would imply that the increased creativity observed in patients with narcolepsy (Study 1) is a trait. If, on the other hand, their network structure is similar to that of control subjects, we could examine whether local, task-related changes in the network of patients with narcolepsy can account for better performance when confronted with a creative task and whether the putative network's restructuring is dependent on brief sleep intrusions/a hybrid state.

To summarize, in light of our other ndings, it is di cult to pinpoint the precise factor responsible for the enhanced creativity in narcolepsy. It could be due to longterm privileged access to REM sleep, as originally hypothesized. But we cannot rule out the possibility that it results from the long-term accumulation of sleep episodes (including the N1 stage and/or REM sleep), or the short-term impact of brief sleep intrusions/a hybrid state on the day of the experiment.

Reconciling the NRT studies

Now that we have discussed how to reconcile ndings from our Studies 1 and 2, could we reconcile some of these results with the literature? We saw in the preceding chapter that sleep has a di erent impact depending on the type of creative task used by researchers (see Figure 2.1). Therefore, I will rst focus on studies that used the same task (NRT) and attempt to reconcile their ndings with the present ones.

To quickly summarize the sleep NRT literature (see Figure 3.2 and here for a detailed description of the studies), Wagner et al. (2004) rst discovered that a full night of sleep (as compared with a similar period of time spent awake) inspired insight. [START_REF] Debarnot | Sleep does not facilitate insight in older adults[END_REF] obtained a similar nding after a full night of sleep as well. Later, [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF] used early vs. late night paradigms to try disentangling whether it was NREM or REM sleep that triggered insight. Yet, they did not replicate their ndings, with no insight-boosting e ect in either the NREM or the REM-rich groups (Figure 5.2). More recently, [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF] found a similar gain in insight to Wagner et al. (2004), but after just 10 minutes of 'quiescence'. Last, we observed a boost in insight associated with the N1 stage that was comparable to (if not higher than) the one observed after a full night of sleep and that vanished when participants reached deeper sleep (N2).

How can we explain the fact that even with a task that appears optimal for sleep to operate on, we still observe contrasting results? Awake quiescence vs. N1 intrusions First, our ndings could t with those of [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF]. The authors concluded that "a short period of awake quiescence facilitates insight, and that sleep is not necessary for this bene cial e ect to emerge" [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF]. However, they did not monitor participants' sleep levels during the resting period, and given the current ndings, it is conceivable that their insight boost was due to N1 intrusions rather than a simple waking rest. Still, one might argue that their subjects had no su cient time to fall asleep (their break lasted 10 minutes compared to 20 minutes in our Study 2). Besides, in Study 2, we observed the rst epoch of N1 after around 13.26 min (9.17 min with the MSE scoring). However, in our study, subjects had to hold an object, which prevented them from easily and rapidly falling asleep: some were focused on not dropping it, while others felt the bottle sliding through their ngers whenever they started to doze o , waking them up. Our design thus certainly delayed sleep onset. In line with this assumption, the literature indicates that subjects reach N1 after about 6 minutes [START_REF] Horikawa | Neural Decoding of Visual Imagery During Sleep[END_REF]. Using a large database of 1,147 fMRI resting-state sessions, Tagliazucchi and Laufs (2014a) also demonstrated that one-third of individuals fell asleep within 3 minutes (even in a noisy fMRI scanner), and one in every two subjects was asleep after 10 minutes (Figure 3.3). Furthermore, we demonstrated that brief sleep intrusions (on average, only one minute of N1) were su cient to inspire insight. We even showed that subjects deemed awake (by traditional scoring), but who had an intermediate level of alpha during the resting period experienced insight, implying that subjects do not necessarily need to completely fall asleep to gain insight. Taken together, it is likely that a non-negligible proportion of participants experienced brief sleep intrusions in Craig et al.

(2018)'s quiescence conditions, which could explain their high (but inferior to ours) rate of insight. Our ndings are, however, more di cult to reconcile with those of [START_REF] Debarnot | Sleep does not facilitate insight in older adults[END_REF] and Wagner et al. (2004) who observed a comparable level of insight to ours but after a full night of sleep, as well as those of [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF] who found no bene t on insight when using a split-night paradigm. In the following subsection, we will discuss an hypothesis for reconciling all of these ndings.

Interrupting an ongoing process

If we push further [START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]'s and Giuditta (2014)'s theories of a complementary, sequential function of sleep stages, one could argue that the N1 stage is rst associated with reviewing novel experiences and abstracting the gist (i.e., the hidden rule), before transferring it to subsequent sleep stages for consolidation. Interrupting such a memory consolidation process prematurely (e.g., before the end of a sleep cycle) may be detrimental to that process. This hypothesis could explain why Wagner et al. (2004) obtained a positive result with a full night of sleep (e.g., undisturbed, complete sleep cycle), in contrast with [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF]'s negative nding obtained with a split, interrupted night paradigm. This idea would also t with our current ndings of a low proportion of insight in the N2 group, which contrasts with the high percentage of insight in the N1 group (both groups having the same amount of N1). Indeed, by interrupting N2, we would have disrupted the ongoing consolidation process, potentially destabilizing the memory traces and impeding their retrieval during retest.

Similarly, rather than being the mere consequence of its interruption, the N2 negative e ect on insight may be related to a phenomenon known as 'reconsolidation'. Reconsolidation describes memory formation as a dynamic process that cycles between labile and stable states; a given memory would thus undergo numerous reconsolidation cycles during its lifetime (which may explain memory transformation with time).

When memories are reactivated (for instance, here during N2), they become labile and susceptible to change [START_REF] Bonin | Reconsolidation and the regulation of plasticity: moving beyond memory[END_REF]. Awakenings at that time, prior to the reconsolidation of these labile memories (certainly in subsequent NREM sleep cycles), would therefore result in memory loss. This hypothesis may also explain why, in Study 3, we did not observe the canonical memory consolidation associated with N2, since we interrupted it early in the rst cycle.

However, our results contrast with those of Lahl et al. (2008) who observed a memory consolidation e ect after only 6 minutes of sleep on average, while the 'interruption' theory would anticipate a memory de cit as they also interrupted the N2 stage. In line

Ingredients for boosting creativity 232

with Lahl et al. (2008), we have seen that the duration of the break (e.g., nap vs. full night) does not appear to be a key element in the sleep-creativity equation (Figure 4.4), casting doubt on the interruption theory (even though nap duration is commonly 90 minutes, which could be su cient in some cases to complete a sleep cycle). Moreover, this 'interruption' theory is based on the assumption that an initial sorting of novel memory traces occurs during the N1 stage prior to memory consolidation in later sleep stages. However, N1 may alternatively be directly involved in the memory consolidation process; in this case, disrupting the N1 stage, as we did in Study 2, should result in an equally low percentage of insight in the N1 group as in the N2 group (in contrast with the observed boost of insight in N1). Last, this 'interruption' theory is based on the assumption that memory processing and insight are inextricably linked, which is supported by several theoretical frameworks and a few research papers [START_REF] Durso | Graph-Theoretic Con rmation of Restructuring during Insight[END_REF][START_REF] Milivojevic | Insight Recon gures Hippocampal-Prefrontal Memories[END_REF], but has yet to be de nitively proven. Altogether, the theory that interrupting an ongoing memory consolidation process may be detrimental to that process is a fairly convincing explanation for harmonizing the NRT literature and the current ndings, though perhaps not fully satisfactory.

Alternatively, the aforementioned discrepancies may be addressed by examining what occurs at the end of sleep rather than during it, a question that we will address in the following chapter.

Chapter Summary -Ingredients for boosting creativity

After reviewing how to create optimal settings for observing a sleep-dependent effect on creativity in the preceding chapter, we explored what ingredients contained within a resting period can boost creativity. We rst con rmed that sleep has a greater impact on creativity than a basic incubation period. Then, we reviewed the sleep parameters that could contribute to such an increase in creativity. Neither the duration of sleep nor its mental content seem to have a signi cant impact on subsequent creativity. In contrast, we showed that sleep stages appear as a crucial factor in modulating the sleep-dependent impact on creativity. Notably, we demonstrated that N1, a stage greatly neglected thus far, appears to act as an insightbooster. It is worth noting that our results in Study 2 could also be explained independently of sleep stages, solely based on brain oscillations (e.g., alpha, delta power). Thus, rather than sleep stages per se, the most critical component here may be brain oscillations (even though both are strongly confounded). We then discussed the ndings from Study 1 in light of the N1 insight-booster e ect, notably whether the N1 stage (instead of REM sleep) might be responsible for the observed increased creativity in narcolepsy. Both options are possible (and not mutually exclusive); future research could evaluate the impact of the two stages on a given creative task. Last, we attempted to reconcile our ndings with the existing literature (on the NRT) using the 'interruption' theory, which o ers a rather plausible explanation for the reported results. In the next chapter, we will discuss another theory and see why sleep o set may be one of the unknowns in the equation. Indeed, our ndings in Studies 2 and 3 were interpreted in terms of sleep onset, but they may also be read in terms of sleep o set. As such, I invite you to complete our experimental design timeline and focus on what happens towards the end of sleep (rather than during sleep).

I give my consciousness the task of nding the answer while I sleep, and in the morning at the threshold of awakening, I nd the information I sought.

-Eileen Garrett

4

Sleep o set, a missing link? So far, we have focused on the pre and break phases, leaving out the post phase, in which performance is retested and compared to the pre phase in order to show a potential sleep-dependent impact. We would assume that by reviewing what happens prior to and during sleep, we would have covered all of the essential components in uencing sleep-dependent processes, since afterwards the period of interest (sleep) is over. However, we will see that sleep o set may help reconciling our ndings with the literature. Before digging further into this topic, I want to emphasize that this entire chapter is purely hypothetical and is only meant to provide food for thought.

Sleep inertia

A large literature (for a review, see Trotti (2017)) has shown that cognitive performance is not fully recovered upon awakening due to a phenomenon known as 'sleep inertia' (SI).

Sleep inertia may be de ned as the 'gradual recovery of waking-like status' [START_REF] Occhionero | Time Course of Sleep Inertia Dissipation in Memory Tasks[END_REF]. This is distinct from the clinical term 'sleep drunkenness', which refers to an abnormal, aggravated form of sleep inertia that is often associated with sleep disorders, particularly in idiopathic hypersomnia (Trotti, 2017). Sleep inertia is manifested by the persistence of EEG sleep features in the rst 5-10 minutes after awakening, with a higher level of slow activity and a lower level of beta power compared to pre-sleep [START_REF] Ferrara | The electroencephalographic substratum of the awakening[END_REF][START_REF] Gorgoni | EEG topography during sleep inertia upon awakening after a period of increased homeostatic sleep pressure[END_REF][START_REF] Marzano | Electroencephalographic sleep inertia of the awakening brain[END_REF]Vallat et al., 2019). It is also associated with a disrupted functional connectivity, as shown by a decrease in the anti-correlation between the task-negative (DMN) and the task-positive (salience, attention) networks (Vallat et al., 2019). SI magnitude and duration seem to be dependent on a number of variables, including the time of the day at which individuals wake up, the kind of cognitive tasks used to assess SI, and, more importantly to us here, previous sleep duration and deprivation (Trotti, 2017). To put it simply, the deeper the sleep before testing, the greater the sleep inertia.

Given the association between sleep inertia and decreased cognitive performance (Trotti, 2017), could this phenomenon account for the reported discrepancies across NRT studies? Indeed, one might argue that [START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF]'s negative nding is due to sleep inertia, given that their subjects were awakened in the middle of the night, when sleep inertia e ects are greatest [START_REF] Scheer | An endogenous circadian rhythm in sleep inertia results in greatest cognitive impairment upon awakening during the biological night[END_REF][START_REF] Silva | Sleep inertia varies with circadian phase and sleep stage in older adults[END_REF].

However, the authors attempted to mitigate this e ect by awakening subjects in light sleep stages. Similarly, one might argue that our opposing ndings between N1 and N2 are due to N2 subjects being more sleepy upon awakening than N1 subjects. Although we cannot de nitely rule out this possibility, I believe it cannot explain our e ect for a variety of reasons that we addressed brie y in the article, but will discuss in more detail below. First, we used a sleepiness scale, and found no between-group di erence in participants' subjective level of sleepiness at the beginning of the post phase. In addition to that subjective measure, we used the well-known Psychomotor Vigilance Task (PVT) to assess the level of vigilance/alertness after the break (before performing the NRT task again). Subjects in the wake and N1 groups had similar vigilance levels, which suggests that the better performance observed in the N1 group compared to the wake group was not due to a renewed level of alertness. While N2 subjects showed mild signs of sleep inertia in the PVT (see the Table S1 of the Study 2), our results suggest that sleep inertia is unlikely to be responsible for the deleterious e ect of N2 in insight for several reasons. First, any behavioral trace of sleep inertia rapidly vanished after the PVT (i.e., performance on the rst NRT block of the post phase was similar in all three groups).

Furthermore, the duration of sleep inertia is still a matter of debate and varies between studies, but overall, it almost never exceeds 30 minutes (for a review, see Trotti (2017)). In Study 2, there was a time interval of ≈10 minutes between the participants' awakening and the start of the post phase. Then, subjects entered the NRT post phase, which comprised a massive number of trials (270 trials) and took non-solvers more than an hour on average to complete. As a result, N2 subjects had at least 150 trials after any signs of sleep inertia would have vanished, leaving plenty of time to discover the hidden rule.

Moreover, if sleep inertia was to blame for our N2 group's low percentage of insight, Wagner et al. (2004) should have found an even lower percentage of insight given that, in their study, subjects slept for an entire night, which should be associated with a higher sleep inertia e ect, as opposed to a total average of 3 minutes of sleep in Study 2. Besides, some studies show no sleep inertia e ect for short naps (less than 30 minutes) even after sleep deprivation (i.e., nocturnal time in bed of 5 hours). On the contrary, they observe immediate performance improvements upon awakening after ≈10-min naps [START_REF] Brooks | A brief afternoon nap following nocturnal sleep restriction: which nap duration is most recuperative?[END_REF][START_REF] Tietzel | The Short-Term Bene ts of Brief and Long Naps Following Nocturnal Sleep Restriction[END_REF][START_REF] Tietzel | The recuperative value of brief and ultra-brief naps on alertness and cognitive performance[END_REF].

Last, Wagner et al. (2004) controlled for the sleep inertia factor and observed no impact of sleep pressure on insight: the percentage of insight was the same whether subjects stayed up during the day or at night (i.e., after sleep deprivation; see here for details).

Altogether, the sleep inertia hypothesis is unlikely to explain our ndings and the literature, which brings us to our nal main hypothesis, which may be the most plausible and will be discussed below in detail.

The awakening stage

In the previous section, we regarded the gray zone between sleep o set and retest negatively, as a time period during which individuals' performance may be jeopardized. For the last hypothesis, we will adopt the opposite approach, seeing that zone as a potential source of creativity. Our idea is reminiscent of the notion of 'carryover', which refers to the assumption that the distinct cognitive state associated with a speci c sleep stage persists for a few minutes after awakening. This notion of carryover has been used to justify the positive (indirect) e ect of REM sleep on creativity [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF]Stickgold et al., 1999;[START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF]. I must pause here to emphasize that, although both sleep inertia and carryover globally pertain to the same phenomenon, they are employed di erently, in my opinion, due to two subtle distinctions. As I just said, the rst is about the perspective adopted when considering that time period (sleep o set) and its impact on performance upon awakening (i.e., negative vs. positive viewpoint). The second, more important, di erence is that sleep inertia is mostly related to preceding sleep depth, whereas the carryover state only considers the sleep stage of awakening (independently of sleep depth, even if both are linked to some degree).

It is worth noting that nearly all of the key studies that demonstrated a speci c e ect of REM sleep on creativity were conducted in a presumed carryover state [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF][START_REF] Fiss | Waking fantasies following interruption of two types of sleep[END_REF]Stickgold et al., 1999;[START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF]. Thus, would the last stage of sleep before awakening have a signi cant impact on subsequent performance? Several lines of evidence indicate that it does, and it may be a missing link in reconciling the literature and the current ndings, as we will see below.

Reconciling the sleep studies using the NRT Returning to the NRT studies, let us see if we can now explain all of the reported ndings simply by examining the awakening stage. Regrettably, a review of the literature reveals that hardly any studies provided this information, making it di cult to evaluate the impact of this variable on the published results. But, we can still perform a thought experience and make educated guesses based on what we know about sleep structure and study design. According to our hypothesis, the late-night group should exhibit a higher proportion of insight than the early-night group since the chance of being awakened from REM sleep is higher in the former situation. However, this is not what they observed: the two groups were associated with similar levels of insight. But interestingly, a careful examination of their Methods section reveals that, regardless of the group, the subjects were awakened in light sleep (N1 or N2) to avoid 'painful' awakenings.

Assuming that the awakening sleep stage is what counts, they possibly eliminated any possible di erence between the early-and late-night groups by doing so. Indeed, the authors did not specify the proportion of each stage awakening, but based on the sleep structure and on the fact that they performed forced (rather than natural) awakenings, N2 awakenings might outnumber N1 awakenings. [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF], on the other hand, reported an increase in insight after a 10-minute resting period. We explained in the last chapter how this gain in insight might be linked to N1 intrusions that were likely to occur in their experimental conditions. Given that attaining deeper sleep (N2) within a 10-min interval (including the sleep latency) is improbable, N1 would be the sole potential sleep stage of awakening (for subjects who possibly fell asleep), which could account for their observed insight-boosting e ect.

Finally, the last NRT study comes from our team, in which we showed a bene cial impact of pure N1 on insight, but that e ect faded with deeper sleep (N2). The last sleep stage before awakening was obviously always N1 in the N1 group (since it was the only sleep stage). But more importantly, in the N2 group, most subjects (93%) experienced N2 as their last sleep stage before awakening. Therefore, in the paper, we interpreted the insight-booster e ect according to the wake-to-sleep transition (i.e., sleep onset), but we could alternatively interpret it according to the sleep-to-wake transition (i.e., sleep o set).

Altogether, this hypothesis could potentially explain all of the relevant published sleep studies on the NRT task (

On the carryover

Unfortunately, this question is di cult to answer because most studies either do not report when the retest occurred relative to awakening, or report a rough time interval (typically 30 minutes; see Figure 4.4). Additionally, this delay is chosen based on the sleep inertia literature (which typically relies on simple cognitive tasks assessing basic vigilance levels), but without a test to verify whether this timing also applies to more creative tasks. But we can still review what we know and make assumptions. Some studies found that REM sleep awakenings resulted in a more creative mindset than N2 awakenings [START_REF] Fiss | Waking fantasies following interruption of two types of sleep[END_REF]Stickgold et al., 1999;[START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF]. Back in the days, [START_REF] Fiss | Waking fantasies following interruption of two types of sleep[END_REF] also conducted a study in which they asked subjects to come up with stories after they were awakened from NREM sleep (N2 or N3) and REM sleep.

They found that stories produced after REM sleep contained more words and ideas, and were more bizarre and visual than stories produced after NREM sleep, a di erence comparable to that seen between NREM and REM sleep dream reports [START_REF] Fiss | Waking fantasies following interruption of two types of sleep[END_REF].

Of note, [START_REF] Fiss | Waking fantasies following interruption of two types of sleep[END_REF] also asked subjects to create stories just after sleep onset (i.e., our standard N1 stage) awakenings, and showed that stories produced after sleep onset (but also after wakefulness) were as complex (number of words and ideas) as those produced after REM sleep. This nding echoes our previous discussion and suggests that the N1 stage could have a comparable e ect on creativity as REM sleep.

All of these studies ascribed their results of an enhanced creativity to the carryover, after awakening, of a creative state such as REM sleep. However, to the best of my knowledge, they did not verify this assumption (e.g., they did not include a delay between a particular sleep awakening stage and the creative test). Indeed, in Stickgold et al. (1999), they had a post group in which testing was performed 5 minutes after awakenings, but 1) subjects in this group could still be in the carryover time-window, and 2) the stages at which subjects were last awakened and their e ect on post-performance are unknown. Similarly, [START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF] had a 'wake' group (conducted 30 minutes after awakening), but did not specify the last sleep stage of awakening and its impact on observed results. Therefore, several scenarios could be imagined if Stickgold et al. (1999), for example, had included a delay between REM sleep awakening and the semantic priming task: either their results of a exible cognition would have vanished, con rming that it was the carryover that was responsible for their results. If, on the contrary, they had observed similar ndings, it would imply that what matters is the last stage of awakening, not the carryover (I admit, the di erence is subtle...). Last, if the e ect is in-between, it would indicate that both (the stage and the carryover) may add for a maximal e ect.

Sleep inertia vs. carryover A cursory examination of the sleep inertia literature (which we can consider as a proxy for the carryover notion) also casts doubt on the concept of carryover. Indeed, the majority of studies report no SI di erence between N2 and REM sleep awakenings; only a general sleep inertia (regardless of sleep stages) when compared to wakefulness [START_REF] Casagrande | Night-time right hemisphere superiority and daytime left hemisphere superiority: a repatterning of laterality across wake-sleep-wake states[END_REF][START_REF] Gorgoni | EEG topography during sleep inertia upon awakening after a period of increased homeostatic sleep pressure[END_REF][START_REF] Jewett | Time course of sleep inertia dissipation in human performance and alertness[END_REF][START_REF] Occhionero | Time Course of Sleep Inertia Dissipation in Memory Tasks[END_REF]. For example, a recent study [START_REF] Occhionero | Time Course of Sleep Inertia Dissipation in Memory Tasks[END_REF] assessed SI and its dissipation across a variety of cognitive tasks (i.e., semantic, episodic, and procedural tasks) and compared performance after awakening from either N2 or REM sleep. They observed an SI e ect on the speed of cognitive task execution and subjective alertness, but this e ect was independent of the sleep stage of awakening [START_REF] Occhionero | Time Course of Sleep Inertia Dissipation in Memory Tasks[END_REF]. Thus, at least on standard cognitive tasks, SI does not appear to di er between N2 and REM awakenings.

To summarize, a part of the literature indicates a higher creativity upon awakening from REM sleep compared to NREM sleep [START_REF] Fiss | Waking fantasies following interruption of two types of sleep[END_REF]Stickgold et al., 1999;[START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF], whereas another portion of it (on sleep inertia) shows no di erence in performance on simple cognitive tasks following NREM vs. REM sleep awakenings. We see two explanations for these divergent ndings: either simple cognitive tasks are not a ected by the carryover state but more complex, creative tasks are, or Stickgold et al. (1999)'s results, for example, are not related to a state carryover, but rather merely to the nal stage of awakening. In that latter scenario, we would expect comparable results to those obtained immediately after awakening by Stickgold et al. (1999) if the sleep o set and testing were separated by a time delay.

REM vs. REM+NREM awakening

The last study that I know of that attributed a creative mind to a REM sleep carryover state yielded some interesting results in support of the awakening stage theory. Indeed, [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF] found that REM sleep played a similar role in facilitating non-obvious, distant associations (on emotional cues) as Stickgold et al. (1999), but only when awakening occurred immediately after REM sleep and this bene cial e ect was lost if it was followed by NREM sleep (Figure 4.2). They interpreted this nding in terms of carryover, speci cally that a REM sleep carryover state would result in exible, creative cognition, as hypothesized in Stickgold et al. (1999). But two lines of argument might contrast with this interpretation. First, the bene cial e ect of REM sleep was not observed for novel stimuli, even when performed right after awakening [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF]. Second, the main retest on primed items was performed 30 minutes after awakening, raising the question of whether they were still measuring the carryover state since SI (a proxy for the carryover) rarely exceeds 30 minutes (Trotti, 2017). Besides, most sleep studies employ that 30-minute delay between awakening and retest to remove the SI e ect (see Figure 4.4). Thus, either the carryover lasts longer than 30 minutes (calling into question all the aforementioned studies' interpretations), or the carryover was over at the time of the retest. If so, [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF]'s results could be explained in two ways: either NREM sleep 'overwrites' the bene cial e ect of REM sleep or subjects simply need to wake up with a creative mindset, for example, to retrieve consciously the work completed during REM sleep. Interestingly, we also observed di erent patterns between the N1 and N1+N2 groups in Studies 2 and 3.

Overall, these studies are compatible with the idea that the last sleep awakening stage (sleep o set) is important for creativity. Then, it is unclear what precise components of the sleep-o set period would be critical. Some authors have assumed that the time interval between awakening and retest is what matters, attributing enhanced creativity to the carryover, upon awakening, of a state conducive to creativity. This concept would, however, warrant further examination, as it is based on a few studies (2)(3) and lacks, to my knowledge, control groups to support this notion. Alternatively, the crucial component might reside in the sleep stage of awakening per se (independently of what happens afterwards). In that case, awakening at a particular stage would be important, for example, to retrieve what has been performed by that stage. The main di erences between this idea and the carryover hypothesis are as follows: 1) The former does not necessitate subjects performing the creative task in a hybrid state combining wakefulness and REM sleep (they could be fully awake) to observe a bene cial e ect on creativity, 2) it rather implies that waking up from the 'right' sleep stage makes crucial information required for creative insights more readily available, and 3) this bene t would not dissipate with time (or at least not within 30 minutes). Still, we discussed in the previous chapter that being in a hybrid state while performing a creative task could account for the present ndings, namely for the increased creativity in narcolepsy found in Study 1, as well as for the N1 insight-booster e ect observed in Study 2. Importantly, both components (sleep o set and hybrid state) are somewhat confounded and not mutually exclusive. Further research is needed to disentangle the two and ascertain whether sleep o set is genuinely a missing piece of the sleep-creativity puzzle and the mechanisms underlying its putative e ect. For example, we could add another group to Study 2 that would be identical to the N2 group but with a nal awakening in N1 (i.e., a N1+N2+N1 awakening group, similar to the design employed in [START_REF] Carr | Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks[END_REF]). If both the pure N1 and N1+N2+N1 groups outperform the N1+N2 group on insight, this would lend credence to the theory that the awakening stage is important. Then, is the bene cial creative e ect of awakening with a creative mindset short-term (i.e., tied to a carryover state) or long-lasting? Adding a condition with a delay between awakening and retest should help answering this question. We could also manipulate the timing of this delay to determine how long the carryover state lasts for creative tasks. Of note, I have alluded to sleep stages thus far, but as we have seen, they may not be perfect and miss some subtle modulations. Therefore, we could as well replace the sentence 'waking up from the right stage' with 'waking up from the right state'.

Whichever hypothesis is correct (or both), we see that what occurs during sleep o set could have a signi cant impact on subsequent creative performance. Late onset of Eureka Indeed, in Study 2, the Eureka moment did not occur immediately upon awakening, but rather after approximately 90 trials with high between-subject variability. This observation contrasts with the popular belief (and Edison's story) that a solution to a problem pops up as soon as we wake up. "The noise [of the sphere drop] would wake Edison, and very often he would awaken with an idea to continue with his project", wrote [START_REF] Jr | Relax: Its Good for You[END_REF]. This surprising nding of a late Eureka is, however, consistent with other studies using the NRT [START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF]Wagner et al., 2004), but the authors never discussed it. How to explain this delayed insight? The moment of Eureka occurred roughly 30 minutes after awakening and could thus correspond to the time for sleep inertia to vanish. This hypothesis is, however, not satisfactory because we did not observe any di erence in the time of the Eureka moment between wake, N1, and N2 subjects (which should increase along with an increase in sleep inertia, although the N2 group was very small in our Study 2). Still, one could argue that the delay must be correlated with a sleep parameter (e.g., the total time spent in the N1 stage, or the alpha/delta power, as these are two critical factors), but we found no correlation between any of these measures and the Eureka moment. Therefore, the most likely explanation is that additional mechanisms (unrelated to sleep) take place following sleep to catalyze its e ect. We brie y mentioned two hypotheses for these mechanisms in the paper, which I will cover in more depth here.

First, this delayed Eureka could correspond to the veri cation stage of the Wallas' model of creativity (see here for a reminder), in which the accuracy of the solution/strategy developed during incubation, is tested. Still, 94 trials on average before Eureka seems like a long time, especially given that we provided feedback to participants at each trial about their correctness. We also looked for evidence of such a veri cation process (e.g., a slowing in solving times in the trials immediately preceding the Eureka moment, but did not nd any; see Fig. S2 of Study 2).

Instead of a conscious veri cation stage, that time interval could alternatively correspond to an unconscious evaluation of the best suitable strategy prior to implementation, in a way similar to drift di usion models (typically used to explain decision-making). In that case, the N1 stage would favor gist abstraction by restructuring task-related memories and storing the hidden rule in a 'strategy-store'. Then, the use of this new strategy would be determined by a second implicit process occurring when confronted again with the task during wakefulness. Precisely, when subjects return to the task, the rst strategy would prevail (because it is the one they have learned and consolidated through practice), until they reach a certain threshold where they have accumulated enough evidence to switch strategies and adopt 'the hidden rule', available thanks to N1. In this context, inter-individual di erences in the moment of Eureka can be explained by distinguishing more exploitative from explorative individuals, who require more or less evidence accumulation before reaching the threshold (see the illustrative Figure 4.3). Of note, I chose for clarity a di erent threshold as an example (and illustrated it in Figure 4.3), but there are other possible scenarios attached to drift-di usion models. For example, individuals may possess a similar threshold but di er in their speed of evidence accumulation (i.e., the slope of the curve on Figure 4.3), or in their o set (i.e., the y-intercept on Figure 4.3). A di erent o set would re ect a cognitive bias for one choice over the other (e.g., in that case, a subject biased towards sticking to the rst, learned strategy). All of these metrics would be in uenced by a variety of factors, including the subjects' background and personality (as aforementioned), but also by other variables such as their motivation or fatigue levels, and would result in various Eureka onsets.

Chapter Summary

We reviewed several hypotheses in an e ort to explain some reported discrepancies in the literature. We ended up emphasizing that sleep o set may o er a missing link (certainly among others), but it is simply an idea that awaits investigation.

Our results also highlight that additional processes appear to take place during the Post phase to catalyze the e ect of sleep. This speculative chapter just sought to take a wider view of this hazy literature to identify future avenues worth exploring that may help clarify the picture. For example, considering the highly organized structure of sleep in humans, it is surprising that the scienti c community (including myself) mostly focuses on quantitative measures of sleep components (e.g., sleep stage duration), while neglecting its qualitative components such as the dynamic of sleep transition (e.g., sleep stage upon awakening). We may bene t from supplementing our conventional measurements with these factors, as well as with intra-stage features (e.g., eye movements common to both the N1 stage and REM sleep), or spectral power. Indeed, we observed that spectral analyses explained our results irrespective of sleep stages in Study 2. These EEG ndings suggest that our traditional scoring approach may not always be optimal for determining the crucial factors involved in an observed e ect of sleep, and may possibly also explain some of the disparities in the literature. Besides, I think that the main contribution of this thesis is to lift the veil on one of these underappreciated sleep components: the wake-to-sleep transition. The next chapter will cover what we have learned from this overlooked gray zone and what remains to be learned.

Ce sommeil ne me fait descendre qu'à une demiprofondeur. J'y dors, mais en touchant faiblement à la veille. Je vois, j'entends, je sens la présence des êtres et des choses, mais le sommeil, en les ltrant, ne m'en laisse passer que l'idée incolore, ce qui peut s'en mêler à la vie fantomale de mes songes. Je otte entre deux mondes1 .

-Henri Bosco

5

Lifting the veil on sleep onset The sleep-onset period, which we all pass through on our way to sleep, may be referred to as the 'Great Forgotten' in the history of sleep. This twilight zone had a golden era of research approximately between 1980 to 1990, but is now mostly ignored by researchers studying sleep physiology or the cognitive functions of sleep. The ephemeral nature of the sleep-onset period is certainly at the foundation of such a scienti c community's lack of interest. Indeed, some researchers may believe that important cognitive processes are unlikely to occur in such a short period of time.

Others might be discouraged by the practical di culties in tracking and capturing this eeting, transitory period.

In the present thesis, we highlighted the need to rehabilitate this twilight zone by showing that critical processes actually occur within a minute time-lapse and by providing methods for capturing that zone. In the following section, we will

expand on what we have learned and what remains to be learned on each of these points.

Critical cognitive processes occur during N1

First, this thesis challenges the intuitive belief that nothing meaningful happens during sleep onset.

Memory reshaping at sleep onset Indeed, we showed that reprocessing of newlyencoded memory traces also occurs during N1 (Study 3). Moreover, N1 function seems to be much more sophisticated than just replaying memory traces. Indeed, our Study 2

suggests that the N1 stage also detects common patterns within them (e.g. the hidden rule). Such a gist detection may be accompanied by the ltering out of super uous information, as implied, for instance, by [START_REF] Lewis | Overlapping memory replay during sleep builds cognitive schemata[END_REF]'s iOtA model (see Figure 5.3). This hypothesis could explain the memory loss of a few items reported in Study 3, as well as the one described earlier by Wyatt et al. (1994) and Wyatt et al. (1997). Besides, forgetting has been hypothesized as a complementary function of sleep in addition to memory consolidation, necessary to prune out irrelevant memory traces and make room for novel learning (for reviews, see Feld and Born (2017) and Poe (2017)).

However, we must remain cautious in interpreting the memory result from Study on the contrary, label the memory traces most in need of being reinforced. In that case, by preventing deeper sleep from supplanting it, we would have disrupted this process, resulting in the forgetting of these few items. Supporting this idea, we found that the Lost objects were the ones that the participants had the greatest di culty encoding (i.e., the labile ones), and some research indicates that labile, weakly-learned information is preferentially consolidated (Creery et al., 2015;Drosopoulos et al., 2007;Schapiro et al., 2018). Further research is thus needed to ascertain the fate of these memory traces. A serial nap paradigm consisting of a N1-nap followed by a memory test and nally a N2-nap could allow addressing this question by following the fate of a particular memory trace through these various sleep stages. Additionally, it is possible that memory representations processed during N1 are labile (because they have not been consolidated in subsequent sleep stages), raising the question of whether our results from Studies 2 and 3 would hold true on a longer timescale (e.g., by adding a long delay between awakening and retest).

Such an experimental design may also aid in addressing the question of why we did not nd the typical sleep-dependent memory consolidation e ect associated with N2 (i.e., memory performance between wake and N2 was similar). In the paper, we postulated that a more consolidated N2 (e.g., with a higher density of spindles) could be required to elicit memory consolidation. However, it is worth mentioning here that most studies do not actually show a memory consolidation e ect per se, but rather a lower forgetting in the post-N2 group compared to the post-wake group. Then, if they combined N1 and wake data (as it may be the case in some research), they would obtain comparable results

to ours in Study 3, namely a higher rate of forgetting in their 'wake' group (possibly driven by N1) than in the N2 group. All of this remains open questions for future experimentation. As aforementioned, it also remains to be determined whether our results could extend to other tasks, such as those requiring access not only to novel memory traces, but also to our long-term memory store (e.g., the RAT/CRA, or riddles). In this regard, a careful examination of hypnagogic experiences suggests that the N1 stage may access and reprocess not only novel memory traces (as demonstrated in Studies 2 and 3), but also long-term memories. As an example, consider the following hypnagogia reported in our Study 2:

" I was inside the Colosseum in Rome when it was still in its prime. I also saw concentric shapes that were shrinking and certain gures that I called functions that I was trying to identify. One that really struck me was what I called the oscillator, which turned one gure into another and then back to the original one.

"

Here, the participant potentially combined ancient memories from the Coliseum of Rome and an oscillator with novel ones from the NRT number-based component.

Consistently, Stenstrom et al. (2012) thoroughly examined hypnagogic contents and found that a great portion of them were composed of fragments of distinct memories separated by a long time interval (sometimes several years) and integrated together into a novel scene (see Figure 5.7). Notably, they also discovered that a great portion of these distal memory sources shared semantic similarity. This o ine reorganization of memories is reminiscent of the integrative encoding and Lewis and Durrant (2011)'s model, in which overlapping features from distinct memories are extracted to form schematas (see Figure 5.3 for a reminder). Thus, these ndings may imply that both our novel and ancient memories undergo reorganization as early as during the N1 stage, but this remains to be con rmed. Indeed, this hypothesis relies on: 1) anecdotal examples of hypnagogic content and a single study based on self-reports (Stenstrom et al., 2012), and 2) the notion that dreaming re ects memory reprocessing, a question that is still under debate (for a reminder, see this box).

The concept of memory network reorganization as a requisite for problem-solving also requires additional research. Indeed, to my knowledge, only one paper [START_REF] Durso | Graph-Theoretic Con rmation of Restructuring during Insight[END_REF] has demonstrated that solving a riddle was associated with a reorganization of the riddle's related network (see here for details on this study). I am currently collaborating on a project to replicate this study with other riddles. This study should allow us: 1)

to validate or refute the notion of memory reorganization as a mechanism underlying problem-solving, and 2) if it works, provide us with a quanti able measure of memory restructuring that could be used to directly investigate the e ect of sleep on it. Of note, there is a second interesting paper for that discussion, which shows that providing participants (during wakefulness) with a link between seemingly unrelated information 5. Lifting the veil on sleep onset 254 triggered a neuronal reorganization of the memory representations. Precisely, this reorganization involved both the formation of novel memory representations that linked related events together and the segregation of isolated information [START_REF] Milivojevic | Insight Recon gures Hippocampal-Prefrontal Memories[END_REF]. Interestingly, this neuronal recon guration was observed in two main regions, the hippocampus and the medial prefrontal cortex. These are the same two regions involved in the hippocampo-prefrontal dialogue described in the rodent literature as a mechanism for sleep-dependent memory consolidation. If we assume that a similar interaction between brain regions leads to the same consequence, it is tempting to extrapolate from [START_REF] Milivojevic | Insight Recon gures Hippocampal-Prefrontal Memories[END_REF] that sleep is a privileged period to allow nding such a critical link between memories and, in the same way, restructuring the memory network (keeping the gist and pruning out unnecessary information), thereby promoting insight.

In any case, we showed that high-level order cognitive processes, which have often been associated with deeper sleep stages, occur earlier, during the N1 stage. That brief moment at the doorway to sleep is thus far from trivial. In the present thesis (Studies 2 and 3), we also revealed the putative existence of two di erent patterns between two seemingly close sleep stages, N1 and N2, on both memory and creativity. To account for these ndings, I proposed in the previous chapter 4 that the last sleep stage before awakening may have an impact on subsequent performance, but this hypothesis remains to be tested. We believe that it is important to rst try replicating our N1 vs. N2 'yin-yang' e ect in future studies with larger sample sizes. Indeed, if the detrimental impact of N2 on insight is con rmed, it would further emphasize the importance of having a reliable technique that captures 'pure' N1 and prevents subjects from entering N2 in order to increase their creativity. The next section will highlight what we have learned about such methods for capturing the 'creative sweet spot'.

Targeting the creative sweet spot

We showed that our bottle was not ideal for capturing the creative sweet spot. Indeed, bottle drops were not directly associated with insight in Study 2, most likely because the bottle captures ne-grained modulations in the wake-to-sleep transition and thus occasionally falls too early (before the sweet spot). In that regard, we view the bottle as a means of preventing subjects from falling too deep into sleep (as it is sensitive to the delta build-up, which appears detrimental to insight), rather than as a means of precisely targeting the creative sweet spot.

More sophisticated methods would be required to speci cally target the creative sweet spot. For example, the previously mentioned 'Dormio' glove, which has been designed to target the N1 stage, might be a viable option. However, it has not yet been marketed and, to my knowledge, the authors have not thoroughly tested whether it does capture the N1 stage e ectively, but they have performed a pilot study that provides encouraging results (see here). However, this method may still not be perfect, as awakening subjects precisely at the onset of N1 might not be su cient to inspire insight (i.e., still too soon). Brain-Computer Interface (BCI) may be the best approach for accurately targeting the creative sweet spot. As the name implies, BCI is an interface that analyzes neural activity in real-time to decide when to perform an action (e.g., stimulating the brain when it detects a slow-wave to increase its amplitude). In our case, it could be con gured to simply trigger an awakening sound in the experimental bedroom whenever the sleeper's neuronal activity reaches the ideal combination: an intermediate x alpha power and a low y delta power (see Figure 5.1).

That being said, our bottle remains an easy and practical method to use in home or work settings where polysomnography and BCI are di cult to implement. Furthermore, as we will see in the following section, it is a valid tool for tracing the sleep-onset period.

Tracing the sleep-onset period

As aforementioned, the di culty in apprehending and capturing the sleep-onset period is another reason, in my view, why the scienti c community overlooked this transitory phase. Indeed, until recently, we lacked a reliable marker capable of accurately capturing the precise moment of falling asleep. As we brie y mentioned in the Introduction, we believe that this di culty stems from the seldom consideration of inter-individual variability in sleep-onset dynamics. Indeed, given that each individual has a unique sleep structure and personal sleep history (i.e., levels of anxiety, sleepiness, ease of falling asleep in new conditions, etc...), there is no reason to assume that all subjects would have the same sleep-onset dynamic, and therefore the same 'turning point'. Was the bottle deliberately dropped by the subjects to get rid of it? Or was our bottle ine ective at detecting sleep onset? Neither of these two, as re ned spectral analysis revealed. Indeed, we observed a signi cant increase in low-frequency power (a sleep marker) in the few seconds before the bottle dropped, even for individuals who dropped it in the absence of any clear signs of sleep. Importantly, random permutation analysis revealed that the delta build-up seen prior to the bottle drop was not as strong elsewhere in the nap. Therefore, our bottle detects subtle modulations in the wake-to-sleep transition that are undetectable to the naked eye. These results further highlight that the sleep-onset dynamic di ers between individuals, with some subjects transitioning to sleep faster than what we would assume using our standard scoring methods, while others exhibit clear indications of sleep for an extended time period before dropping the bottle. Thus, alpha disappearance does not necessarily equal sleep onset, which could be explained by [START_REF] Prerau | Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics[END_REF]'s observation that the appearance of low-frequencies does not always coincide with alpha-dropout, with the two occasionally separated by a few minutes (see Figure 2.3).

Besides, the model developed by [START_REF] Prerau | Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics[END_REF] nicely exempli es the intersubject heterogeneity in sleep-onset dynamics. As a reminder, their model combines various metrics (i.e., EEG spectral power, muscular activity, and behavioral responses) to calculate, in real-time and continuously, a wake probability (for more detail, see this section of the Introduction). With it, the authors showed that everyone has a unique sleep-onset phenotype, which may even vary within the same individual from one night to the next (Figure 5.3). However, despite its e ectiveness and precision, this model has three drawbacks.

First, it relies on active behavioral tasks that require the subject to complete a task while falling asleep, which may interfere with this process. Second, it does not pinpoint a moment at which we could wake the subjects up. Third, their model relies on complex algorithms that, for the time being, are inaccessible to those who do not have the necessary equipment or programming skills. Our bottle thus represents signi cant progress in this eld, as it seems to capture each individual's sleep onset moment without the need for any additional equipment (e.g., polysomnography, programming skills).

Furthermore, the bottle drop was associated with not only electrophysiological evidence of sleep onset, but also with additional sleep onset indicators, such as sleep perception and hypnagogic experiences.

Hypnagogia Indeed, most subjects (81.48%) reported that they had been drifting to sleep when the bottle dropped. Moreover, the bottle was also a great 'hypnagogiacatcher': the percentage of reported hypnagogia at bottle drop was signi cantly higher (63.64%) than the one without bottle drop or after random micro-sleep episode awakenings (collected in Study 3), as well as higher than the one reported by [START_REF] Hori | Topographical EEG changes and the hypnagogic experience[END_REF]. However, some studies reported an exceedingly high percentage (93-99%) of hypnagogia after N1 awakenings [START_REF] Foulkes | Mental activity at sleep onset[END_REF][START_REF] Horowitz | Dormio: A Targeted Dream Incubation Device[END_REF]. This discrepancy, we think, is related to how hypnagogia is de ned.

In Studies 2 and 3, we chose not to use the traditional de nition of dream, which encompasses any mental content that occurs during sleep. Indeed, because we were studying the wake-to-sleep transition (with frequent periods of wakefulness interspersed with micro-sleep episodes), it is di cult to say whether a reported 'wake-like' mental content quali es as hypnagogia. Thus, we adopted a more conservative de nition, classifying a report as hypnagogia only if its content was 'involuntary, spontaneous, perceptual, and bizarre' (i.e., unusual perceptions, non-wake-like thoughts). But if we had used the most liberal de nition of dreams (i.e., any reported mental content), we would have been at 100% of reported hypnagogia at bottle drop. Consistently, [START_REF] Nielsen | Microdream neurophenomenology[END_REF] noted in his review that when focusing on complex dreaming involving 'hallucinated dramatic episodes', the aforementioned high percentage drops to 31-76%.

As an example, [START_REF] Horowitz | Dormio: A Targeted Dream Incubation Device[END_REF] assumed that they could use the conventional de nition (i.e., any reported mental content), since their device was intended to detect the N1 stage. A careful examination of their reports, however, suggests that some of them may correspond to awake reports (although again, this is a matter of debate),

or at least reports that we did not label as hypnagogia (e.g., planning, down-to-earth thoughts). Here is an example of one of their hypnagogia: "I was thinking about how someone else would have to fall asleep here" [START_REF] Horowitz | Dormio: A Targeted Dream Incubation Device[END_REF].

Lifting the veil on sleep onset 260

In sum, we have learned several things about hypnagogia:

• Our bottle is an e ective way of capturing these ephemeral experiences.

• We con rmed Hori's pioneering ndings that hypnagogic experiences can occur far earlier than 'standard' sleep (i.e., during epochs considered as wakefulness according to the standard criteria).

• Given the aforementioned individual variability in sleep-onset dynamics, we would gain in e cacy (e.g., number of reports) if we did not rely solely on standard sleep stages to collect hypnagogia, as the latter may miss some hypnagogic experiences that take place during 'o cial' wakefulness.

• We also con rmed that phenomenological experiences that occur throughout the wake-to-sleep transition are diverse. They do not simply consist of geometrical forms, but also include complex hallucinations that resemble full-edged dreams.

To fully appreciate their richness, I encourage you to watch our 'Hypnagogia

Best-Of' video by clicking on this link. This straightforward method could be used by anyone desiring to study the wake-to-sleep transition.

How pervasive is sleep?

The ndings of my thesis highlight how porous the frontier between wakefulness and sleep is (even in healthy subjects; e.g., during the N1 stage, or local sleep intrusions such as before the bottle drops), and how these sleep/wake overlaps may have a substantial impact on cognition. In this section, I will explore this putative 'continuum' between wakefulness and sleep, as I think it is important to have this theoretical discussion.

Sleep and wakefulness are often viewed as distinct entities. I'd like to temper this viewpoint by highlighting that the gap between the two is much narrower than we believe; rather, they both seem to exist on a continuum on which we alternate during a 24hour day. This porous frontier becomes evident when phenomenological experiences are considered. Our lexicon to describe them is vast: daydreaming, mind-wandering, mindblanking, hallucinations, parahypnagogia, hypnagogia, dreaming... This abundance of terms creates confusion regarding their de nitions, and researchers frequently mix them up. Rightfully so? In other words, are they separate phenomena or do they all refer to the same construct with varying degrees of intensity? Many pieces of evidence point to the latter. We have already covered the overlap between dreams and hypnagogia in the Introduction, but these two also have a lot in common with mind-wandering. [START_REF] Occhionero | Phenomenal consciousness in dreams and in mind wandering[END_REF] even proposed that mind-wandering can occur during sleep onset, stressing how di cult it is to see a distinction between mind-wandering and hypnagogia. Back in 1969, the psychologist Charles [START_REF] Tart | Altered states of consciousness: a book of readings[END_REF] already noted:

"

The hypnagogic state is by no means con ned to the beginnings of nocturnal sleep. I would speculate that there are many times when we believe we are just 'thinking deeply' or 'concentrating' in which we momentarily slip into a hypnagogic state.

"

Similarly, given the striking similarities between dreaming and mind-wandering, [START_REF] Fox | Dreaming as mind wandering: evidence from functional neuroimaging and rst-person content reports[END_REF] posited that dreaming is simply an intensi ed form of mind-wandering.

Indeed, the mental contents of both (dreaming and mind-wandering) are primarily audiovisual and emotional, more bizarre and fantastic than goal-directed thoughts, rely on long-term memory, and are related to current concerns. They are also accompanied by a relative lack of meta-awareness (to varying degrees), and they exhibit similar patterns of brain activity, including activation of areas associated with inwardly-directed thoughts (the DMN) and deactivation of PFC executive regions [START_REF] Christo | Mind-wandering as spontaneous thought: a dynamic framework[END_REF].

Thus, all of the characteristics that di erentiate mind-wandering from goal-directed waking thoughts are found in dreams, but exacerbated. In the same vein, hallucinations have been proposed to be an intensi ed form of mind-wandering [START_REF] Fazekas | Hallucinations as intensi ed forms of mind-wandering[END_REF].

As a result, we have a plethora of terms that seem to refer to the same construct.

This conundrum stems from our outdated binary view of sleep vs. wakefulness, which led to a multiplicity of terms to di erentiate spontaneous thoughts supposed to occur during wakefulness (e.g., mind-wandering) from those ostensibly occurring during sleep In this framework, we could represent all of the di erent kinds of spontaneous thoughts on a spectrum with varying degrees of sleep intrusion -from local sleep to full hybridity, and nally up to global sleep -, each associated with a speci c kind of mental content. For example, we could predict that the degree of bizarreness of those spontaneous thoughts would increase with the degree of sleep pervasiveness (Figure 5.6). This rede nition, in addition to clearing up this blurry background and providing a quantitative measure, could also help shed light on the dynamic of so-called wakefulness.

To date, considerable e orts have been made to capture the complexity of sleep by subdividing it into several stages characterized by varying sleep depth. We could do the same with wakefulness, whose complexity has thus far been overlooked. Most authors conducting 'wake' studies on cognition assume that subjects are awake but do not monitor their vigilance/sleep level with EEG measurements. In addition to improving our understanding of the wakefulness ne-grained dynamic, systematically monitoring putative sleep features in awake subjects may help in reconciling results

from disparate studies, particularly on creativity. Indeed, given the present ndings of a boost in creativity after only one minute of the N1 stage, sleep intrusions could account for part of the results' variance reported in the literature on incubation and on sleep/creativity (e.g., are wake control groups genuinely wake controls?). In line with this idea that hybrid states may favor creativity, [START_REF] Baird | Inspired by Distraction: Mind Wandering Facilitates Creative Incubation[END_REF], for example, found that individuals with a proclivity for mind-wandering score higher on creativity tests (i.e., the AUT). Interestingly, [START_REF] Tan | Mind Wandering and the Incubation E ect in Insight Problem Solving[END_REF] also reported that subjects who found the hidden rule in the NRT (the task we used in Study 2) reported signi cantly more mind-wandering than non-solvers over the previous incubation period. Given that [START_REF] Andrillon | Predicting lapses of attention with sleep-like slow waves[END_REF] found that mind-wandering was associated with sleep intrusions, it is plausible that sleep intrusions are responsible for the boost in creative performance associated with mind-wandering in [START_REF] Tan | Mind Wandering and the Incubation E ect in Insight Problem Solving[END_REF]. This observation also ts countless anecdotes describing the emergence of ashes of insight during 'o ' periods [START_REF] Gable | When the Muses Strike: Creative Ideas of Physicists and Writers Routinely Occur During Mind Wandering[END_REF], when the mind wanders (for anecdotes, see this box).

Given that memory and creativity seem to be inextricably linked, monitoring sleep levels in studies performed during 'wakefulness' could also help reconcile contrasting results on memory and 'wake rest'. Indeed, given that we observed a memory e ect after a short intrusion into sleep and that subjects are often sleep-and ca eine-deprived (even in wake studies), sleep intrusions are likely to pervade the wakefulness studies on memory and impact their results. Notably, some studies report a memory consolidation e ect following a 'wake rest' in humans (for a review, see [START_REF] Wamsley | Memory Consolidation during Waking Rest[END_REF]). However, in contrast to sleep studies, these wake rest studies rarely compare post-rest performance to baseline (pre), but rather compare it to another type of rest, active rest (e.g., performing a spot-the-di erence task) (Brokaw et al., 2016;[START_REF] Craig | Rest boosts the long-term retention of spatial associative and temporal order information[END_REF][START_REF] Dewar | Brief wakeful resting boosts new memories over the long term[END_REF][START_REF] Dewar | Boosting long-term memory via wakeful rest: intentional rehearsal is not necessary, consolidation is su cient[END_REF]. Therefore, they do not show that wake rest improves memory (in fact, there often tends to be a decline in performance between pre and post), but rather that passive wake rest is associated with less forgetting than active wake rest.

Similarly, [START_REF] Gottselig | Sleep and rest facilitate auditory learning[END_REF] found a bene cial e ect of wake rest compared to a busy break, but found the same e ect without any break, implying that it was not resting per se that was responsible for their results.

Additionally, some of the aforementioned studies associated their ndings to a 'wake rest', but they either:

• Did not verify that their subjects stayed awake during the resting period [START_REF] Craig | Rest boosts the long-term retention of spatial associative and temporal order information[END_REF][START_REF] Dewar | Brief wakeful resting boosts new memories over the long term[END_REF][START_REF] Dewar | Boosting long-term memory via wakeful rest: intentional rehearsal is not necessary, consolidation is su cient[END_REF].

• Or mixed N1 with wakefulness and attributed a memory consolidation e ect to a 'wake rest' with a group of subjects who spent an average of 3 minutes in N1 [START_REF] Humiston | A brief period of eyes-closed rest enhances motor skill consolidation[END_REF]. This study is a good example of one research that combined wakefulness and N1 data, as aforementioned.

Of note, research also indicates a correlation between fMRI reactivations of learning content during wake rest (usually of 8-10 minutes) and subsequent memory performance [START_REF] Gruber | Post-learning hippocampal dynamics promote preferential retention of rewarding events[END_REF]Schapiro et al., 2018;[START_REF] Tambini | Persistence of hippocampal multivoxel patterns into postencoding rest is related to memory[END_REF], but the subjects' vigilance states during the o periods are rarely monitored (which is di cult to do in fMRI studies). Some ask the subjects to perform another task or to keep their eyes open, but Tagliazucchi and Laufs (2014a) found that subjects could also fall asleep with their eyes open. They also reported that roughly half of the subjects fell asleep during a 10-minute resting period in an fMRI scanner (Figure 3.3). I want to emphasize here that I am not implying that their ndings are necessarily due to sleep intrusions, but rather that their data may contain a mixture of sleep intrusions (at least locally, as in mind-wandering) and fully awake subjects, which would result, in the best scenario, to noisy data, and in the worse scenario, to a di erent interpretation of their ndings.

Overall, systematically tracking putative sleep intrusions into wakefulness (including local sleep) could help us better understanding the wake-sleep precise dynamic across a 24-hour cycle, as well as gaining a clearer picture of its impact on cognition.

Chapter Summary

We showed that crucial cognitive processes take place during the N1 stage and that, with the appropriate tools, this gray zone can be captured and investigated. Thus, our ndings highlight the need to pay more attention to this transitional period in the future. They also further emphasize the ne line that exists between wakefulness and sleep, as well as how sleep intrusions may pervade our lives far more frequently than we realize. Indeed, sleep intrusions (at least locally) may be the common denominator for the plethora of spontaneous thoughts reported in the literature and to which we would devote the majority of our existence. Therefore, I tantalizingly propose to combine all of these terminologies (e.g., hypnagogia, mindwandering, dream...) into a single term: spontaneous thought, accompanied by a quali er, the degree of sleep pervasiveness. Besides, we addressed the importance of constantly monitoring sleep levels even in studies conducted during 'standard' wakefulness, since sleep intrusions may explain some of the literature's con icting results. Doing so may contribute to the uni cation of disparate but closely related research areas such as the incubation, mind-wandering, and sleep literature. In the last chapter of my thesis, I will explore the potential long-term applications and perspectives of this eld. questions could be addressed in future studies (for a non-exhaustive summary, see the box entitled 'Research agenda' at the end of this chapter). As we near the conclusion of this thesis, I will take a step back and brie y outline the major applications and perspectives that I foresee for this eld. Obviously, more research is needed before these long-term applications can be considered, but let us conclude with hopes for the future of this research area.

Clinical applications

Certainly because I did my PhD in a hospital, I envision clinical applications for my present ndings. The rst one is obviously linked to Study 1, which involved patients with narcolepsy. The main message here is directed at physicians who can encourage

Perspectives and Applications

269

patients with narcolepsy to fully use their creative potential. Indeed, informing them of their increased creativity may act as a silver lining to their disorder and help them cope with this debilitating disease. Additionally, we showed in Study 2 that the bottle detects micro-sleep episodes, which are a good marker of excessive daytime sleepiness [START_REF] Tirunahari | Microsleep and sleepiness: a comparison of multiple sleep latency test and scoring of microsleep as a diagnostic test for excessive daytime sleepiness[END_REF]. Thus, if this nding is con rmed in future studies, the bottle may be a valuable tool for studying and diagnosing disorders of hypersomnolence.

For example, patients with excessive daytime sleepiness (including narcolepsy, idiopathic hypersomnia, or sleep apnoea) must take a test that measures how alert they are during the day and their ability to remain awake in resting conditions during a de ned period of time. Additionally, the bottle could be used to study the mechanism of sleep onset in patients su ering from the inverse problem, chronic insomnia, which a ects ≈10-30% of the adult population [START_REF] Bhaskar | Prevalence of chronic insomnia in adult patients and its correlation with medical comorbidities[END_REF][START_REF] Leger | Prevalence of insomnia in a survey of 12,778 adults in France[END_REF][START_REF] Roth | Insomnia: De nition, Prevalence, Etiology, and Consequences[END_REF]. In insomniacs, there is a frequent discrepancy between the actual and perceived times of falling asleep and total sleep duration, a phenomenon referred to as 'sleep misperception'. In that case, the This desynchronization would increase uncertainty in wakefulness probability, hence lowering the chance of subjects perceiving themselves to be asleep (rightfully so). In that case, patients with insomnia would not misperceive their sleep; rather, our standard sleep scoring would misperceive the situation by missing this desynchronization (as we saw in Study 2). This in-between state could prevent patients with insomnia from completely and rapidly losing consciousness. Furthermore, according to our theory, we might anticipate their hypnagogic experiences to be more ordinary, given the low degree of sleep pervasiveness associated with the desynchronization. The presence of wake-like hypnagogic experiences, combined with an 'hyper-awareness', may reinforce their perception of being fully awake, despite the fact that they are on the verge of sleep (Figure 6.1). Our bottle could help testing these assumptions, as we showed that the bottle is: 1) sensitive to low-frequency accumulation (which traditional scoring methods are blind to), and 2) an e ective 'hypnagogia-catcher', which may assist in identifying a putative di erence in insomniacs' hypnagogic experiences. Of note, the same approach could be used in patients with narcolepsy to determine whether their sleep onset dynamic and hypnagogia di er from those of normal subjects.

Overall, it would be interesting to better understand how dynamic neurophysiological changes accompany the gradual descent into sleep, to explore how these changes relate to cognitive content (e.g., task-focused, mind-wandering, hypnagogia...), and meta-cognition (e.g., being aware of falling asleep). Moreover, studying the sleep-onset period could be crucial for better understanding how these parameters may vary within individuals and between populations (e.g., in sleep disorders characterized by disrupted wake-sleep transitions, such as insomnia or narcolepsy). As [START_REF] Prerau | Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics[END_REF] wrote:

"

Given the importance of sleep onset dynamics, the ability to track the continuous dynamical properties of the SOP in a principled, automated manner could provide critical insight into the pathophysiology of these populations, aiding in both diagnosis and treatment.

"

But this is not the sole application of this eld; in fact, as we will discuss now, studying the sleep-onset period may o er an ideal research model. (see here for a reminder). Dreams are di cult to access and, more importantly here, we lack a dream temporal marker, which prevents researchers from correlating a given dream report with its corresponding EEG segment. These typical problems could be bypassed by investigating the sleep-onset period. Indeed, because hypnagogic experiences (or 'micro-dreams') can emerge after only 1 second of alpha-dropout [START_REF] Nielsen | Microdream neurophenomenology[END_REF], they are easier to collect in large quantities and to pinpoint temporally (e.g., by asking the participants to execute a hand switch whenever they experience one, as in [START_REF] Nielsen | Describing and modeling hypnagogic imagery using a systematic self-observation procedure[END_REF]). Besides, [START_REF] Germain | EEG power associated with early sleep onset images di ering in sensory content[END_REF] Indeed, these hypnagogic fragments would be less a ected by the so-called 'forward momentum', which refers to the fact that "experiential events activated at one time may remain active for an indeterminate duration and continue to shape and color events arising later" (e.g., we might report at N2, a content experienced at N1 or shaped by N1 content) [START_REF] Nielsen | Microdream neurophenomenology[END_REF]. These large amounts of 'pure' data could then be used to not only advance our knowledge of the neural signature of dreams, but also to peer directly into the dreaming world and examine its functions. Indeed, using a dream incubator like the one developed by [START_REF] Horowitz | Dormio: A Targeted Dream Incubation Device[END_REF], we may exert control over the hypnagogic content, allowing us to assess the impact of a given dream on subsequent performance in a cognitive task, for example.

Of note, one could argue that N1 dream fragments are, however, more di cult to disentangle from mind-wandering thoughts (for example), given their close proximity to wakefulness. First, I would like to highlight that all sleep reports are classi ed as dreams even though some are clearly wake-like thoughts. Sleep researchers (including myself) usually do not question their oneiric nature because they are collected during sleep, but they might also be 'tainted' by wakefulness (e.g., it might have been preceded

6. Perspectives and Applications 274 by an arousal, a higher degree of hybridity between sleep and wakefulness, or it could have been a wake thought that occurred after awakening if there is a delay between awakening and report). Now, returning to the initial problem, we could simply apply a more conservative de nition of dreams for N1 reports to avoid any wake contamination, as we did in Studies 2 and 3. However, this may result in the omission of some wakelike mental contents that would have occurred during N1. Therefore, based on our discussion on spontaneous thoughts, another possibility would be to examine the degree of sleep pervasiveness prior to the report (be it gathered during the N1 or N2 stage)

to determine how to label it (dream or wake thought).

Overall, the sleep-onset period not only provides us with an ideal research model for advancing our understanding of a wide range of critical topics (e.g., dream and consciousness correlates, dream functions...), but it may also serve as a vehicle for increasing our self-awareness and abilities.

Increase our self-awareness and abilities

"

Men share a world during the waking state, but everybody has their own universe when sleeping.

Heraclitus of Ephesus "

This nal section mirrors my preamble in that it is about gaining access to a eeting portion of our lives, as well as nding what sleep can o er us in terms of selfimprovement that wakefulness cannot.

Given that dreams are entirely ours (shaped by our memories, concerns, aspirations...), accessing the hypnagogic state could indeed serve as a 'portal to introspection'. I am thinking, for example, of those individuals who complain about never dreaming (which, as we reviewed here, is unlikely). Our bottle could assist them in e ortlessly capturing their hypnagogia, allowing them to realize that they do dream. It could also be useful for people who already know that they dream to increase their self-awareness (e.g., what is important to them, etc...), but also to develop one of the most important skills (if not the most important) in today's world: creativity.

A 'genius gap' Indeed, we showed that the sleep-onset period, which everyone goes through at least once a day, is a creative sweet spot. If these results are con rmed in the future and extended to other types of creative tasks, this twilight zone might provide a unique opportunity for anyone desiring to easily increase their creativity (e.g., at home or at work

"

This report nicely illustrates how the hypnagogic state enables the "mind to freely wander through novel, weak, and strange semantic associations, but maintains a capacity for introspection, idea selection, and retention" (Horowitz, 2019). In a similar vein, we could directly evaluate people's capacity to solve a problem while in that zone by presenting them with a riddle, inducing a hybrid state in which they retain some degree of consciousness, and asking them to try solving the riddle while in this state. It is worth noting that in such instances, it may be important to keep sleepers just at the doorway to sleep (as in Horowitz ( 2019)) to prevent them from losing consciousness completely. As [START_REF] Mavromatis | Hypnagogia : the nature and function of the hypnagogic state[END_REF] again said:

"

It is only by oscillating in and out of hypnagogia, thus allowing the waking mind some room for judgement, or by learning to remain passive-receptive in hypnagogia that one can appreciate the metaphoric aspect of the imagery of the state.

"

One strategy for training sleepers to stay on the brink of sleep would be to use alpha/theta EEG-neurofeedback methods [START_REF] Gruzelier | A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration[END_REF]. Here, participants' alpha and theta brainwaves are recorded and converted into sounds delivered to participants (Figure 6.3; one sound, like babbling brook is sent when alpha>theta, whereas another sound is played, like crashing waves, when theta>alpha level). Participants' goal state is to have a higher theta than alpha, so to hear the crashing waves sound. Of note, it is also possible to set amplitude thresholds and send a low/high pitch sound whenever they are exceeded. Like that, subjects can practice entering this hybrid state and could then use it, for example, to solve riddles (Figure 6.3). Besides, some studies report an increase in creative performance following alpha-theta training [START_REF] Gruzelier | A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration[END_REF][START_REF] Gruzelier | EEG-neurofeedback for optimising performance. II: Creativity, the performing arts and ecological validity[END_REF].

Alternatively, our bottle may be e ective for maintaining subjects at the doorway to sleep, as our participants reported that it prevented them from de nitively drifting to sleep on several occasions, since the bottle slipped through their ngers as they dozed o , awakening them and preventing them from completely losing consciousness. This 'hydrid' state naturally echoes the one found in lucid dreaming (i.e., being conscious of dreaming while dreaming; Voss et al. ( 2009)), which is interestingly more prevalent during the transition from wakefulness to sleep (e.g., when sleep is fragmented; Gott et al.

(2020)). Besides, some methods for inducing lucid dreams rely on awakenings (i.e., the wake-back-to-bed method; Erlacher and Stumbrys (2020) and [START_REF] Schredl | Inducing lucid dreams: The wake-up-back-to-bed technique in the home setting[END_REF]). Consistently, we found that lucid dreamers with narcolepsy had higher creativity scores on questionnaires than non-lucid dreamers [START_REF] Lacaux | Increased creative thinking in narcolepsy[END_REF]. Later, [START_REF] Scarpelli | Dream Activity in Narcoleptic Patients During the COVID-19 Lockdown in Italy[END_REF] also reported a relationship between lucid dream frequency and creativity in narcolepsy.

Unlike lucid dreaming, however, the hypnagogic state is normally accessible to everyone and does not necessitate disrupting sleep quality and structure (Vallat and Ruby, 2019). On the contrary, we showed in Study 2 that, in order to access the creative sweet spot (i.e., medium level of alpha and low level of delta), subjects need to fall asleep but without too much sleep pressure (otherwise, they rapidly enter deeper sleep).

Therefore, the idea here would be to sleep normally during the night and then take 'recreational naps' (instead of 'power naps' that are intended to quickly 'revitalize' the sleeper) with the bottle. Obviously, later, the bottle could (and should) be complemented by more advanced sleep engineering that can precisely target that zone (e.g., the Dormio device or BCI approaches such as alpha-theta training neuro-feedback).

And who knows, perhaps one day we will be able to create directly from that state, with algorithms decoding our mental content in real-time and transmitting it to a robotic arm that will paint our hypnagogic world as we experience it [START_REF] Appel | Communicating with a sleeping person[END_REF].

Research agenda

• Compare the impact of the N1 stage and REM sleep on creativity

• Determine whether the increased creativity observed in narcolepsy is traitor state-dependent, either through longitudinal studies or by examining whether the structure of their semantic memory network di ers from that of control subjects (and whether it depends on sleep intrusions)

• Test the fate of items lost during the N1 stage (Study 3) with serial naps (e.g., N1 nap -test -N1+N2 nap -retest)

• Examine the relationship between insight and memory during the N1 stage

• Investigate whether naps containing only N1 have a lasting impact (e.g., at least several hours) on subsequent performance or whether observed changes are only temporary

• Assess whether the N1 stage could boost performance on other creative tasks than the NRT (e.g., RAT, AUT, riddles)

• Test the hypothesis that sleep helps creative problem-solving via the restructuring of memories

• Con rm the di erent e ects of N1 and N2 that we observed on memory and creativity

• Investigate the hypothesis that the last sleep stage upon awakening has an impact on subsequent cognitive abilities (especially for creative problemsolving)

• Develop precise methods for targeting the creative sweet spot (e.g. BCI)

• Assess the link between hypnagogia and memory reprocessing/insight A Memodream project

A.1 Introduction

Every day, we perform motor sequences that constitute the foundation of many of our everyday activities, such as typing, writing, playing an instrument, or simply speaking. We ultimately learn and automate them via repetition, but not exclusively.

Indeed, motor performance typically increases rapidly at the start of training, then gradually over time and successive trials, before reaching an asymptotic level at the conclusion of a day's training [START_REF] Gheysen | Taking the brakes o the learning curve[END_REF][START_REF] Karni | Functional MRI evidence for adult motor cortex plasticity during motor skill learning[END_REF]. What then enables one to surpass this threshold and continue progressing from a complete novice to a genuine piano master, if not practice? The answer can be found in what happens between practice sessions, as performance continues to improve even after a delay without practice [START_REF] Cohen | Motor sequence consolidation: constrained by critical time windows or competing components[END_REF], implying that motor memory is also consolidated in a delayed manner. We now know that sleep plays a crucial role in this delayed consolidation of newly-acquired motor memories. Indeed, an abundant literature shows that motor performance is at least stabilized [START_REF] Nettersheim | The role of sleep in motor sequence consolidation: stabilization rather than enhancement[END_REF], and frequently improved after a period of sleep, but not -or to a lesser extent -after an equivalent period of wakefulness [START_REF] Debas | Brain plasticity related to the consolidation of motor sequence learning and motor adaptation[END_REF][START_REF] Doyon | Contribution of night and day sleep vs. simple passage of time to the consolidation of motor sequence and visuomotor adaptation learning[END_REF][START_REF] Fischer | Sleep forms memory for nger skills[END_REF][START_REF] King | Sleeping on the motor engram: The multifaceted nature of sleep-related motor memory consolidation[END_REF]Walker et al., 2002a). Improvement can even be observed on a test conducted several nights after learning [START_REF] Walker | Sleep and the Time Course of Motor Skill Learning[END_REF] Given the extended literature on replays as a core mechanism in memory consolidation models (Rasch and Born, 2007;Rasch and Born, 2013), memory trace reactivation represents a promising candidate for motor memory consolidation. Consistent with this notion, some studies have demonstrated that arti cially reactivating a motor sequence during sleep with 'targeted memory reactivation' improved later motor performance on that sequence [START_REF] Antony | Cued Memory Reactivation During Sleep In uences Skill Learning[END_REF][START_REF] Cousins | Cued Reactivation of Motor Learning during Sleep Leads to Overnight Changes in Functional Brain Activity and Connectivity[END_REF][START_REF] Schönauer | Strengthening procedural memories by reactivation in sleep[END_REF]. While these ndings support the idea that reactivation of a motor memory trace during sleep is key for subsequent performance, such a hypothesis remains di cult to verify. Indeed, the sleeper's cognitive activity is virtually inaccessible, leaving researchers with just the brain activity to determine if a memory trace is reprocessed during sleep. There are now emerging classi cation tools for detecting reactivation patterns based on EEG activity, and a few studies have provided evidence for a role of reactivation during NREM sleep in motor memory consolidation [START_REF] Belal | Identi cation of memory reactivation during sleep by EEG classi cation[END_REF][START_REF] Eichenlaub | Reactivation of Motor-Related Gamma Activity in Human NREM Sleep[END_REF]. Still, researchers have no way of knowing whether or not (and when) the memory traces of interest will be reprocessed during the resting period, which requires considerable patience and the deletion of a signi cant portion of data prior to seeing an event of interest.

Another approach is to assess sleep-dependent reorganization of brain memory networks. Indeed, reactivation is thought to result in a progressive remodeling of memory networks in order to stabilize the new, labile memory traces [START_REF] Frankland | The organization of recent and remote memories[END_REF]). Yet, until recently, we again had only indirect evidence for this notion in human studies, in the form of changes in brain activity before and after sleep (and their impact on motor performance) [START_REF] Debas | Brain plasticity related to the consolidation of motor sequence learning and motor adaptation[END_REF][START_REF] Fischer | Motor memory consolidation in sleep shapes more e ective neuronal representations[END_REF]Walker et al., 2002a). In 2017, a seminal work managed directly testing whether learning a new motor skill results in subsequent network reactivation and reorganization during sleep [START_REF] Vahdat | Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI (L. Davachi[END_REF]. The authors observed that the brain network engaged during learning transiently reactivated at the beginning of the resting period, before declining in functional connectivity (during both wakefulness and NREM sleep). Then, if followed by NREM sleep (but not during a wake period), the cortically-dominant 'learning' pattern was progressively replaced by another, subcortically-dominant network whose connectivity increased with the NREM sleep duration. Notably, this pattern, dubbed the 'consolidated' network, was prominent the next morning during recall. Additionally, the activity of one of its regions (the putamen) during sleep predicted performance's improvement. Overall, [START_REF] Vahdat | Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI (L. Davachi[END_REF] demonstrated that NREM sleep is necessary for the reorganization of newly acquired motor traces, which underlies motor memory consolidation.

Our project seeks to replicate and extend the ndings of this one-of-a-kind study.

First, [START_REF] Vahdat | Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI (L. Davachi[END_REF] focused exclusively on the impact of NREM sleep on memory consolidation, leaving out the second main sleep stage, REM sleep. Given that some theories emphasize the complementary, sequential function of these two stages (Giuditta, 2014), we may be missing a signi cant part of the sleep-dependent consolidation process by not including REM sleep. Besides, theoretical frameworks suggest that REM sleep is particularly involved in memory reorganization [START_REF] Landmann | The reorganisation of memory during sleep[END_REF][START_REF] Lewis | How Memory Replay in Sleep Boosts Creative Problem-Solving[END_REF]. Second, we sought to clarify the role of reactivation in this process. Indeed, it is debatable whether what they observed (i.e., activity of a subset of brain regions that were active during learning) quali es as a reactivation. For example, it could be a residue of earlier activity given that it quickly returned to baseline in both wakefulness and sleep conditions. Controlling which material is reactivated may thus be useful in gaining a better understanding of its role in motor memory reorganization and consolidation.

To do so, we resorted to a rare population, patients with narcolepsy. First, patients with narcolepsy provide us with the opportunity to investigate the role of REM sleep in motor memory consolidation. In healthy subjects, REM sleep only appears after around 90 minutes of sleep, making it almost impossible to collect in an fMRI scanner given that fMRI run duration is limited and sleeping within an fMRI scanner is challenging for the average person (it is noisy and uncomfortable). In contrast, because patients with narcolepsy experience excessive daytime sleepiness, they can easily and rapidly fall asleep, even in a noisy fMRI scanner, and they also frequently rapidly transition into REM sleep, a phenomenon known as 'sleep onset in REM sleep periods' (SOREMPs).

Furthermore, the vast majority of them (80%) are lucid dreamers (Dodet et al., 2015;Rak et al., 2015), which means that they are conscious of dreaming while dreaming and can signal their lucid episodes to the experimenters via an ocular code captured by EOG electrodes (La [START_REF] Berge | Lucid dreaming veri ed by volitional communication during REM sleep[END_REF]. Some of them can even control part of their dream scenario. Therefore, lucid dreamers with narcolepsy can 'replay' a motor learning in their dreams, with the replay being timestamped on the recording (thanks to the ocular codes). With this model, we could potentially compare brain activity associated with a REM sleep (lucid dream) replay of the motor task to that elicited by the re-execution of the motor sequence during wakefulness or in imagination. Altogether, lucid dreamers with narcolepsy represent an excellent research model to examine how sleep dynamically consolidates motor learning in both NREM and REM sleep, with the ability to control and investigate the brain correlates of task-related reactivation.

A.2 Primary objectives

• Follow the destiny of a new motor trace over time, from learning, consolidation, up to recall, as in [START_REF] Vahdat | Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI (L. Davachi[END_REF]. Precisely, we want to assess changes in the functional connectivity of 'learning' and 'consolidated' patterns during o ine periods of wakefulness, NREM sleep and REM sleep, as well as during the re-execution of the motor sequence during REM sleep (lucid dreaming).

• Examine the e ect of those consolidation periods (and their motor trace remodeling) on behavioral performance.

Auxiliary goals

This large-scale project should also allow us to address ancillary, yet important questions:

• Determine which brain regions are involved when a motor sequence is executed, imagined or replayed in a dream (and how similar they are). Here, we expect to replicate, with a larger sample size, a research conducted by [START_REF] Dresler | Dreamed movement elicits activation in the sensorimotor cortex[END_REF] that showed a relative concordance between the brain regions engaged during the execution of a simple motor action in wakefulness and during its execution in lucid dreaming.

• Expand our understanding of the neuroscience of lucid dreaming. Indeed, inconsistent ndings about the brain correlates of lucid dreaming are reported in the literature; a study with a larger sample size (one subject in Dresler et al. ( 2012))

could help in clarifying this research eld. We will speci cally examine if the prefrontal cortex is more activated in lucid REM sleep and whether it exhibits increased gamma activity (Dodet et al., 2015;[START_REF] Dresler | Neural Correlates of Dream Lucidity Obtained from Contrasting Lucid versus Non-Lucid REM Sleep: A Combined EEG/fMRI Case Study[END_REF][START_REF] Voss | Induction of self awareness in dreams through frontal low current stimulation of gamma activity[END_REF][START_REF] Voss | Lucid Dreaming: A State of Consciousness with Features of Both Waking and Non-Lucid Dreaming[END_REF].

A.3 Methods

A.3.1 Participants

We recruited 20 adult narcoleptic lucid dreamers who, by de nition, fall asleep easily, rapidly enter REM sleep, but also regularly experience lucid dreams (at least once a week), and are capable of controlling their dream scenario and signaling it to the experimenters via an ocular code.

A.3.2 Experimental design

Task Participants completed a nger-tapping task, which consisted of tapping on a keyboard as quickly as possible a 5-digit sequence (4-1-3-2-4, where 1 and 4 corresponded to the index and little ngers, respectively) using their non-dominant hand. During each phase detailed below, accuracy and inter-key duration (speed) was measured, patients' brain activity was recorded using combined EEG-fMRI techniques, as well as EOG, EMG (electrodes on the chin and on the exor and extensor of the forearm), and ECG.

Familiarization phase To ensure that patients understood the task and the sequence, they rst had to complete the motor sequence three times in a row without error.

Performance Start Prior to training, participants' initial, baseline performance was examined to compare it to their nal performance. Participants had to complete eight 30second blocks of a dual-task, 4 of which were on the aforementioned 'complex' sequence and the remaining 4 blocks were on a control, 'simple' motor sequence (4-3-2-1). The dual task required participants to rapidly and precisely tap the matching motor sequence while simultaneously undertaking an auditory discrimination task in which they had to say out-loud whether the tones they heard were high or low pitch.

Training phase Next, they entered the training phase, which consisted in 28 consecutive blocks of practice, 14 on the complex, learned sequence, and 14 on the simple sequence. This latter simple sequence was used to di erentiate sequence-speci c e ects from unrelated practice-induced motor improvement. Again, participants were instructed to type the sequence as quickly and precisely as possible. One block automatically ended after the patient had executed 60 nger motions, which corresponds to about 12 accurate sequences. This strategy ensured that the number of nger movements remained constant regardless of the participant's speed. A 15-second rest interval separated each block, and each touch press and timing were recorded.

Pre and post break phases Throughout the day, participants' performance was evaluated using two tests administered before and after four 30-minute breaks. A 'speed' test that measured the speed at which the sequence was executed, and a 'dual-task' test that assessed the degree of automaticity of the movements by examining the impact of an auditory discrimination task on the execution of the motor sequence. The dual-task should allow us to capture subtle changes that the speed test could miss if participants' performance was at the ceiling. At the end of each of these 4 intermediary phases, participants had a 45-min waking rest outside the fMRI scanner.

Break phases

The breaks included: one wakefulness block during which participants were asked to execute the complex motor sequence awake and in imagination, and three napping blocks during which participants could sleep. In the instance of lucid dreaming, participants were told to change the content of their dreams in order to perform the motor task as many times as possible in their dreams. For each of these three conditions (execution, imagination, and lucid dreaming), participants were instructed to eye-code (left-right-left-right) before and after the motor sequence to provide a temporal marker of the 'replay' on the fMRI and EEG signals, followed by a 10-s pause and repetition of this pattern (ocular code -motor sequence -ocular code -pause). The order of the napping/waking breaks was counterbalanced across subjects.

Performance End At the end of the day, participants' nal performance was once again tested on the dual-task and on the speed test (on both the simple and complex sequences, 4 blocks each).

The

A.4 State of the project

We already collected all of the data (N = 20 participants, 55 naps in total). Participants reported that they slept in 91% naps and that they were lucid dreaming in 56% of those naps (28/50). Preliminary polysomnographic data analysis con rms numerous sleep episodes. However, after many pre-processing attempts, part of the EEG data remains However, some of the data is still noisy. Surprisingly, the motor command seems to be sent to the arm muscles (but may be blocked before its execution), but it is reassuring to note that the chin muscle tone stayed at.

Last, we began performing preliminary analyses on the behavioral data. M endeleïev aurait eu l'idée de son tableau périodique des éléments dans un rêve. Une révélation de cette organisation « logique » des atomes lui serait apparue. La légende raconte également que Newton était en train de dormir au pied d'un pommier lorsqu'un fruit de cet arbre lui tomba sur la tête. Avant ce réveil un peu brutal, peut-être était-il alors en plein sommeil paradoxal (voir encadré) et songeait-il alors à la gravité !

UNE PORTE SUR UN AUTRE MONDE

Le rêve est en effet une porte ouverte sur un autre monde qui nous est propre. Leur interprétation est un des substrats des psychanalystes qui trouvent, à travers eux, un accès à notre inconscient. Peuplés de personnes vivantes, décédées ou imaginaires, de formes, d'animaux ou de tout autres choses qui nous apparaissent sur le moment comme étant bien réelles, le rêve est aussi un moment important de la créativité de notre cerveau. Sans atteindre le degré démontré chez les patients étudiés, elles peuvent s'en approcher en augmentant la durée de leur sommeil. La durée du sommeil paradoxal (SP) y est directement liée car il arrive à la fin de la nuit de sommeil. Il est facile de faire l'expérience que les matins de grasse matinée, nous avons plus tendance à nous souvenir de nos rêves que les autres nuits. Le premier conseil est donc de dormir plus. Les patients narcoleptiques se souviennent aussi plus de leurs rêves, ce qui pourrait constituer une banque de données dans laquelle puiser leurs inspirations créatives. Ainsi, noter ses rêves afin de mieux s'en souvenir pourrait également aider. Mais nous pensons que la créativité est surtout liée à cet accès privilégié, direct, au SP. Ce serait plus le fait d'accéder au SP puis de se réveiller qui aiderait à être créatif. Et, là, seules les personnes narcoleptiques bénéficient de cette clé. Elles ont un « superpouvoir » que les autres n'ont pas. Une seule consigne sera donnée à la participante confortablement installée dans son fauteuil : tenir dans sa main une petite gourde en plastique. Si Léticia s'assoupit, l'objet tombera par terre et la réveillera immédiatement. « Notre hypothèse, c'est que l'insight est peut-être favorisé par la première phase du sommeil, celle de l'endormissement, que l'on appelle le stade N1. C'est une phase très intéressante, qui ressemble par certains points au sommeil paradoxal, mais finalement assez méconnue. » Ce stade N1 intéresse notamment les chercheurs pour les phénomènes particuliers qui s'y produisent, appelés hypnagogies. Il s'agit d'expériences sensorielles -principalement des images, parfois des sons -qui apparaissent de manière spontanée lors de l'endormissement, presque comme des hallucinations. Sleep leads to a disconnection from the external world. Even when sleepers regain consciousness during rapid eye movement (REM) sleep, little, if any, external information is incorporated into dream content [1][2][3]. While gating mechanisms might be at play to avoid interference on dreaming activity [4], a total disconnection from an ever-changing environment may prevent the sleeper from promptly responding to informative events (e.g., threat signals). In fact, a whole range of neural responses to external events turns out to be preserved during REM sleep [5][6][7][8][9]. Thus, it remains unclear whether external inputs are either processed or, conversely, gated during REM sleep. One way to resolve this issue is to consider the specific impact of eye movements (EMs) characterizing REM sleep. EMs are a reliable predictor of reporting a dream upon awakening [10,11], and their absence is associated with a lower arousal threshold to external stimuli [12]. We thus hypothesized that the presence of EMs would selectively prevent the processing of informative stimuli, whereas periods of REM sleep devoid of EMs would be associated with the monitoring of external signals. By reconstructing speech in a multi-talker environment from electrophysiological responses, we show that informative speech is amplified over meaningless speech during REM sleep. Yet, at the precise timing of EMs, informative speech is, on the contrary, selectively suppressed. These results demonstrate the flexible amplification and suppression of sensory information during REM sleep and reveal the impact of EMs on the selective gating of informative stimuli during sleep.

NARCOLEPSIE EN UN MOT

Sous la vidéo de Léticia, les oscillations se font plus lentes

RESULTS

We used a neural decoding approach allowing us to track the selective processing of speech in a cocktail-party paradigm. Participants (N = 18) heard two competing auditory streams while their electroencephalogram (EEG) was recorded. One stream, played in one ear, was meaningful (e.g., informative speech), while the other stream, played in the other ear, had normal syntactic and phonological properties but was meaningless (i.e., Jabberwocky speech as in Lewis Carroll's Jabberwocky poem). Participants were asked to focus only on informative speech and ignore Jabberwocky speech (Figure 1). They first performed this task in wakefulness and were then allowed to sleep during a morning nap, at times of high rapid eye movement (REM) sleep pressure (STAR Methods). We tracked speech processing by using a stimulus reconstruction approach, which allows the continuous reconstruction of a sound's envelope based on its neural responses [13,14]. We showed previously that selective orientation toward either informative or Jabberwocky speech could be decoded from scalp EEG using this technique [15]. When participants are instructed to focus only on the informative stream and then enter non-REM (NREM) sleep, EEG markers reveal that informative speech is overall still enhanced, but it is specifically suppressed during sleep slow waves, a hallmark of NREM sleep [15]. Here, we used the same approach to evaluate whether informative speech was still preferentially processed during REM sleep and, in particular, whether the presence of eye movements (EMs) plays a role in gating the processing of external information. We first found that both informative speech and Jabberwocky speech could be reconstructed across wakefulness, light NREM sleep, and REM sleep, revealing preserved auditory encoding across vigilance states (Figure 2A; p < 0.05 for all conditions corrected for multiple comparisons). We replicated, with this independent dataset, our previous results that informative speech was preferentially reconstructed over Jabberwocky speech in

ll

OPEN ACCESS Jabberwocky for wake, effect size, r = 0.86, p < 0.001; light NREM sleep, r = 0.51, p < 0.05; Figure 2A). We show additionally that these results extend to REM sleep (informative versus Jabberwocky, r = 0.65, p < 0.01; Figure 2A). These results are unlikely to be attributed to a bias in our decoding model as both informative and Jabberwocky speech were equally reconstructed when presented alone in both ears during wakefulness (informative versus Jabberwocky, non-significant, Bayes factor: 3.80; Figure S1B). We then computed a decoding score corresponding to the proportion of trials with a higher reconstruction score for informative speech compared to Jabberwocky speech. We found that decoding performance was significantly above the chance level of 50% for both wakefulness and REM sleep (wake, r = 0.83, p < 0.001; REM, r = 0.61, p < 0.01; Figure 2B). Yet, decoding performance was lower in REM sleep than during wakefulness (wake versus REM sleep, r = 0.81, p < 0.001; Figure 2B).

To investigate whether this difference resulted from the disruption of auditory encoding for all streams or more specifically from informative speech, we compared reconstruction scores for informative and Jabberwocky speech separately. We found that informative speech and Jabberwocky speech are differently affected across wakefulness and REM sleep (t(677.35) = À4.41, p < 0.001 for the interaction informative versus Jabberwocky 3 wake versus REM sleep). Post hoc analyses revealed that the reconstruction of informative speech was selectively modulated by the transition to REM sleep (wake versus REM sleep, r = 0.68, p < 0.01; Figure 2C), while Jabberwocky speech remained unaffected (wake versus REM sleep, non-significant, Bayes factor: 3.64; Figure 2C). Thus, decreased decoding performance during REM sleep appears linked to a selective reduction in processing informative speech, rather than a general decline in the encoding of auditory signals.

We thus tested whether EMs, a marker of oneiric activity that occurs during REM sleep, are associated with the selective gating of informative speech. We first removed EM-related activity from EEG using independant component analysis (ICA) to avoid muscular contamination of speech reconstruction (STAR Methods). Our analyses revealed an inverse correlation between the amount of EMs per trial and the reconstruction of informative speech (p < 0.001; Figure 3A). Crucially, this was not the case for Jabberwocky speech (p > 0.05; Figure 3B). This contrast between informative and Jabberwocky speech is not due to a floor effect in the reconstruction of Jabberwocky speech, which was still above chance, albeit at a lower level than informative speech. It suggests that EMs do not affect the processing of Jabberwocky speech and additionally reinforces the view that artifacts caused by EMs do not reduce the quality of stimulus reconstruction. It also suggests that the presence of EMs selectively modulates the processing of informative signals. To confirm this interpretation, we investigated the temporal relationship between the occurrence of EMs and the selective gating of informative speech, by performing a time-resolved analysis of reconstruction scores around EMs. Within a 10 s window around the onset of EMs, we found that informative speech was amplified before EMs ([À7.8, À2.8]s, d = 0.63, P cluster < 0.05), but this effect disappeared during and after EMs (Figure 3C). Contrastingly, no such modulation could be evidenced for Jabberwocky speech. As EMs can occur in bursts, the reconstruction around a given EM may be impacted by the presence of surrounding EMs. To test for this potential contamination, we investigated the time course of the reconstruction score at the beginning and end of burst EMs. Our analysis revealed that the amplification of informative speech was present before the beginning of bursts and then disappeared at their onsets ([À4.6, À0.2]s, d = 1.48, p < 0.001; Figure S2A). Conversely, we observed that the First, participants listened to 12 stories in both ears, 6 informative stories followed by 6 Jabberwocky stories (training phase). Jabberwocky stories have normal syntactic and phonological structures but are made of novel/meaningless words. Then, participants were instructed to focus on the informative story that was played in one ear while ignoring the Jabberwocky story that was played in the other ear (test phase). After 8 trials during which participants had to perform the task in wakefulness (wake test phase), participants were allowed to fall asleep while the trials from the wake test phase were continuously played (sleep test phase: ~90 min; see Figure S4 for two examples of hypnograms and Table S1 for sleep statistics). Novel trials with new informative (blue) and Jabberwocky (red) stories were played whenever participants were sleeping. To track speech processing using cerebral activity during the test phase, training trials were first used to map the EEG response to variations of the sound envelope. The obtained linear model was then used to reconstruct a sound envelope from the filtered EEG recorded during wake and sleep test trials. A correlation score was computed by comparing the predicted sound envelope with the envelope of each auditory stream. Trials with a higher reconstruction score for the informative speech compared to the Jabberwocky speech were scored as correct and the percentage of correct trials per condition defined the decoding performance.

Report amplification of informative speech was regained only after burst offsets ([5.0, 7.5]s, d = 0.98, p < 0.05; Figure S2B).

We then refined our analysis by inspecting isolated EMs (i.e., with no more than one additional EM nearby). We compared reconstruction scores obtained at the onset of isolated EMs with that computed on the entire REM sleep period. We confirmed that the presence of isolated EMs was indeed modulating the selective processing of informative speech (t(1180) = À3.52, p < 0.001 for the interaction informative versus Jabberwocky 3 isolated EM versus REM sleep; Figure 4A). Post hoc analyses revealed that informative speech was overall selectively amplified during REM sleep but selectively suppressed at the onset of isolated EMs (informative versus Jabberwocky in REM sleep, r = 0.52, p < 0.05; during isolated Ems, r = À0.67, p < 0.05; informative versus 0 during REM sleep, r = 0.86, p < 0.01; Jabberwocky versus 0 during REM sleep, r = 0.63, p < 0.05; Figure 4A). We then confirmed that such selective suppression of the informative stream was restricted to the period surrounding the onset of isolated EMs ([À2.6, 1.5]s, d = À0.73, P cluster < 0.05; Figure S2C). We further checked that removing eye-movement artifacts with ICA was not responsible for our effects and we obtained similar results with and without the ICA procedure (Figure S3). These analyses confirm that the presence of EMs is tightly associated with the selective suppression of informative speech.

We finally inspected more systematically the distinction between tonic (tREM) and phasic (pREM) REM sleep. Temporal windows of 20 s were scored as pREM sleep in the presence of bursts of EMs, and as tREM sleep otherwise. In agreement with the literature [16,17], we obtained ~20% of pREM sleep (Table S1). Congruent with the results above, we found that the selective processing of informative speech is distinct during pREM and tREM sleep (t(1198) = À2.07, p < 0.05 for the interaction tonic versus phasic REM 3 informative versus Jabberwocky speech; Figure 4B). Post hoc analyses revealed that the selective amplification of informative speech was present during tREM sleep (informative versus Jabberwocky, r = 0.55, p < 0.05) but absent during pREM sleep (not-significant, Bayes factor: 5.36). These analyses confirm that periods of sustained eye-movement activity prevent the preferential processing of informative speech.

DISCUSSION

Our study first confirms that auditory stimuli are still processed by the sleeping brain, in line with results showing a preservation of neural responses to external sounds in the auditory cortex during REM sleep [5,6]. They further add evidence that salient stimuli are selectively processed during REM sleep [7-9, 18, 19]. More crucially, presenting competing auditory streams allowed us to show that informative stimuli can be flexibly enhanced or suppressed depending on the presence of EMs. Such results are consistent with previous reports showing that responses to auditory stimulation and deviant tones are reduced in the presence of EMs during REM sleep [20,21], as well as the elevation of arousal thresholds during phasic compared to tonic REM sleep [12]. By showing nevertheless that this stimulus gating did not affect the encoding of meaningless speech during REM sleep, we provide first evidence of a selectivity of sensory suppression mechanisms during EMs.

While the mechanisms underlying sensory disconnection have been relatively well studied during NREM sleep, such investigations have been so far limited for REM sleep [4]. It was first proposed that a gating mechanism would prevent auditory information from being transmitted to higher-order associative regions during sleep, but, to date, such cortical gating has not been evidenced in REM sleep [6]. Other studies alternatively suggested that endogenous activations, potentially associated with the generation of dreams, could interfere with the processing of external events [4]. In the present study, we tracked the neural processing of two competing streams differing in terms of meaningfulness. The selective processing of meaningful stories during REM sleep suggests that associative regions are also involved in the selection of stimuli based on their informative content, ruling out the notion of a thalamic [22] or cortical [23] gating of external inputs during REM sleep. However, attention may also be impaired during REM sleep, as observed in other altered states of consciousness such as hypnosis [4]. Here, we found that selective amplification of informative speech was overall maintained during REM sleep, even if reduced compared to wakefulness. This suggests that attentional resources might, at least partially, be preserved across vigilance states. Because EMs are closely associated with internal activity, we propose that our results buttress the ''informational gating'' hypothesis, i.e., a competition between the processing of internal information versus external information during REM sleep [4,24].

The ''informational gating'' hypothesis suggests a pivotal role for selective processing to balance internal versus external demands depending on stimulus properties (e.g., informative stimuli) and spontaneous activity (e.g., dreaming activity). Representations of competing speeches are encoded separately in the auditory cortex [25], allowing in our study for the selective suppression of informative speech while leaving the Jabberwocky speech unaffected. Such selective suppression may reflect the presence of ongoing high-order internal activity, such as dreaming. Indeed, substantial literature investigating neural processes during eye movements indicates a link between EMs and dreams (see [26] for a recent review) [10,[26][27][28][29][30][31][32]. Yet, dreaming has also been reported outside EMs, during both REM and NREM sleep [33,34]. Assessing the impact of other markers of dreaming activity (e.g., EEG spectral changes [35]) on sensory processing should allow investigating whether our results generalize to other correlates of dreaming or whether they are specific to the occurrence of EMs.

Functional neuroimaging studies indicate a competition between internal and external processing during EMs. Indeed, higher-order cortices associated with spontaneous activity correlate negatively with the activity of sensory areas during REM sleep, and a thalamo-cortical network may favor internal activity at the expense of sensory processing [21,36,37]. By blocking the processing of salient information-known to be more susceptible to wake up the sleeper [7,38]-suppressive mechanisms of sensory stimuli may endorse the role of sleep protection, preventing informative signals from interfering with internal activity. Here, we hypothesized that meaningless signals would be considered as background noise and thus would not interfere with internal processes, such as memory consolidation or dreaming activity. Our results support this hypothesis, but it remains unsettled whether Jabberwocky stories would also be suppressed during EMs if they were presented alone rather than in competition with informative stories. However, even if such a suppression were observed, it would not Report necessarily go against a selective suppression of informative signals during EMs, as meaningless speech presented alone could be treated as informative inputs. It is also unknown whether the selective suppression of informative stories during EMs depends on task instructions, and whether our results would be changed if participants were asked to focus on Jabberwocky stories instead. Further studies are thus necessary to establish how and to what extent internal activity in REM sleep affects the sensory processing of external events.

Finally, our study provides further evidence of a close link between perceptual processing and sleep depth. Using the same approach, we previously found that the selective amplification of informative speech occurs during light NREM sleep but disappears in deep NREM sleep, with a selective suppression of informative speech during slow waves [15]. What we observed in this new study strikingly mirrors these results by showing that the selective amplification of informative speech occurs during tonic REM sleep but disappears during phasic REM sleep, with a selective suppression of informative speech during EMs. Despite the drastic physiological differences between NREM and REM sleep, these results provide a surprisingly coherent picture of the relationship between the selective processing of informative signals and sleep depth.

Overall, our study highlights the role of flexible stimulus selection in the balance between sensory processing and internal activity during REM sleep. The fact that informative stimuli are selectively suppressed, in particular around EMs, buttresses the existence of an informational gating mechanism during REM sleep. Our results provide further support for a distinction between phasic and tonic REM sleep based on the selective gating of informative stimuli and is coherent with reports of poor integration of external stimulation into dreaming activity. Extension of this paradigm to situations where internal activity can be reported (e.g., mind-wandering or using a serial awakening paradigm to probe dreaming activity [33]) would allow us to directly investigate whether the occurrence of internally generated conscious contents is specifically associated with the selective gating of sensory information.

STAR+METHODS

Detailed methods are provided in the online version of this paper and include the following:

d KEY RESOURCES

Contents 1 . 1

 11 The de nition of sleep . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 The physiology of sleep . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Sleep mentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 A blurry frontier between wakefulness and sleep . . . . . . . 18

Figure 1 . 1 .

 11 Figure 1.1. Half awake, half asleep. Examples of unihemispheric sleep in dolphins (A), ducks (B) and frigatebirds (C). Adapted from Mukhametov et al. (1977), Rattenborg et al. (1999), and Rattenborg et al. (2016).

Figure 1 . 2 .

 12 Figure 1.2. Hypnogram of a healthy individual. NREM (4 substages: S1-S4, now S3 and S4 are merged) and REM sleep stages alternate cyclically, with a predominance of deep NREM sleep (N3) earlier in the night, followed by a prevalence of REM sleep later in the night. Adapted from Feld and Diekelmann (2015).

Figure 1 . 3 .

 13 Figure 1.3. The sleep cycle. Electrophysiological signature of each sleep stage.

Figure 1 . 4 .

 14 Figure 1.4. Illustration of awakenings and arousals. Photographs taken with a 6-hour time-lapse to illustrate brief awakenings during the night, responsible for movements. The Sleep of the Beloved, by Paul Maria Schneggenburger.

Figure 1 . 5 .

 15 Figure 1.5. Mental reports over years.From Nielsen (2000).

Figure 1 . 6 .

 16 Figure 1.6. A blurry frontier between wakefulness and sleep. (A) Illustration of localized (in space and time) sleep-like slow-waves in awake subjects. (B) These slow-waves can be seen with scalp EEG or with intracranial recordings (associated with episodes of neuronal silencing). (C) Local sleep increases with time spent awake, and especially in brain regions engaged in a given task ('use-dependent'). Local sleep episodes can also impact both behavior and subjective experiences. From Andrillon et al. (2019).

Contents 2 . 1

 21 Tracing the moment of sleep onset . . . . . . . . . . . . . . . . 21 2.2 The N1 stage vs. REM sleep, a striking resemblance . . . . . .

Figure 2 .

 2 Figure 2.1 A); the rst two sub-stages correspond to our standard scoring of wakefulness, the following six sub-stages to N1, and stage H9 corresponds to the beginning of N2.

  when the epoch is composed of low-voltage theta waves (Figure 2.1 A).

Figure 2 . 1 .

 21 Figure 2.1. Hori classi cation. (A) EEG characteristics of the nine sub-stages, (B) Mental activity, behavioral response, and subjective perception of sleep along the 9 Hori sub-stages. Adapted from Hori et al. (1994).

Figure 2 . 2 .

 22 Figure 2.2. Simultaneous theoretical plot of all of the metrics tested to trace the sleep-onset period. All of the measures do not agree on a common moment of sleep onset. Abridged version, adapted from Ogilvie (2001).

Figure 2 . 3 .

 23 Figure 2.3. Tracing the sleep onset process (A) The wake probability curve shows sudden awakenings within the sleep-onset period that were not captured by standard scoring methods. (B) Persistence of behavioral responses in the absence of alpha rhythm. The hypnogram indicates N1 epochs where the wake probability curve depicts a high probability of wakefulness based on both the persistence of behavioral responses and a delayed increase in slower frequency bands (delta/theta). (A,B) The corresponding spectrograms, behavioral responses, wake probability curves, and hypnograms are displayed in order from top to bottom. Adapted from Prerau et al. (2014).

"

Figure 2 . 4 .

 24 Figure 2.4. An hypnagogia-catcher. This gure illustrates the technique that Thomas Edison and Salvador Dali used to capture this eeting moment that is sleep onset. According to the story, they napped with an object in their hand, which supposedly fell when they dozed o , awakening them just in time to capture inspirational ideas. ©Je Warren (left drawing), and right panel from Dali (1992).

2 .

 2 The doorway to sleep 29 subjects right on time to examine the speci c role of sleep onset on cognitive processes.

Figure 2 . 5 .

 25 Figure 2.5. A micro-dream, signaled via hand switch (B) after one second of alpha dropout (A). From Nielsen (2017).

Figure 2 . 6 .

 26 Figure 2.6. Narcolepsy, a sleep-wake state instability. (A) Hypocretin-producing neurons are found in the hypothalamus. This peptide then travels throughout the brain to wake-promoting areas. Adapted from Bassetti et al. (2019). (B) In narcolepsy, most of these neurons die o ; prepro-hypocretin transcripts and cells are detected in the hypothalamus of controls (left panel) but not in narcoleptic subjects (right panel). From Nishino et al. (2010).

Figure 2 . 7 .

 27 Figure 2.7. Excessive daytime sleepiness in narcolepsy. The lack of hypocretin causes wake-sleep instability, resulting in a fragmented sleep and an excessive daytime sleepiness, with multiple sleep intrusions throughout the day (often with direct transitions to REM sleep, SOREMs). The upper panel depicts a schematic narcoleptic hypnogram, while the lower panel illustrates a healthy hypnogram.

Figure 2 . 8 .

 28 Figure 2.8. Cataplexy. Left panel: Photographs of a cataplectic episode. From Overeem et al. (2001). Right panel: An electromyography (EMG) recording during a cataplectic episode (shaded area), which shows a loss of muscle tone on several electrodes. From Bassetti et al. (2019).

Figure 3 . 1 .

 31 Figure 3.1. The two-stage model of memory consolidation. Adapted from Diekelmann and Born (2010).

Figure 3 . 2 .

 32 Figure 3.2. Replay as consolidation. (A) Schematic representation of place cells ring during exploration and of a replay during sleep. Adapted from Lewis and Bendor (2019) and Wilson and McNaughton (1994). (B) Disrupting replay impairs learning. Adapted from Girardeau et al. (2009) and Ólafsdóttir et al. (2018). (C) Odor-cue is associated with improved memory along with the activation of hippocampal and necortical regions. Adapted from Diekelmann and Born (2010) and Rasch et al. (2007). (D) Odor-evoked brain reactivation during sleep promotes memory consolidation. Adapted from Rasch (2018) and Shanahan et al. (2018).

Figure 3 . 3 .

 33 Figure 3.3. The hippocampo-cortical dialogue. Each subpanel's description can be found directly written on each sub gure. Adapted from Ólafsdóttir et al. (2018).

Contents 4 . 1

 41 The de nition of creativity . . . . . . . . . . . . . . . . . . . . . 49 4.2 Approaches to measuring creativity . . . . . . . . . . . . . . . . 53 4.3 How does creativity emerge? . . . . . . . . . . . . . . . . . . . . 58 4.4 Neuroscience of creativity . . . . . . . . . . . . . . . . . . . . . . 67 4.5 The ideal cocktail for creative sparks . . . . . . . . . . . . . . . 69

  The two de ning elements of creativity What are the common aspects between the preceding examples? Two factors should easily stand out (Figure 4.1).

Figure 4 . 1 .

 41 Figure 4.1. The de ning factors of creativity.Something is considered creative if it is both original and appropriate (i.e., adapted to a given environment). This illustration was inspired by[START_REF] Abraham | The Neuroscience of Creativity[END_REF].

Figure 4 . 2 .

 42 Figure 4.2. The Number Reduction Task. Adapted from Wagner et al. (2004).

Figure 4

 4 Figure 4.3. The 9 dots problem. Upper panel: classical unsuccessful attempt, and lower panel: example of one solution.

Figure 4 . 4 .

 44 Figure 4.4. The memory network. (A) The painting A tree at the end of stairs, by Leon Spilliaert, to illustrate a memory network 4 . (B) Example of my memory network for the word Winter.

4. 3 . 3

 33 Exploring the memory network for creative sparks

Figure 4 . 5 .

 45 Figure 4.5. Network restructuring and insight. Reproduction from Durso et al. (1994).

Figure 4

 4 Figure 4.6.Progressive insightrelated network reorganization. Adapted from[START_REF] Durso | Graph-Theoretic Con rmation of Restructuring during Insight[END_REF].

Figure 4 . 7 .

 47 Figure 4.7. Network structure of high vs. low creative individuals. Adapted from Kenett et al. (2014).

Figure 4 . 8 .

 48 Figure 4.8. Creativity and top-down control. Adapted from Abraham (2014).

Figure 4 . 9 .

 49 Figure 4.9. Three major brain networks. Brain regions that constitute the Default Mode Network, the Central Executive Network, and the Salience Network. From Dai et al. (2019).

Contents 5 . 1

 51 How does sleep boost creativity? . . . . . . . . . . . . . . . . . . 72 5.2 Sleep onset, a creative zone? . . . . . . . . . . . . . . . . . . . . 88

Figure 5 . 1 .

 51 Figure 5.1. Sleep inspires insight. Results from Wagner et al. (2004).

  Wagner et al. (

Figure 5 . 2 .

 52 Figure 5.2. Comparison between NRT studies. Yordanova et al. (2008) (in dark gray) failed to replicate Wagner et al. (2004)'s positive impact of sleep on insight (light gray). Adapted from Verleger et al. (2013).

Figure 5 . 3 .

 53 Figure 5.3. Memory overlaps and schemata.The overlapping replay of related memories selectively strengthens shared elements, progressively building schemata (i.e., gist). Global downscaling, on the other hand, "erodes the more idiosyncratic aspects of each memory". From[START_REF] Lewis | Overlapping memory replay during sleep builds cognitive schemata[END_REF].

Figure 5 . 4 .

 54 Figure 5.4. Sleep replays and generalization. Inter-mnesic trace reactivations, a mechanism for rule extraction and generalization. From Cherdieu et al. (2017).

Figure 5 . 5 .

 55 Figure 5.5. Semantic priming task. (A) Examples of prime-target pairs and reaction times. (B) Results fromStickgold et al. (1999). Of note, the higher the value, the greater the priming e ect, as priming is expressed as the di erence between RT unrelated and RT related (e.g., 600-560 = 40ms).

  attempted to reconcile by proposing a two-step model, an extension of the aforementioned iOtA framework, named the BiOtA (Broader iOtA). According to the BiOtA, NREM sleep (especially SWS) would rst reprocess newlyencoded items and form a new representation of them containing only the gist (Figure 5.6 A and C) before integrating it into our memory network. Then comes REM sleep, which would look for common structure between this new representation and the older ones and reorganize the network to link them (Figure 5.6 B and D).

Figure 5 . 6 .

 56 Figure 5.6. The BiOta model. (A) During NREM sleep, the consolidation of episodic memories from astronomy lessons learned through teachers, books, and television results in the formation of a semantic representation of the solar system. (B) This representation is distinct from the pre-existing ones in the cortex (e.g., atom, concentric circles). Replays of these schema during REM sleep would reveal the shared structure, and the semantic knowledge space would be restructured to allow for their connection altogether. (C,D) Identical to (A,B), but more detailed. From Lewis et al. (2018).

Figure 5 . 7 .

 57 Figure 5.7. Hypnagogia binds together distant memory sources. Temporal characteristics of memory sources pertaining to hypnagogic reports. From Stenstrom et al. (2012).

  Stenstrom et al. (2012) demonstrated that hypnagogic dreams combine semanticallyrelated information from memories separated by large temporal windows ranging from 10 minutes to 15 years (Figure 5.7). Thus, N1 dreams, like REM sleep dreams, appear

"

  Let us learn to dream . . . but let us beware of publishing our dreams before they have been put to the proof by the waking understanding. " Creativity would thus be a dynamic process characterized by transitions between idea generation and evaluation. As Girn et al. (2020) highlighted in their model (the Dynamic Framework of Thought), an intermediate level of deliberate constraints (i.e., cognitive control), as found at sleep onset, would be optimal for doing both (Figure 5.8).

Figure 5 . 8 .

 58 Figure 5.8. The Dynamic Framework of Thought. The optimum degree of cognitive control (i.e., deliberate constraints) for creativity would be somewhere in between to allow for both idea generation and evaluation. Sleep onset might provide this ideal environment. From Girn et al. (2020).

Figure 5 . 9 .

 59 Figure 5.9. Sleeping vs. Resting on it. The percentage of subjects who gain insight is comparable after a full night of sleep versus only 10 minutes of quiet rest (quiescence). Adapted from Craig et al. (2018) and Wagner et al. (2004).

  de nitively convinced us to investigate what occurs during the sleep-onset period. Craig et al. (2018) used the same paradigm as Wagner et al. (

Figure 5 .

 5 Figure 5.10. Dream Incubator. Left panel: Picture of the Dormio device. Right panel: Detailed protocol. In brief, subjects were instructed to think about a tree while they drifted o to sleep. Then, when the device detected sleep onset, it played an awakening sound and probed the subjects' mental content. From Horowitz et al. (2020).

Figure 5 . 11 .

 511 Figure 5.11. Sleep onset and creativity. Compared to wake, dream incubation of the word tree during N1 (as well as during N1 alone, without incubation) is associated with enhanced tree-related creativity. From Horowitz (2019).

Figure 5 .

 5 Figure 5.12. Does Dormio accurately detect the N1 stage? From top to bottom: PSG scoring of a 65-min session with Dormio. From Horowitz (2019).

Contents 6 . 1

 61 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 6.2 Main objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Study 1

 1 sought to examine the putative correlation between REM sleep and the long-term development of creativity. In contrast, Studies 2 and 3 used a more classical pre/post approach to investigate the direct e ect of the N1 stage on creative problem-solving. 6.2.2 Study 2: Assess the impact of the N1 stage on creative problemsolving Thomas Edison invented a technique (holding steel spheres in his hands) to capture sleep onset-related hypnagogia from which he drew inspiration for his work. Study 2 sought to conduct a scienti c examination of Thomas Edison's intuition.

  1990| BRAIN 2019: 142;[START_REF] Viens | A re ned switch-activated time monitor for the measurement of sleep-onset latency[END_REF] -1999 C. Lacaux et al. C. Lacaux et al.

Figure 2

 2 Figure2Self-reported assessments of creativity in healthy controls and in subjects with narcolepsy. (A) Boxplots of the scores of controls (pink) and subjects with narcolepsy (blue) on the TCP (total score, followed by the three associated creative profiles). On each box, the central thick line indicates the median, the cross represents the mean, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers and the outliers are plotted individually using circles. (B) Distribution of the scores obtained on the CAQ for both groups. Note the skewed distribution of the data with a higher proportion of outliers (i.e. individuals with high levels of creative achievement) in the narcolepsy group compared to the control group. n = 126 for control and n = 185 for narcolepsy. **P 5 0.01 and *P 5 0.05 for between group differences (Z-test).

Figure 3

 3 Figure 3 EPoC test in healthy controls and in subjects with narcolepsy. (A) Correlation circle of the principal component analysis on the eight variables of the EPoC test. Histograms showing the distribution of data for the total score of the EPoC test (B), score of convergent (C) and divergent (D) creative thinking in control and narcolepsy groups. Note that each dimension comprises four subtests. For better visibility, black curves graphically represent each data distribution. The vertical black bar represents the median, and the horizontal black bar on top of each graph represents the mean AE SD. To highlight the difference between groups, the distribution' curves of control and narcolepsy groups were additionally superposed at the lower part of each graph (B-D). n = 30 for both control and narcolepsy groups. **P 5 0.01; the exact P-value is displayed if P 4 0.05, for between-group differences obtained with linear mixed model analyses. (E) Spearman correlation matrix between the creativity and achievement questionnaires and the divergent and convergent scores. ***P 5 0.001; **P 5 0.01; *P 5 0.05 for correlations between variables.

Figure S2 -

 S2 Figure S2 -Self-reported assessments of creativity in controls and subjects with narcolepsy in two different countries. (A) French group: Mean scores ± SD of controls and subjects with narcolepsy on the Test of Creative Profile (total and its three associated creative profiles, left panel) and on the Creative Achievement Questionnaire (CAQ, right panel). For clarity, error bars of the CAQ were not displayed on the graph as the scores were highly skewed with SD = 8.55 in 83 controls and SD = 31.78 in 110 subjects with narcolepsy type 1 and type 2. (B) Italian group: Same legend as A. Note that SD = 4.03 in 43 controls and SD = 9.84 in 64 subjects with narcolepsy type 1. **P < 0.01, * P < 0.05, and ns, non-significant for a difference between control and narcolepsy groups with the Z-test.

Figure S3 -

 S3 Figure S3 -Evaluation of Creative Potential test in control and narcolepsy groups. Mean scores ± SD for divergent (graphic and verbal) and convergent (graphic and verbal) creative thinking. Note that each dimension comprises two subtests. N = 30 for both control and narcolepsy groups. **P<0.01, *P<0.05, and exact P values are displayed if P > 0.05, for differences between control and narcolepsy groups obtained with linear mixed model analyses.

Figure S4 -

 S4 Figure S4 -A closer look at the five factors used to evaluate convergent creative thinking. (A) Mean scores ± SE of controls and subjects with narcolepsy on all the convergent tasks for each of the five factors. (B) Spearman correlation matrix between the five factors (orange dotted line) and between the five factors and the questionnaires of creativity (white dotted line). **P<0.01, *P<0.05, and exact P values are displayed if P > 0.05, for differences between the variables of interest obtained with linear mixed model analyses.

  Overall, we identi ed a 'creative sweet spot' during the sleep-onset period. Hitting it requires participants to balance falling asleep without falling into too deep sleep. Our ndings also reveal the existence of two opposing e ects on insight between two seemingly close sleep stages, N1 and N2, emphasizing the importance of disentangling them when studying the impact of sleep on cognition. By identifying sleep onset as a key period for inspiring insight, our study provides a well-identi ed, brief window to focus on when investigating the neural mechanisms of creativity, as

Fig. 1 -

 1 Fig. 1 -Experimental paradigm. (A) Top: An illustrative NRT trial.Subjects were presented with an 8-digit sequence (composed of1, 4, and 9) and instructed to find the final solution as quickly as possible by applying two rules in a stepwise manner: reporting the same digit if the previous and next digits are identical ('same' rule) and reporting the remaining third digit if they are different('different' rule). Unbeknownst to them, the second response was always the final solution (hidden rule allowing them to answer much faster). Adapted from(1). Bottom: Protocol timeline. Subjects completed two blocks (30 trials each) of the NRT, then had a 20min break followed by 9 additional blocks. (B) Break period. Participants rested in a chair, eyes closed, in a dark room while holding a bottle (circled in red) in their right hand. They were told to report out loud any mental content if the object fell. (C) Illustrative polysomnographic recording. In this example, the participant was in N1 when the bottle fell (dashed line), waking him up. He then reported a hypnagogic experience ("I saw a big cliff and I was climbing it. Then boom, it [the bottle] fell and brought me back to reality").

  of N1 but without any signs of deeper sleep stages, N = 24), and the "N2" group (subjects who had at least one 30-second epoch of NREM sleep stage 2 or N2, N = 14, including 3 who directly fell into N2 without passing by N1). Critically, all groups were exactly in the same conditions during the incubation period. Subjects in the N1 group were awake most of the time, spending only 1 minute in N1(1.35 ± 1.20 min); subjects in the N2 group spent a similar time in N1 than the N1 group (mean = 1.79 minute ± 2.11, Mann-Whitney, U = 156.5, z = -0.33, p = 0.73) plus an average of 4.18 (± 2.94) minutes in N2.

Fig. 2 -

 2 Fig. 2 -N1 inspires insight. (A) Percentage of solvers pre-sleep (all groups) and post-sleep (Wake, N1, and N2 groups defined according to their polysomnographic activity during the break, NPre = 103, NWake = 49, NN1 = 24, NN2 = 14). (B) Solving time before and after the "Eureka" moment for all solvers (N = 53) and non-solvers (N = 50). Each dotted line represents a subject. Note that 4 solvers did not exhibit a clear shift in performance before and after the identified Eureka moment. They explicitly reported the hidden rule during debriefing and explained that they did not use it as they considered it as cheating. (C) Trial number at which the Eureka moment occurs during the Post phase. (D) Illustration of the Eureka moment (dark dotted line) automatically detected by an algorithm that identifies the most abrupt change in solving time for both a solver (left panel) and a non-solver (right panel). ***, p <0.001; **, p<0.01; *, p<0.05; n.s. for non-significant differences (Fisher Test for comparisons of proportions between groups, Wilcoxon signed-rank for comparisons between two paired samples: solvers and non-solvers and Kruskal-Wallis for comparisons between all three groups).

Fig. 3 -

 3 Fig. 3 -Alpha and delta power capture the sleep-onset creative sweet spot. (A)Average power spectrum over the occipital electrode during the Break (N=78 recordings) for participants who slept (blue) or not (green), further split between solvers (continuous line) and non-solvers (dotted line). We identified two clusters of frequency associated with insight, in the alpha([9, 9.8]Hz, black line) and the delta bands([3.2, 4.4]Hz, red line) (all pcluster<0.001). (B, D) Average power in the delta (z-scored across all participants) and alpha bands (squared z-score) for solvers (yes) or non-solvers (no), further divided into participants who slept (blue) or not (green). Z-score values show the linear effect of delta (ANOVA with between-subjects' effects of Sleep andInsight; effect of Insight: p=0.019) whereas the squared z-score values show the quadratic effect of alpha (p=0.013). Individual subjects are represented by circles. Horizontal lines show the 1st, mean and 3rd quartiles. (C, E) Same as (B, D) but delta and alpha power were binned into three levels and the proportion of participants reaching insight (yes=1, no=0) was computed for each bin (gray circles and error bars are represent the mean and standard error of the mean, SEM).

Fig. 4 -

 4 Fig. 4 -Edison's technique is a reliable marker of sleep onset. (A) Average delta power around bottle drops ([-50, 10] s) in subjects who dropped the bottle (Real, N=39 individual recordings; when applicable, averaged across multiple drops, N=8/39). To approximate what would have happened if bottle drops were independent of participants' vigilance state, we show the corresponding average delta power when selecting 100 randomly drawn times for each recording (Rand). Shaded areas/dotted lines show the SEM and solid lines, the mean. The horizontal black line shows the significant cluster between real and rand data (p<0.001). (B) A 1st-degree polynomial was fitted to the average delta power time-course [-20, -2] s obtained for each recording and the corresponding slope is shown for real and random times. (C) Percentage of hypnagogic experiences collected after awakenings from N1 (control experiment) and in subjects withand without drop. ***, p<0.001; (Chisquared Test). (D) Proportion of 30s-sleep epochs (standard sleep criteria) after each drop for real and random times. (B, D) For Real, circles denote subjects who slept/had micro-sleep episode(s) (blue) or remained awake (without any micro-sleep, green). Horizontal lines show the 1st quartile, mean and 3rd quartiles. **, p<0.01; *, p<0.05 (Wilcoxon signed rank).

23 ± 3 .

 233 -109 participants were recruited for this study, 6 of whom were excluded from analysis (3 for technical problems, 2 for sleep disorder suspicion, and 1 decided to prematurely stop the experiment). We thus included a total of 103 healthy participants (73 females, age 23.58 years) in this study. They were screened out for exclusion criteria such as excessive daytime sleepiness, history of sleep, neurological or psychiatric disorders. Additionally, participants who fall asleep easily were selected, as measured by the Epworth scale (mean score = 9.63 ± 2.44). To further facilitate sleep onset, we asked participants to sleep about 30% less than usual during the night preceding the experiment (either by going to bed later or waking up earlier) and to avoid stimulants on the day of the experiment. Participants confirmed that they followed the sleep deprivation instruction on the day of the experiment (self-report). They were paid 10€ per hour as compensation for their participation. All subjects provided their written informed consent prior to the onset of the study. The study protocol was approved by the local ethics committee (Comité de Protection des Personnes, Ile-de-France III, Paris, France, approval number 2019-A00562-55).Behavioral task -We used the same Number Reduction Task and stimuli as[START_REF] Craig | Rest on it: Awake quiescence facilitates insight[END_REF] (

4 .

 4 Post-Phase. Then, approximately 10 minutes after awakening (a time interval that included a questionnaire on hypnagogia, the instructions, and a Psychomotor Vigilance Test), they performed the Post session, which consisted of 9 blocks of 30 trials each (always containing the regularity) and lasted 64 min on average.To control for any groups' differences in vigilance state, participants also performed a 3-min Psychomotor Vigilance Test (PVT)(40) at the beginning of the Pre and Post phases. Here, participants had to respond to visual stimuli as quickly and accurately as possible (inter-trials interval varying between 1 and 4s including a 1s-feedback screen), and performance was measured as the average reaction time to the appearance of the visual cue. As additional controls, we also assessed their subjective levels of sleepiness, boredom, concentration, and motivation on a five-point scale (as in[START_REF] Yordanova | Di erential associations of early-and late-night sleep with functional brain states promoting insight to abstract task regularity[END_REF] (41) before and after the Pre and Post sessions.

EEG recordings -

 recordings Subjects were continuously monitored with video-polysomnography during the experiment. The montage included 3 EEG channels (FP1, C3 and O1), electro-oculograms (EOG) with electrodes placed on the outer canthi of the eyes, chin electro-myogram (EMG), a microphone and infrared video recordings (Brainet, Medatec Ltd, France). The impedances of electrodes were generally below 5kΩ. EEG signals were referenced to A2 (right mastoid) and sampled at 250 Hz. Sleep scoring -EEG signals were bandpass filtered between 0.1 and 40 Hz and EOG signals between 0.3 and 15 Hz (two-pass Butterworth filter, 5th order). EMG signal was obtained with a local derivation and bandpass filtered between 10 and 100 Hz (two-pass Butterworth filter, 5th order). The vigilance state of participants during the break was scored offline by two experienced scorers (CL and DO), blind to experimental conditions. The inter-rater agreement was very good (Kappa coefficient = 0.83) and remaining disagreements were examined by a third expert scorer (SL).

Model 6 :

 6 Insight ~ 1 + Sleep_Group + Delta_Power + Alpha_Power 2 + Alpha_Power + Delta_Power 2 + PVT_RT + Bottle_Drop + Epworth_Score + Micro_SleepGeneralised Linear Models were fitted with the glm function from the 'stats' package in R. We examined the Akaike Information Criterion (AIC) to determine which model fitted the data best (AIC for models 0 to 6: 109.3, 103.2, 100.5, 94.4, 95.9, 97.2, and 102.1). Accordingly, Model 3 was the model that provided the best fit. The only model within 2 AIC units of the best model was Model 4. We report the effects associated with Model 3 in the Results section and Table

Fig. S1 .

 S1 Fig. S1. Performances before and after Eureka. Accuracy (A) and speed-accuracy trade-off (B) prior and after "Eureka" for all solvers (left panel, N = 53) and non-solvers (right panel, N = 50). ***, p <0.001; n.s. for non-significant differences (Wilcoxon signed rank for comparisons between two paired samples).

Fig. S2 .

 S2 Fig. S2. No evidence for an implicit knowledge before Eureka Mean Solving Time on the last block of the Pre phase (A), and on the Post phase until the Eureka moment (B) for both solvers post and non-solvers (here Eureka was approximated as the mean Eureka moment of solvers post, namely trial 94). (C)Difference in solving time (last block of pre minus first block of post) for N1 and N2 groups. n.s. for non-significant differences between groups (Wilcoxon).

Participants - 52

 52 healthy volunteers (49% females,24.83 ± 4.45 years) participated in the study. They were screened out for exclusion criteria such as excessive daytime sleepiness, history of sleep, neurological or psychiatric disorders. To facilitate sleep onset, we asked participants to sleep about 30% less than usual during the night preceding the experiment and to avoid stimulants on the day of the experiment. Subjects were paid 10€ per hour as compensation for their participation (plus a bonus based on their performance). All subjects provided their written informed consent prior to the onset of the study. The study protocol was approved by the local ethics committee (Comité de Protection des Personnes, Ile-de-France III, Paris, France).

  Subjects were constantly monitored with video-polysomnography for the entire duration of the experiment. The montage included 3 EEG channels (FP1, C3 and O1), electro-oculograms (EOG) with electrodes placed on the outer canthi of the eyes, chin electromyogram (EMG), a microphone, and infrared video recordings. The impedances of electrodes were generally below 5kΩ. EEG signals were referenced to A2 (right mastoid) and sampled at Sleep scoring -EEG data was bandpass-filtered between 0.1 and 40 Hz and EOG derivations between 0.3 and 15 Hz (two-pass Butterworth filter, 5th order). EMG signal was obtained with a local derivation placed on the chin, which was bandpass-filtered between 10 and 100 Hz (two-pass Butterworth filter, 5th order). Participants' vigilance states during the resting period were scored offline by two experienced scorers according to the standard sleep scoring guidelines of the American Academy of Sleep Medicine, AASM[START_REF] Berry | The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications[END_REF]. There was a high concordance between these two independent scorers (CL and DO, Kappa coefficient > 0.8) and remaining disagreements were examined by a third expert scorer (SL).

Figure 1 -

 1 Figure 1 -Experimental paradigm. (Top) Protocol timeline.Subjects first learnt the precise location of 48 pictures, which were presented within a grid. They were tested on their memory for these objects' locations before and after a 30-min resting period, during which they could sleep while being continuously monitored via polysomnography. They were regularly awakened (approx. every 6 minutes) by a sound to prevent them from sleeping too deeply and to probe their mental content. Based on their vigilance states during the resting period, participants were divided into three groups: Wake (N =16), N1 (N = 15), and N2 (N = 21). (Bottom) Visuospatial memory test. Each object appeared at a wrong position ('start') and the subjects had to drag it to its correct ('real') location as precisely as possible. Here, the object would be considered incorrect as the location provided by the subject is outside the circle's perimeter, which represents the threshold for correct objects (for more details, see the Methods).

  Figure 2 -Memory loss at sleep onset. (A) Delta (pre-post) distance and (B) delta (post-pre) accuracy (percentage of correct objects) for each group (Wake, N1 and N2). Negative values thus indicate forgetting for both measures. The delta distance was computed on the items that were correct during the Pre test (items sufficiently encoded to potentially benefit from memory consolidation during the break). (C) Forgetting rate for each group: � 𝑁𝑁𝑁𝑁 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑁𝑁𝑁𝑁 𝐶𝐶𝐿𝐿𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐿𝐿 𝑃𝑃𝐶𝐶𝐶𝐶 � × 100. (D) Number of repetitions during the learning phase for the Always Good (correct in pre and post) and Lost items. For each box, horizontal lines represent the 1 st , mean, and 3 rd quartiles.Individual data is also depicted by circles alongside their boxplot. Kruskal-Wallis tests were performed for comparisons between all three groups; when appropriate, post-hoc comparisons with Tukey correction have been computed. One-sample Wilcoxon signed-rank was used for comparison with zero level, and two-samples for comparisons between two paired samples: Always Good and Lost. Stars are used alone to report the p-values obtained when comparing data to zero, stars with a line for between-group differences, and stars above a square bracket for post-hoc comparisons. *** p<0.001; **, p<0.01; *, p<0.05, ns, for non-significant differences.

Figure 3C ;

 3C Figure 3C; r = -0.39, p = 0.019, Pearson's correlation), revealing that the more participants'

Figure 3 -

 3 Figure 3 -Neurophysiology of memory loss. (A) Average power spectrum and (B) alpha/theta ratio over the occipital electrode during the entire Break for each group (Wake, N1, and N2). *** p<0.001; **, p<0.01; *, p<0.05 (One-way ANOVA for comparisons between all three groups, and Tukey-Kramer post-hoc comparisons with correction for multiple comparisons). (C) Pearson correlation between the forgetting rate and the alpha/theta ratio for all the subjects who slept (N1 and N2 groups). Both the raw individual data (circles) and a glm fit with 95% confidence interval (line+shaded area) are plotted. Rho and p-values are displayed in the figure.

Figure 3 . 1 .

 31 Figure 3.1. The hidden rule.

Figure 3 . 2 .

 32 Figure 3.2. The experimental design.

Figure 1 . 1 .

 11 Figure 1.1. Quick summary of the ndings.

1 :

 1 Increased creative thinking in narcolepsy

Contents 2 . 1

 21 The experimental task . . . . . . . . . . . . . . . . . . . . . . . . 206 2.2 Pre-task exposure is necessary . . . . . . . . . . . . . . . . . . 211 2.3 Individual di erences and abilities . . . . . . . . . . . . . . . . 215 2.4 Delay between the pre and break phases . . . . . . . . . . . . . 217 During my PhD, I found a clear-cut e ect of the N1 stage on insight in Study 2

  conclude their study by stating: "Our study adds to the accumulating evidence that sleep does not provide a more suitable environment for problem solving than the wake state. The main function of the reorganization of memory content during sleep might thus lie instead in the extraction of common features of multiple experiences". This hypothesis of sleep acting on gist abstraction but not on memory reorganization is possible, and further research should investigate if our results in Study 2 can be applied to that third task category. Thus, task selection does seem to have a crucial impact on the subsequent e ect of sleep on creativity (for a summary schema, see Figure 2.1).

Figure 2 . 1 .

 21 Figure 2.1. Task choice and sleep impact. A schematic overview of the di erent types of creative tasks used in the sleep-creativity literature and the reported impact of sleep on them. The color green indicates a positive e ect, while the color orange denotes a mitigated e ect.

Figure 2 . 2 .

 22 Figure 2.2. Sleep and the extraction of hidden regularities. Reproduction of ndings from Lerner and Gluck (2019).Note that the terms 'implicit vs. explicit' refer to whether subjects gained an implicit or explicit knowledge of the regularity.

Figure 2 . 3 .

 23 Figure 2.3. Who is the culprit? Summary of the follow-up experiments we plan on conducting to determine what factor(s) may be responsible for the lack of insight gain in Study 3.

Figure 2 . 4 .

 24 Figure 2.4. Implicit knowledge of the rule. ∆ Accuracy between trials following vs. trials breaking the rule for all solvers and non-solvers.

Figure 2 .

 2 Figure 2.5.Setting the optimal conditions. A schematic representation of the various factors that may in uence sleep's e ect on creativity. The color green symbolizes a positive impact, the color orange, a mild e ect, the color red, no e ect, and the color gray denotes an unknown or inconclusive e ect. Of note, the professions' colors were randomly chosen and were just intended to illustrate inter-individual di erences.

Contents

3. 1

 1 Sleep outweighs incubation . . . . . . . . . . . . . . . . . . . . . 219 3.2 Sleep duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 3.3 Mental content . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 3.4 Sleep stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Figure 3 . 1 .

 31 Figure 3.1. An untypical cognition prior to sleep onset. Stickgold et al. (1999)'s results on the semantic priming task.

Figure 3 . 2 .

 32 Figure 3.2. Summary results of the NRT studies. The rst author of each paper cited is displayed on the gure. The percentage of insight in the Pre group is taken from Study 2, but is similar in the other studies. The color red indicates no e ect on insight, while the color ocher indicates a positive e ect.

Figure 3 . 3 .

 33 Figure 3.3. Rapid shift from wakefulness to sleep. (A) Graph showing the probability of subjects remaining continuously awake as a function of time. After 10 minutes, ≈ 50% of subjects had experienced at least one epoch of sleep. (B) The probability of subjects reaching the N1 stage as a function of time. Within a few (3-4) minutes, one-third of participants reached the N1 stage. The dashed vertical line marks 10 minutes. Adapted from Tagliazucchi and Laufs (2014a).

Figure 3 .

 3 Figure 3.4. A complex but incomplete model. A schematic illustration of the numerous factors that may in uence sleep-dependent action on creativity. A solid link denotes an in uence, a dotted link suggests an uncertain/mitigated in uence, and no link indicates no apparent in uence.

Contents 4 . 1

 41 Sleep inertia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 4.2 The awakening stage . . . . . . . . . . . . . . . . . . . . . . . . . 237 4.3 On the carryover . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 4.4 Additional processes to catalyze the sleep e ect . . . . . . . . . 244

  [START_REF] Debarnot | Sleep does not facilitate insight in older adults[END_REF] andWagner et al. (2004) demonstrated a bene cial e ect of sleep on insight using a full night paradigm. It is unknown from which stage the subjects were awakened, but given the prevalence of REM sleep towards the end of the night, the odds that subjects were awakened in REM sleep (or N1) are high. Because these two stages are associated with creativity, they may explain the gain in insight observed in these studies. On the other hand,[START_REF] Yordanova | Shifting from implicit to explicit knowledge: di erent roles of early-and late-night sleep[END_REF] used a split-night paradigm, with early and late night groups to separate the e ects of NREM and REM sleep on insight.

  Figure 4.1). Let us now dig a bit further into this idea and ask a follow-up question: Is the increase in creativity attributable to a carryover of a creative stage (such as the N1 stage or REM sleep), or would we observe this e ect regardless of the time interval between sleep o set and retest?

Figure 4 . 1 .

 41 Figure 4.1. The NRT literature in regard to the awakening sleep stage. The colors indicate whether the resting phase had a positive impact on insight (green) or not (red). The interrogation point represents an assumption.

Figure 4 . 2 .

 42 Figure 4.2. Sleep awakening stage and exible cognition. A creative mindset was found only after REM sleep awakening. If REM sleep was succeeded by NREM sleep, the e ect vanished. Note that they found an e ect only on emotional cues (in red). From Carr and Nielsen (2015).

4. 4

 4 Additional processes to catalyze the sleep e ect In the preceding section, we addressed the idea that sleep o set might be important to consider in addition to what occurs during sleep. Moreover, our nding of a delayed Eureka in Study 2 suggests the existence of additional mechanisms operating during the Post phase, which catalyze the e ect of sleep.

Figure 4 . 3 .

 43 Figure 4.3. Late onset of Eureka. (A) Each individual moment of Eureka is plotted per group (from Study 2). (B) A drift di usion model is proposed to account for inter-individual di erences in the moment of Eureka. Subjects need more or less evidence, depending on their nature (explorative/risky = ochre color; conservative = brown color), to reach the threshold at which they switch strategies and use the next strategy (the hidden rule), available thanks to N1. Of note, other scenarios are possible, such as a di erent speed of evidence accumulation (the slope of the curve), or a di erent o set (the y-intercept).

Figure 4 . 4 .

 44 Figure 4.4. Summary table on the sleep and creativity literature. The table represents a nonexhaustive review of the research on sleep and creativity examining the impact of several putatively important factors (e.g., sleep duration, carryover, sleep stages...). The colors depict a speci c e ect of REM sleep (violet), SWS (dark blue), the N1 stage (sky blue), sleep in general (green), incubation (orange), or no e ect at all (red). The symbol ' > ' indicates a higher creativity.

Contents 5 . 1

 51 Critical cognitive processes occur during N1 . . . . . . . . . . . 251 5.2 Targeting the creative sweet spot . . . . . . . . . . . . . . . . . 255 5.3 Tracing the sleep-onset period . . . . . . . . . . . . . . . . . . . 256 5.4 How pervasive is sleep? . . . . . . . . . . . . . . . . . . . . . . . 260

3

  for several reasons. First, we found no e ect on insight in Study 3 as compared to Study 2 (we have already discussed the putative reasons of such discrepancies in the chapter 2), preventing us from achieving our original goal of investigating the destiny of memory traces in relation to creativity. Moreover, although the N1 stage was associated with memory loss, subjects who continued into deeper sleep (N2) showed less forgetting. Thus, instead of having an active role in forgetting, the N1 stage might,

Figure 5 . 1 .

 51 Figure 5.1. Targeting the creative sweet spot. The gure depicts two putative methods for hitting the creative spot: (A) Horowitz et al. (2020)'s Dormio device and Brain-Computer Interface (B). The sketch in (B) was adapted from the website 'Potioc'.

Figure 5 . 2 .

 52 Figure 5.2. Inter-subject variability in bottle drop timing. Micro-sleep episode duration (in seconds) per subject before the rst bottle drop.

Figure 5 . 3 .

 53 Figure 5.3. Intra-individual variations in the sleep-onset phenotype. Sleep-onset dynamic of the same subject on two succeeding nights (A and B, respectively). Note the smooth transition to sleep in the rst night, contrasted to the second night's discontinuous transition, marked by a brief arousal period in the middle of the SOP. Also note the slight uctuations in the wake probability (sometimes reaching 0.6), which contrasts with the smooth hypnogram that always depicts wakefulness. From Prerau et al. (2014).

  bottle appears as a reliable tool for detecting the precise moment of sleep onset, speci c to each individual, where all measures (electrophysiological, phenomenological, subjective perception, and behavioral -the bottle drop can be considered a measure of muscular relaxation) converge to indicate sleep onset.

  (i.e." dreaming), or in-between (e.g., hypnagogia). However, a growing body of research indicates that sleep and wakefulness often co-occur, as evidenced by the phenomenon of 'local sleep' (for more information, see section1.4). Thus, all of these terms may refer to the same phenomenon with varying degrees of 'sleep pervasiveness', which would account for their striking phenomenological similarity. Accordingly, 31% of reported hypnagogia in Study 2 were collected during so-called wakefulness (as de ned by standard scoring classi cation), but we detected sleep-like EEG slow-wave activity prior to the bottle drop with more precise analyses. Recently,[START_REF] Andrillon | Predicting lapses of attention with sleep-like slow waves[END_REF] also observed the presence of spatially and temporally localized slow-waves (typically seen at the transition to sleep) when subjects had attentional lapses (i.e., behavioral errors) and reported mind-wandering/mind-blanking (Figure5.5). The authors were also able to di erentiate between mind-wandering and mind-blanking states based on the neuronal location of these slow-waves (frontal for mind-wandering, frontal and posterior for mind-blanking; Figure5.5).

Figure 5 . 4 .

 54 Figure 5.4. The predominance of spontaneous thoughts. Pie chart showing the distribution of spontaneous thoughts in our life (roughly two-thirds of our time is dedicated to them).

Figure 5 . 5 .

 55 Figure 5.5. Localized sleep-like activity and spontaneous thoughts. (A) Average waveform and scalp topographies of wake vs. sleep slow-waves. (B) Scalp topographies for slow-wave density, amplitude, upward and downward slopes for the di erent mental states (ON = task-focused; MW = mind-wandering; MB = mind-blanking). Adapted from Andrillon et al. (2021).

Figure 5 . 6 .

 56 Figure 5.6. A spectrum between sleep intrusion and phenomenological experiences. Varying degrees of sleep intrusion are associated with di erent kinds of spontaneous thoughts. We hypothesize that the degree of spontaneous thoughts' bizarreness or disconnection from the environment would increase with the pervasiveness of sleep.

  bottle could be used to investigate what causes this sleep misperception by examining how the dynamic neurophysiological and phenomenological changes associated with the gradual descent into sleep are potentially altered in insomnia. For example, returning back to the Prerau et al. (2014)'s model, one could anticipate that, in insomnia, alpha dropout rarely coincides with low-frequency increase; instead, it may be accompanied by an abnormally prolonged latency between the two (Figure 6.1).

Figure 6 . 1 .

 61 Figure 6.1. Sleep-onset dynamic in insomnia.

Figure 6 . 2 .

 62 Figure 6.2. Dream correlates, a hot research topic. Timeline showing the number of papers published each year when searching in PubMed, for neural correlates of dreams.

  demonstrated the feasibility of assessing micro-dreams in laboratories and their potential utility for studying dream neural correlates, as they observed a congruent relationship between micro-dream sensory modality and coherent EEG activation in corresponding sensory regions. Horikawa et al. (2013) also successfully decoded the contents of N1 dreams from the measured brain activity (with fMRI). Moreover, alike our discussion on REM sleep vs. the N1 stage, these micro-dreams do not su er from contamination from other sleep stages.

Figure 6 . 3 .

 63 Figure 6.3. Alpha-theta training to boost creativity. First, subjects are trained to learn how to reach a hybrid state with auditory neuro-feedback. Following training, they are given a riddle, and asked to try solving it within such a hybrid state. The alpha/theta curves are in Johnson et al. (2013).

  Figure A.1 below summarizes the experimental protocol's timeline.

Figure A. 1 .

 1 Figure A.1. Memodream experimental design.

  noisy, making sleep scoring di cult. Indeed, EEG data was initially very artifacted by the fMRI scanner (for an illustration, see Figure A.2).

Figure A. 2 .

 2 Figure A.2. EEG preprocessing. Left panel: raw EEG data. Right panel: EEG data after removing the gradient artifact. Note that the signal is very noisy due to cardio-ballistic artifacts.

Figure A. 3 .

 3 Figure A.3. Brain regions associated with motor execution compared to rest (last dual-task, grouplevel analysis).

Figure A. 4 .

 4 Figure A.4.A lucid dreamer replays the motor sequence in his/her dreams. The gure depicts the EEG trace of a subject who signaled a lucid dream episode with an ocular code (left-right-leftright, in red), then took control of the dream scenario to perform the motor sequence (visible on arm EMG electrodes, in green), before concluding the episode with another ocular code (on the next page). Surprisingly, the motor command seems to be sent to the arm muscles (but may be blocked before its execution), but it is reassuring to note that the chin muscle tone stayed at.

  First, as expected, we observed a signi cant improvement in the speed of execution on the dual task from start to end for both the complex (∆ End-Start = -128.43 ms, p < 0.001) and simple (∆ End-Start = -120.21 ms, p < 0.001) motor sequences. Second, we investigated if the session number (time) and condition (sleep vs. wake, solely based on the break labels, not on sleep scoring) were related to performance improvement. A two-factor repeated ANOVA showed a signi cant e ect of both factors on the speed of execution (and on the speed/accuracy trade-o ) for the simple motor sequence (session: p = 0.002; condition: p = 0.026), and a signi cant e ect of the session number for the complex sequence (Centre national de référence pour la narcolepsie, hôpital de la Pitié-Salpêtrière) et ses collègues sont partis de di érentes études suggérant un lien entre le sommeil paradoxal, cette phase où les rêves sont concentrés, et la créativité. « Il est peu probable que la créativité intrinsèque soit atteinte instantanément à la suite d'une sieste », écrivent-ils cependant. C'est pourquoi les chercheurs ont décidé de faire appel à des « champions» du rêve et du sommeil paradoxal, comme les désigne Delphine Oudiette: des patients atteints de narcolepsie, chez qui sa cosignataire Isabelle Arnulf avait noté qu'ils se livraient fréquemment à des loisirs créatifs.Rêveurs lucides Les personnes narcoleptiques s'endorment subitement pendant la journée, tombent directement en sommeil paradoxal, peuvent entrer en catalepsie (perdre tout contrôle musculaire, en restant conscient), font parfois l'expérience d'hallucinations au moment de l'endormissement, et sont souvent des rêveurs lucides, c'est-à-dire qu'elles savent qu'elles sont en train de rêver et qu'elles peuvent prendre le contrôle de leurs songes. Plusieurs études ont aussi montré que le rêve lucide est associé à une plus grande créativité. L'équipe française s'est donc associée à des neurologues de l'université de Bologne pour tester celle de sujets narcoleptiques. Elle a notamment utilisé un test mesurant le type de créativité selon trois pro ls -« novateur », « imaginatif », « chercheur ». Un second questionnaire demandait aux sujets de faire état de leurs capacités dans dix domaines créatifs (arts, écriture, découverte scienti que, cuisine. . . ).En n, une épreuve formelle mesurait les capacités de créativité divergente (inventer autant de ns possibles à une histoire; dessiner autant d'objets possibles à partir d'une forme donnée) et convergente (inventer une histoire à partir de trois personnages imposés ; incorporer une série de formes dans un dessin original). Malgré leur plus grande fatigue -« ils devaient constamment lutter contre le besoin de dormir» -, il en ressort que les sujets présentant une narcolepsie « ont un potentiel créatif plus élevé que les sujets du groupe contrôle», concluent les chercheurs. Et ce, que leur narcolepsie soit de type 1 (liée à une dé cience dans la production d'un neurotransmetteur, l'hypocrétine) ou de type 2, dont la physiopathologie reste inconnue. Malgré ce potentiel marqué, seule une minorité avait à son actif des réalisations de type professionnel, comme un roman, une sculpture ou une oeuvre musicale, note l'étude. Peut-être leur fatigue chronique les empêche-telle de mener leurs idées à terme, avancent les chercheurs. Près de la moitié (43%) des sujets testés étaient des rêveurs lucides, contre 3% dans le groupe contrôle. Savoir si le rêve lucide est la cause ou la conséquence de la plus forte créativité « reste une question ouverte », selon les auteurs. Ils soulignent aussi que les personnes narcoleptiques se souviennent mieux de leurs rêves, « qui pourraient leur fournir une source plus vaste d'idées d'où tirer leur inspiration créative».Sommeil paradoxalCes observations sont en tout cas à rapprocher d'un modèle de l'activité cérébrale qui voit dans le sommeil paradoxal et les rêves un moyen de générer des scénarios permettant au sujet d'anticiper une foule de situations rencontrées pendant l'éveil. L'étude de Brain fait écho aux propres observations d'Emmanuel Mignot, qui dirige le Centre des sciences et de médecine du sommeil à Stanford (Californie), dont les patients présentent souvent un pro l créatif. « Mais il y a un biais, préciset-il, car ils sont très éduqués. Certains sont artistes, et j'ai même quelques cas qui n'aiment pas être traités avec des sédatifs (Xyrem), car leurs rêves sont supprimés. » Le « visionnaire» George Church préfère lui aussi éviter les médicaments, susceptibles d'enrayer la machine à rêves de la narcolepsie. « Etre di érent permet de penser de façon décalée », dit-il dans l'article de Stat, où il plaide aussi pour une meilleure inclusion de la neurodiversité présente chez des personnes autistes ou sou rant de troubles obsessionnels compulsifs. « Il y a des forces cognitives dans chaque di érence, acquiesce Delphine Oudiette. La narcolepsie est très gênante si vous voulez devenir chau eur routier, mais si vous êtes chercheur, cela peut être une vraie force. » Hervé Morin 72 L'HUMANITÉ DIMANCHE DU 14 AU 20 NOVEMBRE 2019 DÉCOUVRIR SCIENCES CERVEAU LE SOMMEIL PARADOXAL, UN LIEU DE RÊVE POUR CRÉER En étudiant des personnes atteintes de narcolepsie, une équipe de chercheurs a découvert que celles-ci étaient bien plus créatives que la moyenne de la population. Certaines arriveraient même à résoudre leurs problèmes de la vie réelle grâce à un accès privilégié à leurs rêves.

Isabelle«

  Arnulf est cheffe du service des pathologies du sommeil à l'hôpital de la Pitié-Salpêtrière. Son service est un centre de référence pour la narcolepsie. De nombreux patients qu'elle reçoit sont atteints de cette pathologie et elle a observé qu'ils semblaient être extrêmement créatifs. « Des études rapportent que le sommeil paradoxal est une phase propice pour la créativité. Or, l'arrivée de cette phase dès le début de l'endormissement est un des critères retenu pour diagnostiquer les patients narcoleptiques. L'intuition d'Isabelle Arnulf partait de cela. C'est pourquoi nous avons mis en place une étude sur ce sujet », explique Célia Lacaux, doctorante dans le service. Effectivement, lorsque nous faisons une courte sieste par exemple, nous restons en sommeil léger sans aller dans le Les narcoleptiques ont une sorte de sixième sens qu'il est difficile, voire impossible d'atteindre en dehors de cette pathologie. sommeil lent profond et encore moins dans le sommeil paradoxal. Chez les personnes atteintes de narcolepsie, en revanche, le sommeil paradoxal arrive tout de suite. Il ne restait plus qu'à vérifier cette hypothèse : les patients atteints de narcolepsie sont plus créatifs que les autres. Pour cela, l'équipe a évalué le niveau de créativité (cf. « Point de vue ») de 185 sujets atteints de narcolepsie et de 126 témoins. L'étude, réalisée en partenariat avec un autre centre de référence des pathologies du SUNLIGHT19 072_HD.indd 72 08/11/2019 14:49:03 DU 14 AU 20 NOVEMBRE 2019 L'HUMANITÉ DIMANCHE 73 SCIENCES DÉCOUVRIR sommeil de Bologne (Italie), a été publiée dans la revue « Brain » en mai dernier. Les résultats montrent que, sur l'ensemble des tests de créativité réalisés, les sujets atteints de narcolepsie ont obtenu des scores plus élevés que les sujets de contrôles », déclare Célia Lacaux. Ces résultats sont importants pour les patients car ils mettent en évidence une créativité supérieure à la moyenne, apportant par là même une note positive à cette maladie très difficile à vivre. Ces patients ont une sorte de sixième sens qu'il est difficile, voire impossible d'atteindre en dehors de cette pathologie. Par ailleurs, cette étude apporte un argument fort sur le rôle joué par le sommeil paradoxal dans la créativité. En revanche, elle ne dit pas si les personnes créatives rêvent plus que les autres. Sans doute l'objet de recherches futures qui trouveront peut-être leurs réponses… dans un rêve ! Il est vrai que c'est un concept complexe et débattu. Afin d'obtenir une image la plus complète possible, nous avons choisi de combiner plusieurs mesures de la créativité. D'une part, nous avons évalué, à l'aide d'un test « papier-crayon », les deux grandes dimensions de la créativité que sont : la « pensée divergente », qui consiste à générer le plus d'idées possible à partir d'une contrainte donnée ; et la « pensée convergente », qui demande d'intégrer plusieurs éléments dans une seule et même production cohérente et originale. D'autre part, nous avons utilisé des questionnaires évaluant le « profil créatif » ainsi que les « accomplissements créatifs » des sujets dans des domaines aussi variés que l'écriture, le cinéma, la cuisine, la musique, les sciences, etc. La personne devait par exemple signaler si elle avait déjà pris un cours, reçu un prix dans un domaine, ou encore si elle avait une activité professionnelle en rapport avec la créativité. Globalement, les sujets narcoleptiques ont obtenu des scores plus élevés que les sujets contrôles, et ce, pour tous les aspects de la créativité que nous avons mesurés. Nous avons aussi pu observer que certains rêveurs lucides étaient très créatifs. Des patients narcoleptiques utilisent d'ailleurs leurs rêves lucides pour résoudre des problèmes qu'ils ont dans la vie réelle ! Des rêveurs lucides ? Normalement, lorsque nous rêvons, nous sommes persuadés que le rêve est la réalité. Les rêveurs lucides, quant à eux, sont des personnes qui ont conscience de rêver lorsqu'ils rêvent. Certains rêveurs lucides peuvent même prendre le contrôle de leur rêve et en modifier le scénario. Rares sont les rêveurs lucides dans la population générale, alors qu'ils sont très nombreux chez les patients atteints de narcolepsie. Ils représentent d'ailleurs pour nous un excellent modèle d'étude des rêves ! Comment des personnes qui ne sont pas narcoleptiques peuventelles acquérir cette étonnante faculté créative ?

(

  *) Au sein de l'équipe du Pr Arnulf et de l'équipe Mov'It à l'Institut du cerveau et de la moelle épinière (ICM), Paris. Elle est rattachée au service des pathologies du sommeil de l'hôpital de la Pitié-Salpêtrière (AP-HP Assistance publique-Hôpitaux de Paris). DE LA LÉGÈRETÉ À LA PROFONDEUR La première étape du sommeil se compose de trois phases distinctes : l'endormissement (la respiration devient plus lente, les muscles se relâchent) ; le sommeil lent léger, qui représente en principe 50 % du temps de sommeil total ; le sommeil lent profond (c'est le moment où l'on récupère de la fatigue physique accumulée durant la journée). Tout l'organisme est au repos, le cerveau émet des ondes lentes et amples. Vient ensuite le sommeil paradoxal : durant cette phase, l'activité cérébrale est très intense et nos yeux présentent des mouvements rapides incessants (REM) sous nos paupières alors que nous sommes dans l'incapacité de nous mouvoir. Cette phase représente 20 à 25 % du temps de sommeil total, avec une plus grande amplitude horaire en fin de nuit. C'est à ce moment-là que nous retrouvons les rêves les plus riches et les plus fantastiques. Source : Cenas -centre du sommeil.

TV report, France 5

 5 This is a brief report from a French television (France 5) on Study 1, a study that examined the relationship between narcolepsy and creativity. You will see some of the tests we used to assess creative potential. To view the video, please click here.Click here to view the video.CPopular article on Study 2 I nsight. Le terme vous est peut-être inconnu, mais vous avez sûrement expérimenté ce qu'il désigne : une soudaine illumination qui vous donne la solution d'un problème auquel vous avez réfléchi longtemps, et qui vous vient alors que vous étiez passé à autre chose. « Ce "moment eurêka", ou insight, apparaît quand l'esprit arrête de se focaliser sur le problème, par exemple lors du repos, même si les mécanismes exacts et les phases impliquées restent encore inconnus », explique Célia Lacaux, doctorante à l'Institut du cerveau, centre de recherche implanté au sein de l'hôpital parisien de la Pitié-Salpêtrière. En 2004, une étude allemande publiée dans la revue Nature mon-trait ainsi qu'une nuit de sommeil favorisait grandement l'insight, en doublant les chances de trouver la solution à un problème donné la veille. Mais en 2018, d'autres recherches menées à l'université d'Édimbourg, au Royaume-Uni, parvenaient aux mêmes résultats en remplaçant la nuit de sommeil par un simple moment d'inactivité, les participants étant assis seuls dans une pièce au calme pendant dix minutes. « C'était assez bluffant ! avoue Célia Lacaux, dont les sujets de recherche concernent les liens entre sommeil et créativité. Toutefois, l'équipe britannique n'avait pas fait d'enregistrements de l'activité cérébrale lors de l'étude. Il est donc impossible de savoir ce qu'il s'est passé pendant ces dix minutes. Certains participants avaient-ils en réalité commencé à s'assoupir ? » Pour savoir si la nuit -ou plutôt le sommeil -porte véritablement conseil, la doctorante a mis sur pied une vaste étude menée au sein du service des pathologies du sommeil, à la Pitié-Salpêtrière. Une unité de diagnostic et de soins où sont également menées des recherches fondamentales, presque toutes mises à l'arrêt par la crise sanitaire. Fin juillet, c'est donc avec soulagement que Célia Lacaux accueille Léticia, l'un des premiers « cobayes » de son protocole à la recherche de l'insight. La jeune femme, étudiante en neurosciences, ne sait rien de ce qui l'attend. Elle a répondu à une petite annonce volontairement vague. Et afin de ne pas influen-de s'assoupir pour trouver la clé de problèmes complexe s ? Reportage à l'Institut du cerveau, à Paris, où l'on traque les « moments eurêka ». mandes sur l'insight. Des suites de chiffres s'affichent sur un écran, et le sujet doit appliquer plusieurs règles mathématiques simples pour les compléter. Mais ce que Léticia ne sait pas, c'est qu'il existe une règle cachée dans cette succession de chiffres, une méthode infaillible pour donner instantanément la bonne réponse en se passant de toutes ces fastidieuses opérations de calcul. C'est là que l'insight peut entrer en jeu. Si, lors d'une première phase de test, l'étudiante ne remarque pas cette règle cachée, toute concentrée sur sa tâche, aura-t-elle un déclic après s'être assoupie ? Après un quart d'heure de test, Léticia est invitée à se reposer quelques instants, sans forcément s'assoupir. « Demander à quelqu'un de dormir, c'est cer les résultats, les autorisations qu'elle signe en arrivant sur les lieux ne sont guère plus explicites sur le contenu de l'expérience. Une dizaine d'électrodes pour suivre les phases du sommeil Léticia commence donc sa matinée par la pose d'une dizaine d'électrodes afin de subir une polysomnographie. Cet examen permet de mesurer en continu différents paramètres, dont l'activité cérébrale. « Le nombre de capteurs dépend de ce que l'on étudie, explique Célia Lacaux en collant méthodiquement chaque électrode sur la participante du jour. Si l'on souhaite une lecture fine de l'activité cérébrale, comme dans l'étude des rêves, on peut monter à plus de 250 électrodes sur le crâne. » Trois seulement seront nécessaires pour cette expérience, plus quelques autres destinées à surveiller le tonus musculaire et les mouvements des yeux. Juste ce qu'il faut pour déterminer si Léticia s'est endormie, et dans quelle phase du sommeil elle se trouve. Pour l'heure, la jeune femme s'installe en salle de repos, dans un confortable fauteuil. Quelques branchements plus tard, un ordinateur situé derrière elle affiche des courbes en temps réel. L'équipe teste le bon fonctionnement de chaque capteur. Les différentes courbes oscillent en réponse à un clignement d'yeux ou à un serrement de mâchoires : les signaux sont clairs, le test en lui-même peut commencer. Il s'agit du même exercice que celui pratiqué dans les précédents travaux des équipes britanniques et alle-2020 I SCIENCES ET AVENIR NUMÉRO SPÉCIAL I 35 REPORTAGE la meilleure façon pour qu'il n'y arrive pas ! Il ne faut surtout pas lui mettre la pression », confiera Célia Lacaux une fois la porte de la salle de repos refermée.

  Pour savoir ce qui passe par la tête d'un dormeur au stade N1 et notamment décrire ces hypnagogies, les équipes de recherche s'appuyaient jusqu'ici sur les courbes du polysomnographe, et réveillaient le participant dès qu'il sombrait dans le sommeil. Avec cette gourde en plastique, Célia Lacaux espère « avoir un marqueur fiable, précis et facile à utiliser de l'endormissement. Nos premiers tests semblent de plus montrer que la gourde tombe après une minute de stade N1, un moment justement associé à un maximum d'hypnagogies. » Léticia a donc comme instruction, si la chute de la gourde la réveille, de décrire immédiatement à voix haute ce qui lui passait alors par la tête. Avec l'intuition, pour l'équipe de recherche, que ces hypnagogies pourraient avoir un rôle dans l'insight. L'idée d'utiliser la chute d'un objet pour permettre un réveil précoce, bien que jamais testée jusqu'ici dans le cadre d'un protocole scientifique, est pourtant loin d'être nouvelle. L'un de ses plus fervents partisans fut l'inventeur américain Thomas Edison. L'homme aux mille brevets, connu pour ne dormir que quatre heures par jour, aurait ponctué ses journées de pauses durant lesquelles il s'assoupissait en tenant dans ses mains des billes en acier. Lorsque ces dernières chutaient, l'inventeur se réveillait et se remettait au travail, notant les idées qui lui étaient venues entretemps. D'autres grands noms -Salvador Dali, Edgar Allan Poe ou Nikola Teslaauraient, dit-on, utilisé des techniques similaires. Avec toujours la conviction que ce moment avant que l'objet ne glisse des mains, donc juste avant de sombrer profondément dans le sommeil, serait propice à la créativité. Pour soumettre cette intuition à l'épreuve de la science, retour à la Pitié-Salpêtrière. La participante du jour est désormais seule dans la salle de repos plongée dans la pénombre. À quelques mètres de là, Célia Lacaux et les deux étudiantes qui l'accompagnent ont les yeux rivés sur l'un des écrans. Sous la vidéo de Léticia allongée dans son fauteuil, captée en temps réel par une caméra infrarouge, les courbes de son activité cérébrale continuent à s'agiter. « Là, on peut voir des oscillations plus lentes. Lorsque celles-ci s'étendent sur la moitié de l'écran, on considère que l'on est entré dans le stade N1 », commente la doctorante. Les minutes passent, les oscillations ralentissent parfois mais reviennent invariablement à la normale. Sur la vidéo qui défile au-dessus des courbes, on distingue toujours la gourde en plastique fermement tenue par la participante. « Elle ne dormira pas », annonce la chercheuse après vingt minutes d'attente. L'équipe retourne au chevet de Léticia pour recueillir ses impressions. « Je suis restée éveillée, mais mon esprit a beaucoup vagabondé, raconte par la première phase du sommeil, celle de l'endormissement, que l'on appelle le stade N1 » Célia Lacaux, doctorante à l'Institut du cerveau la jeune femme. À un moment, j'ai notamment revu les chiffres du test défiler dans mon esprit. » Debout à ses côtés, la chercheuse note l'information. Elle confiera un peu plus tard : « Le fait d'avoir revu le problème pendant qu'elle se reposait pourrait correspondre à une hypnagogie, peut-être survenue pendant les courtes phases de ralentissement de l'activité cérébrale que l'on a observées. » Cette simple vision furtive pourrait-elle aider le cerveau à trouver la règle cachée du test ? Pour répondre à cette question, Léticia, comme les autres participants de l'étude après leur moment de repos, qu'ils aient dormi ou non, est une nouvelle fois laissée seule face à un écran la bombardant de séries de chiffres à compléter. Une heure plus tard, la participante est arrivée au bout du test, sans jamais avoir repéré la règle cachée. Tout juste a-t-elle trouvé légèrement étranges les dernières suites de chiffres. « À la fin, nous avons ajouté quelques suites qui ne collent pas avec la règle cachée, explique Célia Lacaux. Nous voulons voir si le cerveau peut parfois faire le travail de manière inconsciente : le participant aurait en quelque sorte intégré la règle cachée à son insu, sans pouvoir la formuler explicitement. Dans ce cas, il devrait avoir plus de mal à résoudre ces exercices finaux. » 60 % des « dormeurs » ont réussi à trouver la règle cachée

  wakefulness and light NREM sleep (informative versus Current Biology 30, 2411-2417, June 22, 2020 ª 2020 The Authors. Published by Elsevier Inc. 2411 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Figure 1 .

 1 Figure 1. Morning Nap Cocktail-Party Paradigm

Figure 2 .

 2 Figure 2. Selective Amplification of Informative Speech across Wakefulness and Sleep (A) The reconstruction scores for informative (blue) and Jabberwocky (red) speech reveal preserved encoding of auditory speech and a selective amplification of the informative speech across wake, light NREM sleep, and REM sleep. Mean and SEM across participants are represented as filled circles and solid vertical lines, respectively. Stars show significance levels against 0 and differences across story types for each vigilance state (***p < 0.001, **p < 0.01, *p < 0.05). See also Figures S1C-S1E. (B) Decoding performances show reduced selective amplification of informative stories during REM sleep compared to wake. Mean and SEM across participants are represented as respectively filled circles and solid vertical lines. Stars show significance levels against chance (dotted line, 50% for each vigilance state) and across vigilance state (***p < 0.001, **p < 0.01, *p < 0.05). (C) Reconstruction scores show a selective modulation of informative speech between wake and REM sleep that is absent for the Jabberwocky speech. Each data point represents the reconstruction scores for each story type across vigilance states for each individual subject (N = 18). Solid lines depict SEM and are centered on the average across subjects for each story type. Distribution of the difference between vigilance states for each story type are modeled by a Gaussian curve and are represented in the top right corner. Solid black lines represent the mean, shaded areas mark the first and third quartiles, and solid lines represent the whiskers. Stars show significance levels against 0 (***p < 0.001, **p < 0.01, *p < 0.05).

Figure 3 .

 3 Figure 3. Selective Suppression of Informative Speech in the Presence of Eye Movements (A and B) The amount of eye movements (EMs) per trial was negatively correlated with the reconstruction scores for the informative (A) but not for the Jabberwocky (B) speech. Values were Z scored for each participant (N = 18) and binned for visual purposes (n = 10 bins on the sorted number of EMs). Mean and SEM across participants are represented as respectively filled circles and solid vertical lines. The dotted line indicates the linear regression fit between variables, and significance of Pearson's correlation scores is reported (***p < 0.001). (C) Time course of the reconstruction score around the onset of EMs shows a drop in the selective processing of the informative speech during EMs. Reconstruction scores of each auditory stream were computed on 4 s sliding windows with 100-ms steps from À10 s to +10 s around the onset of EMs (t = 0 s). Mean and SEM are represented respectively with solid lines and shaded areas. Blue horizontal line denotes significant clusters of reconstruction scores differing from 0 for informative speech (p < 0.05 after cluster correction; STAR Methods). See also Figures S2 and S3A.

  1,2 and Delphine Oudiette1,2 Some studies suggest a link between creativity and rapid eye movement sleep. Narcolepsy is characterized by falling asleep directly into rapid eye movement sleep, states of dissociated wakefulness and rapid eye movement sleep (cataplexy, hypnagogic hallucinations, sleep paralysis, rapid eye movement sleep behaviour disorder and lucid dreaming) and a high dream recall frequency. Lucid dreaming (the awareness of dreaming while dreaming) has been correlated with creativity. Given their life-long privileged access to rapid eye movement sleep and dreams, we hypothesized that subjects with narcolepsy may have developed high creative abilities.

To test this assumption, 185 subjects with narcolepsy and 126 healthy controls were evaluated for their level of creativity with two questionnaires, the Test of Creative Profile and the Creativity Achievement Questionnaire. Creativity was also objectively tested in 30 controls and 30 subjects with narcolepsy using the Evaluation of Potential Creativity test battery, which measures divergent and convergent modes of creative thinking in the graphic and verbal domains, using concrete and abstract problems. Subjects with narcolepsy obtained higher scores than controls on the Test of Creative Profile (mean AE standard deviation: 58.9 AE 9.6 versus 55.1 AE 10, P = 0.001), in the three creative profiles (Innovative, Imaginative and Researcher) and on the Creative Achievement Questionnaire

(10.4 AE 25.7 versus 6.4 AE 7.6

, P = 0.047). They also performed better than controls on the objective test of creative performance (4.3 AE 1.5 versus 3.7 AE 1.4; P = 0.009). Most symptoms of narcolepsy (including sleepiness, hypnagogic hallucinations, sleep paralysis, lucid dreaming, and rapid eye movement sleep behaviour disorder, but not cataplexy) were associated with higher scores on the Test of Creative Profile. These results highlight a higher creative potential in subjects with narcolepsy and further support a role of rapid eye movement sleep in creativity.

Table 1

 1 Demographic and clinical characteristics of controls and subjects with narcolepsy

	Group

Treated patients, n (%) 138/168 (82.1) 1/82 (1.2) 50.0001 Stimulants, n (%) 114/168 (67.9) 1/82 (1.2) 50.0001 Anti-cataplectic drugs, n (%) 52/168 (30.1) 0/82 (0) 50.0001 Sodium oxybate only, n (%) 44/168 (23.8) 0/82 (0) 50.0001

Measures are presented as the mean AE standard deviation or n (%). NA = not applicable.

Table 2

 2 Impact of narcolepsy symptoms and medical treatments on the scores of the creativity questionnaires in subjects with narcolepsy

	Scores	TCP (0-100)		P	CAQ		P
	Patients	With	Without		With	Without
	Cataplexy	58.7 AE 9.3	59.6 AE 10.1	0.51	10.1 AE 25	8.3 AE 15.5	0.59
	Hallucinations	60.8 AE 8	57.7 AE 10.5	0.03	8.3 AE 14.4	10.3 AE 26.3	0.56
	Sleep paralysis	62.1 AE 8.4	57.2 AE 9.8	50.001	9.4 AE 15.2	9.5 AE 25.1	0.97
	Clinical REM sleep behaviour disorder	61.8 AE 8	57.8 AE 10.1	0.01	10.5 AE 22.2	9 AE 8	0.69
	Lucid dreaming	61 AE 0.9	57.3 AE 1	0.009	13.6 AE 29.8	8 AE 22	0.09
	Medical treatments a
	Stimulant drugs	59.6 AE 9.6	59.9 AE 10.1	0.87	8.6 AE 15.6	17 AE 41.3	0.08
	Anti-cataplectic drugs	56.7 AE 9.9	59.9 AE 10.1	0.17	5.8 AE 12.3	17 AE 41.3	0.07
	Measures are presented as the mean AE standard deviation.

a

Comparison with untreated patients (control, n = 30).

Table 3

 3 Correlations between measures of sleepiness, depressive and anxiety symptoms, and the scores on the creativity questionnaires in subjects with narcolepsy

	Correlation with	TCP	P	CAP P
	Hospital Anxiety and Depression Scale
	Depression score, 0-21	0.23	0.03 0.11	0.30
	Anxiety score, 0-21	0.16	0.13 0.03	0.8
	Measures of sleepiness
	Epworth sleepiness score, 0-24	0.19	0.01 0.12	0.11
	Mean daytime sleep latency, 0-20 min	0.15	0.08 0.10	0.26
	Sleep onset in REM periods, 0-5	À0.001 0.99 0.08	0.33

  | 1997 Downloaded from https://academic.oup.com/brain/article-abstract/142/7/1988/5506053 by PCH AGAM user on 24 July 2019

Table S1 -Demographic and clinical characteristics of the subjects with narcolepsy and of the healthy controls having completed the Evaluation of Creative Potential test

 S1

	Group	Narcolepsy	Control	P-value
	No.	30	30	NA
	Age, y	38.9 ± 14.8 39.5 ± 16.2	0.88
	Education level, 1-7	6.5 ± 1	6.2 ± 1.2	0.30
	Female gender, N (%)	14/30 (46.7) 17/30 (56.7)	0.51
	Right-handed, N (%)	25/30 (83.3) 27/29 (93.1)	0.37
	Narcolepsy symptoms
	Epworth sleepiness score, 0-24	13.9 ± 5.4	6.7 ± 2.7	<0.0001
	Cataplexy, N (%)	16/30 (53.3)	0/30 (0)	<0.0001
	Sleep paralysis, N (%)	11/30 (36.7) 1/30 (3.3)	0.004
	Hypnagogic hallucinations, N (%)	15/30 (50)	1/30 (3.3)	<0.001
	Clinical REM sleep behaviour disorder, N (%) 8/30 (26.7)	1/30 (3.3)	0.009
	Lucid dreaming, N (%)	16/30 (53.3)	3/30 (10)	<0.001
	Hospital Anxiety and Depression Rating Scale
	Depression score, 0-21	6.7 ± 3.3	4.7 ± 3.0	0.013
	Anxiety score, 0-21	8.0 ± 3.9	6.8 ± 2.9	0.14
	Multiple sleep latency tests
	Mean sleep latency, min	5.9 ± 3	Not done	NA
	Sleep onset in REM periods, 0-5	3.2 ± 1.1	Not done	NA
	Treatments
	Treated patients, N (%)	26/29 (89.7)	0/30 (0)	<0.0001
	Stimulants, N (%)	21/29 (72.4)	0/30 (0)	<0.0001
	Anti-cataplectic drugs, N (%)	4/29 (13.8)	0/30 (0)	0.035
	Data are presented as the mean ± SD or N (%).

Table S2 -Comparisons of the scores on the questionnaires of creativity in untreated patients vs. patients treated with drugs prescribed for narcolepsy.

 S2

		Number of	Test of	P*	Creative	P*
		patients	Creative		Achievement
		concerned	Profile (0-100)		Questionnaire
	Untreated patients	30	59.9 ± 10.1	NA	17 ± 41.3	NA
	Treated patients	138	59 ± 9.7	0.66	8.2 ± 15.6	0.054
	Stimulant (all)	114	59.6 ± 9.6	0.87	8.6 ± 15.6	0.08
	Methylphenidate	29	61.5 ± 8.3	0.51	9.6 ± 11.2	0.36
	Modafinil	68	58.6 ±10.3	0.56	7.3 ± 14.1	0.09
	Pitolisant	18	61.1 ± 6.7	0.65	12.2 ± 24.8	0.66
	Anti-cataplectic	52	56.7 ± 9.9	0.17	5.8 ± 12.3	0.07
	drugs (all)
	Sodium oxybate	44	55.7 ± 10	0.	5.9 ± 13.2	0.10
				08
	Antidepressants	13	64 ± 7.8	0.20	10.1 ± 11.9	0.56
	Data are presented as the mean ± SD. * Comparison with untreated patients (control).

Table S3 -Association between narcolepsy symptoms and sleep measures and the mean score of the Evaluation of Creative Potential test in subjects with narcolepsy Score on the Evaluation of

 S3

		Creative Potential test (1-7)	T-test	P
	Patients	With	Without
	Cataplexy	4.5 ± 0.9	4.0 ± 0.9	-1.51	0.14
	Hallucinations	4.2 ± 1.0	4.4 ± 0.9	0.44	0.66
	Sleep paralysis	4.4 ± 1.1	4.2 ± 0.8	-0.54	0.59
	Clinical REM sleep	4.5 ± 0.7	4.2 ± 1.0	-0.64	0.53
	behaviour disorder
	Lucid dreaming	4.3 ± 1.1	4.3 ± 0.8	-0.009	0.99
			Correlations coefficient	P
	Hospital Anxiety and Depression Scale
	Depression score, 0-21		0.01		0.94
	Anxiety score, 0-21		0.21		0.29
	Epworth sleepiness score, 0-24	0.08		0.67
	Mean daytime sleep latency, 0-20 min	-0.15		0.45
	Sleep onset in REM periods, 0-5	-0.31		0.10

Table S4 -Correlations between raw scores for each of the four divergent subtests of the EPoC and their standard scores on the Likert (7-point) scale

 S4

	Spearman Rho	P
	Divergent
	Graphic (abstract) Graphic (concrete) Verbal (story beginnings) Verbal (story endings) Sleep onset is a creative sweet spot 0.98 <0.001 0.97 <0.001 0.97 <0.001 0.98 <0.001 2

Transition

In Study 1, we discovered that patients with narcolepsy exhibit a higher creative potential, implying that REM sleep has an impact on the long-term development of creativity. However, patients with narcolepsy not only have privileged access to REM sleep, but also to the sleep-onset period, which they frequently traverse throughout the day. Is sleep onset associated with enhanced creativity? This question will be addressed in Study 2.

2.1 Digest

State of the art

Wagner et al. (2004)

experimentally established the adage 'Sleep on it' by demonstrating that sleep inspires insight (i.e., the sudden discovery of a solution

  Such a procedure was inspired by the famous inventor Thomas Edison, who allegedly napped while holding spheres in his hands. He reckoned that the spheres would noisily drop as soon as he fell asleep, waking him up just in time to capture sleep-inspired ideas. Here, we tested Edison's intuition that there is a fleeting, propitious moment to insightful thoughts within the sleep onset period (e.g., reaching the hypnagogic period, but awakening before deeper sleep arises). Throughout the break, participants' vigilance state was monitored via videopolysomnography (simultaneous EEG, EOG, and EMG recordings). After the break, subjects were tested again on 9 blocks of NRT (Post phase).

Table S2 . Predicting insight with pre-nap, nap and post-nap features

 S2

				Wake		N1		N2	P
	MSE stands for Micro-Sleep Episodes, and MSE duration refers to MSE cumulated time. p-values
	Demographic data are displayed (ANOVA or Kruskal-Wallis Tests when appropriate: ordinal data or violation of
	normality assessed with the Shapiro-Wilk test) and significant differences between groups are
	Age highlighted in bold. When appropriate, post-hoc comparisons (Tukey-Kramer) have been 23.22 ± 3.57 23.17 ± 3.56 24.79 ± 4.49 0.43
	Laterality (right-handed), % computed and we report significant differences between Wake and N2 (+), Wake and N1/N2 (*), 81.63 79.17 78.57 0.95
	Gender, women, % N2 and Wake/N1 (#), and between all groups ($). 77.55	58.33		85.71	0.12
	Educational level			6.65 ± 1.57		6.63 ± 1.82	6.79 ± 1.22	0.41
	Epworth Score			9.47 ± 2.65		9.21 ± 1.69	9.14 ± 2.21	0.56
	Field of expertise (1-4)	2.02 ± 1.22		2.04 ± 1.04	2.08 ± 1.0	0.86
	Cofactors
	Coefficient	Estimate	Standard-Error	Chi-squared	P-Value
	Accuracy Pre, %			94.51		92.78		93.69	0.76
	Solving Time Pre, secs Sleep Group	-0.64	13.93	0.27	15.24		15.21 6.24	0.24 0.01 (*)
	PVT RT Pre, msecs			303.3 ± 58.9	294.3 ± 35.2 312.8 ± 36.1 0.23
	PVT RT Post, msecs Delta_Power		-0.71	281.1 ± 26.4 0.31	281.7 ± 30.4 304.4 ± 38.5 0.03+ 5.99 0.01 (*)
	Motivation (1-5) Sleepiness (1-5) Alpha_Power 2		-0.72	3.43 ± 1.08 2.50 ± 1.07	0.30	3.21 ± 1.24 2.90 ± 0.85	3.08 ± 1.19 2.58 ± 1.00 8.11	0.49 0.40 0.004 (**)
	Concentration (1-5)			3.29 ± 0.76		3.04 ± 1.05	3.31 ± 0.85	0.57
	Boredom (1-5)			1.84 ± 0.92		2.02 ± 1.04	2.58 ± 0.81	0.03+
	Enigma expertise (1-3)	1.37 ± 0.67		1.54 ± 0.72	1.29 ± 0.61	0.36
	Sleep data
	Wake duration, min			21.60 ± 1.19	20.15 ± 1.76 15.43 ± 4.28 <0.001$
	N1 duration, min			0 ± 0		1.35 ± 1.20	1.79 ± 2.11	<0.001*
	N2 duration, min			0 ± 0		0 ± 0		4.18 ± 2.94	<0.001#
	Sleep latency, min			NA		14.46 ± 4.45 11.18 ± 5.04 0.04
	MSE data
	MSE duration, min			0.16 ± 0.25		1. 90 ± 1.13	2.30 ± 1.89	<0.001*
	Wake-to-MSE transitions, N	2.16 ± 3.50		14.29 ± 9.82 15.57 ± 10.96 <0.001*
	Bottle data
	% of subjects with drop	34.69		79.17		78.57	<0.001*
	MSE duration before fall, secs 3.0 ± 6.38		64.77 ± 43.51 75.11 ± 61.89 <0.001*
	MSE bouts before fall, N	0.83 ± 1.79		8.35 ± 6.24	9.16 ± 6.46	<0.001*
	Last MSE before fall, secs	1.39 ± 2.17		10.31 ± 7.28 8.21 ± 6.8	<0.001*
	Table S1. Demographic and sleep characteristics of each group. Measures are presented as the
	mean ± the standard deviation or in percentages. NWake = 49, NN1 = 24, NN2 = 14. Educational

level is scored according to the International Standard Classification of Education

[START_REF] Schneider | Class and Stratification Analysis[END_REF]

. Fields of expertise are categorized as follows: 1 for Sciences, 2 for Humanities, 3 for Arts, and 4 for Others. Enigma expertise is self-reported with 1 for No, 2 for A little, and 3 for Yes. Subjective ratings of motivation, sleepiness, concentration, and boredom were collected at the start of the Post phase. . Estimates, Standard Errors, Chi-Squared and P-values for Model 3 (Insight ~ 1 + Sleep_Group + Delta_Power + Alpha_Power 2 ), the best fitting model obtained in our analyses. Significant effects are highlighted in bold fonts.

Table 1 .

 1 Subjects in the Wake

	and N1 groups only differ by the amount of time spent in N1 (respectively, 0 vs. 4.40 ± 3.75
	min). Subjects in the N2 group spent a similar amount of time in N1 than the N1 group (mean

± SD = 3.78 minutes ± 3.57, z = 0.45, p = 0.65, Mann-Whitney U test; BF01 = 2.86) plus an average of 7.22 (± 5.02) minutes in N2.

Table 1 -Demographic and sleep characteristics of each group

 1

. Measures are presented as the mean ± the standard deviation or in percentages for proportions. N Wake = 16, N N1 = 15, N N2 = 21. Educational level was scored according to the International Standard Classification of Education

  Indeed, one could imagine the bottle as a screening test at the DMV, allowing to select and refer to the sleep lab, individuals who let the bottle fall after a mean sleep latency of less than 19 minutes (this timing is used to determine if someone can drive). Finally, because the bottle detects micro/local sleep episodes (undetectable on polysomnographic recordings) in 'awake' subjects (according to the standard criteria), this technique may be even more accurate than the existing one. Indeed, these local sleep episodes, accompanied by muscle relaxation (which caused the bottle to drop), may be su cient to cause a car accident (e.g., releasing the steering wheel of the car).

	These sessions must be terminated if three consecutive epochs of sleep (whether N1,
	N2 or REM sleep) are detected. However, in practice, all sessions are often completed
	regardless of what happens (no sleep, brief sleep episodes, or even long naps), because
	otherwise, sleep doctors or technicians would have to x the screen (without nodding
	or being distracted) for 40 minutes 4 times per day and score EEG traces in real-time.
	This is not always possible due to hospital constraints and it could lead to premature
	termination of the tests, if done incorrectly.
	But, it would be feasible with the bottle as a termination signal, as it does not require
	polysomnographic recording or sleep scoring. Patients would just be instructed to call
	the technicians if it dropped to end the test. Thus, this method could save both medical
	personnel and patients considerable time. It could also result in signi cant
	cost savings for hospitals by potentially freeing up some beds quicker, saving on
	polysomnographic expenses, if being done in Department of Motor Vehicle (DMV)
	o ces.

This test, known as the 'Maintenance of Wakefulness Test' (MWT), consists of four 40-min sessions during which participants sit in a dimly lit bedroom and are instructed to stay awake, without being permitted to employ any technique to help them stay awake (e.g., pinching, singing, talking, etc...). This test is typically used to evaluate if a patient is t to drive safely without endangering himself or others.

  6.2 Sleep onset, an ideal research modelWe have already discussed whether the N1 stage could provide a creative advantage over REM sleep, but this is still an open question. However, one thing is certain: the N1 stage has a practical advantage over REM sleep for researchers. Indeed, because REM sleep usually occurs after 90 minutes of sleep, it is di cult to collect easily and rapidly in the laboratory. But more importantly, researchers investigating REM sleep are also confronted with the 'contamination problem': because REM sleep is preceded by other sleep stages (at least in healthy subjects), it is impossible to isolate the speci c e ect of

REM sleep on a cognitive process. None of these di culties exist when studying the N1 stage: it is readily accessible, facilitating the collection of large amounts of data in a short period of time, which is essential for replication and experimenters (no time or money wasted). Moreover, we can avoid the contamination problem by preventing deeper sleep from supplanting N1 with methods such as our bottle. We thus have a unique opportunity to easily and rapidly investigate the e ect of a single, pure sleep stage on a variable of interest (e.g., memory, creativity, etc...).

  ). Subjects could practice entering and remaining within this creative sweet spot (e.g., with alpha/theta neurofeedback techniques, see below for more details). They could then use this state to escape rational thinking and explore all of the world's possibilities, which could help them solve day-to-day problems (i.e., problem It's like producing a movie, just more flexible with my angle of view and props. I cannot control what I will see next, new scenes just switch in with no relation to previous ones... It helped me understand more about what I can do with hallucinations, I've never spent this much time unconstrained and free, it helps me see more in the world. I started with a tree, but actually I can walk everywhere with this task, I can walk into the lego o ce and see it covered in trees, I can walk into my own building and see my professor turn into a tree, it became supremely absurd, but that made me see all ways I could use a tree in ways I never imagined before, so now I can see the trees with a new sort of eye because I will maybe continue challenging myself on the task. And it inspired me to learn a new way of thinking... Just now it was as if my scenes I was seeing changed every once a second, so that new ways to use a tree came up every second and I couldn't even keep up... at the beginning of my dreaming experience I was seeing scenes that were the same size and functionally as real trees, but then the second time I was much bigger than the trees and I could eat them like nger food. You wouldn't come up with that idea at the beginning, but this time I had hundreds of them. The hope is to break out of the banal stories. Which is why I liked this experiment.

incubation), nd creative inspiration, or simply enjoy this fantastic world. Horowitz (2019)'s unpublished data, which I previously mentioned, provide promising results in this regard. Indeed, the authors observed that incubating the word 'tree' during sleep onset favored subsequent creativity around that word. Here is an example of one of the hypnagogia they collected using this method

(Horowitz, 2019)

: "

  . However, the above studies focused mainly on the impact of sleep a posteriori on the consolidation of motor memory, but do not inform us on what happens during sleep. Indeed, because studying what happens during sleep is di cult, the precise sleep-dependent dynamic neural changes that drive motor memory consolidation remain unclear.

  Pendant que l'équipe retire les électrodes collées sur le crâne de Léticia, cette dernière apprend l'existence de la règle cachée. Une révélation qui ne la surprend pas vraiment. « Lors de la seconde phase de test, j'avais le sentiment qu'il y avait une astuce à trouver, mais dès que je commençais à y réfléchir, on passait à la série suivante. » Aurait-elle eu un « moment eurêka » si elle avait pu s'assoupir pendant la pause ? Impossible à dire. En tout état de cause, ses résultats rejoindront ceux du groupe témoin rassemblant les participants ne s'étant pas endormis, et pourront être comparés à ceux du groupe des « dormeurs » ayant laissé tomber leur gourde. « Notre objectif est d'avoir au moins trente participants dans chaque groupe pour voir s'il y a des différences significatives au niveau du nombre d'insights rapportés », précise Célia Lacaux. La doctorante rappelle à quel point il est difficile d'avoir des données lorsque l'on étudie le sommeil : « Même si nous choisissons des gros dormeurs qui affirment pouvoir s'assoupir un peu n'importe où, arrivés ici, ils savent qu'ils sont filmés, que tout est enregistré. Et le contexte hospitalier est assez stressant, surtout en ce moment. » Quant aux appareils développés pour effectuer des polysomnographies à domicile, la doctorante les juge encore trop peu précis. Les chercheurs doivent donc s'armer de patience et répéter les tests sur des dizaines de personnes jusqu'à obtenir des résultats statistiquement exploitables. À l'heure où nous écrivons ces lignes, sur vingt-quatre participants au test, dix se sont assoupis pendant la pause, lâchant leur gourde au sol. Parmi eux, six ont réussi à trouver la règle cachée. 60 % d'insight dans le groupe des « dormeurs », c'est plus du double que dans le groupe témoin. S'il est encore trop tôt pour tirer une conclusion avec un échantillon aussi restreint, ces résultats semblent plus que prometteurs. n Speech is encoded in cortical activity during rapid eye movement (REM) sleep d Informative speech is selectively processed over meaningless speech during REM sleep d Informative speech is on the contrary selectively suppressed during eye movements within REM

		Report
	Sleepers Selectively Suppress Informative Inputs
	during Rapid Eye Movements
	Highlights	Authors
	d A neural decoder tracks speech processing in a cocktail-	Matthieu Koroma, C elia Lacaux,
	party paradigm during sleep	Thomas Andrillon,
		Guillaume Legendre, Damien L eger,
		Sid Kouider
		Correspondence
		sid.kouider@ens.fr
	Master 2 research paper In Brief
		Sleepers are sensitive to stimuli during
		rapid eye movement (REM) sleep, yet how
		they protect internal activity from external
		interference is poorly described. Koroma
		et al. show that informative signals are
		overall selectively amplified during REM
		sleep but conversely selectively
		suppressed during eye movements, a
		marker of dreaming activity.
	Pendant que Léticia
	se repose, l'équipe
	surveille sur un
	polysomnographe
	son activité
	cérébrale (courbes
	noires), ses
	mouvements
	oculaires (en
	rouge) et le tonus
	musculaire de son
	visage (courbe
	verte), donnant
	des indices sur
	son niveau d'éveil
	ou de sommeil.
		YANN CHAVANCE
		PHOTOS : FRANÇOIS GUÉNET/DIVERGENCE
		POUR SCIENCES ET AVENIR

OCTOBRE/DÉCEMBRE 2020 I SCIENCES ET AVENIR NUMÉRO SPÉCIAL I 37 D d

TABLE

  Reconstruction around eye movements during REM sleep d QUANTIFICATION AND STATISTICAL ANALYSIS SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. cub.2020.04.047.

d RESOURCE AVAILABILITY B Lead Contact B Materials Availability B Data and Code Availability d EXPERIMENTAL MODEL AND SUBJECT DETAILS d METHOD DETAILS B Stimuli B Experimental protocol B EEG recordings B Sleep scoring B Stimulus Reconstruction B

This paper was the result of my M2 research. viii

The rst criterion (predominance of the theta rhythm) is however necessary and su cient to score the N1 stage.

Historically, polysomnograhic traces were produced on paper using ink-writing pens, limiting the available space to epochs of 20-30 seconds. While sleep recordings are now conducted digitally, the practice of scoring sleep in

30-second windows has remained the standard approach in humans.

This comparison between the moment of sleep and death is a long-time story. The greek god Hypnos (sleep) and Thanatos (death) were brothers.

[START_REF] Hori | Topographical EEG changes and the hypnagogic experience[END_REF] also includes quiet wakefulness and the rst minutes of N2 within the sleep-onset period, emphasizing that it may begin earlier and end later than N1 stage.

Rightfully so, as it literally means 'leading to sleep', from the Greek terms: hypnos (sleep) and agein (guiding).

To judge creativity, experts often cite PotterStewart (1964): "I know it when I see it".

As the musician Charles Mingus said "Creativity is about more than just being di erent. Anybody can play weird, that's easy."

For more information, please nd his web-series here and a TED talk on the subject here. Of note, this notion could account for the phenomenon of 'multiple discoveries': individuals with relevant knowledge would come up with the same idea at the same time. As Victor Hugo so eloquently put it, "There is one thing stronger than all the armies in the world, and that is an Idea whose time has come. "

I confess that it adds little to the understanding of this thesis, but I wanted to include one of my favorite painters.

Sorbonne University, IHU@ICM, INSERM, CNRS UMR7225, F-75013 Paris, France

AP-HP, Ho ˆpital Pitie ´-Salpe ˆtrie `re, Service des Pathologies du Sommeil, National Reference Centre for Narcolepsy, F-75013 Paris, France

ReceivedSeptember 18, 2018. Revised February 18, 2019. Accepted March 27, 2019. Advance Access publication May 29, 2019 ß The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com Downloaded from https://academic.oup.com/brain/article-abstract/142/7/1988/5506053 by PCH AGAM user on 24 July 2019

Downloaded from https://academic.oup.com/brain/article-abstract/142/7/1988/5506053 by PCH AGAM user on 24 July 2019

Correlations are Pearson's correlation coefficients. 1994 | BRAIN 2019: 142; 1988-1999 C. Lacaux et al. Downloaded from https://academic.oup.com/brain/article-abstract/142/7/1988/5506053 by PCH AGAM user on 24 July 2019

English translation: "How many times (hundreds, thousands of times) has the night provided me with solutions to problems [...] I would nearly say that every two or three mornings, I discover an answer within myself, deposited at I am not sure what hour of the night."

Patients with narcolepsy also have a disturbed sleep consistent with a wake-sleep instability, as evidenced by a decreased delta power in their N1 stage (the same EEG frequency that we found to be detrimental to creativity) and an increased alpha power in REM sleep(Christensen et al.,

2015).

English translation: "This sleep just brings me halfway down. I sleep in it, but with a faint touch of wakefulness. I see, I hear, and feel the presence of beings and things, but sleep, by ltering them out, allows only a colorless idea of them to get through, which can be mixed with the ghostly life of my dreams. I am oating between two worlds."

English translation: "Ah! There will come a time when each profession will have its own dreamer, its oneiric guide, and each factory will have its own poetic o ce!"

Remerciements

Acknowledgements

We warmly thank the subjects with narcolepsy and the controls who participated in the study. The authors obtained permission from HoGrefe to reproduce parts of the EPoC material in this article.

Acknowledgments

: We thank S. Leu, P. Dodet, J.B. Maranci, B. Dudoignon and M.A. Dalloz for their help with sleep scoring, K.A. Paller for helpful comments, and M. Craig for providing us materials for the task. Funding: Doctoral school ED3C (CL), Research grant from Société Française de Recherche et Médecine du Sommeil (CL), and INSERM research endowment (DO). Author contributions: Conceptualization: CL, DO; Methodology: CL, TA, DO; Investigation: CL, CB, YI, AF; Visualization: CL, TA; Supervision: IA, DO; Writing: CL, TA, DO. Competing interests: The authors declare that they have no competing conflict of interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials, using the following link: https://osf.io/dkbw7/?view_only=19a6f2d822234f84a8675073173d065e

Acknowledgements

This research was supported by the doctoral school ED3C and by the SFRMS (Société Française de Recherche et Médecine du Sommeil). We thank S. Leu for helping with sleep scoring, C. Bastoul and A. Le Coz for helping with the acquisition of some data, and J. Frain for helping with the experimental task piloting.

ACKNOWLEDGMENTS

This research was supported by ANR grants (ANR-10-LABX-0087 and ANR-10-IDEX-0001-02), the European Research Council (ERC project METAWARE to S.K.), the CIFAR (to S.K.), the DGA (to M.K.), and the International Brain Research Organisation (IBRO) and Human Frontier Science Program (HFSP, LT000362/2018-L) to T.A. We thank Shihab Shamma and Daniel Pressnitzer for discussion and Nicolas Obin and Axel Roebel for their help in constructing the stimuli.

Funding

The study was financed by the Big Brain Theory grant from the IHU@ICM (Brain Research Institute) of Pitie ´-Salpe ˆtrie `re Hospital to I.A.

Data availability

The data that support the findings of this study are openly available at https://figshare.com/articles/Increased_creative_ thinking_in_narcolepsy/7795805.

Data availability

All the relevant data is available upon reasonable request. Inquiries should be directed to the corresponding author.

Competing interests

The authors report no competing interests.

Supplementary material

Supplementary material is available at Brain online. Divergent verbal (story beginnings): Similar to the divergent verbal (story endings) but participants were given the ending of a story and had to invent as many beginnings as possible in 10 minutes. Convergent graphic (concrete): Similar to convergent graphic (abstract) but with 8 concrete objects. Convergent verbal (story with characters): Subjects were asked to invent a story in 10 minutes in which there were three imposed main characters.

Supplementary figures

For each test, subjects received the instruction to name their work and to try to produce original drawings/stories, different from what the others could have produced.

SUPPLEMENTARY MATERIALS

Supplementary Text

Figs. S1 to S2

Tables S1 andS2 Supplementary Video

Author contributions

CL and DO designed the study. CL collected the data. CL and TA analyzed the data. CL, TA, and DO wrote the paper. DO and IA supervised the study.

Conflict of interest

The authors declare no competing interests.

SUPPLEMENTARY FIGURES

Results

We found no between-group di erence in the number of participants who discovered the rule (Figure 3.3; Wake = 16%, N1 = 12%, and N2 = 9%; χ 2 (2) = 0.59, p = 0.78). Of note, there was also no di erence between the Pre (6.35% of solvers) and Wake groups (p = 0.35). Overall, regardless of group, the percentage of solvers was low, with only 7 solvers post out of 59 subjects, corresponding to 11.86% of insight post. As a result, we were unable to conduct additional analyses due to a lack of statistical power. We plan on performing controls to gure out why this rule is so di cult to identify.

Part III GENERAL DISCUSSION

Harnessing the creative power of sleep

In the following chapters, I will discuss how to reconcile the present ndings with the existing literature on the topic. I deliberately chose the review format for two main reasons. First, I think it is important to have a broad perspective rather than one that is narrowly focused on my own results. Second, a cursory examination of the relevant literature reveals a muddled research area in need of clari cation. Indeed, given its relative contemporaneity, we only have a few studies to mull over. And these few studies used a wide variety of tasks and conditions to control for potential confounding variables, resulting in under-powered groups with small sample sizes (including ours).

Additionally, contradictory results are reported from one study to the next; even the few seminal papers that we keep citing (Cai et al., 2009;Stickgold et al., 1999;Wagner et al., 2004;[START_REF] Walker | Cognitive exibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving[END_REF] have rarely been replicated. In such a confused background, it is di cult to gain a clear picture of the speci c e ect of sleep on creativity. Yet, one thing that comes up is that sleep in general seems to have an impact on creativity (see Figure 4.4), but only in speci c circumstances. I tried my best to identify these key factors that intervene all along this intricate path, beginning with the Pre phase (i.e., setting the optimal conditions before sleep) and continuing with the resting phase, where I will discuss what may be important for boosting future performance. I will cover them in such a chronological order and include points of discussion on the current ndings throughout. However, even after accounting for these factors, we will see that we still struggle to reconcile our ndings with the literature, hinting that we might miss signi cant ingredients to reach a satisfactory model of sleep and creativity. I will propose ideas to explain some of the remaining inconsistencies, but I am well aware that these are just hypotheses among many others that could certainly better account for the literature (but I cannot nd better ones).

Consistently, in the literature, full night of sleep vs. nap (typically lasting 90 minutes) paradigms show a similar bene t of sleep on creative problem-solving (Figure 4.4), suggesting that sleep duration is not a crucial parameter for boosting creativity.

Mental content

Several authors have suggested that dreams play a part in creative problem-solving (Barrett, 1993;[START_REF] Ritter | Creativity-the unconscious foundations of the incubation period[END_REF]Stickgold and Walker, 2004), inspired by the numerous anecdotes of artists and scientists who reported ashes of creative inspiration after a dream. Consistent with this hypothesis, we found a positive correlation between creative pro les in patients with narcolepsy (Study 1) and both hypnagogic hallucinations and lucid dreaming abilities (typically associated with a high dream recall frequency). A year later, D'Anselmo et al. ( 2020) published a similar result, showing that hypnagogic hallucinations were positively linked with creative achievement. These ndings are in agreement with several other studies that report a relationship between dream recall frequency and creativity (Schredl, 1995;Schredl, 2000a;Schredl and Erlacher, 2007).

We tried to address this relationship between dreaming and creativity in Studies 2 and 3, but we found no link between task-related hypnagogia (or hypnagogia in general) and post-sleep insight (or with memory loss in Study 3). We did, however, observe a signi cant amount of task incorporation into hypnagogic content, even when restricting analyses solely to direct allusions to the task (as opposed to more speculative, metaphorical ones), such as hearing the sound '1,4,9' or visualizing some task images. This level of task-related hypnagogia (39.47% of reported hypnagogia) was even higher than that commonly observed in the literature (around 7.2% in Stickgold et al. (2000)), despite the fact that subjects in Stickgold's study played Tetris for several hours prior to sleep, compared to only 30 minutes here. This observation that hypnagogic content integrates recent awake experiences further lends credence to the notion that memory reprocessing takes place in the borderland between wakefulness and sleep. However, whether task-related hypnagogic experiences play a role in

Concluding Remarks

In the present thesis, we took a risky path by attempting to investigate two hazy processes: creativity and the doorway to sleep, but chose to trust our forefathers' intuitions, which were often correct.

Besides, we showed that this twilight zone that acts as the doorway to sleep is far from being trivial, as high-order cognitive processes occur during that period, with an impact on creativity comparable to (if not greater than) that seen after a full night of sleep. I hope that this work will contribute to the rehabilitation of this in-between zone (and of sleep in general). I also hope that, in the future, we will shift away from a binary view of wakefulness vs. sleep toward one that considers and values intermediate states. Indeed, there is now more and more convincing evidence to support the idea that we spend the bulk of our lives in what could be termed 'reveries', suggesting how crucial they must be. As [START_REF] Liberson | EEG Records, Reaction Times, Eye Movements, Respiration, and Mental Content During Drowsiness[END_REF] said on these "vague types of thinking in the midst of our daily activities": " It may represent a biological necessity to turn the environment out of focus, to blur for a time the 'here and now', and to allow a less realistic relationship to take place between various elements of our mental life.

"

Because this research area is still in its infancy, the main contribution of this thesis, in my opinion, is not so much to advance scienti c knowledge as it is to open up new avenues for future research with whispered suggestions. Therefore, I hope that I have persuaded you to continue constructing this modest but promising edi ce. As F.G. Hedge once said:

"

Dreaming is an act of pure imagination, attesting in all people a creative power, which, if it were available in waking, would make everyone one a Dante or Shakespeare.

"

What if it were available now... Appendices (p=0.008). However, these are only very preliminary analyses; we need the sleep scoring to establish the precise impact of each sleep stage on subsequent performance.

A.5 Expected results

• We anticipate a progressive regional shift in brain activity from a more cortical pattern during learning to a more subcortical pattern during recall (as seen in [START_REF] Vahdat | Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI (L. Davachi[END_REF]).

• We also expect to replicate [START_REF] Vahdat | Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI (L. Davachi[END_REF]'s ndings that the connectivity index (CI) of the learning pattern gradually returns to baseline and is replaced by the consolidation pattern whose connectivity would increase with time spent in NREM sleep (but not after resting periods; Figure A.6). When NREM sleep is followed by REM sleep, we may predict a greater reorganization of the motor trace (and perhaps even the emergence of a novel pattern). In contrast, for pure REM sleep naps (without prior NREM sleep), these changes may not take place if we consider that reactivation (which is mostly associated with NREM sleep) is the trigger of memory reorganization. • Following this line, higher changes in connectivity index could be observed for episodes of lucid REM sleep with a re-enactment of the motor sequence in sleep.

• We also hypothesize that the greater the variations in CI during sleep, the greater the following behavioral performance.

• At rst, we assumed that these modi cations would only a ect the complex sequence. However, given that the behavioral results suggest motor learning for the simple sequence, we could predict CI changes for this sequence as well. (phasic REM sleep,N = 8). Mean and SEM of reconstruction scores across participants are represented respectively as filled circles and solid vertical lines. Stars show significant difference against 0 and interactions across story types and vigilance states (***p < 0.001, **p < 0.01, *p < 0.05). See also Figures S2A andS2B.

DECLARATION OF INTERESTS

The authors declare no competing interests.

Report KEY RESOURCES TABLE RESOURCE AVAILABILITY

Lead Contact

Further information and requests should be directed to and will be fulfilled by the Lead Contact, Sid Kouider (sid.kouider@ens.fr).

Materials Availability

The stimuli that support the findings of this study are available from the corresponding author upon reasonable request.

Data and Code Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

42 French-native speakers (mean: 25.1 years old, min: 18, max: 31, 17 females) with self-declared normal hearing and no history of sleep disorders were recruited for this study. Participant sample size was chosen based on previous results using the same methodology [15]. Easy sleepers (Epworth Sleepiness Score, mean: 12.5, min: 10, max: 15) with normal or evening chronotypes (Morningness-eveningness score, mean: 42.1, min: 31, max: 54) were selected through online questionnaires to facilitate propensity to fall asleep in the morning. To increase sleepiness and ensure high REM sleep pressure during the experiment, participants were required to sleep about 30% less during the night preceding the experiment (verified with actimetry) and to arrive at 7.30 a.m. at the sleep laboratory. They were also deprived from stimulants (e.g., caffeine) the day of the experiment. The present protocol has been approved by the local ethical committee (Comit e de Protection des Personnes, Ile-de-France I, Paris, France) and all participants provided informed consent.

METHOD DETAILS Stimuli

Stimuli used for this study were identical to Legendre and colleagues [15]. Eighty meaningful (informative) and meaningless (Jabberwocky) stories, i.e., texts for which content words (e.g., nouns and verbs) were replaced by French pseudo-words, and matched in length, syntax, word-frequency and phonemic properties. Stories were generated using IRCAMTTS, a state-of-the-art Text-tospeech MATLAB software [39]. Using the IRCAMTRAX module of Logic Pro software (Apple), we then manipulated the voice to generate two copies of each audio story, one pronounced by a low-pitched voice and one by a high-pitched voice. Eighty pairs of informative and Jabberwocky were then formed and matched in duration and acoustic properties (73.57 ± 5.16 s, mean ± SD

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Let others pride themselves about how many pages they have written; I'd rather boast about the ones I've read.

-Jorge Luis Borges