Chloé Stengel
email: chloe.stengel@icm-institue.org

Marine Vernet

Julià L Amengual

MD Antoni Valero-Cabré
email: antoni.valerocabre@icm-institute.org

Tristan Moreau

Adrien Martel

Causal role of high-beta right fronto-parietal synchrony in the modulation of human conscious visual perception

Keywords: visual perception, TMS, cortical oscillations, inter-regional synchrony visual perception, TMS, neural noise, stochastic resonance, interhemispheric differences Conscious visual perception, Cortical oscillations, Crossmodal perceptual facilitation, Decision making, Signal Detection Theory, Rhythmic transcranial magnetic stimulation, Electroencephalography, Auditory cortex, Oscillatory entrainment

First, I would like to thank Toni who welcomed me into his team 5 years ago. Thank you for the trust you placed in me and for all the opportunities to learn, to travel, to meet passionate researchers that I was given as your student. Your energy and enthusiasm for the research we have done together pushed me to work harder and better and I am proud of what we accomplished.

A heartfelt thank you as well to Marine, who was my first teacher when I first came into the lab and knew nothing about EEG or TMS. I hope you can see through the work of this thesis how far I have come since then and I do believe a large part of everything I now know came from you.

You taught me to be rigorous in everything I do and you continue to be an example of this quality.

I would also like to thank Julià and Tristan, who were always generous with their time and shared their expertise with me when I had technical questions. Thank you for the precious feedback you gave me throughout the years and the new ideas you contributed to take our results even further.

Thank you to Anna, Monica and Fanny also who I may not have gotten to work very closely with but who have followed my journey and were always here with a smile and an encouraging word or available for any help I needed.

I would like to thank Marisa Carrasco for inviting me into her lab in NYU for two months and for offering me a wonderful learning experience, both scientifically and personally. I would also like to thank all the members of her lab who were so welcoming to me and a particular thank you to Yong-Jun Lin for his critical feedback on our TMS-EEG data.

Thank you to Gregor Thut and Valentin Wyart for accepting to be part of the committee following me through the year of my PhD. Thank you for your attentive feedback. I would also like to thank the members of the jury for my defense: Til Ole Bergmann, Laura Dugué, Vincenzo Romei, Rachel Sherrard and Gregor Thut for accepting to come review my work.

Thank you to the foundation Naturalia & Biologia for providing the funding which enabled me to present my work in international conferences and to visit a partnering lab in NYU.

Thank you to Clara, Chris and, a little bit later, Marcela, the little group of PhD students of the Frontlab. Thank you Clara for your generosity and your patience. You have helped me so much either by taking the time to come help me for experiments or simply by being right alongside me as we both experienced the same difficulties and frustrations and we solved them together.

Thank you Chris for your enthusiasm about science and research and for all the philosophical debates. Your passion for your work was an inspiration. Thank you Marcela for your kindness and your optimism. You are such a caring person and a single conversation with you is enough to make anyone feel better after a rough day.

A huge thank you to Hughes who has been a great friend to me ever since my very first year in the lab as a master student. Yes, you make very strange jokes sometimes but you are amazingly kind and you have always been there to cheer me on, believing in me and encouraging me to have ambition in my future as a researcher. I have faith that one day you will be able to start your own PhD and I am waiting for the day where I will be able to read your thesis! Thank you to all the students or visiting researcher who passed through the lab throughout the years, Frederica, Juan, Rocio, Lydia, Justine, Valentin, Roberto, Jeremy, Angelina, Fanny, Patrick and many others. All of you brought life into the lab and I am very glad to have met each and every one of you. I must add a very special thank you to those of you who helped me during my very long experimental sessions, either as pilot subjects or as an additional set of hands to set up the EEG. Thank you for not minding getting EEG gel all over your hands and clothes and for hours spent in a damp basement with me! And thank you to Adrien, who may only have joined the lab a few months before the end of my PhD, but who has been with me in the lab the whole long months of July and August while I was writing this thesis. It was a bonding experience! Enfin, en français, je voudrais remercier mes parents, qui m'ont soutenue pendant toutes mes études mais qui se sont toujours assurés qu'avant la réussite académique j'étais surtout heureuse dans ce que je faisais. Une mention particulière pour ma mère et sa 'pensée positive' qu'elle a fait marcher en ma faveur plus d'une fois.

Merci à ma soeur qui m'a toujours encouragée à sortir de ma zone de confort, à vouloir plus et à ne pas avoir peur de saisir de nouvelles opportunités. Je n'aurais pas été aussi ambitieuse dans mes études sans toi.

Un énorme merci également à Mathilde et Léa qui ont une telle confiance en moi qu'elles m'ont offert alors que j'avais à peine 20 ans ma propre médaille de prix Nobel, en pâte à modeler, certes, mais dont je ne suis pas moins fière et qui trône toujours sur ma bibliothèque.

SUMMARY

Two decades of studies on the role of oscillatory activity and network synchrony have provided extensive evidence supporting the contribution of these mechanisms to a large variety of cognitive processes and behaviors. In the domain of visuo-spatial attention, a process that mediates our ability to focus, select and extract relevant visual information from natural environments, theoretical and experimental evidence have suggested a role for high-beta phase synchrony, or the lack thereof, mediating top-down attentional influences on human conscious visual perception. Such contributions have proven to be site-and network-specific, hence calling for a systematic exploration of further coding contributions for fronto-parietal nodes in a bilaterally distributed network with bearing on orientation of attention and perception.

The studies included in the current doctoral dissertation used MRI neuronavigated Transcranial Magnetic Stimulation (TMS) in either rhythmic patterns designed to entrain highbeta oscillations or arrhythmic patterns designed to induce different levels of neural noise and desynchronization. TMS patterns were delivered trial-by-trial to the right and left Frontal Eye Fields (FEF) while participants carried out a visual detection task, in which they had to report the presence of lateralized near-threshold Gabors titrated at 50% visibility. In parallel, by means of concurrent scalp EEG recordings, we aimed to better understand the influence of entrained oscillations and noise patterns in the generation of frequency-specific synchrony, and ultimately assess the ability of the probed regions and TMS-coding patterns to modulate conscious access for near-threshold lateralized visual stimuli.

The INTRODUCTION of this dissertation summarizes the latest knowledge with regards to the role of oscillations, synchrony and neural noise in the coding, transfer and processing of information subtending the orienting of spatial attention and the modulation of visual perception. Complementarily, we also review the features and application of brain stimulation technologies, and in particular rhythmic TMS, to identify the relevant cortical regions and characterize the oscillation and synchronization/desynchronization-based coding mechanisms involved in enabling attentional orienting and the facilitation of conscious perception. The introduction is completed with a short section presenting the SPECIFIC AIMS, stating the underlying question pursued by the different studies of the dissertation, including their relevance, methodological approaches and a priori hypothesis and prediction for outcomes. A detailed section of GENERAL METHODS presents, discusses critically and justifies the choice of behavioral paradigms, stimulation technologies patterns, experimental designs and EEG recording, data processing and measures employed in the three sets of studies included in the dissertation.

The RESULTS section integrates 3 different projects using each time the format of scientific papers. The two papers included in PROJECT 1 used concurrent rhythmic TMS-EEG approaches (high-beta 30 Hz rhythmic TMS vs random TMS 4 pulse bursts) to probe the contribution of the right FEF to conscious visual sensitivity (d'), as measured from the Signal Detection Theory. They showed that high-beta rhythmic TMS patterns increase local and interregional synchronization in a right lateralized fronto-parietal attentional network. This outcome supports a causal role for episodic high-beta oscillations entrained prior to target onset in the facilitation of conscious visual perception, likely via top-down attentional orienting mediated by the fronto-parietal dorsal attentional network. The paper in PROJECT 2 uses very similar TMS-EEG approaches, probing the role of the left FEF with TMS patterns similar to those used previously (high beta 30 Hz rhythmic TMS vs 3 different non-frequency specific TMS 4 pulse bursts: non-uniform rhythmic, irregular and random patterns) inducing different levels of local noise during task performance. Our data showed that, in this region, arrhythmic or irregular patterns of TMS increased neural noise locally and also throughout nodes of the bilateral dorsal attentional network. None of the tested patterns showed an impact on perceptual sensitivity (d').

Nonetheless, based on prior evidence collected in our lab for an improvement of visual sensitivity following arrhythmic TMS bursts, we provide preliminary evidence for a causal relationship between TMS-induced optimal levels of neural noise and enhancements of conscious visual perception. Finally, the paper presented in PROJECT 3 explored the impact of different patterns of TMS-generated sounds sharing a similar temporal structure with the electromagnetic patterns tested in prior study projects (30 Hz rhythmic sham TMS, random sham TMS 4 pulse bursts and single sham TMS pulse) on evoked and oscillatory EEG activity and also conscious visual perception correlates. None of the clicking sound patterns were able to impact visual sensitivity (d') neither did they entrain frontal or fronto-parietal oscillations.

Nonetheless, irrespective of TMS pattern type, stimulation phase-locked oscillations in central contacts and decreased response criterion (c), rendering participants less conservative when making perceptual decisions.

Taking all studies together, we CONCLUDE that oscillatory and phase-synchrony contributions to visual perception probed with causal methods were site-, network-and patternspecific. To this regard, our TMS-EEG approach attested a potential influence of right frontal (right FEF) high-beta oscillations and fronto-parietal synchronization to conscious visual perception. In a homotopic left fontal site (left FEF), we obtain preliminary evidence of 'stochastic-resonance-like' effects of graded neural noise levels facilitating visual perception, but further studies will be needed to better pinpoint this finding. Finally, at difference with active electromagnetic TMS, sham TMS-generated sounds in rhythmic high-beta patterns failed to entrain rhythmic activity or modulate visual sensitivity. Stimulation wise, concurrent TMS-EEG recordings demonstrated the ability of some active TMS patterns to modulate, during their delivery, oscillatory activity and inter-regional cortical synchrony, while other active TMS patterns proved able to modulate neural noise levels in a TMS pattern-dependent manner. In the GENERAL DISCUSSION we highlight further interpretations of these results in the wider context of the existing literature on the anatomical and physiological correlates of spatial attention and the top-down modulation of visual perception as well as the future technological advances in the field of non-invasive brain stimulation to manipulate oscillations and synchrony for fundamental and clinical research.

RÉSUMÉ (Français)

Deux décennies de recherche sur le rôle de l'activité oscillatoire et de la synchronisation des réseaux neuronaux ont fourni de nombreuses preuves de la contribution de ces mécanismes à une grande variété de processus cognitifs et de comportements. Dans le domaine de l'attention visuo-spatiale, qui est notre capacité à se focaliser, sélectionner et extraire des informations visuelles pertinentes dans notre environnement naturel, des preuves théoriques et expérimentales soutiennent le rôle de la synchronie des oscillations neurales à une fréquence beta-haute (ou de son absence) dans l'attention et la modulation de la perception visuelle consciente. De telles contributions se sont révélées spécifiques à des sites corticaux et à des réseaux neuronaux, appelant ainsi à l'exploration systématique des stratégies de codage des noeuds au sein d'un réseau fronto-pariétal bilatéral de l'attention et de la modulation de la perception consciente.

Les études incluses dans ce mémoire de thèse doctorale utilisent la Stimulation Magnétique Transcrânienne (SMT) sous la forme des rafales rythmiques conçues pour entraîner des oscillations beta-hautes ou arythmiques afin d'induire différents niveaux de bruit neural et de désynchronisation des rythmes cérébraux. Les rafales de SMT sont délivrées essai-par-essai sur les champs oculomoteurs frontaux (en anglais, FEF) des hémisphères droit et gauche, tandis que les participants effectuent une tâche de détection visuelle dans laquelle ils doivent détecter et localiser à droite ou à gauche la présence d'une cible visuelle au seuil de détection (c'est-àdire, adapté en contraste à un taux de visibilité de 50%). En parallèle, au moyen d'enregistrements d'EEG de surface, nous avons cherché à mieux comprendre l'influence des oscillations ou du bruit neural entraînés par la SMT sur la génération de la synchronisation locale ou inter-régionale à une fréquence spécifique, et à évaluer la capacité des régions cérébrales étudiées et des rafales de SMT à moduler l'accès conscient des stimuli visuels latéralisés présentés au seuil de visibilité.

L'INTRODUCTION de ce mémoire résume l'état de l'art en ce qui concerne le rôle des oscillations, de la synchronie et du bruit neural dans le codage, le transfert et le traitement de l'information sous-tendant l'orientation de l'attention spatiale et la modulation de la perception visuelle. Par ailleurs, nous détaillons également les caractéristiques et l'application des technologies de stimulation cérébrale non-invasives, et en particulier de la SMT rythmique, pour identifier les régions corticales et caractériser les mécanismes de codage basés sur l'activité oscillatoire et la synchronisation/désynchronisation des réseaux impliqués dans l'orientation de l'attention et la facilitation de la perception consciente. L'introduction est complétée par une courte section présentant les OBJECTIFS SPÉCIFIQUES, exposant les questions sous-jacentes poursuivies par les différentes études incluses dans ce mémoire, y compris leur pertinence, leurs approches méthodologiques et leurs hypothèses et prédictions à priori. Une section détaillée de MÉTHODES GÉNÉRALES présente, discute de manière critique et justifie le choix du paradigme comportemental, des technologies et motifs de stimulation, du design expérimental ainsi que de l'enregistrement, du traitement de données et des mesures d'analyse d'EEG utilisées dans les trois séries d'études incluses dans ce mémoire.

La section RESULTATS intègre 3 projets différents présentés à chaque fois sous la forme d'un article scientifique. Les deux articles inclus dans le PROJET 1 utilisent des approches EEG-SMT rythmiques (rafales de SMT rythmique beta-hautes à 30 Hz versus rafales de SMT aléatoires de 4 impulsions : non-uniformes-rythmiques, irrégulières et aléatoires) afin d'explorer la contribution du FEF droit à la sensibilité visuelle consciente (d '), telle que mesurée par la Théorie de Détection du Signal. Les rafales de SMT rythmiques à une fréquence beta-haute augmentent la synchronisation locale et inter-régionale sur un réseau attentionnel fronto-pariétal latéralisé à droite. Ces résultats corroborent le rôle causal des oscillations épisodiques dans une bande de fréquence beta-haute entraînées avant l'apparition de la cible dans la facilitation de la perception visuelle consciente, probablement via des effets descendants de l'attention médiés par le réseau fronto-pariétal dorsal de l'orientation de l'attention. L'étude du PROJET 2 utilise des approches EEG-SMT très similaires à celles déjà mentionnées, pour explorer cette fois le rôle du FEF gauche, avec des rafales de SMT périodiques proches de celles utilisées précédemment (rafales de SMT rythmique à 30 Hz et 3 motifs de rafales de SMT non spécifiques en fréquence à 4 impulsions) induisant différents niveaux de bruit neural lors de l'exécution d'une tâche d'accès à la perception consciente. Nos données montrent que, sur cette région, des rafales non spécifiques en fréquence de SMT augmentent le bruit neural, localement et également tout au long des noeuds du réseau bilatéral de l'attention. Aucun des motifs de rafales de SMT délivrées n'a montré d'impact sur la sensibilité perceptuelle (d'). Néanmoins, selon des résultats antérieurs obtenus dans note laboratoire qui ont montré une amélioration de la sensibilité visuelle à la suite de rafales de SMT non spécifiques en fréquence, nous fournissons des preuves préliminaires d'une relation de cause-à-effet entre les niveaux optimaux de bruit neural induits par la SMT et les améliorations de la perception visuelle consciente.

Enfin, l'étude présentée dans le PROJET 3 examine l'impact de différents types de sons périodiques générés par les impulsions SMT, avec une structure temporelle similaire aux rafales électromagnétiques testés dans les projets d'études précédentes (rafales SMT placebo rythmiques à 30 Hz, rafales SMT placebo aléatoires de 4 impulsions et des impulsions uniques de SMT placebo), sur l'activité EEG évoquée et oscillatoire, ainsi que sur la perception visuelle consciente. Les rafales sonores ne montrent pas d'impact sur la sensibilité visuelle (d') ni aucuns signes électroencéphalographiques d'entrainement oscillatoire frontaux ou frontopariétaux. Néanmoins, quel que soit le type de rafale SMT placebo délivrées, elles ont abouti à une synchronisation en phase l'activité oscillatoire du cortex auditif et ont diminué le critère de réponse (c), engendrant des stratégies moins conservatrices lors de la prise de décisions perceptuelles.

Considérant l'ensemble de nos résultats, nous CONCLUONS que les contributions oscillatoires ou de la synchronie de réseau sur la perception visuelle consciente étudiées avec des méthodes causales sont dépendantes du site et réseau stimulé ainsi que de la structure temporelle de la rafale magnétique. À cet égard, notre approche SMT-EEG a attesté une influence potentielle des oscillations dans une bande de fréquence beta-haute au niveau du cortex frontal droit (FEF droit) et la synchronisation fronto-pariétale dans l'hémisphère droit sur la perception visuelle consciente. Dans la région homotope à gauche (FEF gauche), nous obtenons des preuves préliminaires d'effets présentant les mêmes propriétés que le phénomène de résonance stochastique, c'est-à-dire une facilitation de la perception visuelle par des niveaux de bruit graduels. Cependant, des études supplémentaires sont nécessaires pour identifier et confirmer les corrélats comportementaux de ce résultat. Enfin, à la différence des rafales de SMT beta-hautes actives, les rafales sonores rythmiques générées par la SMT placebo ne parviennent pas à entraîner d'activité neurale rythmique ni à moduler la sensibilité visuelle. En ce qui concerne la stimulation SMT, le couplage avec des enregistrements EEG nous a permis de démontrer la capacité de certaines rafales de SMT active à moduler l'activité corticale oscillatoire et sa synchronisation, tandis que d'autres motifs de rafales de SMT permettent la modulation du niveau de bruit neural. Dans la DISCUSSION GÉNÉRALE, nous présentons les interprétations de nos résultats dans le contexte plus large de la littérature existante sur les bases anatomiques et physiologiques de l'attention spatiale, la modulation de la perception visuelle consciente et les futurs développements technologiques dans le domaine de la stimulation cérébrale non invasive afin de manipuler les oscillations cérébrales et la synchronie à des fins expérimentales ou cliniques. Electrophysiological recordings of neural activity at any scale, either electroencephalographic (EEG) activity from large neuronal assemblies [START_REF] Berger | Über das Elektrenkephalogramm des Menschen[END_REF], in vivo local field potentials produced by local neuronal clusters [START_REF] Gray | Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex[END_REF], or in vivo [START_REF] Alonso | Subthreshold Na+-dependent theta-like rhythmicity in stellate cells of entorhinal cortex layer II[END_REF] and in vitro [START_REF] Draguhn | Electrical coupling underlies high-frequency oscillations in the hippocampus in vitro[END_REF] single-cell voltage changes, reveal patterns of rhythmic activity which have been referred to as neural oscillations. This neurophysiological phenomenon is characterized by highly regular, repetitive and synchronous activity patterns, which ensure the precise timing of neuronal activity, and can operate in a wide range of frequencies across brain sites and neural circuits.

Oscillations were initially reported as particularly prominent during sleep or in situations in which consciousness was decreased and neural systems did not seem to be involved in a specific behavior [START_REF] Steriade | Synchronized sleep oscillations and their paroxysmal developments[END_REF]. For this reason, they were considered unrelated to cognitive processes, and their physiological and behavioral role was long ignored, or considered an irrelevant by-product or epiphenomenon bearing no role on human behaviors. Two decades ago however, neural oscillations started to be revisited with renewed interest and since then the number of studies addressing the role of oscillations in cognitive functions such as memory, attention or perception has skyrocketed (reviewed in Buzsáki & Draguhn, 2004).

Amongst cognitive functions that have been widely shown to be subtended by oscillatory activity is attentional orienting in space. Attention is the process by which we select information in our crowded environment [START_REF] Desimone | Neural Mechanisms of Selective Visual Attention[END_REF]. In spite of the high processing power of the human brain, our senses have a limited capacity to simultaneously uptake information from the inner and outer environment. To face the challenge of overcrowded environments, to which we are often exposed, attention acts as a selective filter that allows us to allocate resources to the most task-relevant stimuli, hence enhance the perception of important inputs and suppress the perception of irrelevant distractors.

Such core function of attention requires brain systems to be able to segment incoming inputs and selectively enhance the processing of some of them at the expense of others which are suppressed. A decade ago, a framework was developed by which neural oscillations synchronized in phase across widely distributed neuronal assemblies connected by white matter pathways, could subtend these core mechanisms (Fries, 2009).

We will highlight the mechanisms by which neural oscillations could subtend the orientation of attention, then review empirical evidence supporting a link between this essential brain function and oscillatory activity and synchronization.

I.1 -Oscillations and synchronization in network communication and information transfer

Under normal conditions, the brain receives simultaneously a very high number of inputs from stimuli present in a visual scene. Each of these incoming stimuli will reach and activate neural assemblies in the early visual cortex. Converging input from several neuronal groups to common neuronal targets is a common neocortical connectivity motif [START_REF] Jones | AN ANATOMICAL STUDY OF CONVERGING SENSORY PATHWAYS WITHIN THE CEREBRAL CORTEX OF THE MONKEY[END_REF], especially in the visual cortex [START_REF] Salin | Visuotopic organization of corticocortical connections in the visual system of the cat[END_REF], hence inputs from competing visual stimuli present in a given visual environment will converge on similar neural assemblies in higher order visual areas.

As a result of this organization of input patterns, high level neurons possess wide receptive fields [START_REF] Gattass | Cortical visual areas in monkeys: Location, topography, connections, columns, plasticity and cortical dynamics[END_REF] and, at any moment in time, neuronal assemblies in higher order visual cortices can receive inputs generated by distinct objects and stimuli present in the visual field. These circuits assemblies cannot respond to several stimuli at the same time as it would give rise to a phenomenon that has been called the "curse of confusion through convergence" (Fries, 2009). To avoid this phenomenon, neural assemblies collecting the converging inputs from lower visual areas need to be able to segment the inputs into distinct visual stimuli to then be able to selectively respond to the inputs that correspond to visual stimuli relevant for the behavior at hand and ignore information from distractor stimuli.

Gamma-band oscillatory synchronization is a well-known mechanism to tackle the so called "binding problem", a phenomenon by which a set of individual features are bound together to build a unified representation of an object [START_REF] Singer | Visual Feature Integration and the Temporal Correlation Hypothesis[END_REF]. The phasesynchronization at gamma frequency between several neuronal assemblies serves to strengthen inter-regional communication and to create a dynamic network processing the same complex stimulus (Fries, 2005(Fries, , 2009;;[START_REF] Tallon-Baudry | Oscillatory gamma activity in humans and its role in object representation[END_REF]. In a seminal paper, Pascal Fries (2009) provides a detailed account on how gamma-band synchronization could help individual neurons deal with the above-mentioned functional segmentation of inputs by means of two mechanisms: feedforward coincidence detection and input gain modulation.

Consider two clusters of neurons entitled 'A' and 'B' both responding to competing stimuli in the receptive field of neurons in a cluster entitled as 'C'. Through a convergence of connectivity, both A and B sustain structural synaptic connections with neurons in C (Fig. 1A, left). Hence when two competing stimuli are present in C's receptive field, both A and B would fire and send an input to C. However, since A and B are involved in coding for competing stimuli, C is unable to respond to both inputs. Hence C has to be able to segment inputs from A and B in order to avoid confusion between the two.

As neurons in cluster A respond to a single object, through the binding by synchronization mechanism [START_REF] Singer | Visual Feature Integration and the Temporal Correlation Hypothesis[END_REF] they will locally synchronize, i.e. progressively phase-lock their activity at a gamma frequency. When all the neurons in A are synchronized, they will reach the phase of their oscillation cycle where the excitability of the neurons is maximal simultaneously and fire within a short time window. All the outputs from neuron group A will reach the dendrites of cluster C in close succession and, through summation of synaptic input, will have higher input gain, hence be more likely to depolarize neurons in cluster C. This phenomenon is called Feedforward Coincidence Detection and it will enable neurons in group A, when they are locally synchronized, to successfully depolarize C periodically when they send a volley of outputs at the excitable phase of their oscillation cycle, and hence, to progressively entrain neuron C to oscillate in a phase-locked manner with neurons in group A (Fig. 1A, right).

Once A and C are synchronized in phase a second mechanism comes into play to ensure that possible inputs from other groups of neurons cannot reach C. Indeed, once C is synchronized with A, its membrane potential oscillates at the same frequency, creating period of lower (i.e., more negative) transmembrane resting potential levels and therefore low excitability for neurons and periods of higher (i.e., more positive hence closer to firing threshold) transmembrane resting potential levels and high excitability. This input gain modulation provides a neuron in cluster C with the ability to selectively react either more strongly to inputs incoming in a phase-locked manner during high excitability phases or less strongly to inputs not synchronized to the rhythms of its oscillation hence coming randomly at any phase of its oscillation cycle (Fig. 1B). Such input gain mechanism would favor inputs coming from neural cluster A, which is synchronized with neurons of cluster C, while lowering the gain of inputs coming from cluster B not synchronized with the latter and therefore coming randomly at times of high or low excitability (Fig 1A,right). and low (bad) excitability phases for neuronal oscillators of cluster C. Neurons in cluster A are synchronized with neurons in cluster C so that inputs from neurons in A will always reach neurons in C during high excitability phases, ensuring a higher gain compared to inputs incoming from neurons in cluster B who reach neurons C at any phase. (Adapted from Fries, 2009).

This mechanism highlights the biased competition between neurons in group A and B.

Both mechanisms of Feedforward Coincidence Detection, which progressively entrains C with the neuron group sending it the most synchronized inputs, and Input Gain Modulation, which renders the input gain in C favorable only to inputs from neurons groups synchronized with it, create a competition between A and B and only one of these competing neuron groups can successfully send inputs to C through a winner-take-all mechanism. An exclusive communication link is established between C and A, excluding inputs from all other competing neuronal groups connected to C. Biased competition, subtended by inter-neuronal gamma-band synchronization, is therefore a very effective model to explain the selectivity of attention, that is its ability to respond to a single set of features in a visual scene containing an infinite number of stimuli [START_REF] Desimone | Neural Mechanisms of Selective Visual Attention[END_REF][START_REF] Reynolds | Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4[END_REF].

It is worth noticing that the emerging properties of selective attentional systems highlighted above emerge from a complex interplay between structural and functional connectivity. The former is essential to build, through learning and Hebbian rules, complex receptive fields in higher sensory cortices. The latter, articulated by means of inter-neuronal gamma-band synchronization, enables a dynamic segmentation of structural connections and a time and space-specific top-down modulation of visual processing via attention.

I.2 -Network synchronization subtending visuo-spatial attention and visual perception

Empirical evidence developed in the last 20 years supports the theoretical framework presented above. In favor of the Feedforward Coincidence Detection mechanism, studies in animal models have shown enhanced local gamma synchronization in visual areas in response to attended stimuli (Fries et al. 2001;Bichot et al. 2005). There is also evidence supporting the enhancement of gamma or high beta interregional synchronization between frontal, parietal and occipital areas either after the presentation of an attended target (Saalmann et al. 2007) or during the orientation of attention in space during a visual search task (Buschman & Miller, 2007). This evidence supports exclusive communication channels between different regions of the attentional network through phase synchronization during the orientation of attention. These findings in animal models have been replicated in healthy human with EEG/MEG recordings, showing the role of fronto-parietal synchronization at high-beta frequencies (15-40 Hz) during tasks manipulating attention (Gross et al., 2004;Phillips & Takeda, 2009) or conscious visual perception, a correlate of attentional orienting (Rodriguez et al. 1999;Hipp et al. 2011).

To further outline the importance of inter-regional synchronization at beta or gamma frequencies for cognitive activity, it should be noted that abnormal (enhanced or reduced) levels of neural synchronization have been shown to be relevant in many pathologies and neural disorders (Uhlhaas & Singer, 2006). In certain conditions, like in schizophrenia or autism, reduced gamma synchronization leads to deficits in object binding and perception [START_REF] Grice | Disordered visual processing and oscillatory brain activity in autism and Williams Syndrome[END_REF]Uhlhaas et al., 2006). In other conditions, such as post-stroke neglect patients, it is an abnormally enhanced local beta synchronization that is detrimental for attentional orienting and perception (Rastelli et al., 2013).

The highlighted evidence enables the characterization of a neural network for the orientation of attention and the top-down modulation of conscious visual perception as well as the relevant frequency bands allowing inter-regional synchronization in this network. The cortical regions of interest highlighted in most anatomical models for this cognitive functions of attention include the bilateral Frontal Eye Fields (FEF) and Intra Parietal Sulci (IPS) as well as visual areas in the occipital lobe like the medial-temporal region (MT) or the primary visual cortex (V1) (Buschman & Miller, 2007;Hipp et al., 2011;Saalmann et al., 2007) (Fig. 2A). The FEF and IPS, as part of the dorsal attention network, have long been identified as crucial regions for orienting attention in space (Corbetta and Shulman 2002;Corbetta et al. 2008) (Fig. 2B). Damage in these two regions has been found to be crucial to explain the deficits and recovery of spatial attention orientating abilities in post stroke neglect patients (Corbetta et al., 2005). In addition to the isolated activity of both of these regions, anatomical connectivity between frontal and parietal areas of attentional networks, supported by the three branches of a white matter tract called the Superior Longitudinal Fasciculus (SLF I, II and III) (Fig. 2C), has been shown to subtend the deployment of spatial attention in healthy participants (Marshall et al., 2015;Thiebaut de Schotten et al., 2011). Moreover, the disconnection of this tract can lead to visuo-spatial attentional deficits in neglect patients (Bartolomeo et al. 2012;Thiebaut de Schotten et al. 2014).

With regards to the relevant synchronization frequency bands for attention, seminal and influential work by Buschman and Miller (2007) in non-human primates highlighted rhythmic activity in the high-beta to gamma range subtending different mechanisms tied to attentional orienting. More specifically, these authors reported a double dissociation with gamma oscillations (35-55 Hz) subtending exogenous attentional orienting (e.g. bottom-up or involuntary) whereas high-beta oscillations (22-34 Hz) underlay endogenous (e.g. top-down or voluntary) attentional processes. Most studies in non-human primates correlated a broad range of frequencies (from 25 to 90 Hz) to the orientating of spatial attention (Bichot et al., 2005;Fries et al., 2001;Saalmann et al., 2007). Nonetheless, analogous follow-up studies in humans reported similar correlations for a narrower range of lower frequency bands (between 15 and 40 Hz), more consistent with high-beta than gamma activity (Gross et al., 2004;Hipp et al., 2011;Phillips & Takeda, 2009;Rodriguez et al., 1999).

Neural oscillations in lower frequencies bands have been associated to other processes at play in spatial attention. Local synchronization in the parietal and occipital cortex in the alpha band is thought to inhibit processing of distractor stimuli (Foxe & Snyder, 2011;Klimesch et al., 2007;Thut et al., 2006;Worden et al., 2000) and oscillations at alpha (Dugué et al., 2011;Mathewson et al., 2009Mathewson et al., , 2011) ) or theta frequency (Huang et al., 2015;Landau & Fries, 2012;Landau et al., 2015) could pace the rhythmic sampling of attention, alternating periods of concentration and periods of shifts of attention. Indeed, a multi-frequency model has been proposed, which integrates the roles of gamma, beta, alpha and theta oscillations in the orientation and reorientation of attention (Fiebelkorn & Kastner, 2019). However, none of these lower frequency oscillations have been associated to inter-regional synchronization and communication (the contributions of frequency bands outside of gamma and high-beta is discussed in more detail in the General Discussion).

All the evidence reviewed above lead to the conclusion that synchronization in the frontoparietal dorsal attention network at a high-beta frequency is related to the orienting of visuospatial attention and conscious visual perception. However, the studies reviewed so far obtained their conclusions from correlations between LFPs or EEG recordings and performance outcomes in attentional and visual perceptual tasks. Consequently, the correlational nature of this evidence did not allow to establish any causal link between these two phenomena cooccurring in time, and could not rule out that cortical oscillations and interregional synchronization patterns were merely epiphenomena, holding no direct contribution to the neural coding subtending cognitive computations. It was only a decade ago, that a new attempt to push progress in this field explicitly advocated to move beyond correlations and called for a need of direct manipulation of rhythmic activity (either to temporally enhance, suppress or replace it) to unearth causal links between cortical oscillations and the modulation of attentional and visual behaviors.

II -Manipulation of brain oscillations subtending attentional and visual behaviors

Traditional As indicated above, direct electrical brain stimulation delivered through intracranial implanted electrodes in patients with medication resistant epilepsy (brief 5-10 second trains of 1, 50 or 60 Hz) to identify seizing foci or deep brain stimulation of the subthalamic nucleus in Parkinson's patients (at high frequency, 90-180 Hz) to prevent tremor, bradykinesia or rigidity provide very interesting opportunities to causally explore the role of oscillations in healthy and pathological structures of unsound brains (Amengual et al., 2017;Cleary et al., 2012).

Nonetheless, in spite of the high spatial and temporal precision and optimal signal-to-noise ratio for intracranial stimulation and recordings, implantation schemes are obviously guided by clinical criteria and hence show considerable variability across patients and provide a very sparse coverage of the cortex. Non-invasive stimulation methods enjoy much more flexibility to explore the same phenomena in a wide variety of cortical regions and patient or healthy subject populations.

II.1 -Non-invasive stimulation techniques to manipulate brain oscillations and synchrony

As summarized above, currently, two main non-invasive approaches have been used to entrain rhythmic activity in the human brain to improve cognition: (1) peripheral sensory stimulation, which uses auditory, visual or tactile sensory pulsed or oscillating patterns applied to peripheral receptors which are conveyed by bottom-up sensory pathways to influence brain systems and networks;

(2) transcranial brain stimulation via magnetic pulses or electrical current fields targeting a cortical area or circuit directly to influence its activity patterns.

Each of these two approaches have strengths and limitations in terms of focality (spatial resolution), timing control (temporal resolution), safety, financial cost, ease of use and portability. The former uses a rather physiological stimulation source which can be made very selective by capitalizing on the modality-specific (somatotopic, tonotopic and retinotopic) organization of afferent receptors and pathways. Nonetheless, its effect depends on the integrity of afferent pathways and these can be modulated (hence dispersed in spatial precision and attenuated in intensity) at every synaptic step from the peripheral receptor to the receiving cortical systems and beyond. The latter can directly target any cortical region with a level of selectivity that depends on the spatial resolution of each technological approach. Nonetheless, focal approaches (TMS) deliver rather intense electrical currents which are far from physiological, whereas un-focal methods (tCS) often lack precision and intensity to produce convincing impact on neurophysiological activity.

Both types of technologies represent unique tools to probe causal links between local and network-mediated oscillatory synchronization on circumscribed anatomical locations and the behavioral effects that these patterns might subserve. For this reason, they have been widely used in the last decade and provided causal evidence for a functional role of cortical oscillations in coding for cognitive functions.

II.1.1 -Rhythmic peripheral sensory stimulation for oscillatory entrainment

Peripheral sensory stimulation is based on conveying rhythmic sensory patterns through the sensory pathways able to reach and influence the activity of cortical systems. Sensory stimuli (usually auditory, visual or less commonly tactile) that are either pulsed (a transient stimulus that is presented repeatedly) or continuously oscillating at a fixed specific frequency can be easily applied to peripheral sensory receptors. Conveyed through afferent sensory pathways, they have been shown to entrain rhythmic activity in the brain within a frequency band which is dictated by the periodicity of incoming stimuli.

A quite common method for sensory entrainment is the use of a visual flicker, in which pulsed visual stimuli are rhythmically flashed while steady-state visual evoked potentials (SSVEPs) from brain systems at the frequency of the flicker (Srinivasan et al., 1999;[START_REF] Vialatte | Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives[END_REF] are recorded via EEG recordings. Similar procedures have been translated to other sensory modalities, and auditory stimuli modulated in amplitude or frequency (Galambos et al. 1981;Picton et al. 2003) presented monoaurally or binaurally via headphones or patterns of rhythmic tactile stimulation applied to skin mechanoreceptors by means of pulsed electrical stimulation, air puffs or piezo-electrical tactile stimulation devices have been used (Nangini et al., 2006).

Entrainment through afferent sensory stimulation typically increases local and interregional synchronization at the stimulus frequency in a wide range of brain areas, not limited to the primary sensory cortices receiving afferent information, but distributed all over the cortex and extending up to frontal systems (Srinivasan et al. 1999;Srinivasan et al. 2006;Srinivasan et al. 2007). However, signals have to progress throughout a whole hierarchy of sensory pathways and synaptic steps before reaching specific cortical regions. Hence this approach cannot achieve high levels of spatial focality and entrains oscillations in a rather widely distributed network, including sub-cortical regions [START_REF] Giraud | Representation of the Temporal Envelope of Sounds in the Human Brain[END_REF]. Moreover, once in primary sensory areas, to reach higher-level associative areas (e.g. frontal or prefrontal areas), input rhythms will need to progress across cortico-cortical relay pathways. Long and multisynaptic afferent subcortical and cortico-cortical pathway (which can be influenced or modulated by other inputs) imply larger time delays and timing variability, making the phase and amplitude of oscillations entrained at destination uncertain or unstable.

II.1.2 -Transcranial brain stimulation technologies for oscillatory entrainment

In this specific context, non-invasive brain stimulation (NIBS) technologies able to directly deliver rhythmic activity to circumscribed regions in the brain and newly entrain oscillations or modulate ongoing rhythmic activity are called to become very useful tools in exploratory or therapeutic endeavors. These approaches induce electric currents directly in the cortex, by-passing sensory cortices, for the entrainment of specific patterns of cortical activity (see Polanía et al., 2018 andValero-Cabré et al., 2017 for recent reviews). The two most widely used NIBS techniques to date are Transcranial Current Stimulation (tCS) and Transcranial Magnetic Stimulation (TMS). These two techniques have very different modes of action and therefore their own set of advantages and limitations.

II.1.2.1 -Transcranial alternate current stimulation approaches (tACS)

Transcranial Current Stimulation (tCS) is achieved by circulating a low intensity current

(1-2 mA, ~0.06 mA/cm 2 ) between at least two electrodes (an anode and a cathode) placed on specific regions of the human scalp (Fig. 3A &B). A substantial portion of the circulating current is generally shunted through the scalp skin (Vöröslakos et al., 2018). Nonetheless part of it will penetrate across the different tissue layers between the skin and the cortical surface (i.e., bone outer and inner tables, and the cerebrospinal fluid cumulated in the epidural and subdural spaces) to reach the pia-mater and spread across rather large cortical areas located between both electrodes [START_REF] Miranda | Modeling the current distribution during transcranial direct current stimulation[END_REF].

The current gradients will polarize electrical charges in the extracellular space in a polarity dependent manner, shifting the resting membrane potential of exposed neurons closer (anodal stimulation) or away (cathodal stimulation) from their firing thresholds, hence increasing or decreasing their probability to generate an action potential when receiving physiological dendritic inputs of sufficient intensity.

If instead of a constant current (tCS modality know as transcranial direct current stimulation or tDCS), an alternating current (AC) is applied, the resting membrane potential and consequently the firing rate probability of neurons influenced by the current field will also fluctuate periodically, following the frequency of the AC signal. This specific modality of tCS is referred to as transcranial Alternating Current Stimulation (tACS) and has been used to noninvasively entrain oscillations in cortical regions [START_REF] Fröhlich | Endogenous Electric Fields May Guide Neocortical Network Activity[END_REF]Herrmann et al., 2013;[START_REF] Merlet | From oscillatory transcranial current stimulation to scalp EEG changes: A biophysical and physiological modeling study[END_REF].

Although tCS devices delivering either tDCS or tACS are recognized as being portable and highly affordable compared to TMS (Fig. 3A &C), these technologies possess a rather poor spatial resolution. Given the diversity of possible electrode montages (particularly when density tCS approaches based on combination of several return electrodes in complex configurations are used) and interindividual differences in head anatomical features, it is not easy to predict how currents applied to the scalp will diffuse transcranially to reach the cortical surface. Indeed, it is generally accepted that induced brain currents will not remain restricted to cortical areas beneath the electrodes but will spread (Bikson et al., 2010;Datta et al., 2012). Moreover, some recent controversy debates if the standard and safe current intensities commonly used are high enough to reach critical current density levels in the cortex (> 0.5 V/m) able to shift transmembrane resting potentials and influence local excitability (Lafon et al., 2017;Vöröslakos et al., 2018;reviewed in Liu et al., 2018). Indeed, to reach meaningful current density levels, stimulation intensities should be of 4 to 6 mA, higher than the currently recommended stimulation intensities [START_REF] Antal | Low intensity transcranial electric stimulation: Safety, ethical, legal regulatory and application guidelines[END_REF]. Additionally, to induce a noticeable behavioral effect, tACS needs to be applied for relatively long periods of several minutes (Nitsche et al., 2008;[START_REF] Nitsche | Transcranial direct current stimulation-update 2011[END_REF], hence it lacks the temporal resolution to either entrain or modulate oscillatory activity at circumscribed time windows during task performance.

Regardless, thanks to its low cost, excellent safety profile, and ease of use, multichannel tACS is probably called to become the tool of choice to flexibly modulate local and interregional synchrony throughout cortical networks in humans. Nonetheless, currently, given the open debate on its potentially too low intensity, its known low spatial resolution and ineffectiveness to entrain episodic short lasting oscillations, tACS is not necessarily the most adapted technology to explore the causal role of cortical oscillations in well-defined anatomical regions at a specific time window during task performance.

II.1.2.2 -Rhythmic transcranial magnetic stimulation approaches (TMS)

TMS is currently the most established non-invasive technology used to activate clusters of neurons responsible for specific behaviors within a rather circumscribed cortical area (estimated ~12-15 mm radius) in healthy humans and patients.

TMS equipment consists in capacitators which charge and store electrical current, which is then briefly circulated (120 to 250 µs) through a stimulation coil (the most commonly used are figure-of-eight coils) made of two contiguous loops of copper wire encapsulated in butterfly shape protective case (Fig. 3C). Following the principles of electromagnetic induction discovered in 1831 by Michael Faraday, the circulation of the high-intensity current generates a brief and rapidly changing magnetic field, called a pulse, which distributes perpendicular to the surface of the TMS coil lying flat on the scalp. Thanks to the electromagnetic induction phenomenon, the magnetic field penetrates painlessly, and with very little distortion, the skull bone and the epidural and subdural spaces filled with CSF to reach the cortex under the coil and induce a current intracranially which will cause the depolarization of clusters of excitable neurons (Hallett, 2007;[START_REF] Kobayashi | Transcranial magnetic stimulation in neurology[END_REF] hosted within a focal area of 12-15 mm radius (see Valero-Cabré et al., 2005 for an estimation in animals models). To achieve its effect, the TMS coil is placed on the scalp region most closely overlying a given cortical target (i.e. the one enabling the shortest straight path to cortical target) using a frameless stereotaxic MRI-based neuronavigation system customized to the anatomy of each healthy participant or patient (Fig. 3D).

Moreover, thanks to its excellent temporal resolution (Hallett, 2007), TMS allows single pulses or multi-pulse bursts arranged in a great variety of patterns to be used in online trial-bytrial designs to impact specific time windows during the performance of behavioral tasks (for recent reviews see Polanía et al., 2018or Valero-Cabré et al., 2017). Likewise, long patterns of so called repetitive TMS (or rTMS) can induce, depending on stimulation parameters (essentially, stimulation frequency, pattern duration and number of pulses, magnetic field intensity and length of inter-burst intervals), excitatory or inhibitory offline modulations of neural activity and associated behaviors, which remain transiently active beyond the discontinuation of pulses.

More interesting for the experimental work presented in this dissertation, either single pulses or, more efficiently, short episodes of the so-called rhythmic TMS (a modality of rTMS delivering short bursts of 4-5 regularly spaced TMS pulses) are being used to manipulate cortical oscillations within a targeted region. The first published precedent using TMS to manipulate ongoing oscillations used the ability of single isolated TMS pulses to phase-reset and synchronize local oscillators operating at the so called 'natural frequency' of the region.

Such an approach has been applied to induce transient increases of oscillation amplitude in several cortical regions (Paus et al. 2001;Rosanova et al. 2009;Van Der Werf and Paus 2006).

Some years thereafter, Thut and colleagues (2011a) put forward the notion that cortical populations of neurons consist in several oscillators, all fluctuating independently at an identical frequency but with a random phase (Fig. 4A). Given their rather natural desynchronized state in awake individuals, their summed spatio-temporal activity patterns tend to cancel off, and scalp EEG or MEG recordings prove unable to reveal clear signs of oscillations with a meaningful amplitude or increases of oscillatory power density in timefrequency analyses.

However, when rhythmic activity from different local oscillators is phase-locked, the amplitude of oscillatory activity at the level of the neuronal assembly increases by summation (instead of cancelling off) allowing the emergence of cortical oscillations visible in scalp EEG or MEG recordings. Single TMS pulses are the simplest stimulation pattern able to phase-lock ongoing un-synchronized oscillatory activity in local circuits. They act as an external force in-phase rhythms will sum up in time and space, increasing the so-called inter-trial coherence (ITC).

Scalp EEG electrodes will record the emergence of cortical oscillations of higher amplitude hence showing higher levels of power density. (Figure extracted From Thut et al. 2011a).

that resets the phase of ongoing oscillators, transiently phase-locking their temporal dynamics, hence increasing for very few cycles, by amplitude summation, the power of the so called 'intrinsic' or 'natural' frequency at which these oscillators normally fluctuate (Fig. 4B).

Although TMS pulses are often seen as alien perturbation phenomena that, by artificially depolarizing neurons, may interfere with their normal coding and behavioural contributions, in the context of the depolarisation of natural oscillators they can also be conceived as low energy stimuli able to enhance, in a specific cortical area, the power of frequency-specific oscillatory activity restricted to the 'natural' frequency of the stimulated area, hence respecting the 'intrinsic' rhythmic activity developed by local circuits.

The phase-reset and phase locking power of single pulses was confirmed experimentally (Rosanova et al., 2009). More specifically, single TMS pulses induced differential increases of oscillatory activity at specific frequency bands, depending on the stimulated cortical-region and similar to the most predominant rhythm at rest. Indeed, single pulses delivered over the occipital cortex (Brodmann Area 19) generated power increases around 11 Hz, in the alpha band which is well known for its role in visual processing, (Klimesch et al., 2007;Sauseng et al., 2005;Thut et al., 2006;Worden et al., 2000). TMS over posterior and superior parietal regions (Brodmann Area 7) selectively enhanced beta oscillatory activity with a peak at 20 Hz. Finally, frontal stimulation (Brodmann Area 6) induced broader-band effects increasing high-beta and gamma oscillatory activity around a 31Hz peak.

Although single pulses could be used to entrain natural oscillations, they are short-lasting and of low amplitude (Van Der Werf & Paus, 2006). Moreover entrainment is limited to the frequencies operating 'naturally', hence difficult to manipulate in exploratory or clinical applications. In an attempt to induce more robust oscillatory entrainment patterns by capitalizing on the phase-locking ability of TMS pulses, rhythmic TMS bursts aligning trains of pulses delivered at a frequency of choice have been developed. These consist in short bursts of pulses (usually 4-5) regularly spaced in time to emulate the periodicity of oscillators in the stimulated regions. As the inter-pulse interval of the burst is tailored to fit a full cycle of a local 'intrinsic' oscillator, each consecutive pulse in the burst will be delivered at the same phase of the oscillation we intend to entrain. The accrual of individual pulses within the burst will progressively phase-reset and phase-lock more and more oscillators, leading to a gradual buildup of a TMS entrained frequency in the targeted cortex (Fig. 4B andC).

Interleaving rhythmic TMS with EEG recordings, [START_REF] Thut | Rhythmic TMS Causes Local Entrainment of Natural Oscillatory Signatures[END_REF] were able to show the effect of 5 pulse TMS bursts delivered to the Intraparietal Sulcus (IPS) at a 10 Hz frequency ('naturally' present in this posterior and superior parietal area) resulting in increases of alpha oscillations power. The amplitude of this oscillation grew progressively higher during the burst and remained increased for a brief time after the 5 th or last pulse of the burst (around one to two cycles, ~150 ms). This seminal paper established the ability of rhythmic TMS to entrain oscillations at a specific frequency dictated by burst frequency and showed that entrainment was particularly effective in boosting 'natural' or 'intrinsic' frequencies at which local oscillators tended to fluctuate.

Since its inception, rhythmic TMS has been used in a variety of brain regions to probe the causal role of frequency-specific rhythmic activity on cognitive processes and behavior. hence were not able to report any effect of rhythmic TMS and oscillation manipulation on behavior. However, subsequent studies developed further uses of rhythmic TMS to provide evidence for a causal role of cortical oscillations in cognitive functions as varied as short-term memory (Sauseng et al., 2009), visual attention (Capotosto et al. 2009(Capotosto et al. , 2012[START_REF] Capotosto | Dynamics of EEG Rhythms Support Distinct Visual Selection Mechanisms in Parietal Cortex: A Simultaneous Transcranial Magnetic Stimulation and EEG Study[END_REF] or the modulation of visual perception (Chanes et al., 2013;Jaegle & Ro, 2014;Romei et al., 2010;Romei et al., 2011).

II.2 -Rhythmic Transcranial Magnetic Stimulation in attentional and visual behaviors

The emergence of rhythmic TMS patterns to manipulate cortical oscillations set the stage to add causal evidence to correlational outcomes linking oscillations, attentional orienting and perceptual modulations (see Section I of this introduction). Rhythmic TMS bursts delivered online during visual detection tasks confirmed a well-documented (Dugué et al., 201;Mathewson et al., 2009;Thut et al., 2006;[START_REF] Van Dijk | Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability[END_REF] role of occipital and parietal alpha oscillations (Romei et al. 2010) and the preferred posterior parietal alpha phase (Jaegle & Ro, 2014) in the modulation of visual detection. This same approach revealed a double dissociation between the role of theta and beta frequencies over the intraparietal sulcus for the perception of global vs. local object features (Romei et al. 2011) previously identified in a correlational study [START_REF] Smith | Perceptual moments of conscious visual experience inferred from oscillatory brain activity[END_REF].

Extending a prior study using single-pulse TMS to prove the causal role of the right FEF in conscious visual detection (Chanes et al., 2012), our team used rhythmic TMS in humans to explore prior correlational intracranial EEG evidence from monkeys highlighting the multiplexing of high-beta vs. gamma rhythms across the same fronto-parietal network to engage endogenous vs. exogenous orienting of attention (Buschman & Miller, 2007) or other correlational evidence in the human brain for a role of fronto-parietal high-beta synchronization in attention and conscious visual perception (Gross et al., 2004;Hipp et al., 2011;Phillips & Takeda, 2009).

Work by our lab employed trial-by-trial bursts of rhythmic TMS at two distinct frequencies (30 Hz vs. 50 Hz) delivered on the right FEF while participants performed a nearthreshold visual detection task (Chanes et al., 2013). The results confirmed in humans previous correlational monkey work (Buschman & Miller, 2007) exploring the roles for high-beta and gamma band activity in an homologue frontal cortical region, the right FEF, and revealed that the episodic entrainment of these frequencies prior to target onset modulated different aspect of a conscious perception paradigm; gamma entrainment (active vs sham 4 TMS pulses at 50

Hz, compared to a non-uniform fixed pattern) decreased response bias (rendering participants less conservative in indicating they had seen a target when in doubt) whereas high-beta oscillations (active vs sham 4 TMS pulses at 30 Hz compared to a non-uniform fixed pattern) increased visual sensitivity (i.e., boosted the capacity to differentiate the presence of a visual target compared to a no-target noise condition).

Given the long-proven network distribution of focally applied TMS effects (Chouinard et al., 2003;Paus et al., 1997;Valero-Cabré et al., 2005), modulations of cortical activity by TMS cannot be reasonably expected to stay confined to the targeted region. As no brain region works in isolation, but rather as nodes linked to complex systems, TMS induced activity spreads to other associated network sites depending on the richness and strength of the connectivity.

Consequently, a single pulse delivered focally on a cortical region will phase-reset local oscillators and increase the amplitude of rhythmic activity in sites distant from the TMS target region (Rosanova et al., 2009). Similarly, rhythmic TMS delivered to frontal regions such as the FEF could have an effect on the topography of synchronization/desynchronization of alpha activity recorded in parieto-occipital areas (Capotosto et al., 2009).

Therefore, local rhythmic TMS can be used to probe a causal contribution of frequency specific cortical oscillations to a task, nonetheless, their use with concurrent mapping technologies sensitive to neural spatio-temporal dynamics such as EEG can serve to monitor its influence on extended neural systems the stimulated region is part of. To this regard, two TMS behavioral studies analyzed individual diffusion imaging tractography datasets (Quentin et al., 2014(Quentin et al., , 2015) ) and reported significant correlations between the facilitatory impact of high-beta right frontal rhythmic TMS stimulation on conscious visual detection and white matter connectivity estimates of the 1 st branch of the right superior Longitudinal Fasciculus (SLF), linking the FEF and the Intraparietal Sulcus (IPS) and subtending the dorsal attentional network (Thiebaut de Schotten et al., 2011).

In conclusion, converging with correlational evidence in monkeys and humans, EEG experiments and theoretical models of oscillatory perturbations, rhythmic TMS has demonstrated an ability to manipulate cortical oscillations. Such effects have been proven to be TMS-frequency dependent, to primarily enhance power of intrinsic frequencies in the tested area, and active during pulse delivery but short lasting thereafter (no longer than two cycles).

Moreover, in the attentional and visual domain, TMS alone or in conjunction with concurrent EEG recordings has been paramount to explore the causal role of episodic high-beta and gamma right frontal rhythms to enable visuo-spatial orienting leading to visual detection improvements.

Further research using similar causal interventional approach will help build a more comprehensive picture of how other brain regions and frequency bands might contribute to the modulation of attention and visual perception in healthy humans. In parallel, an emerging research field is attempting the translation of oscillatory manipulation principles to effective treatments for the rehabilitation of cognition in human neurological patients.

II.3 -Rhythmic Transcranial Magnetic Stimulation in neuropsychiatric rehabilitation

Beyond their use in experimental science to probe causal relationships in the brain, noninvasive stimulation approaches have shown promise as treatments for patients with altered cognition. Brief rhythmic TMS bursts applied for exploratory purposes induced short-lasting effects that are essentially restricted to the duration of the stimulation train (and one to two cycles beyond for the frequency of interest). This limitation makes them suited for trial-by-trial exploratory studies in cognition, operating episodically in relatively shorty time windows prior or during task events, on the other hand it does not enable longer-lasting modulations of neural rhythms that could be used for therapeutic purposes. It remains, however, controversial if the use of long TMS stimulation patterns may be able to operate beyond the discontinuation of the stimulation trains and modulate ongoing oscillations or network synchrony in a way that can be predicted according to input parameters (reviewed in Polanía et al., 2018;Valero-Cabré et al., 2017). Such longer lasting effects are paramount to support future uses of rhythmic TMS stimulation to correct cognitive symptoms linked to abnormal oscillations or synchrony patterns in the context of neuropsychiatric diseases.

Repetitive TMS (or rTMS), consisting in series of pulses or bursts tested in a wide range of low or high frequencies (usually conventional rTMS at 1, 3, 5, 10 or 20 Hz, or patterned TMS such as continuous (cTBS) or intermittent (iTBS) theta burst, which consists in bursts of 3 pulses at 50 Hz repeated every 200 ms, i.e. at 5 Hz, for longer periods of time, from 30 seconds to 30 min) have been shown to lastingly modulate measures of motor cortico-spinal excitability [START_REF] Gangitano | Modulation of input-output curves by low and high frequency repetitive transcranial magnetic stimulation of the motor cortex[END_REF][START_REF] Huang | Theta Burst Stimulation of the Human Motor Cortex[END_REF][START_REF] Maeda | Modulation of corticospinal excitability by repetitive transcranial magnetic stimulation[END_REF][START_REF] Pascual-Leone | Responses to rapid-rate transcranial magnetic stimulation of the human motor cortex[END_REF], visual evoked potentials [START_REF] Aydin-Abidin | Effects of repetitive TMS on visually evoked potentials and EEG in the anaesthetized cat: Dependence on stimulus frequency and train duration: rTMS effects on cat VEPs[END_REF]Thut et al., 2003) and, most relevant for this thesis, offline cortical oscillatory activity [START_REF] Chen | Low-frequency rTMS over lateral premotor cortex induces lasting changes in regional activation and functional coupling of cortical motor areas[END_REF][START_REF] Schindler | Theta burst transcranial magnetic stimulation is associated with increased EEG synchronization in the stimulated relative to unstimulated cerebral hemisphere[END_REF][START_REF] Strens | The effects of subthreshold 1 Hz repetitive TMS on cortico-cortical and interhemispheric coherence[END_REF]Thut et al., 2003;[START_REF] Woźniak-Kwaśniewska | Changes of oscillatory brain activity induced by repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex in healthy subjects[END_REF].

Generally speaking, neurophysiological offline or after-effects have been proven to last for up to 30 min (see [START_REF] Thut | A Review of Combined TMS-EEG Studies to Characterize Lasting Effects of Repetitive TMS and Assess Their Usefulness in Cognitive and Clinical Neuroscience[END_REF] for a review), depending on stimulation site and TMS pattern specification (frequency, type, number of pulses or bursts, duration etc.). For conventional rTMS (pure frequencies between 1 to 20 Hz), a commonly applied rule of thumb has established that after-effects are effective for a period of time which is ~50% of the pattern duration. Patterned TMS (cTBS and iTBS) have been found to induce long lasting after-effects of 20 to 60 minutes, after a pattern lasting 20-190 seconds in the cortico-spinal tract [START_REF] Huang | Theta Burst Stimulation of the Human Motor Cortex[END_REF]. The accrual of rTMS daily sessions repeated at intervals of <24 hours showed a potential to induce even longer-lasting effects [START_REF] Bäumer | Repeated premotor rTMS leads to cumulative plastic changes of motor cortex excitability in humans[END_REF][START_REF] Maeda | Modulation of corticospinal excitability by repetitive transcranial magnetic stimulation[END_REF] and backed up promises of therapeutically meaningful outcomes in neuropsychiatric diseases.

Stimulation regimes based on daily rTMS sessions protocols tested in multicentric clinical trials have been approved by the US Food and Drug Administration (FDA) to treat medication-resistant depression [START_REF] George | Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: A sham-controlled randomized trial[END_REF][START_REF] O'reardon | Efficacy and Safety of Transcranial Magnetic Stimulation in the Acute Treatment of Major Depression: A Multisite Randomized Controlled Trial[END_REF], which is one of the most clinically established application for rTMS. Likewise, pre-clinical and clinical rTMS studies are currently being conducted for the treatment of Obsessive-Compulsive Disorder [START_REF] Dunlop | Reductions in Cortico-Striatal Hyperconnectivity Accompany Successful Treatment of Obsessive-Compulsive Disorder with Dorsomedial Prefrontal rTMS[END_REF] or positive symptoms of schizophrenia such as auditory verbal hallucinations (reviewed in [START_REF] Thomas | Brain connectivity and auditory hallucinations: In search of novel noninvasive brain stimulation therapeutic approaches for schizophrenia[END_REF], see Thomas et al., 2019).

Additional neuropsychiatric conditions or symptoms in which rTMS is being tested in clinical trials include, in order of relevance: post stroke rehabilitation (motor paralysis and spasticity, aphasia, attentional awareness disorders, hemianopsia and scotoma), tinnitus, pain, movement disorders (dystonia, essential tremor and Parkinson disease), neurodegenerative conditions such as Alzheimer disease or semantic dementia (memory, language, executive and spatial deficits), anxiety disorders, substance abuse, disorders of consciousness and epilepsy (see [START_REF] Lefaucheur | Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS)[END_REF] for an extensive review).

With the exception of Parkinson's tremor and post-stroke hemineglect and hemianopsia, rTMS therapeutic regimes are still far from considering strategies that target focal or networkwide oscillatory and synchrony abnormalities. This situation could be explained by the need for more solid and detailed evidence (i.e., networks, regions, frequency bands, time-windows and underlying mechanisms) on oscillatory and synchrony contributions to normal cognition and their potential role in the physiopathology of neuropsychiatric symptoms. Additionally, skepticism about the efficacy of current non-invasive brain stimulation technologies (such as rTMS or tACS) to modulate brain oscillations and associated behaviors in a predictable manner are slowing down developments and dampening enthusiasm.

Work focused on a human cognitive system with relatively well-established physiological, anatomical and behavioral bases, subtended by cortical sites accessible to transcranial brain stimulation, and pursuing a thorough causal characterization of contributing oscillatory coding processes and their manipulation becomes paramount for the field. To this regard, by probing and extending existing knowledge on fronto-parietal attentional systems and further exploring their role in the modulation of visual perception (a behavior which is easy to characterize and quantify), with an emphasis on its oscillatory basis, we will pave the way to devise novel non-invasive stimulation treatment strategies that rehabilitate spatial attentional and visual disorders in neurological patients. Then, on the basis of the lessons learned, similar approaches and rationales can be progressively extended to other cognitive functions and their dysfunctions.

III -Neural noise, stochastic resonance and the modulation of visual perception

In prior sections of this introduction, we have extensively reviewed existing evidence supporting a behavior-specific role of local oscillatory activity and interregional synchrony;

with a special focus on anatomical systems and frequency-specific coding strategies subtending attentional orienting and perception. We have highlighted (and shown experimental evidence of) how the emergence of frequency-specific oscillations, a highly predictable, regular and synchronous fluctuation of activity, enabled a most efficient processing of top-down attentional allocation or bottom-up saliency, facilitating perception (Fries, 2005(Fries, , 2009;;[START_REF] Singer | Visual Feature Integration and the Temporal Correlation Hypothesis[END_REF][START_REF] Tallon-Baudry | Oscillatory gamma activity in humans and its role in object representation[END_REF].

Despite such a strong focus in brain oscillations and network synchrony as one of the fundamental pillars of neural coding, research has also inquired about the potential role of neural noise (i.e. a highly random and unpredictable neural signal) in the modulation of cognitive processing leading to specific behaviors. One of the most influent concepts to this regard lies at the core of the so-called Stochastic Resonance Theory. This well-established framework in signal processing has theorized and shown experimentally that the addition of an optimal level of sensory noise (i.e., not too high, not too low) to a weak signal can unexpectedly enhance stimulus saliency hence its ability to be perceived (detected or discriminated) instead of blurring it.

To this regard, regular frequency-specific oscillations and neural noise, two different and in some ways opposite patterns of cortical activity produced by joint or segregated neural systems, may both be called to show a complementary role in neural coding strategies, through different mechanisms, and contribute together to the modulation of local and network synchronization events. Some indirect hints challenging the notion that increases of oscillation power or synchrony is necessarily favorable to efficient coding of cognitive operations and ultimately lead to better performance can be found in neuropsychiatric diseases. This evidence would leave some room for neural models of cognitive coding in which systems generating controlled levels of random neural noise would be as important as local and long-range neural networks able to generate episodic brain rhythms.

III.1 -Cognitive impairments associated to abnormal oscillations and synchrony

The evidence reviewed in the preceding sections of this introduction seems to strongly support the notion that the lack of cortical oscillations are in general considered to cause pathological states and explain cognitive impairment [START_REF] Grice | Disordered visual processing and oscillatory brain activity in autism and Williams Syndrome[END_REF]Uhlhaas et al., 2006).

Nonetheless, neurophysiological evidence in well-known neuropsychiatric diseases also suggest a detrimental role for cortical oscillations or excessive frequency-specific synchrony.

For example, in Parkinson patients an excess of high-beta synchronization in cortical motor system and basal ganglia loops slows movements and increases rigidity [START_REF] Witcher | Neuronal oscillations in Parkinson's disease[END_REF].

In epilepsy, abnormally high levels of local gamma synchronization often precedes the onset of a seizure, whereas generalized epilepsy can be characterized by very high levels of gamma synchronization throughout large cortical regions, leading to impaired behavior and loss of consciousness (Uhlhaas & Singer, 2006). Therefore, although synchronization subtends the activation of cognitive operations and behavioral facilitation, the desynchronization of neural activity (i.e. preventing the build-up of temporally synchronized activity in specific frequency bands) can also be the necessary condition to avoid signs of pathology.

In hemispatial neglect, an attentional awareness disorder impairing patient's ability to orient attention hence consciously detect, localize or discriminates perceptual events occurring the left hemispace or hemibody (Bartolomeo, 2007), strongly synchronized left frontal beta oscillations (~13-14 Hz) prior to target onset correlates with trials in which this right hemisphere stroke patients omit visual targets displayed in the left visual hemifield (Rastelli et al., 2013). Conversely, in healthy participants, a pre-target onset desynchronization of left frontal beta activity has been associated to successful ability to anticipate the appearance of a target in a contingent negative variation paradigm (Gómez et al., 2006). Similarly, the levels of left frontal beta-band desynchronization predicted the detection of supra-threshold somatosensory stimuli during a backward masking paradigm (Schubert et al., 2009). A desynchronization of alpha rhythms is also observed in occipito-parietal cortex contralateral to the attended visual hemifield in spatial cueing paradigms (Sauseng et al., 2005;Thut et al., 2006;Worden et al., 2000) and the level of pre-stimulus alpha power is negatively correlated with visual detection (Mathewson et al., 2009) and discrimination performances (Hanslmayr et al., 2007). Such findings have led to the hypothesis that alpha oscillations suppress processing of sensory stimuli (reviewed in Foxe & Snyder, 2011).

In an attempt to add causality to the above-reported correlational studies, and at the same time explore potential interhemispheric differences in coding between the right FEF (thoroughly explored in Chanes et al., 2013;Quentin et al., 2014 and2015) and the left FEF for the modulation of conscious visual perception, Chanes et al. (2015) tested the impact on visual detection performances of a 30 Hz rhythmic TMS pattern, designed to entrain frequencyspecific high-beta oscillation in the left FEF, compared to a fixed non-uniform TMS pattern and a random TMS pattern (both non-frequency-specific), all made of 4 pulses covering the same time window (i.e., same duration from the 1 st to the 4 th pulse). Unexpectedly, but coherent with the notion of left frontal high-beta synchronization impairing visual perception (or conversely, supporting the notion of visual performance facilitation with left frontal beta desynchronization), these authors reported bilateral and unilateral right improvements of conscious visual detection sensitivity with non-uniform and random TMS patterns, respectively, whereas rhythmic high-beta stimulation that had proven successful in the right FEF failed to show any effect on visual perception (Chanes et al., 2015).

This outcome nuanced prior conclusions on the causal proven beneficial role of high-beta and gamma oscillations in attentional orienting and conscious visual perception. More specifically, it suggested for the first time that disabling the build-up of high-beta left frontal rhythms with TMS patterns that either entrained mixed or random frequencies on each trial (but never a pure 30 Hz rhythm) facilitated conscious visual performance. Most importantly, it pointed to a potential interhemispheric asymmetry in coding strategies between the left and right fontal nodes (left and right FEF) as key regions of a bi-hemispheric fronto-parietal network (Corbetta et al., 2005) devoted to the orientation of spatial attention activated prior to target onset. Within such a complex system, oscillatory activity and desynchronization via the injection of noisy patterns may engage within a bi-hemispheric network complementary mechanisms facilitating pre-target attentional processing.

III.2 -Stochastic Resonance Theory, modulation of neural coding and information processing

How may the addition to the brain of pulsed electromagnetic noise inducing cortical neurons to fire asynchronously improve neural processing and behavior? The most currently plausible answer comes from the domain of physics. In nonlinear systems (i.e. a system whose output is not proportional to the strength of the inputs it receives), a phenomenon referred to as 'threshold Stochastic Resonance' (or threshold SR) occurs where the addition of controlled levels of noise to a signal can enhance the detection of weak stimuli (Moss et al. 2004). Neurons are nonlinear systems as they will trigger an action potential (which can be recorded

electrophysiologically as a single cell potential or a spike) following an 'all-or-none rule', whereby only if the trans-membrane resting potential exceeds a specific voltage threshold the cell depolarizes producing a spike or a burst of spikes.

With regular extracellular electrophysiological recordings, in such system, variations of membrane potential can only be monitored by following the sequence of triggered spikes. If

the resting trans-membrane potential remains at subthreshold voltage levels, any voltage fluctuations below that threshold will remain invisible to our recordings. The addition of electrical noise to this system increases the volatility of the membrane potential and, when the potential becomes positive and nears the threshold, it may stochastically (i.e. randomly) 'push' the transmembrane resting potential to reach and overpass the depolarization threshold, so that high values of membrane potentials will finally manifest electrophysiologically as a spike or a spike burst, reflecting a transient state of high neuronal excitability. Resonance (SR), showing that only an optimal level of noise improves signal processing whereas too much noise will be detrimental to signal processing. (Adapted from Moss et al. 2004). (C) Example of Stochastic Resonance with white noise added externally to a weak visual stimulus. Without noise, the weak signal (black and white Union Jack flag) is unrecognizable as only few pixels pass the threshold.

The addition of a low (optimal) level of noise helps the lightest pixels pass the threshold, making the signal clearer and more recognizable. Too high levels of added noise drown out the signal and make it unrecognizable again. (Adapted from Schwartzkopf et al 2011).

In the case of a natural oscillator, a subthreshold transmembrane potential (Fig. 5A, shown as the thick black line) fluctuating cyclically never crosses the threshold and therefore never manifests as a spike train. Nonetheless, when an optimal level of noise (Fig. 5A, light grey line) is added to this subthreshold signal, it helps the membrane potential supersede the threshold during phases of high excitability (i.e. high positive voltage levels close to depolarization threshold) and manifest as a train of recorded electrical spikes (Fig. 5A, top).

The train produced by the neuron is noisy but the oscillation frequency of its neuron transmembrane potential remains clearly visible simply by following the temporal dynamics of the depolarization spikes.

In order for the Stochastic Resonance phenomenon to facilitate the detection of a given signal (in our case, electrical fluctuations linked to neuronal activity), the magnitude of the injected noise (electrical current inducing a voltage change in resting transmembrane potentials) needs to be high enough so that when added up in time and space to the subthreshold levels, the signal plus noise crosses the 'visibility' threshold (in our case neuronal firing threshold).

However, if the intensity of the added noise is too high, it will cause the neuron to spike at random times, blurring the regularly fluctuating temporal structure of the generated spike trains. As the quality of the information contained in the spike train deteriorates, any subthreshold variations of transmembrane resting potential will become invisible. For this reason, the stochastic resonance phenomenon requires to identify the level of noise that maximizes the mutual information (i.e. the amount of information that can be gained about one variable through the observation of another variable) between spike train dynamics generated by injected noise alone and the one generated when combining subthreshold neural signals and injected noise (Fig. 5B &C).

Stochastic Resonance is a very robust phenomenon that has been shown to occur with signal and noise of different types and sources (e.g. pixels within a low contrast image or sounds during a low volume phone conversation), hence manipulated at will to enhance the salience of weak signals making them more easy to be processed in image processing or telecommunications engineering.

In the brain, electrical neural noise is ubiquitous in local and extended circuits. At each level across signal transduction in the neuron and synaptic transmission between pre-and postsynaptic neurons, noise is injected into the system (see Faisal et al. 2008 for a review).

More specifically, the (1) opening/closing dynamics of ion channels in neuronal membrane at each level will sum up across individual neurons to then be further accrued depending on the connectivity patterns of the neuronal network (Faisal et al., 2008).

Such a high level of cumulated noise has long been thought as be detrimental to information processing (Shannon, 1948). In response, research in this domain has been traditionally directed to identify the mechanisms by which brain systems limited neural noise and gained in efficiency, for example by averaging redundant information or, in sensory processing, relying on prior knowledge about the natural world to discriminate between signal and noise (see Faisal et al., 2008 for a review). However, the Stochastic Resonance framework postulates that neural noise is not an accidental byproduct of neural activity flowing across networks that is potentially harmful to signal processing, but when dosed adequately, rather an essential ingredient of efficient signal transmission and processing. A growing body of experimental evidence is now supporting this same notion and suggesting an active physiological role for noise generation systems in neural processing.

III.3 -Neural noise and Stochastic Resonance in the modulation of perception

In a wide variety of organisms, from invertebrate to mammals, individual cells have evolved a capacity to use external sources of noise to improve signal processing, following the principles of Stochastic Resonance (SR). The transduction of bioelectrical signals through membrane ion channels can be improved with a noisy electric field applied to the cell membrane (Bezrukov & Vodyanoy, 1995). In peripheral sensory systems, sources of noise, whether naturally present in sensory systems such as Brownian motion of hair bundles in the cochlea (Jaramillo & Wiesenfeld, 1998), or added experimentally (Collins et al. 1996;Cordo et al. 1996;Douglass et al. 1993) have been found essential for neurons to respond to weak afferent sensory stimuli. At the behavioral level, it has also been repeatedly demonstrated that optimal levels of random noise added to weak visual (Simonotto et al., 1997), auditory (Zeng et al. 2000) or tactile (Collins et al. 1996;Iliopoulos et al. 2014) stimuli, increase the signal saliency and detectability. Detection improvements have been found to follow an inverted U-curve across noise intensity, suggesting as the SR theory postulates, that only accurately dosed levels of noise can boost the identification of weak signals. Indeed, increasing or decreasing noise levels outside of such ideal window or the addition of an optimal level of external noise to suprathreshold stimuli leads to a deterioration of the signal and decreasing perceptual sensitivity (Iliopoulos et al. 2014;Manjarrez et al. 2007). Moreover, across participants, optimal levels of noise required to maximize sensory sensitivity are quite variable and need to be individually customized (Groen and Wenderoth 2016;Iliopoulos et al. 2014;Kitajo et al. 2003).

Investigations of SR effect in human cognition have pinpointed where in our nervous systems this phenomenon may become crucial for coding and transmission. Although SR is at play in sensory receptor and afferent peripheral systems, the same phenomenon operates in neurons and networks of the central nervous system. Evidence has shown that, for facilitation to occur, adequate levels of noise do not need to be conveyed afferently by the exact same receptor system that will uptake the signal. Indeed, nonfiltered high-frequency electrical noise applied to one finger improves detection of weak electrical impulses delivered to adjacent fingers of the same hand (Iliopoulos et al. 2014). Similarly, a noisy visual stimulus monocularly delivered to the left eye improved the perception of a weak signal delivered to the right eye (Kitajo et al., 2003). As visual signals from both eyes converge in primary visual areas, the process subtending such perception improvements can only occur at the cortical level.

Furthermore, SR is a cross-modal phenomenon that works with adequate noise levels and weak signals from different sensory modalities. Indeed, long before it was theorized, audio white noise was shown to improve visual performance following the rules of SR, i.e.

improvement of perceptual sensitivity following an inverted-U function across noise levels [START_REF] Harper | Signal detection analysis of effect of white noise intensity on sensitivity to visual flicker[END_REF]Manjarrez et al. 2007). Confirming this cross-modal character, audio white noise has been shown to improve not only the perception of auditory signals but also boost the salience of visual and tactile stimulation (Lugo et al. 2008). In sum, cross-modality could be a defining feature of SR which takes place in multisensory neurons located in sensory association cortical areas (Manjarrez et al. 2007).

SR-like phenomena have also been assessed in scalp EEG datasets of cortical activity and verified that either power of sensory entrained oscillations or the amplitude of evoked potentials generated by weak stimuli also followed an inverted U-curve with increasing levels of externally added noise. More specifically, in the somatosensory (Manjarrez et al. 2002) and visual (Mori & Kai, 2002) sensory systems, the addition of peripheral stochastic noise, tactile and visual respectively, to a weak periodic stimuli of the same modality, facilitated the entrainment of cortical oscillations at the stimulus frequency, an effect that wore off when noise intensity passed a threshold level. Similarly, in a visual steady-state protocol, the addition of visual noise to an oscillating spatial grating improved oscillation power at the frequency band dictated by the rhythm of grating fluctuations and increased the amplitude of visual evoked potential generated by its onset (Srebro & Malladi, 1999). Taken together, these studies published nearly two decades ago build precedence that following the principles of Stochastic Resonance (as illustrated in Figure 5), the entrainment of frequency specific brain oscillations by a rhythmic sensory source can also be facilitated by the addition of adequately dosed noise.

Importantly, in all the above-discussed studies, noise was conveyed to cortical systems by means of randomly fluctuating sources of sensory stimulation exposing peripheral receptors (retinal cells, cochlear cells, skin mechanoreceptors) to such patterns (auditory, visual or tactile). Yet, as indicated previously in this section, neuronal and synaptic components embedded in complex cortical networks contain also a lot of intrinsic sources of noise thanks to variability of processes such as synaptic transduction, local potential and action potential generation, axonal conduction and stimulus propagation across neural networks (Faisal et al. 2008). Externally produced and peripherally conducted stochastic noise, as described above, will hence depend on mixed contributions from controlled external and internal sources of noise along a multi-synaptic afferent sensory pathway (which will be in turn modulated also by the former), difficult to tease apart.

In this regard, Aihara and colleagues (2008) This approach diverges from other tCS stimulation technologies (such as tDCS delivering a constant polarizing current, or tACS which uses an oscillating current at a single frequency) by being able to deliver broad-band rhythmic currents which can eventually mimic white noise.

To do so, random current intensities chosen from a normal distribution (usually with mean 0) are delivered to the brain at each stimulation sample. Noise intensity delivered by tRNS can be flexibly manipulated by varying the standard deviation of the normal distribution from which current intensities are chosen; a higher noise standard deviation resulting in higher mean amplitude of delivered current and vice versa.

The frequency power spectrum of white tRNS noise should be flat, and characterized by an electrical signal that encompasses all frequencies with the exact same amplitude.

Experimental studies have shown, however, that only high-frequency bands (100-640 Hz) of the tRNS power spectrum generated with tCS devices seem to be responsible for modulating neuronal excitability (Terney et al., 2008). Consequently, more recent studies have delivered not pure white noise but rather "high frequency" tRNS which consists in a high-frequency broadband current between 100 and 640 Hz.

In spite of the pending conceptual and technological breakthroughs to be overcome, pioneering evidence on the potential role of cortical noise in coding and information processing has been developed thanks to the advent of this technology. In the attentional and visual domain, varying tRNS intensities delivered over the occipital cortex have shown to exert a similar impact than externally added visual noise in the modulation of perception for visual near-threshold stimuli (Groen & Wenderoth, 2016), suggesting that transcranial stimulation by tRNS may have the potential to modulate perception in a SR-like manner.

Additionally, much like the use of non-invasive stimulation compared to sensory steadystate stimulation, direct cortical noise modulation with tRNS has enabled the translation of SR principles to cognitive activities other than perception, such as decision making. Recently, it has been demonstrated that the addition of an optimal noise level over the visual cortex does not only improve stimulus sensitivity but also but the rate of evidence accumulation needed to trigger a response (Groen et al., 2018).

Transcranial random noise technologies have rapidly expanded during the last decade in exploratory or clinical applications. Nonetheless, tRNS suffers from the limitations associated to tCS methods, such as unknown physiological mechanisms, weak intensity (Vöröslakos et al., 2018;Liu et al., 2018), poor focality (Bikson et al., 2010;Datta et al., 2012) and difficulties of recording concurrent EEG activity (Noury et al., 2016;Noury & Siegel, 2018). Additionally, the lack of solid neurophysiological models on the role of noise in processing has limited the value of the outcomes of tRNS studies.

The evidence gathered thus far points clearly toward a crucial influence and physiological role for 'internal' and 'external' sources of noise in coding, signal transmission and information processing in brain systems. Consequently, noise in the recording of neuronal activity can no longer be dismissed as simply epi-phenomenological, irrelevant for human cognition or simply detrimental for neural processing, hence to be avoided at all costs.

In this exciting but challenging context, the development of novel methods to quantify and manipulate cortical 'intrinsic' noise in brain cortical systems will be necessary to extend our current knowledge. Work should focus on developing well-tested alternative technologies such as TMS, in novel burst configurations aiming to inject random electrical signals and increase neural noise, and capitalize on its higher intensity, focality and compatibility with concurrent EEG recordings.

Ever since its first application in experimental neuroscience, TMS was though to act by inducing neural noise is the stimulated cortex. Originally, it was hypothesized that TMS activated neurons in the targeted cortical region indiscriminately, or randomly, and therefore added noise and interfered with task-related patterns of neural activity to create was has long been called a 'virtual lesion' (Miniussi et al., 2013;Walsh & Cowey, 2000). Empirical evidence has shown that short TMS bursts (3 pulses at 15 Hz) can increase 'internal' noise levels in the left V5/MT region of the cortex, as measured by a decrease in the slope (i.e. an increase in response variability) of the individual psychometric function for the discrimination of motion direction with increasing motion coherence (Ruzzoli et al., 2010). It was further demonstrated that similar short TMS bursts (3 pulses at 20 Hz delivered on left V5/MT) modulated motion discrimination performances following the inverse U-curve characteristic of SR (Schwarzkopf et al., 2011). Indeed, on stimuli with weak coherence for motion (i.e. subthreshold stimuli) only TMS pulses of medium intensity (and not too low or too high intensity) increased motion discrimination performances; whereas on motion stimuli with strong coherence (i.e. suprathreshold stimuli) medium or high intensity TMS pulses decreased performances. All of this evidence is coherent with a modulation of neural noise by short episodic TMS patterns.

The proposed mechanism for its action on neural activity later evolved with the emergence of evidence for state-dependent effects of TMS, with a preferential activation of task-irrelevant neurons (Silvanto et al., 2008 for a review) rather than an indiscriminate activation of all neuronal population in the stimulated cortex which would increase background noise levels. This was supported by evidence that single TMS pulses (delivered on V1 or V5/MT) do not increase neural noise but instead specifically suppress the strength of taskrelated signals during visual and motion discrimination tasks [START_REF] Harris | The Functional Effect of Transcranial Magnetic Stimulation: Signal Suppression or Neural Noise Generation[END_REF]Ruzzoli et al., 2011). TMS could therefore reduce or change the ratio between noise and signal in neural processing (Miniussi et al., 2013). Such findings are promising for the development of TMS patterns able to selectively manipulate neural background noise or signal strength with high spatial and temporal resolution in order to probe the causal role of noise in neural coding and information processing.

SPECIFIC AIMS

The long-term goal of the larger project in which this dissertation is embedded is to develop the use of non-invasive stimulation technologies to advance the causal characterization of normal and dysfunctional oscillatory activity and network synchrony patterns subtending physiological and pathological cognition in the domain of visuo-spatial attention and the modulation of conscious visual perception. We aim to characterize sophisticated anatomical and physiological models of visuo-spatial attentional networks, including relevant nodes, oscillation frequencies and crucial time windows that, once identified, can be modulated to either improve attentional orienting, visual awareness and perceptual performance in healthy participants or restore brain functions in neurological patients.

Indeed, prior evidence has shown that local oscillations and network synchronization play a key role in the emergence of a wide variety of cognitive processes, by enabling data coding, information transfer or signal processing in brain systems. Likewise, pathological oscillatory activity or network synchrony has been found to be associated with cognitive deficits, neurological symptoms and altered behaviors. Therefore, by mapping the detailed contributions of episodic oscillatory activity or synchrony states causally involved in visuo-spatial attentional and/or perception, we will be able to devise rehabilitation strategies for patients affected by visuo-spatial attention or visual perception disorders such as hemispatial neglect or hemianopsia.

The short-term goal of this thesis is to characterize the anatomical, neurophysiological and behavioral contributions of frontal cortical regions, considered key nodes of a bilaterally distributed fronto-parietal dorsal network, with bearing on the orienting of spatial attention, the modulation of visual perception and providing conscious access for near-threshold perceptual stimuli. To this end, we will use Transcranial Magnetic Stimulation (TMS) applied to specific cortical regions and delivered during specific time windows in brief frequency-specific patterns, engineered to entrain local oscillations, or non frequency-specific patterns designed to either prevent oscillations from building up or to increase neural noise. These TMS patterns will be applied to healthy human participants to episodically manipulate on a trial-by-trial basis the neural activity in the left and right Frontal Eye Fields (FEF). Impact will be evaluated by means of concurrent EEG recordings and conscious detection performance paradigms for low contrast lateralized visual targets.

In such context, the doctoral research project presented in this dissertation will develop the following three specific aims:

SPECIFIC AIM 1: We will use TMS-EEG approaches to characterize neurophysiologically the causal contributions of right frontal high-beta oscillations and fronto-parietal synchronization on conscious visual perception in healthy human participants.

A bilateral but right dominant fronto-parietal network linking the Frontal Eye Field (FEF) and the Intra Parietal Sulcus (IPS) and subtended by the 1 st branch of the Superior Longitudinal Fasciculus (SLF) has long been proposed as involved in attention orienting and the top down modulation of visual perception. Rhythmic TMS studies have reported that the entrainment of high-beta (~30 Hz) oscillations in a right frontal node (FEF) of this network plays a causal role in the modulation of conscious visual detection (Chanes et al., 2013;Quentin et al., 2014Quentin et al., , 2015)). However, the lack of concurrent EEG recordings in these studies precluded a better understanding of the neurophysiological mechanisms behind this rhythmic TMS-induced high-beta oscillatory entrainment. By means of coupled TMS-EEG recordings, we here aim to better understand the local role of high-beta rhythmic activity delivered to the right FEF and explore the network effects, likely via a fronto-parietal dorsal attentional network, of frontal stimulation and their contribution to the modulation of conscious visual detection in healthy participants.

We hypothesize that 30 Hz rhythmic TMS delivered over the right FEF will locally entrain highbeta activity and boost high-beta right lateralized fronto-parietal synchrony which will in turn increase high-beta oscillations in the posterior parietal cortex, distant from the frontal cortical target on which rhythmic TMS will be originally applied.

SPECIFIC AIM 2: We will use TMS-EEG approaches to pinpoint the neurophysiological mechanisms subtending local and network influences of neural noise induced in the left frontal cortex and its effects on visual perception in healthy human participants.

Prior causal evidence hypothesized a beneficial role for neural noise induction across a left lateralized fronto-parietal network (Chanes et al., 2015) We hypothesize that TMS sounds (for any of the patterns types and sound modalities tested) might improve conscious visual detection performance preferentially for ipsilateral targets with regards to the sham stimulated hemisphere. We do not expect 30 Hz rhythmic sham TMS to significantly entrain high-beta cortical oscillations in fronto-parietal locations. However previous evidence (Romei et al. 2012) leads us to hypothesize that sham TMS sounds might be able to phase-reset cortical oscillations.

GENERAL METHODS

The work presented in the following result chapters (Projects 1, 2 and 3) combines three categories of methodological approaches: (1) behavioral evaluations of conscious detection for lateralized near-threshold visual targets, (2) frontal non-invasive brain stimulation with TMS bursts and (3) scalp EEG recordings of brain activity.

In all the four studies presented in this and, by virtue of such effects, modify the behavioral visual performance that these regions and their associated networks contribute to. Throughout the experimental sessions and concurrently with TMS delivery, EEG activity was recorded with a 60 scalp electrode net to assess the impact of brain stimulation on evoked and oscillatory neural signals. In addition to these three experimental approaches, participant's gaze was recorded during the behavioral task by means of an eye-tracking system to ensure correct central fixation during each trial. In parallel, the localization of the TMS coil on the scalp (coil center location, orientation angle and tilt angle)

was guided and continuously monitored by means of an MRI-based frameless neuronavigation system that ensured accurate and consistent transcranial stimulation of cortical targets throughout stimulation blocks and sessions.

In this chapter, we detail methods associated with each of these three methodological approaches and address the challenges of combining them in a complex experimental setup in human healthy participants. This methods chapter will also provide the occasion to specify and justify the common methodological choices made in designing our studies.

I -Behavioral paradigm to assess visual performance

Healthy adult human participants, males and females, were requested to perform a conscious visual detection task with lateralized near threshold visual stimuli, based on a spatially un-cued version of the Posner paradigm. We quantified conscious detection performance changes caused by different conditions of TMS stimulation (brain site, TMS pattern and TMS modality). We ultimately aimed to assess how such behavioral outcomes could be modulated by the manipulation of neural activity with TMS patterns in frontal regions of the dorsal attention network.

I.1 -Near-threshold lateralized visual detection paradigm

The Posner paradigm [START_REF] Posner | Orienting of Attention[END_REF][START_REF] Posner | Attention and the detection of signals[END_REF]) was designed as a very simple and barebones perceptual task allowing to study the effects of visuospatial attention and topdown sensory modulation on conscious visual perception and the relationship between these two processes. In this paradigm, participants are asked to fixate a central cross on a computer screen and consciously report the presence of a visual target that could appear in multiple locations outside of fixation (participants are asked to give a response that equates to "I saw a target"). Prior to target onset, participants are presented with a cue alerting them of the imminent target onset. Such a trial is called a 'neutral' detection trial. Additional elements can then be added to this model of a trial to modulate the orientation of the spatial attention of participants. In Posner and colleagues' experiments, the pre-target cue was modified to include some spatial information that would orient participant's attention to one of the spatial locations in which the target could appear. In our case, we used pre-target bursts of TMS to modulate the participant's attentional state. This paradigm is well-known and well tested and has been used several times in our team to explore the causal modulation of conscious visual perception with brain manipulations via TMS (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015).

The detection task, synchronized with the delivery of the TMS pulses (see below for methodology of TMS procedure), was executed in a desktop computer (HP Z800, Hewlett Packard) using an in-house MATLAB R2012b (Mathworks) script with the Psychtoolbox extensions (Brainard, 1997).

Participants were comfortably seated in front of a computer screen and a keyboard with their heads resting on a chin-rest, placed 57 cm away from the center of the screen, to ensure a stable positioning of their eyes. Each trial started with the presentation of a central fixation cross (size 0.5x0.5 o ) and a right and left rectangular placeholders (6.0 x5.5 o ) located 8.5° away from the fixation cross in the center of the screen. The placeholders indicated the two locations at which the peripheral target could later appear. Participants were asked to fix their gaze on the central cross the whole time it was on the screen. This fixation screen lasted between 1000 and 1500 ms to ensure sustained fixation and avoid predictability of upcoming trial events.

Then, the central cross increased its size (0.7 x 0.7 o ) for 66 ms, acting as a cue to alert the participants of the imminent apparition of the target.

In 80% of the trials, the target appeared with equal probability in the left or right rectangular placeholder 233 ms after cue offset and was kept on the screen for 33 ms. The other 20% of trials were 'catch trials' in which no target was shown. The window with the response cue was shown 1000 ms after target offset. On it, two arrow-like signs (">>>" and "<<<"), pointing to the left and the right towards the two rectangular placeholders, were presented below and above the fixation cross. The location of the arrows was randomized trial by trial, so that participants could not prepare their motor response and had to wait for the apparition of the response screen to know if the arrow pointing to the location where they saw the target was the upper or lower arrow.

This was done to ensure that the processing of the visual target was temporally separated from motor decisions and responses. Premotor and primary motor areas are adjacent to frontal regions, such as the FEF, involved in attentional orienting and the modulation of conscious visual perception. Hence, we aimed to ensure that brain activity patterns linked to these two functions were fully temporally segregated on scalp EEG recordings. Consequently, participants were not instructed to respond quickly but instead to be as accurate as possible in their responses.

To provide a response, participants were required to keep the middle, index and thumb fingers of their left hand placed above three keyboard keys: an upper key (corresponding to the 'd' letter key), a lower key (corresponding to the 'c' letter key) and the space bar. If the participants had seen the target they indicated the location (upper or lower) of the arrow that pointed toward the side (left/right) where they had seen the target. If the participants had not seen the target they pressed the space bar. The trial ended after participants provided a response. While performing the detection task, we ensured that the participants kept their gaze fixated on the central fixation cross and did not move their eyes to the peripheral placeholders.

We monitored both eyes positions throughout the experiment with a remote camera-based eye tracking system (Eyelink 1000, SR Research). A trial was considered non-fixated if at any time between the onset of the alerting cue and target offset, subject's eyes position was recorded more than 2° away from the center of the fixation cross on the computer screen. Subjects were immediately alerted that they had broken fixation at the end of the trial and the non-fixated trial was randomized to be presented again later in the experimental block. The eye-tracking system was calibrated before the start of the experimental session and re-calibrated at the beginning of each testing block. If for any reason the gaze tracking became inadequate (for example when participants moved their heads to a largely different position) the system could also be exceptionally recalibrated during a block at the end of a non-fixated trial.

I.2 -Visual target properties, features and titration procedures

A Gabor patch (0.5 o /cycle sinusoidal spatial frequency, 0.6 o exponential standard deviation) oriented vertically was used as a target stimulus. Gabor patches are widely used as visual stimuli in psychophysics experiments because they have been shown to optimally activate neurons in early visual areas [START_REF] Daugman | Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters[END_REF]. We adjusted the contrast of the target individually to a 50% detection titration level for each participant. The use of low contrast, near-threshold Gabors made an otherwise very simple detection task very challenging for our healthy participants. We made sure that the task was demanding enough so that participants missed to consciously report at least half of the target, hence leaving room for potential improvements or degradations of conscious detection performance under the impact of TMS patterns.

The 50% detection level contrast was determined for each participant during a calibration block performed before the start of the experimental session. We followed a one-up/one-down staircase titration procedure (Cornsweet, 1962). The staircase method presents the advantage of being very efficient and completed in relatively little time because it requires few trials to reach a threshold. At the start of the titration block, stimulus contrast was set at a Michelson contrast of 1 which is the method for quantifying stimulus contrast most adapted to gratings or periodic stimuli like Gabor patches [START_REF] Kukkonen | Michelson contrast, RMS contrast and energy of various spatial stimuli at threshold[END_REF].

Participants performed the detection task described above and, at the end of each trial, contrast was adapted: if the presented Gabor was reported as 'seen' its contrast was decreased by one step of contrast, whereas if the participant missed to report a present target the Gabor contrast level was increased also a step. The initial contrast step was set to the initial contrast level of 1 Michelson contrast unit and on each response reversal (the target was missed when in the previous trial it has been correctly reported as 'seen' or vice-versa), contrast step was divided by two. However, throughout the titration procedure, Michelson contrast of the Gabor target and the contrast step were always kept between 0.005 and 1. The 50% detection contrast was considered to be reached when Gabor contrast varied by less than 0.01 Michelson contrast during five consecutive titration trials. The 50% detection contrast level was determined twice using this procedure. If the two measured contrasts differed by less than 0.01 Michelson contrast, they were averaged and used as the fixed 50% detection contrast for the following experimental blocks. If not, the 50% detection contrast was determined again. Note that during the titration block no active stimulation was delivered, subjects only received sham TMS patterns (see below for details on the TMS procedure).

Control analyses performed on the three datasets that were employed in the results chapters of this dissertation confirmed the reliability of our titration procedure. This sanity check showed, that, on average, the groups of participants participating in the three studies of this thesis verified ~50% detection rates (Fig. 2, grey bar plot representing mean performances across experimental blocks). However, we noticed that detection performance tended to decrease with the accrual of experimental blocks (Fig. 2). The long duration of some of our experimental sessions (~4 hours, including the laborious setting of the EEG cap) made some participants suffer a steep drop in their detection performance rates towards the end of the experimental session, no doubt because of a state of cumulated fatigue. In earlier studies using this same task by our team, this drop in performance was avoided by continuously adjusting target contrast every 20 trials along each block and experimental session. However, earlier studies analyzed the impact of TMS patterns on conscious visual detection performance but did not record ongoing EEG signals (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015).

In the three datasets presented in this thesis, we aimed to compare EEG signals across trials and experimental blocks. To this end, we kept the physiological properties of target stimuli identical throughout experimental trials to ensure that the amplitude of the EEG responses to the target onset would not be modulated by a higher or lower target contrast. The drop of detection rate revealed by our sanity check should not bias outcomes, since the observed drift in performance did not reach significance for any of the three datasets. Indeed, one-way ANOVA analyses conducted on measures of detection rate for each dataset and experimental session independently revealed no main effect of block number (all p > 0.06). Moreover, the order of experimental TMS conditions across sessions (experimental blocks) was counterbalanced across participants to avoid systematic biases caused by fatigue on specific TMS conditions. However, it is interesting to notice that for the data in Dataset 1 and 2 we found a significant negative correlation between the baseline detection performance (detection rate in sham trials) and the improvement of target detection by active TMS (difference in detection rate between active and sham trials) (dataset 1: R 2 =0.118, p<0.01; dataset 2: R 2 =0.047, p<0.05; dataset 3: R 2 =0.027, p=0.19), which indicates that the lower the detection rate of the participant is in any experimental block, the higher the improvement of detection performances by TMS will be in this block. Such a significant relationship between baseline detection rate and magnitude of TMS effect highlights the need to carefully titrate individual performances prior to the start of the experimental session and further indicates that the detection task needs to be made difficult for healthy subjects in order to observe a potential effect of TMS.

I.3 -Experimental blocks and session organization

The experimental session consisted in several experimental blocks each testing a different TMS pattern, which were divided into sub-blocks of 20 trials. The presentation of each type of trials (no target present / right target / left target, and active / sham TMS patterns) was randomized for each sub-block. Each sub-block embedded an equal number of sham and active TMS trials delivering the same pattern and also the same number of left and right stimulus presentations, including 20% of 'catch trials' in which no target was presented.

Each experiment started with a titration block until a stable 50% detection contrast was reached. Participants then performed a training block in which active TMS trials (half of the trials were active and half were sham, see below for details on the TMS procedure) were introduced and participants were given the chance to get used to active stimulation.

Participants' detection performance, calculated only on sham TMS trials, was checked to make sure it remained stable around 50% detection rates even with the introduction of active TMS At the end of each sub-block during training, and the end of every two sub-blocks during experimental blocks, participants were provided, via a message displayed on the computer screen, some feedback on task performance. Information provided included: (1) a warning note if the alarm rate, i.e., trials in which participants indicated having 'seen' a target when no target was presented on the screen ('catch trials'), was higher than 50%;

(2) the percentage of correctly detected targets for which they made mistake when reporting target localization (likely by being too liberal with regards to reporting the presence of a target in a position where none was presented, or by selecting the incorrect response key when reporting target position); and finally

(3) the percentage of trials in which they had violated gaze fixation requirements. After the presentation of the feedback, participants were invited take a short break before starting the next sub-block.

I.4 -Subjective and objective measures of perception

The visual detection paradigm used in our three studies is a subjective task that relies on the introspective report of participants acknowledging if and how they perceived a target. The subjective evaluation of conscious perceptual representations is challenging as experimenters need to trust the ability of participants to accurately report recent conscious experiences ('seen'

or 'unseen') when these could suffer assessment biases guiding their decision to report whether they perceived a stimulus or not [START_REF] Merikle | Perception without awareness. Critical issues[END_REF].

Other paradigms designed to avoid reliance on subjective reports claim to provide a more objective measure of perception [START_REF] Marcel | Conscious and unconscious perception: Experiments on visual masking and word recognition[END_REF]. In such tasks, participants are requested to give a forced-choice response on a stimulus, in visual categorization or identification tasks, regardless of whether they acknowledged to have 'seen' the stimulus or not. The perception of the stimulus is then evaluated on the basis of task performance: if performance nears chance levels, it is concluded that it was not perceived; whereas when performance is above chance levels, it is inferred that the stimulus was indeed perceived.

There is a longstanding and still ongoing debate concerning the use of subjective reports or objective discrimination paradigms to study perception and conscious access which is beyond the scope of this dissertation (see [START_REF] Merikle | Perception without awareness. Critical issues[END_REF][START_REF] Weiskrantz | Consciousness and Commentaries[END_REF][START_REF] Block | Perceptual consciousness overflows cognitive access[END_REF] for some representative references on this topic). Nonetheless, indirectly addressing our choice of a single conscious detection task, prior experiments from our lab assessed during the same trial the impact of TMS on a forced-choice discrimination task (Gabor line orientation, right or left tilting) and our subjective visual detection task (reporting absence or presence of visual target)

both similarly titrated (50-75% correct performance) and performed sequentially about the same previously displayed near-threshold lateralized Gabor. Interestingly they consistently failed to show modulations by frontal and posterior parietal TMS on the former but not the latter visual task (Chanes et al., 2012(Chanes et al., , 2013(Chanes et al., , 2015;;[START_REF] Quentin | Frontotectal white matter connectivity mediates facilitatory effects of non-invasive neurostimulation on visual detection[END_REF].

It is difficult to rule out if the dissociation found could be explained by the specific order in which the two tasks were performed (1 st the forced choice discrimination and 2 nd the subjective detection task), differences in titration levels applied to each (50% vs 75% correct performance) or differences in cognitive demands and the anatomical and physiological neural substrates subtending and modulating each task. Nonetheless, leveraging on this same debate, Posner and colleagues observed in seminal studies that identical manipulation of attention orientation had stronger effects on subjective reports than forced-choice discrimination paradigms [START_REF] Posner | Orienting of Attention[END_REF]. They postulated that this is due to the increased complexity of forced-choice tasks compared to simple 'seen'/'not seen' reports. Indeed, discrimination or categorization of a visual object requires additional processing compared to simple perception tasks. Moreover, brain networks [START_REF] Lau | Relative blindsight in normal observers and the neural correlate of visual consciousness[END_REF] and oscillatory signatures [START_REF] Benwell | Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance[END_REF] involved in subjective reports of a conscious experience and those involved in objective categorization or discrimination tasks have been hypothesized to be distinct and differentially modulated by spatial attention [START_REF] Dehaene | Conscious, preconscious, and subliminal processing: A testable taxonomy[END_REF].

Accordingly, for further studies, we retained the visual task (subjective detection reports) that had been shown, in prior studies by our team, to be consistently modulated by frontoparietal TMS delivered either in single pulses or rhythmic TMS bursts. Prior knowledge, gathered by our team, of effective TMS timings, patterns and relevant frequencies enabling an effective modulation of conscious detection performances strengthened this choice (Chanes et al., 2012(Chanes et al., , 2013(Chanes et al., , 2015;;[START_REF] Quentin | Frontotectal white matter connectivity mediates facilitatory effects of non-invasive neurostimulation on visual detection[END_REF].

I.5 -Signal Detection Theory and visual performance outcome measures

As indicated above, in a conscious detection task based on 1 st person reports the subjective dimension of the response and assessment biases leveraged to take the decision to deem a perception as deserving to be reported as 'seen' or 'unseen' can be a source of uncertainty and behavioral noise. Indeed, even when possessing the same amount of information about a target stimulus, participants may adopt varying response strategies. For example, very conservative participants may decide to report a target as 'seen' only when absolutely sure that it was present, failing to acknowledge present targets he/she may have been simply unsure to have seen according to such a strict personal criterion. In contrast, more liberal participants might systematically acknowledge to have 'seen' a target upon the faintest perceptual sensation of having seen a target on the screen, hence showing very accurate performance for targets effectively displayed, but also reporting positively on targets that weren't actually presented on the screen during 'catch trials' and/or making localization mistakes when asked to indicate if targets had appeared 'right' or 'left'.

This subjective decision-making threshold has to be differentiated from the perceptual abilities of the participant. The characterization of this different profiles to avoid experimental noise in our behavioral measures is achieved by applying the principles of Signal Detection Theory (SDT) (Green & Swets, 1966;Stanislaw & Todorov, 1999). SDT is employed in situations where participants are requested to provide a binary response to distinguish between the presence of a signal and noise. More importantly, SDT offers a method to separate perception and decision-making processes from responses produced by participants. participants will respond that a target has been seen. The likelihood ratio (β), is the ratio, calculated at the decision criterion, of the likelihood that a signal was presented on the screen or just noise. (Adapted from Stanislaw and Todorov, 1999).

In the SDT framework, perception is conceptualized as a process that is not discrete or deterministic, but strongly influenced by noise and random variability (Fig. 3). Therefore, the model represents perception along a continuum, referred as the decision axis, measuring the amount of information collected about a perceptual event (in our case a faint visual target 50% visibility contrast presented on a computer screen). Given that perception is noisy, a varying amount of information will be collected by the participant at each trial when a target is presented (or in trials when no target is presented) and therefore the event 'target present on the screen' is represented not as a single point but as a continuous distribution along this decision axis with a specific mean and standard deviation. It is assumed that noise is normally distributed, therefore on the decision axis two normal distributions, with different means but equal standard deviations, are displayed: the distribution of stimulus-related information collected by a participant when there is only noise on the screen (this distribution has a mean value of '0´

because no stimulus is presented on the screen, therefore there is no information to be collected)

and the distribution of a visual signal when indeed there is one (in our case a visual target, i.e.

Gabor) present on the screen in addition to noise.

The perception of the subject is measured by a variable called perceptual sensitivity (d') which is the distance between the mean of the two distributions. Perceptual sensitivity measures how much the presence of the signal amongst the noise shifts the distribution along the decision axis. The more the presence of a target on the screen shifts the 'signal' distribution, the less the 'noise' and 'signal' distribution overlap and the easiest it is for the participant to distinguish signal from noise. Therefore, the d' measures the ability of the participant to detect the visual target. A d' value of 0 means that the two distributions overlap and the participant cannot distinguish between signal and noise. The maximal value of d' is infinite, meaning that the two distributions do not overlap at all and the participant can perfectly distinguish signal from noise.

The subjective decision criterion (c) of the subject is the value on the decision axis above which the subject decides enough information has been collected so that he/she can consciously acknowledge he/she saw the target ('yes, I saw a target'). If this threshold is not exceeded, he/she will respond that he/she did not detect a target. This decision criterion can be moved along the decision axis to be more or less conservative independently of the d'. A criterion of 0 means the subject has no bias (i.e. the decision criterion is placed when the probability for signal or noise, considering the information collected on the decision axis, is equal). A negative criterion means the subject is liberal, hence more likely to acknowledge and respond there is a target in case of doubt. Finally, a positive criterion means the subject is conservative, hence more likely to respond that no target was presented when in doubt. The subjective decision criterion of the participant can also be expressed as a likelihood ratio. At a single trial, when a certain amount of information is collected about the presence of a target, the likelihood ratio measures how likely is it that this amount of information can be collected in a trial when there was a target present, compared to trials when there is only noise. The subject will acknowledge a target was present if this likelihood ratio exceeds a certain threshold (b). A ratio b of 1 means the subject has no bias, hence there is the same probability for the presence of a signal or noise.

Liberal participants have a threshold ratio below 1 whereas conservative participants have a ratio above 1.

The theoretical model proposed by the SDT presented above manifests on the participant's performance as different types of responses during a visual perception task with near-threshold low contrast stimuli (see Fig. 3, on the right). Indeed, across trials, the subject could either correctly report the presence of a target (this trial is classified as a 'Hit') or miss the presence of a target (this trial is classified as a 'Miss'). On catch trials where no target is displayed on the screen, the participant could correctly report that no target was present (the trial is classified as a 'Correct Rejection') or mistakenly report to have seen a target (the trial is classified as a 'False Alarm'). The measures of perceptual sensitivity and subjective decision criterion defined by the SDT are derived from the proportion of 'Hits' and 'False Alarms' trials according to the following formulas:

# = '( () -'( () = - 1 2 2 '( () + '( ()4
= exp 9 '( () : -'( () : 2 ;

Where '( is the inverse of the normal cumulative distribution function, H is the percentage of 'hit' trials and FA is the percentage of 'false alarms' trials.

In cases in which the percentage of False Alarms is 0 (i.e., subjects were so conservative that they never made a mistake by acknowledging the presence of a target during a 'catch trial')

or the percentage of Hit trials is 1 (i.e., subjects correctly reported the presence of all targets), the '( function results into infinite values, which prevents the calculation of inter-subject averages or the performance of statistical analyses on these measures. The common correction for such cases is to use a percentage instead of 1, where N is the number of trials from which this percentage is calculated (Macmillan & Creelman, 2004). For 'false alarms' trials, N would be the number of catch trials whereas for 'hit' trials, N would be the number of trials in which the target was present.

In the case of our lateralized conscious visual detection task, since participants were not only required to respond if the target was present or not but also indicate its location (left/right of the fixation cross) a trial was only considered a 'hit' if the participant: (1) correctly reported the presence of a target and (2) also correctly identified its location. Consequently, a new category of trials arose when participant correctly identified the presence of a target but incorrectly reported its right or left location. These trials were called 'error' trials and where excluded from the analysis given that it was impossible to rule out whether the participants correctly detected the target but pressed the wrong button to report its location or whether they wrongly detected a target in a location in which none was displayed. Having the information of where the subject saw a target enabled us to have separate proportions of 'Hit' and 'False Alarm' trials for targets presented in the 'left' and 'right' visual hemifield, hence to calculate separate perceptual sensitivity and decision criterion measures for both visual fields.

To ascertain the impact of different TMS patterns and conditions tested in our studies on the subjective conscious visual detection task, we performed three-way repeated measures ANOVA, with within-subjects factors Visual Field (left and right targets), TMS pattern (see below for a description of the TMS patterns tested) and TMS condition (active and sham), on perceptual sensitivity (d') and response criterion (c and b). According to the main effects and/or interactions revealed by the ANOVA, we performed t-students tests pairwise comparisons between specific conditions (find further methodological details in result chapters for Projects 1, 2 and 3).

II -Transcranial Magnetic Stimulation

In our three studies, while participants performed a visual detection task, we used TMS to causally interact (either entrain, modulate or interfere) on a trial-by-trial basis with local neural oscillatory activity patterns during a short time window preceding visual target onset.

With TMS, we aimed at manipulating visuo-spatial attention orienting abilities that ultimately influence the perception of visual stimuli and their conscious access. Previous correlation and causal evidence indicates that in frontal regions (FEF) of the right hemisphere, high-beta cortical oscillations within a dorsal fronto-parietal network might subtend the orientation of attention and the facilitation of conscious visual perception (Buschman & Miller, 2007;Chanes et al., 2013;Gross et al., 2004). In contrast in the left hemisphere, less uniformly synchronized (in a sense more noisy) patterns of activity operating in the left frontal regions (~FEF) might be beneficial for visual detection (Chanes et al., 2015;Rastelli et al., 2013). To this regard, the addition of specific levels of noise to neural signals has been shown to improve detection at threshold through a mechanism called Stochastic Resonance (Kitajo et al. 2003;Lugo et al. 2008;Groen and Wenderoth 2016). We tested a variety of TMS patterns to entrain either highly regular and highly synchronous oscillatory activity or less synchronous and hence more noisy activity in left and right frontal nodes of the dorsal attentional orienting network.

We chose the use of TMS over non-invasive electric stimulation because magnetic stimulation benefits from a much better spatial (Bikson et al., 2010;Valero-Cabré et al., 2005) and temporal (Hallett, 2007;Nitsche et al., 2008) resolution, allowing us to modulate neural activity trial-by-trial in a very specific pre-target onset time window by acting on circumscribed focal brain regions, something which would be difficult to do with modalities of transcranial electric current stimulation (tCS) such as tDCS, tACS or tRNS. Compared to tCS, the use of TMS bursts, either in rhythmic or random patterns, ensures an undisputed ability to effectively reach and induce depolarization effects on brain cortical regions and, importantly, allows to record and analyze concurrent EEG activity patterns which are only temporally but not continuously artifacted by the presence of an electrical field (Noury et al., 2016;Rogasch et al., 2014).

II.1 -Stimulation parameters

The delivery of TMS was synchronized with the timings of the visual detection task (Fig. 1). The MATLAB (R2012b, Mathworks) script running the behavioral task sent triggers to a high temporal resolution multichannel synchronization device (Master 8, A.M.P.I.) which was in turn connected by BNC cables conveying TTL trigger pulses to operate a biphasic rTMS stimulator (Super Rapid 2, Magstim) which sent TMS pulses through a standard 70 mm diameter figure-of-eight TMS coil.

We delivered short TMS bursts (4 pulses, in a span of 100 ms), starting 133 ms and ending 33 ms before the onset of a Gabor patch participants were asked to consciously detect. TMSentrained oscillations are short lasting, and changes in power does not extend beyond 1-2 cycles after the delivery of a single pulse (Van Der Werf & Paus, 2006), or the last TMS pulse in a short burst (Thut et al., 2011). The effects of TMS on behavior have also been shown to be short-lasting, with effects of single TMS pulses on conscious visual perception dissipating for intervals longer than 80-100 ms between a TMS pulse and visual target onset (Chanes et al., 2012). For this reason, we delivered our last TMS pulse 33 ms (i.e. one full cycle of a 30 Hz oscillation) before target onset. This time interval ensured, on the one hand, that the last cycle of 30 Hz oscillations entrained by TMS would very likely be still ongoing at the time of Gabor onset and, on the other hand, that the interval between the last TMS pulse and the presentation of the Gabor patch was short enough to exert a robust effect on conscious visual behavior performance. It should be noted that unpublished data from our team (see PhD thesis [START_REF] Chanes | Frontal and parietal contributions to visual perception in humans (Université Pierre et Marie Curie -Paris VI)[END_REF] suggests that the phase of the entrained high-beta oscillation at visual target onset had no major effect on the TMS-driven modulation of visual sensitivity (d') tested in a similar task.

We set stimulation intensity at 55% of the maximum stimulator output of our TMS machine. This is in contrast with many TMS studies that individually customized TMS intensity to an index of cortical excitability determined in the primary motor regions of each participant such as the resting motor threshold (RMT) [START_REF] Rossi | Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research[END_REF]. This is possible because the primary motor cortex is an area that is easily accessible to TMS and provides a visually detectable (hand muscle twitch) or measurable response to stimulation (hand muscle motor evoked potential, MEP), to obtain an estimate of cortical excitability. For this reason, a majority of TMS studies adjust stimulation intensity to the individual RMT, which is the intensity at which a single TMS pulse can elicit a motor response in resting hand muscles of at least 50 µV peak to peak in 50% of the trials (Rossini et al., 2015). However cortical excitability in the primary motor cortex does not necessarily predict cortical excitability in other regions of the brain (Kähkönen et al. 2005;Stewart et al. 2001). We therefore decided not to adjust stimulation intensity on the basis of the individual RMT, but instead we relied on several independent TMS experiments by our team, stimulating the same cortical regions to assess TMS-driven modulations on a very similar or identical visual detection task as the one we used in studies presented in this dissertation. These TMS studies reported robust modulation of conscious visual perception by stimulating at 45% of the maximum rTMS machine output with the TMS coil placed directly on the participant's scalp (see Chanes et al., 2013Chanes et al., , 2015;;Quentin et al., 2015). Nonetheless, in the studies of this dissertation, we compensated the increased distance between coil and scalp due to the extra-thickness added by the presence of TMS compatible EEG electrodes (~5 mm), and estimated that stimulation intensity had to be increased by 10%

and reach a total 55% of maximum stimulator output. Importantly, we tested that this level of intensity permitted our TMS equipment to recharge its capacitors fast enough (in <20 ms), hence be able to consistently deliver without losing any power bursts of 4 pulses at the frequency (30 Hz) probed in our studies.

In any case, to allow for comparison with other TMS studies, at the end of each experimental session, we determined the individual RMT in the right and left hemisphere. We localized the cortical hotspot for the abductor pollicis brevis (APB) muscle as the coil position over the primary motor cortex (M1) yielding the strongest thumb motor activations following a single TMS pulse. Once the hotspot was found, coil position was fixed above this position and stimulation intensity was progressively lowered to reach the TMS intensity at which 5 out of 10 single pulses yielded an activation or the APB. This intensity was defined as the RMT.

For all studies, we report the average stimulation intensity expressed as the mean ± standard deviation percentage that the fixed intensity of 55% of the maximal motor output used in our studies represented with regards to individual RMT.

II.2 -Design of rhythmic and random TMS patterns

In our three sets of studies, we delivered four different TMS patterns, designed to either entrain cortical oscillations or neural noise patterns. First, following the example of alpha ( 10Hz) rhythmic TMS patterns developed by Thut and colleagues (2011) to progressively entrain cortical oscillations, we delivered a rhythmic TMS bursts made of 4 pulses regularly spaced in time to build a 30 Hz frequency, i.e. with a constant inter-pulse interval of 1/30 seconds (~33 ms) which corresponds to a full cycle of a 30 Hz oscillation.

We also designed 3 other non frequency-specific TMS patterns (Fig. 4). The design of these patterns was carefully controlled so their impact on perception could be compared to the main high-beta rhythmic pattern. All the patterns contained the same number of pulses (i.e. 4

pulses) to ensure they delivered the same exact amount of total stimulation, (i.e. that the targeted region received the same amount of energy from all patterns). Additionally, the onset timing of the 1st and 4th pulse remained fixed across all 4 TMS patterns to ensure stimulation was delivered across the same time window (100 ms) and that the interval between the 4 th and last pulse of the burst and the target onset was kept constant. On top of these free design choices, we were constrained by technical limitations that compromised the variety of patterns we could deliver. Indeed, at least 20 ms were required by the TMS stimulator to recharge after each pulse and be able to accurately deliver a new pulse at the established intensity (55% maximum stimulator output), hence two adjacent pulses could not be delivered less than 20 ms apart.

Non frequency-specific patterns were originally designed to avoid carrying a unique frequency so that, when contrasted with rhythmic patterns, one could isolate the contribution of a high-beta 30 Hz frequency to the TMS-driven modulation of conscious visual detection.

Additionally, we made the hypothesis that given their mixed temporal structure containing different inter-pulse intervals, hence more than one pure high-beta frequency, non frequencyspecific patterns would carry higher levels of noise and increase power in a broader frequency band than high-beta 30 Hz rhythmic patterns designed to entrain pure 30 Hz oscillations. All the non frequency-specific patterns contained unequal interpulse intervals. In the nonuniform rhythmic pattern the two middle pulses where anticipated and delayed, respectively, by 9 ms from the timing of the regular rhythmic pattern. However, a potential design weakness of this pattern, is that, even if it is not completely regular (and does not contain a single frequency), it does contain a repeating inter-pulse interval of 24 ms at the beguining and end of the burst (Fig. 4) which could be held responsible for entraining oscillations outside of 30 Hz.

A second non frequency-specific pattern was designed, the random pattern, in which the onset time of the two middle pulses were randomly jittered trial-to-trial with the constraint that they had to be shifted at least 3 ms away from the onset time in the rhythmic pattern to make sure that random patterns would never deliver a perfectly regular 30 Hz frequency. However, since the timing of the two middle pulses were randomly jittered around (preceding or following) the onset times they had in the rhythmic frequency-specific pattern, on average over all trials the onset times of the pulses in the random pattern is quite close to the timings for the 30 Hz frequency-specific rhythmic pattern.

The last pattern we designed, the so called irregular pattern, seeked to avoid the shortcomings of the two previous patterns. It had fixed pulse onset times that were chosen randomly with the only constraint that all 3 inter-pulse intervals within the burst had to have different lengths. The non-uniform rhythmic and random patterns have been used as successful controls to rhythmic stimulation in previous work by our team (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015). The irregular pattern is a new pattern engineered to induce the maximum level of noise in neural systems.

Given the technical constraints limiting the design of TMS bursts, our 4 patterns could be claimed to be quite similar to one another with regards to their temporal structure. However, the role of oscillations in stimulus coding or information processing enabling cognitive operations is supposed to be exquisitely fine-tuned to specific frequencies and hence such strong inter-pattern similarity could play to our advantage and be considered a strength of our experimental design. By keeping the TMS patterns so similar to each other, any statistically significant difference in perceptual detection measures or EEG activity across TMS patterns could only be explained by virtue of their differences of temporal structure. Said otherwise, at the risk of minimizing or cancelling inter-pattern differences on behavioral and electrophysiological correlates, this design provides maximal sensitivity to isolate the impact of pattern frequency in oscillatory entrainment, and assess the influence of different levels of neural noise on brain systems.

Since its inception as a causal mapping technique in cognitive neuroanatomy TMS effects interfering with cognitive processes were hypothesized to be caused by neural noise (Walsh & Cowey, 2000). However, in spite of research directed to demonstrate this hypothesis in visual systems (Ruzzoli et al., 2011(Ruzzoli et al., , 2010;;Schwarzkopf et al., 2011;Miniussi et al., 2013), different complex non-synchronous TMS patterns have never been systematically compared to study their impact on neural activity and specific behaviors.

Instead, since 2008 tCS based-technologies such as transcranial Alternate Current Stimulation (tACS) and transcranial Random Noise Stimulation (tRNS) characterized by their ease and flexibility of use, low cost and excellent safety profile have gained increasingly popularity as technologies of choice to entrain oscillations and induce neural noise, respectively. tACS uses a continuous sinusoidal current oscillating at a single frequency, to entrain rhythmic cortical activity patterns or phase-synchronize regions. tRNS delivers a wide variety of asynchronous stimulation, from mixed broadband patterns across specific bands of choice to electrical white noise (Terney et al., 2008). Additionally, it allows to manipulate the magnitude of delivered noise by changing the intensity of the current (Groen & Wenderoth, 2016). Despite these apparent advantages, both tACS and tRNS suffer from similar limitations as any tCS technologies, hence we found both of them unsuited to the purposes of the studies presented in this dissertation.

First, given their poor temporal resolution, which at the very best is in the order of seconds or minutes (Nitsche et al., 2008), neither tACS nor tRNS are adapted for the delivery of episodic patterns targeting specific time windows in a trial-by-trial basis in the context of a task. Second, all tCS technologies including tACS and tRNS have a rather poor spatial resolution, which makes them suited to act on large cortical areas, but unable to achieve the necessary spatial specificity to target specific nodes of the dorsal attentional network such as right or left FEF [START_REF] Datta | Transcranial current stimulation focality using disc and ring electrode configurations: FEM analysis[END_REF]. Third, the effects of tCS modalities such as tACS and tRNS are considered hard to model accurately. Hence it is quite complex to predict which cortical regions are impacted and how much intensity these regions will receive using a given montage (Bikson et al., 2010;Datta et al., 2012). Fourth, the presence of a continuous electrical artifact, either constant for tDCS, oscillating for tACS or broadband for tRNS makes it extremely challenging to record online cortical activity with scalp EEG and characterize their effects on oscillations or neural noise (Noury et al., 2016;Noury & Siegel, 2018). Fifth and last, a vigorous debate is currently questioning if the highest levels of tCS current provided by commercial devices (limited for technical and safety reasons to ~1.5-2 mA with 25-35 cm 2 electrodes) may reach cortical systems with the sufficient strength to significantly shift the resting membrane potential of stimulated neurons (Lafon et al., 2017;Vöröslakos et al., 2018;reviewed in Liu et al., 2018).

As a result, its ability to induce consistent improvements of cognitive functions (Horvath et al., 2015) or genuinely modulate cortical rhythms in healthy participants (Asamoah et al., 2019) remains controversial, could be in part explained by peripheral sensory effects (Asamoah et al. 2019) and might require currents of at least 4 to 6 mA to be overcome the high resistivity of the skin to electrical currents (Liu et al., 2018).

Given the above-mentioned limitations of tCS, and to be able to compare current findings to prior outcomes obtained with online trial-to-trial rhythmic TMS bursts assessing conscious visual perception (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015) we opted for using this same technique to entrain oscillations or induce neural noise levels. To the best of our knowledge, our studies will spearhead the manipulation of neural noise levels with TMS bursts and scalp EEG recordings. Should it prove possible, this approach carries a lot of promise for experimental work assessing site, time and pattern specific effects of noise in brain coding and processing.

In addition to active TMS control patterns, we also delivered sham TMS trials, interleaved with active TMS trials during the same blocks. To this end, we used a second TMS equipment (Magstim, Super Rapid 2) attached to a TMS coil positioned in a sham configuration, i.e., on a neighboring region adjacent of the actively site, with the left edge of the coil in contact with the scalp and its stimulation surface oriented perpendicular to the skull to direct the magnetic field away from the scalp. Sham stimulation aimed to mimic the clicking noise characterizing the delivery of active TMS pulses and was used to control for rhythmic or random sound stimulation patterns, collaterally associated with TMS delivery without effectively stimulating the brain.

However, delivering standard sham stimulation with a second TMS coil placed close to the active TMS coil, as described above, can be cumbersome and logistically demanding. It requires a second rTMS machine and a coil (both, quite expensive materials to be used for a placebo effect) and an articulated mechanic arm holder (Manfrotto) placed concurrently as close as possible on the participant's head. For this reason, we tested replacing the second TMS coil and rTMS machine by an audio speaker mounted on the active coil playing a recording of the clicking sounds of TMS (TMS-like sounds) on a trial-by-trial basis with the exact same onset time as active pulses.

To generate the sound file reproducing a TMS clicking sound, we recorded the sound generated by 100 single TMS pulses (Aiwa CM-S32 stereo microphone) and averaged the individual waveforms to build a sound template emulating the waveform of the TMS clicking sound. The envelope of this waveform was then adjusted so that, when played through our speaker (Mobi Wavemaster), the audio of the click sounded as similar as possible to the sound of an active single TMS pulse delivered through a TMS coil placed on the participant's head.

Systematic debriefing indicated that participants who underwent two experimental sessions, one with sham TMS delivered through a TMS coil placed perpendicular to their scalp and a TMS-like sham sound delivered through a speaker mounted on the active coil, were unable to detect the difference between the two sham conditions.

II.3 -Cortical target selection and MRI-based frameless neuronavigation

In our studies we focused on stimulating either the right or the left Frontal Eye Fields (FEFs), both key frontal nodes of a bi-hemispheric dorsal attentional orienting network (Corbetta & Shulman, 2002;Vernet et al., 2014). These two sites were identified and labelled in MNI space as spherical regions of interest of 5 mm radius centered on Talairach coordinates x=31, y=-2, z=47 for the right FEF and x=-32, y=-2, z=46 for the left FEF (Paus, 1996). A T1weighted MRI scan (3T Siemens MPRAGE, flip angle=9, TR=2300 ms, TE=4.18 ms, slice thickness=1mm) was acquired for participants of all studies to label the target and neuronavigate the TMS coil. The transformation matrix from normal to native space was computed for each individual MRI scan using the SPM8 toolbox running on MATLAB R2017b. Then, the regions of interest centered on the right and left FEFs were denormalized into the MRI native space for each individual subject and used as a target for TMS stimulation.

The targeting of our cortical targets was controlled throughout the experiment by an MRIbased frameless neuronavigation system (Brainsight, Rogue Research). During each block, the TMS coil was hand-held and kept on the labelled sites within ~3 mm of the target. The theorized trajectory of the TMS field penetrating through bone and meninges to reach the cortical surface had to be adapted to each participant's head shape. Nonetheless, the TMS coil was placed and angled on the scalp so as to leave the shortest possible distance between the center of the stimulation coil and the cortical region of interest. To the best of our abilities, on each individual, the TMS coil was kept tangential to the scalp, with a fixed anterior-to-posterior and lateral-to-medial orientation angle (~45 o to the interhemispheric longitudinal fissure, handle pointing rostral and lateral). We also used the lowest tilt angle (<10 o ) that the curvature of the scalp on the contact zone allowed, ensuring the shortest straight path between the center of the coil stimulation surface and the aimed cortical target on the right or left FEF.

This rigorous placement of the coil is paramount since the intensity of the magnetic field generated by the coil decreases exponentially with distance [START_REF] Mcconnell | The transcranial magnetic stimulation motor threshold depends on the distance from coil to underlying cortex: A replication in healthy adults comparing two methods of assessing the distance to cortex[END_REF][START_REF] Stokes | Simple metric for scaling motor threshold based on scalp-cortex distance: Application to studies using transcranial magnetic stimulation[END_REF]. If the center of the coil is not placed at a tilt angle that ensures maximal tangentiality and the shortest straight path to the cortical target, the magnetic field has to cross a longer distance to reach the cortical surface and the intensity of the induced electrical current is weakened. Once the optimal TMS coil position, orientation angle and tilt was found for an individual participant the neuronavigation software (Brainsight, Rogue Research) recorded the coil's spatial coordinates allowing us to maintain across blocks and sessions performed on different days the exact same TMS coil position.

III -Concurrent TMS-EEG recordings of brain activity

To evaluate the online effects of TMS on brain activity and better understand the features of TMS-induced oscillatory entrainment or TMS-generated neural noise, we recorded scalp EEG activity prior, during and following stimulation. To this end we applied and developed in our lab existing methods for the recording of concurrent TMS-EEG datasets, to study either TMS-Evoked Potentials (TEPs) or the impact of magnetic stimulation on time-frequency electrophysiological activity [START_REF] Bonato | Transcranial magnetic stimulation and cortical evoked potentials: A TMS/EEG co-registration study[END_REF]Paus et al., 2001;Van Der Werf & Paus, 2006;Van Der Werf et al., 2006), a procedure that has seen increasing developments during the last decade. However, concurrent TMS-EEG recording presents some extremely challenging technical problems due to the high amplitude artifacts generated on EEG recording during TMS pulse discharge.

III.1 -Electromagnetic TMS-EEG artifact removal and data cleaning procedures

The discharge of a magnetic pulse by TMS generates a short-lasting electric field many orders of magnitude higher than the electric fields originating from the brain that conventional EEG systems are designed to record. The discharge of a TMS pulse will therefore saturate conventional EEG amplifiers and result in artifacted signals for up to hundreds of milliseconds post-pulse (Ilmoniemi & Kičić, 2010) preventing the accurate recording of crucial post pulse or intra-burst effects. TMS can also induce eddy-currents in the conventional EEG contacts which could significantly warm up the electrode surface with the accrual of magnetic pulses (Ilmoniemi & Kičić, 2010) although this risk has been greatly minimized with new slitted electrode designs and materials [START_REF] Thut | A new device and protocol for combining TMS and online recordings of EEG and evoked potentials[END_REF][START_REF] Virtanen | Instrumentation for the measurement of electric brain responses to transcranial magnetic stimulation[END_REF]. Finally, electrical recharge artifact can be observed as spikes in between pulses as capacitors uptake new current to deliver the upcoming pulse [START_REF] Ilmoniemi | Dealing with artifacts in TMS-evoked EEG[END_REF]. Precautionary measures can be implemented during recordings to minimize TMS artifacts, such as employing TMScompatible EEG amplifiers and electrodes designed to limit the impact of stimulation artifacts during online recordings (Ilmoniemi & Kičić, 2010). Additionally, ensuring very low skinelectrode impedances (ideally <5 kOhm with passive electrodes, <35 kOhm with active electrodes) helps minimizing artifact duration (~6-12 ms) [START_REF] Veniero | TMS-EEG co-registration: On TMSinduced artifact[END_REF]) and thus preserves the most amount of un-artifacted neural signal in between the pulses of a burst.

Nonetheless, a TMS-generated pulsed electromagnetic artifact cannot be totally avoided and will always leave a trace in EEG recordings. Artifacts will need to be carefully cleaned from the EEG datasets before further evoked potential or time-frequency analyses can be undergone.

Additionally, the TMS field spreads onto branches of the spinal (XI st cranial nerve) or facial (VII th cranial nerve) nerves or directly into the neck, scalp and facial muscles (trapezius, temporalis, masseter, orbicularis, frontalis muscles) and can cause involuntary muscle twitches, resulting in mechanical electrode displacement and evoked electromyographic (EMG) activity which generate artifact scalp EEG signals (Ilmoniemi & Kičić, 2010). Moreover, somatosensory inputs associated with scalp tapping and facial muscle twitches will also convey afferent proprioceptive and tactile inputs via branches of the trigeminal (V th cranial nerve for frontal, temporal, parietal regions) and the spinal (XI st cranial nerve for occipital regions) cranial nerves, and hence induce evoked activity in contralateral central-parietal EEG contacts.

Finally, the loud clicking noise associated with the delivery of magnetic pulses has the potential to activate cochlear auditory receptors by mechanical transduction via the external and middle ear (and also through direct bone vibration of the cochlea) and may generate in EEG recordings time-locked auditory evoked potential (Nikouline et al. 1999) in contralateral and also ipsilateral central-temporal EEG contacts. Occasionally, in some participants, automatic startle responses triggered by intense and unexpected stimulation accompanying sensations (sound and/or tactile tapping sensations) may also manifest as eyelid blinks or eye movements, which tend to significantly dwindle as participants get habituated to stimulation.

Given all these sources of non-neural electrical activity, extensive cleaning of TMSrelated artifacts from EEG recordings is paramount for further analyses. To this end, several TMS-artifacts cleaning methods have been proposed. The first methods subtracted TMS-EEG time series obtained during a control condition in which TMS was delivered with the brain 'at rest' (i.e., while no activity was engaged via a stimulus or task) from TMS-EEG time series of interest obtained when TMS was delivered on a stimulus-or task-activated brain (Thut et al. 2003;[START_REF] Thut | A new device and protocol for combining TMS and online recordings of EEG and evoked potentials[END_REF]. However, in addition to auditory and somatosensory evoked potentials related to sensations accompanying TMS delivery (sound and tapping), this subtractive approach cancels from the signal the cortical physiological activity elicited by the delivery of the magnetic pulse itself. It is hence well suited to characterize EEG activity changes produced on task-related brain activity correlates by the delivery of a TMS pulse, but remains however blind to TMS-induced changes in cortical activity. And yet, to index levels of local cortical excitability or to pinpoint functional connectivity features of brain networks [START_REF] Nikulin | Modulation of electroencephalographic responses to transcranial magnetic stimulation: Evidence for changes in cortical excitability related to movement[END_REF]Rosanova et al., 2009;Van Der Werf & Paus, 2006;Van Der Werf et al., 2006) TMSinduced brain activity cannot be disregarded as artefactual activity and hence subtracted from EEG traces during artifact cleaning.

Alternative cleaning methods have applied Kalman filters to remove the electrical artifact that arise from the strong currents induced on scalp EEG electrodes during the delivery of magnetic pulses [START_REF] Morbidi | Offline removal of TMS-induced artifacts on human electroencephalography by Kalman filter[END_REF]. Others have used Principal Component Analysis (PCA)

to identify topographies related to TMS artifacts originated outside of the brain, segregate such topographies from sources of biologically meaningful brain activity, then subtract the former from unprocessed raw EEG signals [START_REF] Litvak | Artifact correction and source analysis of early electroencephalographic responses evoked by transcranial magnetic stimulation over primary motor cortex[END_REF]. Nonetheless, the variety of methods developed over the years that we reviewed here are yet to be proven consistent and effective for TMS-EEG analyses and require further development and sophistication.

Pending further progress, the most widely applied approach for TMS-artifact cleaning is based on Independent Component Analysis (ICA), a method able to decompose a mixed signal back into the independent sources that compose it, even in cases where no information about the original sources and how they are mixed is available [START_REF] Hyvärinen | Independent component analysis: Algorithms and applications[END_REF]. ICA makes a number of assumptions we need to be aware of. First, mixed signals decomposed by the ICA must be a linear summation of the signal from all its independent sources. Second, the timecourses of the source signals must be statistically independent from each other at any point in time (i.e., knowledge about the time course of one source provides absolutely no information about the time-course of any of the other sources). Third and last, the mixing matrix (similar to a weighting matrix) that describes how sources sum up together to produce the mixed signal must not change over time. These three assumptions apply plausibly to EEG mixed signals [START_REF] Onton | Imaging human EEG dynamics using independent component analysis[END_REF] and ICA assumes that such signals are a linear sum of activity coming from multiple sources (cortical sources that have functionally specific signals and are therefore independent from one another as well as sources outside the brain generating artifacts), that are stationary in space and therefore can be characterized by a stable topography in electrode space.

The ICA procedure decomposes the EEG signal into source signals, including artifactual signals, and can, once the artifactual sources have been identified, remove artifact from EEG signal by a simple subtraction of artifactual sources from the mixed EEG time series.

ICA has therefore proven to be a very powerful method for artifact correction and has long been used to clean of a wide variety of EEG artifacts such as eye blinks, EMG activity or 50/60Hz noise from power lines [START_REF] Iriarte | Independent component analysis as a tool to eliminate artifacts in EEG: A quantitative study[END_REF][START_REF] Jung | Removing electroencephalographic artifacts by blind source separation[END_REF]. However, compared to other artifacts cleaned with ICA, TMS artifact present particularities that require a careful use of this approach. In the first milliseconds following pulse delivery, TMS artifacts have a much higher amplitude than other EEG artifacts. When such high amplitude artifact are present in datasets, they will dominate the topographies of the components identified by the ICA and distort those corresponding to components associated to neural signal [START_REF] Hernandez-Pavon | Uncovering neural independent components from highly artifactual TMS-evoked EEG data[END_REF]. Moreover, if the assumption that the EEG signal is composed of the sum of spatially stable sources can be generally considered to be correct, this might not apply during the early part of the TMS artifact. This is because the first 10 ms of the EEG signal following the pulse contain several transient spatial components [START_REF] Litvak | Artifact correction and source analysis of early electroencephalographic responses evoked by transcranial magnetic stimulation over primary motor cortex[END_REF] and make it difficult for ICA to identify a component corresponding to this early portion of the TMS artifact. Contrary to the later part which is very consistent trial-to-trial, hence can be cleaned effectively by subtraction of TMS-artifact template, the first 10 ms of the TMS artifact show a more stochastic timecourse that is not well corrected by the subtraction of a template [START_REF] Thut | A new device and protocol for combining TMS and online recordings of EEG and evoked potentials[END_REF]. For these reasons, the early epoch of the TMS-artifact (usually 10-15 ms of signal) must be removed from the TMS-EEG signal before ICA can be applied to clean the remaining parts of the artifact. It on EEG contacts in the neighborhood of the TMS coil scalp location and also if their timecourse shows sharp peaks exclusively time-locked to the TMS-pulse onset time (Rogasch et al., 2014). In the case of repetitive or rhythmic TMS delivered at a specific frequencies (in our studies high-beta stimulation at 30 Hz), the TMS-artifact components can also be identified by a power spectrum showing a strong peak at the frequency of stimulation [START_REF] Hamidi | Brain responses evoked by high-frequency repetitive transcranial magnetic stimulation: An event-related potential study[END_REF].

All the components identified as artefactual can then be removed from the EEG signal.

A drawback of using ICA as a cleaning method for TMS-related artifacts is that the components separated by ICA are independent from one another. However, TMS-related artifacts and also part of the TMS-induced cortical activity are time-locked to the onset of the TMS-pulse. This correlation in time violates the assumption of independence of the sources and might make it difficult for the ICA to correctly separate TMS-related artifact from cortical activity. Using ICA to clean the TMS-artifact might therefore be too stringent and remove from the EEG signal some of the physiological responses to TMS together with the artifact [START_REF] Hamidi | Brain responses evoked by high-frequency repetitive transcranial magnetic stimulation: An event-related potential study[END_REF]. A potential solution in case too many components that seem to contain both artifact and cortical activity are identified, is to perform two rounds of ICA. The first ICA will remove the brunt of high amplitude TMS-artifact whereas the second ICA might better separate less prominent residual TMS artifacts from cortical activity [START_REF] Hamidi | Brain responses evoked by high-frequency repetitive transcranial magnetic stimulation: An event-related potential study[END_REF]Rogasch et al., 2014).

Despite this limitation, most artifactual components identified by ICA do not mix artifact and neural signals (Rogasch et al., 2014) and any TMS-induced activity that does not share a precise topography with the TMS artifact and is not strictly time-locked with pulse onset will be correctly separated by ICA [START_REF] Hamidi | Brain responses evoked by high-frequency repetitive transcranial magnetic stimulation: An event-related potential study[END_REF].

Finally, it is worth mentioning that in experiments contrasting active TMS with a sham TMS control condition, or any other control condition bearing no stimulation, it is important to apply the exact same cleaning procedure (artifact removal, interpolation and ICA cleaning) to all the conditions to be compared. This ensures that any difference observed in the EEG signal with or without active stimulation cannot be attributed to artifacts introduced in the signal by the TMS-artifact cleaning procedure.

III.2 -Concurrent TMS-EEG recordings and EEG data pre-processing

We recorded EEG signals with a TMS-compatible system (BrainAmp DC and BrainVision Recording Software, BrainProducts GmbH) with a 60 electrodes net spread on the scalp following to the international 10-20 system. We placed the reference on the tip of the nose, far from the position of our TMS coil (on ~FC2 and ~FC3 electrodes, for the right and left FEF respectively) to prevent the electromagnetic TMS artifacts to impinge on our reference signal, and the ground on the left earlobe. We recorded EOG signals from 4 additional electrodes positioned on the left and right temples and above and below the right eye. To minimize the duration and the amplitude of the TMS-EEG artifact we monitored the impedances throughout each experimental sessions, before every evaluation block, and we kept them at all times below 5 kOhm [START_REF] Veniero | TMS-EEG co-registration: On TMSinduced artifact[END_REF]. To best capture high frequency variations of TMS artifact and also to minimize its duration we digitized the signal at a high sampling rate of 5000 Hz [START_REF] Veniero | TMS-EEG co-registration: On TMSinduced artifact[END_REF], which was the highest allowed by our EEG equipment.

All EEG analysis and preprocessing were performed on MATLAB R2017b with the FieldTrip toolbox (Oostenveld et al., 2011), an open-source toolbox for EEG analyses. The EEG and EOG signals were epoched on a [-2 2] s window centered on the target onset. A first automatic analysis removed trials in which the fixation requirements, monitored by an infrared camera-based eye tracking system (Eyelink 1000), on the central fixation cross were violated.

These events were however very rare as for a majority of studies of this dissertation participants were warned about incorrect fixation. Also note that any trial where fixation was broken was repeated in randomized order in the sub-block. The timings of the triggers commanding the delivery of TMS pulses were also automatically checked post-hoc and the extremely rare cases of trials in which triggers were not delivered within an acceptable timing (i.e. they varied by more than ± 3 ms from their correct onset time) were also excluded from further analyses.

Following automatic rejection of trials, all remaining trials were inspected visually and trials containing blinks or muscle artifacts within an epoch of [-500 500] ms around target onset, which corresponded to the window of interest in which to compute and analyze time-frequency measures, were also excluded.

On the remaining trials, the electromagnetic TMS artifact was cleaned following the ICA cleaning procedure highlighted in a prior section. Data within a [-4, +12] ms window centered on the onset time of each TMS pulse (active or sham) was removed and interpolated with a shape-preserving piecewise cubic interpolation. Datasets were down-sampled to 500 Hz to reduce the volume of our files and processing time. Then time series corresponding to all experimental conditions were gathered together in a single EEG dataset on which ICA was applied. We visually inspected all independent components and identified artifact components according to the features detailed in previous section. We then removed signal components corresponding to eye movements, electrode malfunction, 50 Hz power line artifacts as well residual TMS artifacts. After ICA cleaning, datasets were separated back by experimental conditions.

III.3 -Control analysis on the TMS-EEG artifact removal and data cleaning procedures

To ensure that the data cleaning procedure did not alter the EEG signals outside of the small window where TMS artifacts were removed and to verify that artifact removal and cleaning procedures applied to our original raw TMS-EEG data could not explain some of the results we hypothesized for our studies, we conducted a control analysis. More specifically, our main experiments were directed to study the EEG local and network-wide correlates of highbeta oscillatory entrainment during the delivery of short rhythmic TMS bursts at 30 Hz compared to random TMS. Hence, we aimed to carefully verify that the different steps of data cleaning highlighted in prior sections could not be held responsible of artificially increasing high-beta rhythms in either sham or active TMS-EEG signals. More generally, since most of our analyses were conducted in the frequency-domain we also verified that our cleaning procedure did not distort the power-spectrum of our original EEG time series.

We artificially added TMS 'artifacts' to series of artifact-free EEG signals obtained were detected on single trials using the automated artifact detection algorithm implemented in the FieldTrip toolbox (Oostenveld et al., 2011). The data was pre-processed to maximize jump artifacts, then the envelope of the signal was z-normalized. Any data sample that crossed the zvalue threshold of 0.8 was considered part of a TMS-artifact (Fig. 5B, left panel, signal in red).

Artifacted samples on single trials were cut from the active TMS dataset and added to epoched resting-state EEG signals (Fig. 5B), generating 150 artificially artifacted resting-state EEG trials. Artificially 'artifacted' data, along with active TMS-EEG data, underwent the artifact removal and cleaning procedure described above (Fig 5C to E).

On both real artifacted TMS-EEG data and artificially 'artifacted' resting state EEG data, we computed, for electrode FC3 (i.e. the closest contact to the TMS coil site in this dataset and hence showing the maximal amplitude of TMS artifact), the power spectrum for frequencies between 6 and 80 Hz on a short time window surrounding the delivery of TMS pulses ([-166 0] ms) using Fourier transformation with multi-taper method. Since the EEG power spectrum data were not normal (deviation from normality asserted with Shapiro-Wilk test, p<0.05) we compared with two-sided Wilcoxon tests (alpha = 0.05) real artifact TMS-EEG data with artificially 'artifacted' resting state EEG data before and after TMS artifact removal and cleaning. We also compared raw resting state EEG data with the artificially 'artifacted' EEG data following TMS artifact removal. We first compared these datasets for the 30 Hz frequency band (the main frequency tested in our studies), and then across all frequencies within a [6 80] Hz window, with Bonferroni correction for multiple comparisons.

We first demonstrate that our method of artificial addition of TMS 'artifacts' to resting state EEG datasets correctly reproduced the amplitude of the artifact from real artifacted data (Fig. 6A). The magnitude of the artifact on the power spectrum at 30 Hz (Fig. 6A, bottom graph) was identical for both real artifacted TMS-EEG data and artificially 'artifacted' EEG resting state data (p>0.4). Comparisons of the two power spectra across all frequencies also revealed no significant differences between real and artificially 'artifacted' EEG data (all p>0.05).

Next, we show that our TMS-EEG cleaning procedure did not generate any change in the power spectrum of the original EEG resting state data (Fig. 6B). Indeed, statistical comparison of the power spectrum of resting state data before the addition of TMS artifacts and after the artifact removal procedure showed not differences in the power neither at 30 Hz (Fig. 6B, bottom graph) nor for any other frequency (all p>0.05). Lastly, we were able to show that, following our TMS artifact removal procedure, the delivery of 30 Hz TMS bursts on active TMS data significantly increased the power at the frequency of stimulation compared to resting-state data (Fig. 6C, bottom graph p<0.001).

Comparison between resting state data and active TMS data also showed significant differences in power for all frequencies within the [6 80] Hz window (all p<0.001, with Bonferroni correction for multiple comparisons).

The result of our control analysis confirmed that TMS artifact cleaning procedure did not introduce or increase in any way 30 Hz frequency power in our datasets. Regardless, it should be kept in mind that, as an additional control, in all our studies and analyses of EEG signal we compare active TMS-EEG data to equivalent sets of sham TMS-EEG data (obtained concurrently in sham trials embedded within the blocks) and that both active and sham TMS datasets undergo exact same artifact removal and data cleaning procedure. Hence, any unlikely artifactual activity introduced in our active TMS-EEG data by the cleaning process should appear as non-statistically significant when comparing active vs sham TMS-EEG conditions.

III.4 -Outcome measures to assess the impact of TMS on oscillatory activity

We transformed cleaned EEG signal into the time-frequency domain using a 3 cycle Morlet wavelet analysis for frequencies between 6 and 50 Hz (50 logarithmically equally spaced frequency points) over the time-window [-500 +500] ms (t=0 centered around target onset) using 10 milisecond steps.

III.4.1 -Outcome measures for local oscillatory activity

Oscillation power was calculated as the squared value of the modulus of the Morlet coefficients for each time-frequency point and expressed in decibels (dB) relative to a baseline prior to the onset of the alerting cue (-233 ms prior to target onset) (Fig. 7A). Inter-Trial Coherence (ITC) measures the stability of oscillation phase across trials at a fixed point in time and for a fixed frequency. ITC is calculated by averaging the oscillation phases at each time-frequency bin across trials and then taking the module of the average complex vector (Fig. 7B).

Phases across trials which are randomly distributed along the oscillation cycle will cancel out and result to an average vector of module=0. Nonetheless, if the phase at a single timefrequency bin is constant and does not vary between trials the average will be a unit vector of module=1 (i.e., a perfect inter-trial phase-synchronization). Any other degree of partial synchronization will provide an ITC value between 0 and 1. ITC expresses the degree of phaselocking or phase alignment of oscillations to a time-locked event in the trial. In the studies presented in this dissertation, we employed ITC to assess the phase-alignment of oscillations to the onset of TMS pulses.

III.4.2 -Outcome measures for inter-regional network synchronization

To measure oscillation synchronization between distant brain regions, we calculated the Phase-Locking Value (PLV) which is a measure of the stability of the phase difference between two signals (Lachaux et al., 1999). The PLV is calculated similarly to the ITC but it is the average across trials at each time-frequency point of the phase difference between two signals instead of the phase of a single signal (Fig. 7C). The PLV is also the module of a resulting average vector in complex space therefore it is also comprised between 0 (random phase difference between signals) and 1 (constant phase difference, or perfect phase synchronization between signals).

When measuring inter-regional synchronization in EEG electrode space one must beware of the volume condition effect (Srinivasan et al. 2007). Following conduction laws, electrical signal from a single cortical source will diffuse in all directions and may be recorded simultaneously by several electrodes on the scalp, and particularly by groups of neighboring adjacent electrodes. Therefore, if the PLV calculated between two neighboring electrodes shows a level of high synchronization, this might be because the two electrodes record EEG signals from common brain sources. In such cases, high synchronization levels do not reflect true phase synchronization but rather the degree of similarity between cortical signals from the same sources recorded by the two neighboring electrodes.

To record and analyze true cortical phase synchronization the part of the signal contributed to by volume conduction has to be identified and eliminated. Several methods have been proposed to this end (Nolte et al., 2004;[START_REF] Nunez | EEG coherency: I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales[END_REF][START_REF] Stam | Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources[END_REF]Vinck et al., 2011) but they all mostly rely on the same rationale. Considering electromagnetic conduction laws for electrical signal, it is widely accepted that a quasi-static approximation holds for EEG.

Said otherwise, the conduction of signals from a cortical source to any scalp electrode is instantaneous, (i.e. has no lag). It follows that if two electrodes record scalp EEG activity from the same brain source, the signal recorded by the two scalp EEG electrodes will be at the same phase and the phase difference between the signal of these two electrodes will always be 0. To be sure to eliminate contributions of volume conduction to our measure of inter-region phaselocking we need to find a measure of phase-locking that is insensitive to signals with a null phase difference.

The representation of phase difference 'j' on a complex plane is given by cos(j) + i×sin(j) where cos (j) is called the real part and sin (j) is called the imaginary part. A phase difference of 0 will have a real part equal to 1, and an imaginary part of 0. Therefore, we project the complex vector of averaged phase differences on the imaginary axis (Fig. 6C) and take the absolute value of the Imaginary part of the PLV (Im PLV) as a measure of interregional phase-synchronization (Nolte et al., 2004). If synchronization between two signals is due solely to volume conduction, the complex vector of averaged phase difference will have a phase of 0 and the Im PLV for this vector will be 0.

Only signals synchronized with a non-null phase difference will contribute to the Im PLV.

The imaginary part of the PLV varies between -1 and 1 according to which of the two signals compared lead or lag in phase. By taking the absolute value of the imaginary part, we ensure that we have a phase-locking value that remains between 0 and 1, ignoring the polarity of the phase difference which is difficult to interpret due to the cyclicity of the phase. Taking only the imaginary part of the PLV is very conservative. If two signals have a small (but not null) phase difference, the Im PLV will be very small, it is therefore possible that we might miss some inter-regional phase-synchronization signal. It is however worth noting that non-null Im PLV provides certainty that this is not an artifact of volume conduction (Nolte et al., 2004).

III.5 Outcome measures to quantify and characterize noise in EEG datasets

In the preceding section, we presented outcome measures to assess oscillation and synchronization. The high relevance of the field in which these measures are applied and the high number of methodological, conceptual and experimental literature contributions addressing cortical oscillations and network synchrony has made these measures well-known and studied. In contrast, the domain addressing the study of noise in neural coding and cognitive processing is still emerging, hence there is less consensus about the most adequate outcome measures available to quantify and describe such phenomena and the conditions and restrictions for their application. Moreover, algorithms to calculate these measures, which can be complex and computationally demanding, are not implemented in conventional open toolboxes of EEG analyses. We made our choice of outcome measures to quantify and evaluate the impact of noise on neural systems from scalp EEG data based on their applicability to physiological signals of very short length but also considering ease of implementation in MATLAB (Mathwork) scripts and computational demands.

At difference to oscillations, noise is a signal that by definition has a degree of randomness or unpredictability. Approaches to objectively quantify such parameter are varied and an active field of development within the signal processing field. Thus far measures of noise can be divided into those computed in the time-frequency domain or in the time domain.

III.5.1. Measures to characterize noise in the time-frequency domain

In the time-frequency domain, a signal that is dominated by a single narrow-band oscillation is not noisy, because a such a signal obeys very simple laws and the fluctuating signal that represents them has very regular time dynamics, with a repeating pattern for each cycle of the oscillation, and it is hence highly predictable. However, a signal made of a mixture of oscillatory frequencies or that is characterized by very broad-band frequency spectrum will be less regular, lack a clear repeated oscillation period, hence less predictable and therefore more noisy. The extreme case is white noise, which has a flat power spectrum and is a completely random signal. In such framework, a first set of measures to characterize noise signals quantify the uniformity of the power distribution from the power spectrum [START_REF] Inouye | Quantification of EEG irregularity by use of the entropy of the power spectrum[END_REF]Rezek & Roberts, 1998;[START_REF] Rosso | Wavelet entropy: A new tool for analysis of short duration brain electrical signals[END_REF] in which a single peak in the power spectrum indicates a low level of noise, whereas a flat power spectrum indicates maximal degree of noise.

As a first estimation of noise in resting state or TMS-modulated EEG signals, we applied an intuitive approach by which we identified peaks in the EEG power spectrum and then quantified their bandwidth (Fig. 8A). A higher number of peaks in the EEG power spectrum, as well as larger width of the power peaks indicates higher noise level.

The power spectrum during the delivery of short TMS bursts was computed as the average of oscillation power from the wavelet analysis over the time window [-0.133 0] ms of interest (spanning from the 1st TMS pulse of our busts to visual target onset, i.e lateralized nearthreshold Gabors). Peaks, or local maxima, were detected in the average power spectrum using the 'findpeaks' function implemented in MATLAB R2017b (Mathworks). For each peak, the program searches for the smallest local minima (or valley) between this peak and the next peak that is higher than the current peak. The height, or prominence, of each peak is calculated relative to this smallest local minima and the width of each peak is calculated at halfprominence (Fig. 8A last panel). Given the low signal-to-noise ratio of EEG data (Kiesel et al., 2008;Ulrich & Miller, 2001), power spectra for individual participants are very noisy hence it is difficult to identify reliable peaks in individual subject data. In order to identify reliable peaks in grand average data while still being able to compute power peak values and width for individual participants and perform group level statistics on this measure, we applied the 'jackknife' procedure. This same approach has been used for the characterization of peak amplitude and latencies in event-related potentials, when having to face the problem of noisy single-subject data with not easily identifiable individual peaks (Kiesel et al., 2008;Ulrich & Miller, 2001). The power spectrum during the delivery of short TMS bursts was computed as the average of oscillation power from the wavelet analysis over the time window [-0.133 0] ms of interest (spanning from the 1st TMS pulse of our busts to visual target onset, i.e lateralized nearthreshold Gabors). Peaks, or local maxima, were detected in the average power spectrum using the 'findpeaks' function implemented in MATLAB R2017b (Mathworks). For each peak, the program searches for the smallest local minima (or valley) between this peak and the next peak that is higher than the current peak. The height, or prominence, of each peak is calculated relative to this smallest local minima and the width of each peak is calculated at halfprominence (Fig. 8A last panel). Given the low signal-to-noise ratio of EEG data (Kiesel et al., 2008;Ulrich & Miller, 2001), power spectra for individual participants are very noisy hence it is difficult to identify reliable peaks in individual subject data. In order to identify reliable peaks in grand average data while still being able to compute power peak values and width for individual participants and perform group level statistics on this measure, we applied the 'jackknife' procedure. This same approach has been used for the characterization of peak amplitude and latencies in event-related potentials, when having to face the problem of noisy single-subject data with not easily identifiable individual peaks (Kiesel et al., 2008;Ulrich & Miller, 2001).

For each subject i in a sample of N subjects, the jackknife procedure computes the grand average signal over a subsample of N-1 subjects by omitting the subject i in the dataset. The peaks in the power spectrum and their width are estimated for each of the N-1 subsampled grand averages. Of course, measures estimated with the jackknife procedure have very low error variance because they are estimated from grand averages over a pool of subject which varies only by one individual. Therefore, the standard error estimated by the jackknife has to be corrected according to the following formula:

. . = √ -1 × Where 's.e.' is the corrected standard error and 'std' the standard deviation of the jackknife subsampled measures. To test for significance, t-and F-statistics must also be corrected for the reduced error variance using the following procedure (Ulrich & Miller, 2001): GHII = ( -1) : ⁄ GHII = ( -1) ⁄

III.5.2. Measures to characterize noise in the time domain

In the time-domain, several measures of signal-to-noise have been developed taking inspiration in entropy measures from information theory framework. Entropy, as expressed in thermodynamics, estimates the degree of 'disorder' of a system and, in information theory, information entropy also relates to the information content of a signal. A signal that expresses unexpected values carries more information than a signal that expresses only predictable values (Shannon, 1948).

Information entropy measures cannot be directly applied to physiological signals, such as scalp EEG, because they are not adapted to signals that are finite. Nonetheless, entropy measures have been developed that take inspiration from information entropy framework measures and adapt them to physiological signals. One of such measures is Approximate Entropy designed to provide reliable measure of entropy on data with a relatively small number of data points (Pincus, 1991). This measure has been shown to be well suited and have good sensitivity when used in EEG data. Nonetheless, it is highly dependent on the parameters used to compute it (Rezek & Roberts, 1998). On our data, we chose to calculate entropy by computing Sample Entropy (SE), which is a measure very similar to Approximate Entropy, i.e.

well adapted to short data segments, but less dependent on its parameters (Costa et al., 2002(Costa et al., , 2005)). SE presents an additional advantage which is to be a very intuitive measure to estimate the unpredictability of a time series or signal.

In simple terms, Sample Entropy calculates the probability to find repeating patterns within a signal. Repeating patterns are identified if each respective time-points in two sequences of m consecutive points are within a distance r of each other (r usually being expressed in percentage of the signal's standard deviation). Follow the example in Figure 8B (central panel), all dots labelled in blue are within a distance 'r' of each other, and the same applies to dots labelled in green or in red. A repeating pattern (i.e., in our example a sequence of a green dot followed by a red and then a blue dot) of 3 consecutive time points (consecutive colored dots in Figure 8B) is thus identified when the same sequence (green, red, blue) is repeated towards the end of the data segment. More formally, any pattern of m consecutive time points u1, u2, … um in the signal is repeated if another sequence of m points v1, v2 … vm (excluding self-matches) is found in which:

½v1 -u1½ ≤ r , ½v2 -u2½ ≤ r, …½vm -um½ ≤ r
The tally of all repeating patterns for each sequence of m consecutive time point yields the probability U m (r) that two sequences of 'm' time points are within a distance 'r' of each other. The same calculation can be performed for patterns of 'm+1' time points. Sample

Entropy is then defined as:

(, ) = -ln ST( () S () Sample Entropy thus represents the conditional probability that, knowing a pattern is repeated for 'm' consecutive time points, it will also be repeated for 'm+1' time points. In other words, SE evaluates the probability that the 'm+1' time point can be predicted when it follows a known pattern of 'm' time points, which translates into a measure of the predictability of the signal. The lowest this probability is, the less predictable the signal is and the higher the entropy of the signal.

To deepen the evaluation of entropy and information content of our signals, we calculate Sample Entropy not only on our original EEG time-series but also on several time scales 't' of our signal, through a procedure called 'coarse graining'. Coarse-grained time series at scales 't' are calculated from the original signal averaged inside non-overlapping time windows of length 't' (Fig. 8B, first panel).

The analysis of the Sample Entropy across different time scales constitutes the measure of Multi-Scale Entropy (MSE) (Costa et al., 2002(Costa et al., , 2005)). Multi-Scale Entropy varies from

Sample Entropy in that it does not evaluate noise levels or unpredictability, but rather signal complexity (Zhang, 1991). Signal complexity differs from entropy in that neither a completely regular signal (with an entropy of 0) nor a completely random signal (with maximal entropy) exhibit a lot of complexity. Such fundamental difference can be easily understood with the example of white noise. At a time-scale of 1, at which entropy is usually calculated, white noise is a completely unpredictable signal and has a very high value of Sample Entropy. However, at higher time scales, when long stretches of data are averaged during the above-defined 'coarsegraining' procedure, the random signal that is white noise will average to a constant signal at 0 and will exhibit very low values of Sample Entropy. White noise therefore exhibits high entropy (i.e, unpredictability) but very low complexity.

The brain is a biological system which operates at several temporal and spatial scales.

Therefore neuronal signals need to be informationally rich (i.e. a non null entropy) at several time scales (Costa et al., 2002(Costa et al., , 2005)). For this reason, it is more biologically meaningful to compare values of complexity across experimental condition than entropy. A signal that exhibits higher Sample Entropy values at a majority of time scales compared to another signal can be considered relatively more complex (Costa et al., 2005).

Lastly, to reduce the dimensionality of the Multi Scale Entropy value, we calculated the area under the curve for Sample Entropy along the different time scales (Zhang, 1991). By doing so, we assumed that a higher area under the Sample Entropy curve of a signal compared to another one reflects higher Sample Entropy at a majority of the time scales and therefore a higher degree of complexity. In our analyses of scalp EEG data across TMS conditions, we set the parameters for Sample Entropy at 'm=2' and 'r' as 15% of the signal's standard deviation (Costa et al., 2005) and we calculated Sample Entropy over 14 time scales. Permutation tests are based upon the simple rationale that, if the null hypothesis is true and samples in 'A' and 'B' are drawn from the same distribution, then the permuted data will be identical to the real data. However, if the null hypothesis is false, the permutated data will be different from the real data.

III.6 -Cluster

For a permutation test to be exact, the null distribution has to be drawn from all possible permutations of samples in 'A' and 'B'. However, for most datasets the total number of possible permutations is far too high and computing an exact permutation test too computationally demanding. On such scenario, Monte-Carlo sampling procedure is used to draw a subset of all possible permutations to estimate the null distribution exclusively in this representative subset.

The higher the number of permutations drawn, the more accurate the p-value estimated with Monte-Carlo sampling will be. Nonetheless there is a trade-off to be accommodated between the accuracy of the p-value and the cost of computing time.

The Multiple Comparison Problem can be solved in permutation tests by comparing the test statitic to a null distribution obtained not for each data point separately, but instead for a single null distribution which represents the complete data space (i.e., sensor space, time space, or time-frequency space). Multiple comparisons are then replaced with a single comparison and there is no Multiple Comparison Problem anymore (Maris & Oostenveld, 2007). However, in that scenario, the null hypothesis becomes that the distributions of conditions 'A' and 'B' are identical for all sensors or time-frequency bin. If this hypothesis is rejected it can be concluded that at least one EEG sensor or time-frequency bin shows significant differences across conditions 'A' and 'B' however the localization of this effect in the scalp topography or in the time-frequency space becomes uncertain. This null hypothesis is known as the global null hypothesis.

Using a global null hypothesis ensures strong sensitivity to differences between conditions, however a price has to be paid in terms of effect localization. It should be noted, however, that when analyzing EEG data, a more data point-specific null hypothesis might be senseless. Indeed, any physiological effects recorded on EEG datasets is unlikely to be localized at a single electrode in the sensor space, instead this effect will be spread in over adjacent contacts. Likewise, EEG dynamic effects evolving over time will last over several consecutive time bins. Therefore, overall, permutation tests are a highly sensitive approach for solving the Multiple Comparison Problem in EEG datasets and are adapted to the high degree of spatiotemporal correlation of such data (Maris & Oostenveld, 2007).

In the analyses done as part of this dissertation (see methods section on each study for further details), we performed data cluster-based non-parametric permutation tests with Monte-Carlo sampling. First, each pair of data points between two conditions were compared with two-tailed paired Student's t-tests. Then, adjacent data points that exceeded the established cluster significance threshold of alpha=0.01 (see [START_REF] Wyart | Neural Dissociation between Visual Awareness and Spatial Attention[END_REF] were clustered together and the sum of t-statistics of each point in the cluster is then assigned as the statistic of the cluster. The cluster statistics were compared to the distribution of the largest cluster statistic over the whole scalp obtained for 10000 random permutations. Any clusters exceeding the significance threshold (alpha=0.05) of the permutation test was considered to show a significant difference between conditions.

In section II of this chapter, we have described and addressed the rationale and assumptions for computing pairwise comparisons across the experimental conditions in our studies. However, given the orthogonal design characterizing such, with TMS pattern (rhythmic, non-uniform rhythmic, random, irregular) and TMS condition (active, sham), it could be argued that for most of our EEG analyses, ANOVA approaches could have been more

powerful to compare our experimental conditions. Notwithstanding, in the case of interactions effects within a factorial design, the use of the cluster-based permutations method to solve the Multiple Comparison Problem becomes highly complex and controversial. The main controversy lies in which permutations between levels of factors should be considered acceptable and will respect the correlation structure of the data. Additionally, considering the restricted possible data permutations for ANOVAs with multiple factors, exact permutation tests for such approaches either lack the power or are simple not possible. In sum, there is currently no consensus on which strategy is more appropriate to approximate permutations tests in factorial designs [START_REF] Anderson | Permutation tests for multi-factorial analysis of variance[END_REF]Edgington & Onghena, 2007;Suckling & Bullmore, 2004). Therefore, for the studies of this dissertation, we prioritized the application of a sensitive method to solve the Multiple Comparison Problem and therefore chose to compute pairwise comparisons between our conditions.

PROJECT 1

Causal role of high-beta oscillations in the right fronto-parietal network for conscious visual detection I -Entrainment of local synchrony reveals a causal role for high-beta right frontal oscillations in human visual consciousness

The following article has been published in Scientific Reports.

Résumé (français)

Des résultats antérieurs étayent le rôle crucial de l'activité oscillatoire dans la perception visuelle.

Mais les oscillations cérébrales

Introduction

High cognitive functions, such as consciousness or attention orienting, do not solely rely on the activity of single cortical regions but require the integration of processes occurring in widely distributed cortical nodes organized in complex networks (Buzsáki & Draguhn, 2004;[START_REF] Varela | The brainweb: Phase synchronization and large-scale integration[END_REF]. In this context, understanding how distant regions communicate as part of a single distributed network during the performance of a cognitive task has become a crucial mission for system's neuroscience.

Early theories of inter-regional communication in the brain have supported the view that anatomical white matter connections subtend long-distance communication [START_REF] Laughlin | Communication in neuronal networks[END_REF][START_REF] Mesulam | Large-scale neurocognitive networks and distributed processing for attention, language, and memory[END_REF]. However, neuronal activity, hence patterns of functional connectivity, is highly volatile and dynamic, fluctuating in the order of milliseconds [START_REF] Bressler | Operational principles of neurocognitive networks[END_REF][START_REF] Britz | BOLD correlates of EEG topography reveal rapid resting-state network dynamics[END_REF]. Consequently, inter-regional communication cannot be solely explained by structural connections, as they lack the flexibility to allow dynamic and selective communication between subsets of brain regions acting as nodes within highly interconnected networks (Fries, 2005).

During the last decade, new mechanistic models have proposed that communication between neural populations is subtended by the synchronicity of their oscillatory activity (Engel et al., 2001;Fries, 2005Fries, , 2009;;Fries et al., 2001;[START_REF] Varela | The brainweb: Phase synchronization and large-scale integration[END_REF]. Such models have claimed that when two natural oscillators in the cortex synchronize in frequency and/or in phase, the spikes sent by a first group of neurons will reach the well synchronized neurons of a target population at their peak of excitability, ensuring a better gain of information transfer between the two neuronal populations and, consequently, more efficient communication. This so called model of communication-through-coherence (Fries, 2005;Fries et al., 2001) has been postulated to be particularly useful to mediate top-down modulation (e.g. by attention or perception) on sensory areas (Engel et al., 2001).

Experimental data in support of long-distance synchronization during visual perception and the orientation of attention have been collected both in animal models (Buschman & Miller, 2007;Gregoriou et al., 2009;Saalmann et al., 2007) and humans (Gross et al., 2004;Hipp et al., 2011;Rodriguez et al., 1999). These studies suggest that fronto-parietal regions synchronize at beta or gamma frequency bands (ranging from 15 to 60 Hz) during episodes of attentional orienting or perception. However, these studies have associated synchronization with specific behaviors solely on the basis of their co-occurrence in time and space, and have proven unable to distinguish causal contributions of oscillatory activity from epiphenomena.

Transcranial Magnetic Stimulation (TMS), a non-invasive technology to stimulate circumscribed cortical regions, used in combination with Electroencephalography (EEG), offers a unique tool to probe the causal implication of oscillatory synchronization between specific anatomical locations and behavioral effects processed by such mechanisms. Indeed, TMS has demonstrated the ability to manipulate non-invasively both behavior (Chanes et al., 2012(Chanes et al., , 2013;;[START_REF] Klimesch | Enhancing cognitive performance with repetitive transcranial magnetic stimulation at human individual alpha frequency[END_REF]Romei et al., 2010;Sauseng et al., 2009) and neuronal activity [START_REF] Valero-Cabré | La stimulation magnétique transcrânienne (SMT) dans la recherche fondamentale et clinique en neuroscience[END_REF], 2007, 2005) by inducing, interfering or modulating ongoing activity in circumscribed cortical sites.

More recently, it has been shown that the delivery of brief bursts of TMS pulses (usually 4-5 pulses) regularly spaced in time (so called rhythmic TMS), building a pure 10 Hz rhythm, progressively phase-locked the natural alpha oscillators over the posterior parietal cortex in the passive non-performing human brain (Thut et al., 2011). Additional studies have reported evidence supporting the ability of rhythmic TMS, applied in a wide range of frequencies, to modulate performance in different cognitive tasks (Jaegle & Ro, 2014;Romei et al., 2011Romei et al., , 2010)).

Finally, a recent study by our group, based on the same dataset we further analyze in the current report, demonstrated that 30 Hz rhythmic TMS delivered over the right Frontal Eye Field (FEF), a region of the dorsal attention orienting network, locally entrained high beta oscillations in the frontal region below the stimulation coil and suggested that such entrainment could be causally linked to improvement of conscious visual detection for lateralized near-threshold targets (Vernet et al. 2019).

Through such studies, rhythmic TMS has been building a solid credibility as a unique causal tool to explore the oscillatory basis subtending the modulation of conscious perception by identifying performance shifts tied to the entrainment of local rhythmic activity at specific frequency bands and cortical sites. However, the role of the inter-regional synchrony between a stimulated region (in our case the right FEF) and other areas of the attention orienting network and their ability to modulate visual perception during the delivery of rhythmic TMS pulses remains rather unexplored. Given evidence showing that TMS-entrained oscillations can spread through connections to distant regions (Rosanova et al., 2009), we here re-analyzed a prior dataset (Vernet et al. 2019), aiming to extend prior results supporting a perceptual modulatory role of local episodic entrainment of high-beta activity in the right FEF. We hypothesized that the brief entrainment of local 30 Hz oscillations by rhythmic patterns of TMS on the right FEF would result in episodic inter-regional synchronization, likely operating within a dorsal fronto-parietal system linking the FEF and posterior parietal areas (Capotosto et al. 2009;Quentin et al. 2014Quentin et al. , 2015)).

We reanalyzed a previously recorded EEG dataset from our group obtained during the stimulation of the right FEF in healthy participants performing a lateralized conscious visual detection task with near-threshold stimuli. We predicted that rhythmic TMS patterns would induce transient inter-regional phase synchronization at a high beta frequency in the fronto-parietal attention network. Such hypothesis would substantiate in EEG recordings as higher values of 30

Hz phase-locking values between frontal electrodes close to the stimulation site and parietal electrodes for rhythmic than for random TMS patterns. We also hypothesized that, through increased fronto-parietal phase synchronization, rhythmic stimulation over the right FEF would distantly entrain high beta oscillations in the parietal cortex. We expected increases of local high beta power and inter trial phase coherence over parietal electrodes during rhythmic compared to random TMS patterns. Crucially, our study was based on a carefully designed control condition (a random TMS pattern) containing the same number of pulses, thus, the same amount of stimulation as the rhythmic 30Hz pattern of interest, but without the frequency-specific spacing of TMS pulses. As in prior publications by our group, this strategy enabled us to isolate the effect of the rhythmicity of the stimulation on inter-regional synchronization as well as the entrainment of local oscillations (Chanes et al., 2013;Quentin et al., 2015;Vernet et al., 2019).

target. After an inter-stimulus-interval of 233 ms, in 80% of the trials a target appeared in the middle of the left or the right placeholder with equal probability. The other 20% of the trials were catch trials in which no target was shown in any of the placeholders. The target consisted of a low-contrast Gabor stimulus (0.5 o /cycle sinusoidal spatial frequency, 0.6 o exponential standard deviation) with vertical lines, appearing for 33 ms. Stimulus contrast was individually adjusted for each participant during a calibration block performed prior to the beginning of the experimental session. At all times, contrast level was never below 0.005, neither higher than 1

Michelson contrast. Similar tasks had been employed in prior publications in our research group (see Chanes et al. 2013Chanes et al. , 2015;;Quentin et al. 2015;Vernet et al. 2019).

Participants were asked to perform a detection task, in which they had to report if they consciously saw the target and, if 'yes', to report on which side it appeared ('right' or 'left'). The response window consisted in two arrow-like signs (">>>" and "<<<") presented simultaneously During the calibration block, target contrast was adjusted to reach a performance of 50% correct detections using a staircase procedure (Cornsweet, 1962). Initially, the Gabor contrast was set very high (Michelson contrast of 1), then, on each trial the contrast was brought up or down

according to the answer of the participant. The initial step in contrast was equal to the initial contrast level (note that, regardless, contrast was always kept higher than 0.005 Michelson contrast). On each change of direction, contrast steps were divided by two. When, in five consecutive trials, contrast varied by less than 0.01 Michelson contrast, we considered that the 50% detection threshold had been reached. The threshold was measured a second time using the same procedure. The two thresholds were then compared. If they differed by less than 0.01

Michelson contrast, the calibration block was terminated and the contrast used during the following blocks was the average between the two thresholds. If they varied by more than 0.01 Michelson contrast, the threshold was determined again. During the calibration block, only sham TMS patterns were delivered on the participant's right frontal cortex. At the end of each subblock, the participant could take a short break.

Before starting the experimental blocks, the participant underwent a training block during which trials with active TMS (see below for further detail on TMS procedure) were introduced.

In all the conditions (no target present, target on the right, target on the left) half the trials were active TMS and the other half sham TMS. The order of presentation of active and sham TMS was randomized for each sub-block of 20 trials. Participants' performance during the training block was checked to ensure that it stayed stable even with the intermixed active TMS trials. At the end of each sub-block in this training period, participants were alerted if their false alarm rate was higher than 50% and they received feedback on their percentage of incorrectly reported target position and on their percentage of incorrect fixation. Between sub-blocks, the experimenter could Fourier spectra. For all trials, complex vectors were reconstructed with a phase (φ) equal to the phase difference between two signals. These complex vectors were averaged over trials. TMS volume conduction was ruled out by calculating the imaginary phase locking value, which consisted in the projection on the imaginary axis of the complex vector averaged over all trials. also manually adjust the contrast. Once the participant reached a stable performance, the experimenter could decide to end the training block and start experimental blocks. Experimental blocks were identical to training blocks (with the same feedback for the participant) except that the contrast was kept constant in all sub-blocks and the participant was allowed to take a short break only every two sub-blocks.

Recording of eye movements

During all blocks, the position of both eyes was monitored on each participant with a remote eye tracking system (Eyelink 1000, SR Research). If the location of the participant's eyes was recorded more than 2° away from the center of the fixation cross at any time between the appearance of the alerting cue and target offset, the trial was considered as non-fixated. The trial was re-randomized amongst the remaining trials in the sub-block and repeated. Non-correctly fixated trials were excluded from any data analysis. At the end of each block, the participant received feedback on the percentage of incorrect fixations to improve the accuracy of their fixations. Using this neuronavigation system the active TMS coil was kept within a ± 3mm radius from the center of the targeted site during the whole experimental session.

TMS

As done previously (Chanes et al. 2013;Quentin et al. 2015;Vernet et al. 2019), the two types of TMS patterns employed in this experiment consisted in a burst made of four TMS pulses, lasting 100 ms (measured from the onset of the first pulse to the onset of the last pulse) and ending 33 ms before the onset of the visual target. Two types of patterns were tested: a rhythmic pattern for which the pulses were delivered regularly at a frequency of 30 Hz (33 ms of inter-pulse interval within the burst) and a random pattern designed to not deliver any specific or pure single frequency. In the random pattern, the time interval between the first and the fourth pulse within the burst were kept identical to those in the rhythmic pattern. However, the onset time of the second and third pulse were randomly jittered below and above their timings in the rhythmic pattern (Fig. 1C). Some constraints applied to this timing randomization. First, since the time needed by the TMS machine capacitor to fully re-charge enough current before delivering the next pulse was limited, two pulses could not be delivered less than 20 ms apart. Second, the onset time of the two middle pulses (the second and the third) had to be shifted at least 3 ms away from the timings of the same bursts in the rhythmic pattern, to ensure that random patterns would never deliver a perfectly regular 30 Hz frequency.

TMS stimulation intensity was set at a fixed level of 55% maximal simulator output (MSO) for all participants. This value is slightly higher than the intensity proven efficient in prior studies by the team (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2014) to take into account the increased distance between the coil and the cortex due to the presence of the EEG electrodes and cap. To allow across-study comparisons, at the end of the experiment, the individual resting motor threshold (RMT) in the right hemisphere was determined visually for each participant as the TMS intensity that yielded a motor activation of the abductor pollicis brevis muscle (thumb motion) in about 50% of the attempts (Rossini et al., 2015). In average, the RMT was 72 ± 9% of maximum stimulator output. The fixed stimulation intensity (55% of maximal machine output) that was used for TMS stimulation corresponded to 78 ± 12% of our participant's individual motor thresholds.

EEG recordings

EEG signals were continuously recorded during all experimental blocks with a TMScompatible system (BrainAmp DC and BrainVision Recording Software, BrainProducts GmbH).

We recorded signals from 60 electrodes spread evenly across the scalp, positioned according to the international 10-20 system, plus a reference on the tip of the nose, a ground on the left ear lobe and 4 additional EOG electrodes positioned above and below the right eye and on each temple. Skin/electrode impedances were maintained below 5 kOhm. The signal was digitized at a sampling rate of 5000 Hz.

EEG epoching and artifact removal procedure

All the EEG data analyses were performed with the FieldTrip toolbox [START_REF] Oostenveld | FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data[END_REF] running on MATLAB 2017b. The EEG and EOG data were epoched across a [-2, 2] seconds window centered on the onset of the target. Trials where the participant did not fixate the central cross were automatically excluded during the task performance by monitoring the position of the eyes using the eye tracking system. Prior to any data analysis, all trials contaminated by blinks were removed by visual inspection. After these procedures, an average of 126±13 trials remained for each experimental block.

To remove the artifact created by the discharge of a TMS pulse, data in a window of [-4 +12] ms centered on the delivery of a pulse was discarded. A second order Butterworth filter (1 to 50 Hz), with forward-backwards filtering, was applied on the remaining data before the discarded data was interpolated using a piecewise cubic spline interpolation. For shorter computation times, data were then down-sampled to 500 Hz before an Independent Component Analysis (ICA) was performed. To make sure that the ICA did not introduce any differences between conditions, trials for all four experimental blocks (whether they were active or sham TMS trials, and whatever the TMS pattern tested) were gathered together and the ICA was computed on this single dataset. This procedure enabled the removal of residual artifacts (including eye movements, electrode malfunctions, 50 Hz power line artifacts and TMS artifacts lasting longer than 12 ms). Components were identified as artifacts based on the guidelines of Rogasch et al.

(2014). On average 9 ± 2 components were rejected. After this procedure, the data was separated into 4 conditions: active rhythmic TMS, sham rhythmic TMS, active random TMS and sham random TMS.

EEG Data Analysis

The epoched EEG signal was transformed into the time-frequency domain using a 3-cycle Morlet wavelet transform on the time window [-500 +500] ms around target onset and for frequencies between 6 and 50 Hz. Three measures relative to oscillatory synchronization were calculated: power, inter-trial coherence (ITC) and imaginary phase-locking value. Power was calculated as the squared value of the modulus of the Morlet coefficients (per each time frame and frequency bin) relative to a baseline window [-500 -300] ms before target onset. This outcome measure shows, in decibels (dB) unit, the increase (positive value) or decrease (negative value)

of power relative to this period.

Inter-trial coherence (ITC) measures phase consistency across trials in a single location (an electrode or a group of electrodes). To measure synchronization between distant regions, we used the phase-locking value (PLV) (Lachaux et al., 1999). This measure reflects the stability of the phase difference between two signals over trials in a specific frequency band. The phase difference between two signals is extracted at all points in time from their Fourier spectra and then averaged across all trials. The PLV is defined as the module of the resulting averaged complex vector across trials (Fig. 1D). The following formula was used for its computation:

X,Y () = Z 1 \I]^_`• b ]•c(\) \I]^_`Z
Where (t) represents the phase difference between the signal recorded by the electrodes x and y at time t and ntrials is the total number of trails in the condition.

The PLV, as the module of a unit vector, is always comprised between 0 (random phase difference between the two signals) and 1 (constant phase difference between two signals) [START_REF] Guevara | EEG coherence or EEG correlation[END_REF].

To avoid controversy on the fact that the PLV might be very sensible to volume conduction, which would bias this parameter to show higher values between neighboring electrodes (Vinck et al., 2011), we considered as a synchrony value the projection on the imaginary axis of the complex vector of the PLV, so called imaginary PLV (Nolte et al., 2004;Vinck et al., 2011).

2 X,Y ()4 = e 1 \I]^_`• b ]•c(\) \I]^_`f
This value of synchrony effictively cancels the contribution to the PLV of signals with a null or close to null phase difference, which is characteristic of two highly correlated signals affected by volume conduction. The normalized value of the imaginary PLV is comprised between -1 (when the two signals compared have a constant phase difference ofπ/2) and 1

(when the two signals compared have a constant phase difference of π/2). We took as variable the absolute value of the imaginary PLV (comprised between 0 and 1) to discard any information about which signal in the pair is lagging in phase behind the other. This information is difficult to interpret due to the cyclicity of the phase and would only render our results more difficult to understand.

In our analysis, we computed the imaginary PLV between the electrode FC2 (the closest to the stimulation coil) and all other scalp electrodes, for the time window [-500, +500] ms centered around the target onset and for frequencies between 6 and 50 Hz.

Behavioral Data Analysis

Performance in the detection task was assessed through the perceptual sensitivity index (d'), a measure from Signal Detection Theory that quantifies objective perception of stimuli presented around the threshold of perception (Stanislaw & Todorov, 1999). Trials were separated as "hits" (when the target was correctly detected), "misses" (when the target was not reported), "false alarms" (when a target was reported for a catch trial, i.e. trials when no target was presented), "correct rejections" (when no target was reported in a catch trial) and "errors" (when a present target was reported on the wrong side of the screen). The perceptual sensitivity index was then calculated from the rate of "hits" and "false alarms". See Vernet et al. (2019) for more detail on this analysis.

Statistical Analyses

We used a 2x2 orthogonal design with TMS pattern (rhythmic, random) and TMS condition (active, sham). Therefore, values of imaginary PLV, power and ITC were compared in two different ways. First, for each TMS pattern (rhythmic and random) we contrasted the active TMS condition and the sham TMS condition. Second, for each stimulation condition (active and sham) we compared the rhythmic TMS and random TMS bursts. Each pair were compared with two-tailed paired Student's t-test (α = 0.01). To correct for multiple comparisons in both topographical and time-frequency maps, we performed cluster-based permutation tests with Montecarlo sampling. This method clustered together neighboring electrodes or time-frequency points that reached significance in the paired t-test, using a single t-value per cluster. A nonparametric permutation test was applied on these clusters (10000 permutations, alpha = 0.05) to determine which clusters survived the correction for multiple comparisons. Cluster-based permutations is a highly sensitive method for correcting for multiple comparisons in EEG data because it is adapted to data which is highly correlated in space and time (i.e. an effect on the EEG signal is likely to be spread over adjacent sensors and consecutive time points) (Maris & Oostenveld, 2007). However, currently no consensus exists on how cluster-based permutations should be applied in factorial designs to evaluate interaction effects between multiple factors (Edgington & Onghena, 2007;Suckling & Bullmore, 2004). For this reason, and driven by our hypotheses of a contrast between rhythmic and random stimulation patterns to isolate the effect of rhythmic structure of the TMS pattern on oscillatory activity, we chose to compute direct pairwise comparisons between our conditions.

To analyze the participants' performance, a 2x2x2 repeated measures analysis of variance (ANOVA) was performed on values of perceptual sensitivity index (d') with stimulation pattern (rhythmic, random), stimulation condition (active, sham) and visual field (left, right) as withinparticipant factors. Planned post-hoc t-student tests were also used for pairwise comparisons. See Vernet et al. (2019) for more detail on this analysis. However, the localization of significant differences in a permutation test is not very precise, the building of clusters of electrodes might blur the effect over larger regions. Moreover, it must not be forgotten that the tested null hypothesis extends to the whole array of electrodes, and cannot be restricted to single electrodes (Maris & Oostenveld, 2007). In order to investigate in further detail the spatial localization of the synchronization induced by rhythmic or random TMS we defined two separate regions of interest, one in the left and one in right hemisphere, including parietal and parieto-occipital electrodes locations.

Results

High-beta right fronto-parietal synchronization

Time-frequency maps of the averaged activity over electrodes in these regions of interest are shown in Fig. 3. Statistical analyses on the time-frequency data confirm that the rhythmic TMS pattern, compared to the random pattern, increased right fronto-parietal synchrony only during the delivery of active TMS and in a frequency band restricted to high-beta (24-45 Hz) oscillations (Fig. 3A). However, contrary to what was suggested by the first topographic analysis, statistics showed no effect of the random TMS on left fronto-parietal synchrony (Fig. 3B comparison between random active and sham conditions).

These topographic and time-frequency analyses on the PLV between frontal and parietal electrodes showed that the delivery of rhythmic TMS patterns over the right FEF induced an increase in inter-regional synchronization in the high-beta band that is short lasting (not exceeding the period of stimulation) and restricted to the right fronto-parietal network, ipsilateral to the stimulation.

Long-distance oscillatory entrainment

Locally, focusing only on parietal and parieto-occipital electrodes, power around 30 Hz in both left and right regions of interest increased significantly in response to active rhythmic TMS delivered frontally (compared to sham rhythmic TMS), but not to active random frontal stimulation (compared to its sham control) (Fig. 4).

The inter-trial coherence (ITC), a measure of phase-locking of local oscillations, was also significantly increased in both left and right parietal regions of interest following active rhythmic TMS to the right FEF (Fig. 5). This time, the direct comparison between the two active conditions (rhythmic versus random pattern) showed that the rhythmic TMS pattern phase-locked local oscillations more strongly on a trial-to-trial basis compared the control random TMS pattern.

TMS-driven effects in the alpha band.

Aside from the modulations of oscillatory activity in the high-beta band, a very clear alpha desynchronization over parietal areas was observed in all 4 conditions during the stimulation (Fig. 4). This desynchronization was obviously not caused by TMS, as none of the TMS patterns showed any significant difference in alpha desynchronization when comparing the active and sham conditions. However, in both the active rhythmic vs. active random and sham rhythmic vs.

sham random comparisons, statistical analyses revealed that the rhythmic pattern induced stronger alpha desynchronization than the random pattern.

On the contrary, at the end of the TMS burst, we observed some increases in phase-locking in the alpha band over the same parietal areas (Fig. 5). Although visible in all 4 conditions (note that, again, this phase-locking shows no significant difference between the active and sham conditions), this phase-locking was stronger in the active random stimulation condition (compared to active rhythmic stimulation) in the hemisphere contralateral to the stimulation (Fig. 5B).

Behavioral effect of induced high-beta synchronization

In addition to electrophysiological results showing an increase in right fronto-parietal synchrony and a distant entrainment of high-beta oscillations in parietal regions, 30 Hz rhythmic stimulation of right FEF modulates behavior in a detection at threshold task, as previously reported in Vernet et al. (2019). A 2x2x2 ANOVA on values of perceptual sensitivity (d') revealed a main effect of stimulation condition (active or sham) F(1,13)=5.33; p < 0.05), with higher levels of visual sensitivity (d'), i.e. better detection performances, in trials with active TMS, regardless of the TMS pattern. The statistical analysis also revealed a significant main effect of visual field (right or left) (F(1,13)=10.14; p < 0.01) with targets appearing in the right visual field generally better detected than targets appearing in the left visual field. No other significant effects were found although the three-way interaction visual field x stimulation pattern x stimulation condition displayed a trend towards statistical significance (F(1,13)= 3.97; p < 0.068).

The triple interaction only approached statistical significance. Nonetheless, on the basis of a strong a priori hypothesis supporting different effects for rhythmic and random stimulation on conscious perception (Chanes et al. 2013(Chanes et al. , 2015;;Quentin et al. 2015 and see Vernet et al. 2019 for more detail on the statistical analysis of behavioral results) student's t-tests were performed to compare active and sham stimulation. These analyses revealed that rhythmic active TMS (compared to sham TMS for the same pattern) increased perceptual sensitivity (d') for targets appearing on the left visual field (p < 0.01) but not for targets appearing in the right visual field (p>0.88) (Fig. 6). No significant differences were found between the active and sham TMS conditions for the random pattern in any visual field (both active vs. sham comparisons p>0.11).

**

Discussion

Here we used rhythmic TMS coupled to EEG recordings during a visual detection paradigm to explore in humans the contributions of 30 Hz inter-regional synchrony within the right dorsal fronto-parietal systems and its causal implications for the modulation of conscious visual perception. We demonstrated that 30 Hz rhythmic TMS patterns delivered over the right FEF induced synchronization in the high-beta band between this area and ipsilateral parietal regions. This increase in synchronization was transient and did not extend beyond the delivery of the last TMS pulse. Since synchronization increases were not observed when participants were stimulated with our control random TMS pattern of equal number of pulses and total duration, we conclude that this effect is dependent on the precise spatio-temporal structure of our high-beta rhythmic pattern. We also posit that very likely such effects on fronto-parietal synchrony are closely related to the local entrainment of a 30 Hz episodic rhythm induced by TMS on the right FEF reported previously on this same dataset (Vernet et al., 2019).

Network effects of focal frontal stimulation

Given that focal stimulation of the right FEF could result, through inter-regional synchronization, in an effect on parietal ipsilateral brain regions, we investigated if the focal entrainment of a 30 Hz frequency in frontal regions reported previously (Vernet et al., 2019) might show signs of spreading to anatomically distant interconnected regions, which were not targeted and neither directly influenced by TMS. For this specific study we focused on EEG leads in posterior parietal regions, such as those around the intraparietal sulcus (IPS) area that has been shown to interact with the right FEF as part of a dorsal attentional orienting network (Capotosto et al. 2009;Quentin et al. 2014;Quentin et al. 2015).

Indeed, as predicted, we found that 30 Hz rhythmic TMS patterns delivered to the right FEF did not only locally entrain high-beta activity (Vernet et al., 2019) but they also increased high-beta power and they phase-locked high-beta oscillations over parietal electrodes, a phenomenon likely derived from previously reported entrainment of cortical oscillations by episodic TMS (Thut et al., 2011). It is interesting to notice, however, that such distant entrainment of high-beta oscillations could be at least partially independent from direct right fronto-parietal synchronization mechanisms, as it was observed for both the left and right hemisphere EEG contacts.

The electrophysiological effects described above, induced by rhythmic TMS bursts delivered shortly before presentation of a low contrast target, were accompanied by modulations of conscious visual performance, consisting in increases of perceptual sensitivity (d') (Vernet et al., 2019). Taken together, these results contribute evidence in favor of the causal implication of high-beta oscillatory activity within a fronto-parietal system potentially involved in the allocation of spatial attention and with bearing on the modulation of conscious visual detection performance.

Additions to previous knowledge on high-beta oscillatory activity in conscious visual perception

These results are consistent with highly influential findings by Buschman and Miller (2007) in non-human primates linking, by means of intracranial recordings, high-beta frontoparietal synchrony with the allocation of endogenous attention in a top-down visual search task and their replication by Phillips and Takeda (2009) employing scalp EEG in humans. Our study used a simple conscious detection task which did not manipulate the allocation of attention by means of spatial or attentional cues. Nonetheless, this same modality of attention might have been engaged in our participants, as on each trial they were cued with a central alert signal to the apparition of an upcoming target with a delay of 233 ms, for which only endogenous attention can uphold expectancy (Carrasco, 2011).

Our results build on the above-mentioned correlational results and, adding the value of causality, provide further proof of a causal relationship between human high-beta fronto-parietal synchrony and the modulation of visual perception. Most importantly, they extend results derived from this same TMS-EEG dataset and suggest that short bursts of focal rhythmic TMS do not only have the ability to locally entrain frequency-specific rhythms within the targeted area dictated by the pace of stimulation as reported previously (Vernet et al. 2019) but, indeed, right frontal local entrainment showed the ability to synchronize in a frequency-specific manner directly stimulated target region (in this case the right FEF) with interconnected regions (such as posterior parietal sites) and entrain rhythms at this same frequency distantly. Last but not least, our analyses support the suitability of rhythmic TMS patterns (built as bursts of individually triggered pulses) delivered onto a specific cortical location and the comparison of their cognitive/behavioral effects with those under the influence of equivalent control random arrhythmic patterns (of equal duration and number of pulses) allowing to isolate the causal contribution of stimulation frequency. Owing to this TMS burst control pattern, the reported TMS-driven electrophysiological effects showing significant differences between active rhythmic and random TMS conditions are unlikely to be artifacts caused by auditory (clicking) or tactile (scalp tapping) stimulation inherent to the delivery of TMS pulses or explained by the impact of a magnetic pulse on the neuronal activity (notice that any of the potential artefactual effect of single pulses would be identically present in both rhythmic and random TMS conditions).

Pioneering research, published prior to ours, already employed rhythmic TMS (without EEG recording) to investigate the causal role of local oscillatory activity on different cognitive functions and behavioral tasks (Jaegle & Ro, 2014;Romei et al., 2010;Romei et al., 2011).

Nonetheless, our study is the first to use coupled TMS-EEG recordings and gather evidence supporting of a potential causal link between a complex cognitive process such as the modulation of conscious perception subtended by long-range systems (such as dorsal attention orienting networks) specifically with interregional synchronization between frontal and parietal sites. connectivity would subtend a frequency-specific synchronization effect between areas of the dorsal attention orienting network responsible for enabling spatial attention, and subserve more efficient communication between frontal and parietal cortical sites. Increased coordination could be beneficial for a fast and flexible allocation of spatial attention which would enhance visual sensitivity and facilitate conscious access.

Although this result was strongly predicted on the basis of prior evidence showing a significant correlation between volume of the SLF I and TMS-induced improvement of visual perception (Quentin et al., 2014(Quentin et al., , 2015)), we did not apply a strong hypothesis on the loci involved in high-beta synchronization in our ad hoc analyses. Instead, we computed measures of synchrony between the electrode overlaying the right FEF and all scalp electrodes of a full array of 60 EEG leads. It is therefore remarkable that statistical analyses reveal a significant synchronization temporally tied to the duration of the delivered rhythmic TMS between the right FEF and electrodes over the right parietal cortex.

Modulations of alpha oscillations

In addition to the modulation of high-beta oscillations, the implemented TMS manipulation also showed effects on alpha band oscillations (Fig. 4 and5). Because these modulations are visible in the active and also sham TMS conditions, we hypothesize that they were caused by the click sound of the TMS and not a direct manipulation of neuronal activity by the TMS pulses. The loud sound that accompanies the delivery of TMS, and that in our task is always delivered 133 ms before the onset of the target, could have an alerting effect and prompt participants to concentrate their attention on the computer screen. The role of alpha desynchronization as a marker of attention orienting is well known (Capotosto et al., 2009;[START_REF] Klimesch | Induced alpha band power changes in the human EEG and attention[END_REF] and the strong alpha desynchronization we observe in all four tested conditions is consistent with this proposition.

We also propose that the phase-locking of alpha oscillations observed after the last TMS pulse is a result of the loud sound, through a different mechanism. It has been previously demonstrated that a sound can cross-modally phase-lock alpha oscillations in occipital cortices (Romei et al., 2012). The interaction between such cross-modal alpha phase-locking and the random stimulation, which is designed to entrain a wide range of frequencies (amongst them alpha) on different trials, could explain the significantly higher alpha phase-locking over the left parietal region in the active random condition compared to the active rhythmic condition. Much remains to be understood about these modulations in the alpha range. However, as modulations in this frequency band were not predicted and are hence outside of the initial focus of this article, further studies regarding the effect on the brain of the sound of sham TMS will be needed to shed more light on these phenomena.

Summary

Our results support the ability to manipulate "at will" interregional synchrony across specific pathways using rhythmic transcranial magnetic stimulation, an observation that might become interesting to systematically map causal synchrony interactions at high beta or other frequency bands, between novel sites and subtending other cognitive processes.

In conclusion, the findings contributed by this study (1) extend prior observations and hypotheses mainly derived from invasive electrophysiological recordings in non-human primate with correlational approaches and adds evidence for causality to previously suspected associations between right fronto-parietal synchronization in the high-beta band and the modulation of conscious visual performance in human participants; (2) They provide more solid ground to hypothesize a role for right frontal oscillations and fronto-parietal synchrony at this frequency band as a relevant physiological coding strategy, likely allowing the orienting of visuo-spatial attention through synchronization in the fronto-parietal dorsal attention network. Important for the field of brain plasticity, (3) our results support the use of non-invasive brain stimulation methods, and in particular rhythmic TMS to engage or modulate local and inter-regional synchrony, either to optimize cognitive performance in healthy participants or to develop novel therapeutic approaches based in the manipulation of abnormal oscillatory activity subtending neurological conditions.

Introduction

A rich and influential literature spanning nearly two decades has highlighted the role of frequency-specific oscillations in the coding of cognitive processes and the execution of behaviors. In the domain of attention, correlational studies in animal models (Buschman & Miller, 2007;Saalmann et al., 2007) and healthy humans (Gross et al., 2004;Hipp et al., 2011;Phillips & Takeda, 2009;Rodriguez et al., 1999) have associated high-beta oscillations in a fronto-parietal network with the allocation of attention in space and the modulation of visual perception.

Completing these findings, non-invasive brain studies in humans have shown that the use of rhythmic TMS bursts to entrain high-beta oscillations in the right Frontal Eye Field (FEF), a right frontal node of the dorsal attentional network (Corbetta & Shulman, 2002), enhanced visual perception (Chanes et al., 2013;Quentin et al., 2015;Vernet et al., 2019). Taken together, these studies provided synergistic evidence supporting a causal role for high-beta oscillations, operating within the right dorsal attention network, in the modulation of conscious visual perception.

However, direct and indirect data have also suggested different coding strategies for the contributions of the left hemisphere homotopic sites to these same cognitive functions. For example, in post stroke visuo-spatial neglect patients, the emergence of a beta rhythm has proved detrimental for the detection of left visual targets as omissions of left visual targets over left frontal regions have been associated with increases of local beta oscillations (Rastelli et al., 2013).

Analogously, desynchronization of beta rhythms in the left frontal cortex of healthy subjects during a pre-stimulus period of attention orientation (Gómez et al., 2006) has been correlated with better somatosensory target detection (Schubert et al., 2009). The latter evidence contradicts the hypothesis of a beneficial role for beta band synchronization throughout a bilaterally distributed fronto-parietal network for endogenous attention orienting, suggesting instead asymmetric coding strategies contributing to the orientation of spatial attention for the left and right hemispheres, with high-beta oscillations being causally related to attention orienting in the right but not the left hemisphere.

The attentional orienting network is known to show strong inter-hemispheric functional and structural asymmetries. Functionally, stronger activations in right hemisphere regions compared to left hemispheric sites have been recorded during attention orientation (Corbetta & Shulman, 2002;Shulman et al., 2010). Anatomically, the Superior Longitudinal Fasciculus (SLF), a white-matter tract linking frontal and parietal regions operating the attentional orienting network has shown a higher volume in the right than the left hemisphere, a feature that scaled significantly with visual detection skills in the left hemifield (Thiebaut de Schotten et al., 2011).

In a first attempt to explore the coding strategies subtending the modulation of conscious visual perception in left frontal sites, compared to those previously described for the right hemisphere, Chanes et al. (2015) stimulated the left FEF with either 30 Hz frequency-specific rhythmic patterns of TMS (aiming to entrain a high-beta oscillation in the stimulated cortex), compared to non frequency-specific or arrhythmic patterns of TMS (which were designed to prevent the entrainment of pure high-beta frequency-specific oscillations). Unexpectedly, result showed that visual perception was enhanced by non frequency-specific TMS patterns, whereas improvements of visual perception in the homologous region in the right hemisphere were driven by 30 Hz rhythmic patterns (Chanes et al., 2013;Vernet et al., 2019). This finding strongly suggested that the left and right FEF do not necessarily use the same coding strategy to facilitate visual detection by enabling attention orienting systems and mechanisms.

Chanes and colleagues put forward the hypothesis that at difference with right frontal rhythmic TMS bursts, which progressively synchronized local neurons at a single frequency, non frequency-specific TMS bursts could enhance perception by adding noise to the targeted left frontal cortex. Additionally, they discussed the possibility that varying inter-pulse intervals and TMS pulses not time-locked (i.e. not in phase) with any single rhythms at any frequency could prevent the build-up of oscillations at frequencies relevant for perception. Finally, the authors also entertained the possibility that non frequency-specific TMS patterns might have boosted cortical oscillations in single or several frequency bands, other than the high-beta rhythm entrained by 30

Hz rhythmic TMS. Nonetheless, the lack of concurrent EEG recordings precluded at the time a more accurate interpretation of such unexpected results and the verification of such predictions.

In this context, we here used coupled TMS-EEG recordings and aimed to further clarify how episodic patterns of arrhythmic or non frequency-specific TMS proved able to modulate patterns of neuronal activity while gauging its ability to influence conscious visual perception.

To this end, we recorded EEG signals during the delivery of short bursts of 30 Hz rhythmic or non frequency-specific TMS on the left FEF while participants performed a conscious visual detection task with near-threshold stimuli. We hypothesized that non frequency-specific TMS patterns would increase the power of cortical oscillations in a broader frequency range than a rhythmic TMS pattern designed to specifically entrain 30 Hz oscillations. We also hypothesized that the increase of cortical oscillations over a broader range of frequencies would manifest as a higher level of noise in EEG cortical signals recorded during non frequency-specific stimulation compared to rhythmic stimulation. Lastly, we searched conceptual support in the phenomenon of Stochastic Resonance according to which the addition of optimal levels of stochastic noise to subthreshold stimuli can boost its saliency, hence facilitate its detectability (Moss et al., 2004). On the latter basis, we hypothesized that an intermediate level of noise induced by non frequencyspecific TMS patterns but not rhythmic patterns would improve visual perception in healthy subjects.

Materials and Methods

Participants

Visual Detection Task

Similar tasks have been employed in prior publications by our research group (see Chanes et al., 2013Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019). The presentation of visual stimuli was controlled by an in-house MATLAB R2012b (Mathworks) script using the Psychtoolbox extensions (Brainard, 1997) and synchronized with the delivery of the TMS pulses (see Fig 1A Participants were presented with a response screen 1000 ms after the Gabor target offset.

They were asked to perform a detection task in which they had to report whether they saw a target and if 'yes' where had the target appeared (left/right of the fixation cross). The response screen consisted in two arrow-like signs (">>>" and "<<<") displayed above and below the central The contrast of the visual target was adjusted to reach the individual threshold contrast where each participant showed consistent 50% detection performance following a one-up/onedown staircase procedure. Gabor contrast was initially set at a contrast level of 1 Michelson contrast. At the end of each trial, target contrast was increased or lowered according to the response of the participant. The initial contrast step was equal to the initial contrast level and upon each reversal in response it was divided by two. Note that, regardless, the contrast of the target throughout the titration procedure was always kept between 1 and 0.005 Michelson contrast. A consistent estimation of the 50% conscious detection threshold contrast was reached when in five consecutive trials target contrast varied less than 0.01 Michelson contrast units. The threshold was measured twice using this exact same procedure. If the two threshold contrasts differed by less than 0.01 Michelson contrast unit, the calibration block was terminated and the contrast used for the rest of the experimental session was the average between the two thresholds. If they differed except that Gabor contrast was kept constant across all sub-blocks and participant were allowed to take a short break (~5-10 minutes) only every two sub-blocks.

Eye movements recording

Throughout the experimental session, the position of both eyes was monitored with a remote eye tracking system (Eyelink 1000, SR Research). In order to ensure fixation during the performance of the visual detection task, if at any point between the onset of the alerting cue and the target offset the position the participant's eyes were recorded more than 2° away from the center of the fixation cross, the trial was labeled as non-fixated and excluded from further analysis.

In such cases, at the end of the trial, participants were alerted that they had violated fixation requirements and the non-fixated trial was randomized again with the remaining trials in the subblock. TMS patterns, the timing of the first and last pulse of the burst were kept identical to those of the rhythmic pattern, whereas the timings of the two middle pulses were modified to create unequal inter-pulse intervals. In the non-uniform rhythmic pattern, the two middle pulses where anticipated and delayed, respectively, by 9 ms. In the random pattern, the timings of the second and third pulse were pseudo-randomly jittered below and above their timings in the rhythmic pattern. This randomization was constrained by two rules. First, to leave enough time for the capacitors of the TMS equipment to recharge between pulses, the inter-pulse interval had to be at least 20 ms long.

TMS

Second, to ensure that the random pattern would never reproduce a regular 30 Hz frequency, the two middle pulses had to be shifted at least 3 ms away from their onset time in the rhythmic pattern. Lastly, in the irregular pattern the onset time of the two middle pulses were delivered randomly within the 100 ms window of the burst, with the same constrains posed for the random pattern but an additional constraint, which was that the 3 inter-pulse intervals in the burst must all have different length, and the timings of the pulses were fixed in all trials. The rhythmic, nonuniform rhythmic and random patterns described above had been used previously in other studies by our team (Chanes et al. 2013(Chanes et al. , 2015;;Quentin et al. 2015;Vernet et al. 2019). For all patterns, the last TMS pulse was delivered 33 ms before the onset of the visual target.

TMS stimulation was fixed for all participants at the level of 55% maximal simulator output (MSO) hence not adjusted to individual resting motor threshold (RMT). Motor cortex excitability has been proven to be a poor predictor of excitability of other cortical areas (Kähkönen et al. 2005;Stewart et al. 2001) and a previous study by our group targeting the right FEF has demonstrated behavioral effects at the group level with a fixed intensity of 55% MSO (taking into account the additional coil-to-cortex distance added by the presence of EEG electrodes) (Vernet et al., 2019). However, to allow across-study comparisons, at the end of the experiment, the individual resting motor threshold (RMT) in the left hemisphere was determined visually on the abductor pollicis brevis (APB) muscle of each participants as the TMS intensity yielding a thumb twitch in about 50% of the attempts (Rossini et al., 2015). On average, the RMT of our cohort of participants reached 66±9% of maximum stimulator output. Hence our fixed TMS intensity translated in a stimulation intensity of 83±12% of individual motor thresholds.

EEG recordings

EEG signals were recorded concurrently with TMS from 60 scalp electrodes with a TMScompatible system (BrainAmp DC and BrainVision Recording Software, BrainProducts GmbH).

Electrodes were spread on the scalp according to the international 10-20 system. The reference was placed on the tip of the nose and the ground on the left earlobe. EOG signals were recorded from 4 additional electrodes positioned on the left and right temples and above and below the left eye. At all times, impedances were kept below 5 kOhms and EEG signal was digitized at a sampling rate of 5000 Hz.

EEG artifact removal procedure

EEG signals were analyzed with the FieldTrip toolbox (Oostenveld et al., 2011) running on MATLAB R2017b. The EEG and EOG data were first epoched in a [-2 2] seconds window centered on the Gabor target onset. Trials in which correct fixation, monitored by an eye tracking system, was violated were automatically excluded. Additionally, the timings of the TTL triggers operating TMS delivery were automatically checked for each trial and those rare events for which the precise onset time was not respected were automatically excluded. Trials containing blinks and muscle artifacts were also excluded following visual inspection of all trials. After exclusions, an average of 121±14 trials remained for each TMS experimental blocks.

The brief electromagnetic field generated by TMS pulses produces a high amplitude artifact on the EEG signals that had to be removed. To this end, data across a [-4, +12] ms window centered on the onset of each TMS pulse was removed and the blank EEG epochs were then interpolated with a shape-preserving piecewise cubic interpolation. The exact same artifact removal and interpolation procedure was applied to sham trials. Once artifacts had been removed, we down-sampled EEG signals to 500 Hz. All trials from each experimental conditions (active/sham trials and trials with the 4 TMS patterns) within each experimental session were gathered into two datasets. Two separate Independent Component Analyses (ICAs) were performed on the data corresponding to each experimental session. Gathering trials across all experimental conditions to undergo the same ICA ensured that the ICA did not introduce differences between the experimental conditions. The artifact components were identified based on the guidelines by Rogash et al. (2014). This procedure enabled us to remove residual TMS artifacts lasting longer than 12 ms which were not removed by data interpolation. We also removed components corresponding to eye movements, electrode malfunctions and 50 Hz power line artifact. We removed on average 9±3 out of 60 components for each dataset.

Once the signal was calculated back to the electrode level, the cleaned EEG datasets were separated into the 8 TMS experimental conditions: active/sham rhythmic TMS, active/sham nonuniform rhythmic TMS, active/sham random TMS and active/sham irregular TMS and datasets evaluating to the same TMS experimental condition from the two experimental sessions were combined. which our 4 types of active or sham TMS bursts were delivered. Second, we focused our analysis on a group of electrodes of interest (electrodes F1, F3, FC1, FC3 in the international 10-20 system) and analyzed modulations of power and ITC over the whole time-frequency space. These electrodes were selected as they were the closes to the center of the TMS coil targeting the left FEF with the shortest straight transcranial path.

EEG data analysis

EEG

We defined the degree of "noise" of a signal as its level of randomness or irregularity, hence its unpredictability. According to this definition, a pure sine oscillation is a completely regular and predictable signal, hence has a noise level of 0. A signal that contains several frequencies and has a very broad-band power spectrum is more irregular, thus more noisy than the former. Finally, at the extreme, white noise which is completely random has a flat power spectrum.

Therefore, to quantify noise in our EEG signal we first quantified the width of the frequency band of oscillations enhanced during stimulation. We averaged the power spectrum over frequencies [6 45] Hz across the time window of stimulation ([-133 0] ms, between the first TMS pulse and the visual target onset) and we detected local maxima in the averaged power spectrum. The width of each local maximum, or peak, in the signal was calculated at half prominence. The prominence or height, of the peak was determined relative to the smallest local minimum located between this peak and the next peak higher than the current peak. Because of the low signal-to-noise ratio of scalp EEG signals, reliable peaks could not be identified on individual datasets (Kiesel et al., 2008;Ulrich & Miller, 2001). We hence calculated the averaged power spectrum during stimulation on the grand average across all participants.

Finally, to estimate the variance in the measure of peak width over our subject group we applied the jackknife procedure. For a sample of N subjects, this method computes, for each subject i (i=1… N), the grand average signal over a subsample of (N-1) subjects by omitting subject i in the dataset. The peaks in the power spectrum and their width are estimated for each of the N subsampled grand averages. As they are estimated from grand averages over a pool of subject which varies only by one individual, measures estimated with this method have a very low error variance and therefore their standard error has to be corrected according to the following formula:

are within a distance r of each other. The same calculation can be done for patterns of m+1 time points. Sample Entropy (SE) is then defined as:

(, ) = -ln ST( () S ()

This measure represents the conditional probability that, knowing that a pattern is repeated for m consecutive time points, it will also be repeated for m+1 time points. Essentially, SE evaluates the probability that the m+1 time point can be predicted when following a known pattern of m time points. The lower this probability is, the less predictable signals are and the higher entropy they have.

SE is calculated for each time series or signals (in our case EEG traces) at several time scales. The signal at time scale t is the original signal averaged inside non-overlapping time windows of length t. The complexity of the signal, as evaluated by MSE, is represented in the evolution of the SE across time scales. Indeed, a very predictable signal will have low SE values at all scales. A signal such as white noise that is very unpredictable will have very high SE at low time scales. However, when long stretches of data are averaged for higher time scales, the random signal that is white noise will average to a constant signal at 0 and will exhibit very low values of SE at high time scales. A complex time serie that exhibits information-wise a very rich signal will

show high SE values at all time scales. Therefore, a signal that exhibits higher SE values at a majority of time scales compared to another signal can be considered relatively more complex (Costa et al., 2005).

To reduce the dimensionality of the MSE value and compare signal complexity over the whole electrode array between our TMS experimental conditions, we calculated the area under the curve for SE along time scales. We took the assumption that higher area under the SE curve for one signal compared to another reflects higher SE at a majority of time scales, therefore higher degree of complexity. We call this measure of area under the SE curve the MSE measure.

We calculated MSE on the time window of stimulation [-133 0] ms (between the first TMS pulse and the target onset) for each electrode. Considering the values of SE at each time scale are more reliable for longer signals, to have the most datapoints possible we computed MSE on data at a 5000 Hz sampling rate. We set the parameters for SE at m=2 and r as 15% of the signal's standard deviation (Costa et al., 2005). We calculated SE over 14 time scales. However we observed that our data segment was too short to yield reliable valued of SE at higher scales (nonfinite values of SE) thus all analyses presented in this study were carried out over 9 scales.

Many other measures of entropy are available for physiological signals however MSE is a measure adapted to relatively short signals, therefore an ideal measure for our concurrent TMS-EEG datasets in which we assessed the impact on EEG activity of magnetic stimulation applied in short bursts (Costa et al., 2005;Pincus, 1991).

EEG statistical analyses

For both topographical and time-frequency maps of power, ITC and MSE, we performed comparisons between active and sham trials for each TMS patterns as well as direct two-by-two comparisons between active trials for the 4 TMS patterns. Each pair were compared with two- A non-parametric permutation test (10000 permutations, Montecarlo sampling method) was applied on the cluster statistics. The statistical results displayed in figures 2, 3 and 5 show clusters that exceeded the significance threshold (alpha = 0.05) of the permutation test. Any effect These outcome measures are calculated from the proportions of different types of responses. For trials in which a target was presented, if the presence and the location of target were correctly reported the trial was counted as a 'hit', whereas if the target was reported as 'not seen' the trial was counted as a 'miss'. For catch trials which displayed no target, the trial was counted as a 'false alarm' if the presence of a target was reported, or as a 'correct rejection' if the target was reported not to be present. Very rarely, participants correctly reported the presence of a target but signaled the wrong location for it. These trials were counted as 'errors' and excluded from the analysis as it was impossible to determine if participants had seen a target in a location where no target was presented (akin to a 'false alarm' trial) or simply pressed the wrong button to report the location of the target. From the rate of 'hit' trials (H) and the rate of 'false alarm' trials (FA), perceptual sensitivity, decision criterion and likelihood ratio were calculated as follows:

# = '( () -'( () = - 1 2 2 '( () + '( ()4
= exp 9 '( () : -'( () : 2 ;

Where '( is the inverse of the normal cumulative distribution function. To avoid infinite values, a null rate of false alarms was corrected to

( :?
and a rate of hit trials of 1 was corrected to 1 -( :?

where N is the total number of trials on which each rate is calculated, following established procedure (Macmillan & Creelman, 2004).

Behavioral data statistical analysis

We

Results

Impact of non frequency-specific patterns of TMS on high-beta oscillations

We first tested the effect of rhythmic and non frequency-specific TMS patterns on highbeta oscillations, which we attempted to entrain in the left FEF with our 30 Hz rhythmic TMS pattern. We examined modulations of high-beta ([25 35] Hz) power for all electrodes during stimulation (time window [-133 0] ms centered on target onset). We show that compared to sham TMS, active TMS significantly increased high-beta power over the whole scalp grid not only during rhythmic bursts but also during non frequency-specific patterns (Fig 2A). Indeed, nonuniform rhythmic, random and irregular TMS patterns, even though they do not contain a specific 30 Hz rhythm, also showed significant increase in high-beta power when comparing active and sham trials. Direct two-by-two comparisons of active trials did not show significant differences in the power for high-beta oscillations during stimulation between rhythmic, non-uniform rhythmic, random or irregular active TMS patterns (data not presented in a figure).

We then analyzed the degree of phase-locking for high-beta activity during stimulation patterns, through the calculation of the inter-trial coherence (ITC), a measure of local phase alignment across trials. Likewise, the comparison between active and sham TMS revealed a significant increase of high-beta phase-locking across the whole scalp for both rhythmic and all non frequency-specific patterns (Fig 2B). However, active random stimulation increased highbeta phase-locking less than the other TMS patterns. Indeed, a direct two-by-two comparisons between active random TMS bursts and active rhythmic, non-uniform rhythmic and irregular TMS bursts revealed significant differences in high-beta phase-locking (Fig 2C). We also noted that two electrodes displayed significantly higher levels of high-beta phase-locking for the active irregular patterns compared to active rhythmic stimulation patterns (Fig 2C ). Both 30 Hz rhythmic TMS and the three non frequency-specific TMS patterns increased amplitude and phase-alignment of high-beta oscillations over the whole scalp during active stimulation.

Direct pairwise comparisons of ITC between active TMS patterns show that random TMS increased high-beta inter-trial phase-locking less than the three other active TMS patterns. A small cluster encompassing 2 electrodes showed higher high-beta phase-locking during irregular TMS than rhythmic TMS, the pattern designed ad hoc to entrain high-beta oscillations in the left FEF.

These results suggest that unexpectedly, non frequency-specific stimulation bursts, designed not to entrain a specific high-beta rhythm, increased high-beta power as strongly as rhythmic stimulation bursts, specifically designed to entrain high-beta cortical oscillations. Also surprisingly, non frequency-specific TMS patterns, namely the non-uniform rhythmic and irregular patterns, also increased high-beta phase-locking as strongly if not more (in the case of irregular TMS bursts) than our pure 30 Hz rhythmic TMS pattern.

Frequency-specific modulations of cortical oscillations by rhythmic and non frequencyspecific TMS patterns

To test the hypothesis that non frequency-specific TMS patterns would increase oscillations in a wider frequency band than the frequency-specific rhythmic 30 Hz patterns, we examined modulations of power and ITC for a group of electrodes of interest (electrodes F1, F3, To quantify the width of the frequency band showing increased power during stimulation, we computed the average power spectrum including the whole stimulation time window ([-133 0] ms, 0 ms corresponding to being the visual target onset). We identified local maxima in the averaged power spectrum and extracted the width of these peaks. The signal-tonoise ratio of EEG data was too low to identify reliable peaks in the power spectrum over the window of stimulation for individual data (Kiesel et al., 2008;Ulrich & Miller, 2001).

Therefore, we used a jackknife procedure to estimate the standard error of the width of the peaks identified on the grand-average power spectrum. Grand-average power spectrum over the stimulation window and estimated power peaks and their widths are shown on the marginal plots for active TMS time-frequency maps in figure 3A, first row. Notice that for active rhythmic, random and irregular TMS, only one peak was identified in the power spectrum at a high-beta frequency (peak at ~28 Hz, ~27 Hz and ~28 Hz respectively). For active non-uniform rhythmic TMS, one peak was reliably identified at a high-beta frequency (peaks at ~31 Hz).

Additionally, 12 out of 15 iterations of the jackknife procedure identified a second peak in the low-beta range (peak at ~15 Hz). The width of the band experiencing power increases for active rhythmic, random and irregular TMS patterns was quantified as the estimated width for the single peak in the high-beta range (Fig 4). Since for active non-uniform rhythmic TMS, two peaks were identified in the power spectrum, we determined that this TMS experimental condition could not be directly compared to the other three which showed increased power oscillations in a single frequency band.

One-way ANOVA on peak-width values of power increase for active rhythmic, random and irregular TMS showed a significant difference between TMS patterns (F(2,41)=3.309, MSE=0.13, p<0.05, after correction for reduced error variance from the jackknife procedure).

Planned two-tailed paired Student's t-test showed that the active irregular TMS increased oscillation power in a significantly wider frequency band than active rhythmic TMS (T( 14 This analysis suggests that non frequency-specific TMS differs from rhythmic stimulation with regards to the frequency band of oscillations they are able to modulate. Indeed, active irregular stimulation increased power and phase-locking in a significantly wider frequency band than active rhythmic stimulation. Moreover, when analyzing the peaks in the power spectrum during active stimulation, non-uniform rhythmic TMS patterns increased oscillation power in two distinct frequency bands (a low-beta and a high-beta band) whereas rhythmic stimulation or other non frequency-specific TMS patterns increased oscillations in a single peak at the high-beta frequency band.

We here hypothesize that the band width of rhythmic activity increased by non frequencyspecific patterns could be used as a measure of the level of noise that TMS patterns induce in a cortical location or network. Narrow-band power increases would give rise to a more regular signals comprised of a single oscillation frequency. In contrast, wider-band power increases, or increased oscillations in several distinct frequency bands, will denote a noisier EEG signal that is less predictable than a single oscillation.

Modulation of signal complexity by non frequency-specific TMS patterns

In an attempt to confirm our conclusions on the modulation of EEG signal noise levels by non frequency-specific TMS patterns, we computed another measure evaluating, this time, the predictability and regularity of EEG signals during stimulation. In the time-domain, we computed a measure of Multi-Scale Entropy (MSE) on the EEG signal during the window of stimulation ([-133 0] ms, 0 being the target onset) for all electrodes. MSE estimates the complexity of a signal by calculating Sample Entropy (SE) at several time scales. This measure estimates the probability to find repeating patterns in the signal hence the predictability of the signal. Lower predictability indicates higher entropy and higher noise levels in the signals. Figure 5A shows the distribution of SE over multiple scales for the electrode group of interest (F1, F3, FC1, FC3). SE gradually increases over increasing time scales (with a steeper increase for active compared to sham stimulation), this evolution is a sign of a complex signal that possesses irregular, non-predictable structures over multiple time-scales (Costa et al., 2002(Costa et al., , 2005;;Zhang, 1991). According to the guidelines provided by Costa et al. (2005) a signal is considered more complex than another one if it has higher values of entropy for a majority of scales. Therefore, to reduce the dimensionality of our entropy measure, we calculated the area under the SE curve over all scales and used this measure as the MSE value in following analyses.

We assessed modulation of area under the MSE curve during stimulation across all scalp EEG electrodes (Fig 5B). Comparisons between active and sham trials revealed that non frequency-specific TMS patterns increased MSE in clusters of sensors located over regions of the fronto-parietal network. Random active stimulation increased MSE over a small cluster of right frontal electrodes, whereas non-uniform rhythmic and irregular active stimulation These results suggest a difference in the modulation of MSE by rhythmic and non frequency-specific TMS, however direct two-by-two comparisons failed to show significant differences between any of the active TMS patterns probed in our study. In sum, the MSE outcome measures seems to confirm prior analysis using the width of peak of power increase. and by comparing active and sham trials, it shows that left FEF stimulation with non frequency-specific TMS patterns, but not with rhythmic TMS, increase signal complexity over frontal and parietal electrodes.

Visual detection performance

As in prior papers by our team on this same issue (Chanes et al., 2015), the impact of rhythmic or non frequency-specific stimulation on conscious visual detection was explored through the calculation of conscious visual detection performance (perceptual sensitivity, noted thus able to test and discard hypotheses about the potential effects of these non frequency-specific patterns on neural signals (Chanes et al., 2015).

Increase of high-beta oscillations by non frequency-specific TMS

First, we were able to demonstrate that against what we initially hypothesized, non frequency-specific TMS bursts did not prevent the build-up of cortical oscillations. In fact, when comparing differences in power during stimulation for high-beta frequency band, our results surprisingly revealed that active non frequency-specific TMS patterns increased high-beta oscillations as strongly as 30 Hz rhythmic TMS patterns (known to entrain high-beta cortical oscillations). Moreover, as previously reported for rhythmic TMS patterns (Thut, Veniero, et al., 2011;Vernet et al., 2019), non frequency-specific TMS bursts were able to phase align cortical oscillations at a level comparable with rhythmic episodic patterns.

As an exception however, the so called random TMS pattern did increase oscillatory phase alignment compared to sham but showed significantly lower impact on phase alignment than highbeta rhythmic TMS or the two other non frequency-specific TMS patterns. The random TMS burst differed from all other TMS patterns tested in that the onset time of the middle pulses (2 nd and 3 rd , out of 4 pulses) within the burst were not fixed but instead, they were randomly jittered from trial to trial. This pulse onset timing variability could explain such a weak (indeed the weakest of all) trial-by-trial phase alignment in response to random TMS.

Previous studies have reported how the injection of an optimal level of peripheral noise to a visual rhythmic stimulus has the ability to enhance cortical oscillations entrained at this same frequency band (Mori & Kai, 2002;Srebro & Malladi, 1999). It is hence plausible that the three non frequency-specific patterns tested in this study ended up acting similarly. The onset timings of TMS pulses in such patterns closely resembled those of a pure 30 Hz frequency, but a slight onset timing shift of the two middle pulses added noise to an underlying 30 Hz frequency. This noise, once it reached a level neither too high or too low, could have enhanced the entrainment of high-beta cortical oscillations, even in the absence of a regular 30 Hz structure in the burst.

Frequency-specificity of effects of rhythmic and non frequency-specific TMS

If they have the similar effects on high-beta oscillations, non frequency-specific TMS patterns differs from rhythmic TMS in the frequency specificity of the oscillations they enhance.

We here showed that, compared to 30 Hz rhythmic episodic bursts, non frequency-specific TMS increases cortical oscillations in the stimulated left frontal cortex (the left FEF) across a broader frequency band extending towards low-beta band. Indeed, when the two patterns were directly compared, irregular TMS increased oscillatory phase-locking in the band more strongly than rhythmic TMS. Additionally, non-uniform rhythmic TMS patterns showed two peaks of increased power, one in the high-beta range and another in the low-beta range. In contrast, rhythmic TMS increased oscillatory power within a single high-beta peak.

The bandwidth of such increases in oscillation power also revealed differences between the three non frequency-specific TMS patterns evaluated in our study. Indeed, our analyses showed that compared to the random TMS pattern, irregular TMS patterns were able to enhance cortical oscillations is a wider frequency band, centered around the high-beta band. Moreover, compared to non-uniform rhythmic TMS, irregular and random patterns enhanced power in a single, albeit wide, frequency band, whereas non-uniform rhythmic TMS influenced cortical oscillations in two distinct frequency bands (high-beta and low-beta).

Increased level of neural noise by non frequency-specific TMS

The distribution of power in frequency bins over power spectral density has been used in electrophysiological signals as a measure of spectral entropy (Rezek & Roberts, 1998). In this respect, the degree of frequency specificity of the oscillatory response to stimulation is taken as a proxy of the level of noise induced by short TMS bursts into the cortex. The synchronization of local oscillators at a single frequency during rhythmic stimulation (Thut, Schyns, et al., 2011;Vernet et al., 2019) gives rise to a regular and predictable oscillating signal with a single narrow power peak and very low entropy. On the contrary, the mixing of frequencies in a large number of frequency bins results into a more complex and unpredictable signal with higher entropy. In this framework, our results show that non frequency-specific TMS induced higher levels of noise in the EEG activity generated by the stimulated cortex than rhythmic TMS bursts.

This conclusion was further confirmed by a second measure, the Multi-Scale Entropy (MSE), a parameter that estimates the evolution of signal entropy over several time scales, higher values of MSE indicating a signal that is not random but complex and rich in information at several time scales (Costa et al., 2002(Costa et al., , 2005;;Zhang, 1991). Only non frequency-specific TMS patterns significantly increased MSE over large clusters of EEG electrodes compared to sham stimulation. Most interestingly, the increase of signal complexity during stimulation with non frequency-specific TMS patterns was found most significant not on EEG electrodes over the stimulated left FEF, but instead those overlying right frontal and bilateral parietal regions. The poor spatial resolution of EEG does not allow us to conclude which cortical regions were the source of this frontal and parietal increase in complexity. Nonetheless, the left FEF is a node of the fronto-parietal network for orientation of spatial attention (Corbetta et al., 2008) and this network is likely to be enabled during a time interval between the onset of an alerting cue and the onset of the visual target (Gross et al., 2004;[START_REF] Kastner | Increased Activity in Human Visual Cortex during Directed Attention in the Absence of Visual Stimulation[END_REF] in which the TMS bursts were delivered. We hypothesize that the increase in signal complexity induced in the left FEF by non frequency-specific TMS was likely spread throughout the dorsal attentional network to the right FEF and bilateral Intra Parietal Sulci (IPS) via transcallosal and structural intrahemispheric connectivity, for instance, along the Superior Longitudinal Fasciculus a bilateral white matter tract linking frontal and parietal nodes of the attention network (Thiebaut de Schotten et al., 2011).

Non-invasive induction of neural noise

To the best of our knowledge, our results offer the first proof, that short TMS bursts can modulate neural noise levels in circumscribed cortical regions. Furthermore, we provide experimental support in favor of the ability of TMS bursts with several types of temporal irregularities to induce distinct levels of cortical noise. Only relatively recently, transcranial Random Noise Stimulation (tRNS) a technique delivering random levels of current mimicking a white noise signal (Terney et al., 2008) has been used to modulate neural noise levels and influence cognition. Through variations in the mean current intensity different levels of cortical noise can be evoked (Groen et al., 2018;Groen & Wenderoth, 2016). In spite of some significant advantages (e.g., lower cost, ease of use, excellent safety profile, portability and possibility of flexible multisite stimulation), compared to TMS approaches, tRNS suffers the limitations of any other transcranial current stimulation (tCS) approaches, essentially, low temporal and spatial resolution and weak intracranial impact (Bikson et al., 2010;Datta et al., 2012;Nitsche et al., 2008;Valero-Cabré et al., 2017). Moreover, concurrent EEG recordings are highly challenging during tRNS given the continuous artifacts generated by scalp electrical currents, and no effective cleaning methods for these artifacts have been found (Noury et al., 2016;Noury & Siegel, 2018).

Accordingly, to our knowledge, no evidence from convincing EEG recordings to confirm the ability of tRNS to manipulate noise levels present in cortical signals have been produced.

Generating experimental proof that tRNS may be able to act by modulating cortical EEG signals is paramount particularly given recent evidence suggesting that tCS intensity levels normally used during electrical stimulation (~2 mA) are not strong enough to reach in sufficient magnitude the cortical surface (Horvath et al., 2015;Vöröslakos et al., 2018). Additionally, recent evidence supports that the behavioral impact of tCS on behavior could be in part driven by the activation of peripheral sensory inputs rather than cortical stimulation (Asamoah et al., 2019).

The possibility to use short TMS bursts to non-invasive manipulate neural noise operating in focal cortical regions and exert an impact on specific behaviors holds considerable promise in the field of neurophysiology, as it could help us better understand neural coding. Indeed, neural noise could play an important role for signal processing in the central nervous system. To this regard, Stochastic Resonance (SR) theory postulates that in non-linear systems, such as neurons, the addition of an optimal level of noise can enhance the information content of a signal (Moss et al., 2004) and, indeed, SR-like beneficial effects of noise have been demonstrated at several spatial scales in the nervous system. External addition of noise improves signal transduction through membrane ions channels (Bezrukov & Vodyanoy, 1995) and single cell responses to sensory stimuli (Collins et al., 1996;Cordo et al., 1996;Douglass et al., 1993;Jaramillo & Wiesenfeld, 1998). At the level of cortical regions, addition of stochastic noise to weak periodic sensory stimuli improved evoked responses recorded by EEG (Srebro & Malladi, 1999) and entrainment of cortical activity at the frequency contained in the stimulus (Manjarrez et al., 2002;Mori & Kai, 2002). Lastly, at the behavioral level, the injection of noise to neural activity in cortical regions facilitates the detection of weak sensory stimuli (Groen & Wenderoth, 2016;Iliopoulos et al., 2014;Kitajo et al., 2003;Manjarrez et al., 2007) and hence improves the coding of higher cognitive processes to enable decision-making or facilitate memory (Groen et al., 2018;[START_REF] Usher | Stochastic resonance in the speed of memory retrieval[END_REF]. Overall, SR effects suggest that cortical processing is very robust to sources of noise inherently present in the brain (Faisal et al., 2008) and very effective at detecting weak stimuli embedded in this noise. By opening the possibility to add noise directly to circumscribed cortical targets instead of via sensory afferent pathways, the use of non frequencyspecific TMS bursts may open new avenues to explore the causal relationship between modulation of neuronal noise levels and cognitive function.

Improvements of visual perception by addition of noise

According to the Stochastic Resonance (SR) theory, the addition of noise can be accompanied by better perception. Along these same lines, an earlier study by our team that inspired the current exploration delivered TMS to the left FEF and showed improvement of visual perception under the influence of two of types of non frequency-specific TMS bursts, whereas rhythmic TMS did not elicit any improvement (Chanes et al., 2015). Unexpectedly, we were unable to replicate this behavioral result even when two of the non frequency-specific patterns we used were identical to the patterns previously tested. Several reasons could explain our inability to modulate conscious visual detection. First, detection improvements for visual stimuli during the addition of stochastic noise seems to systematically follow an inverse U-shaped curve (Collins et al., 1996;Simonotto et al., 1997). Accordingly, only an optimal level of noise added to sensory stimuli drives detection improvements, whereas noise levels below or above have either no effect or a detrimental impact. It has been shown that the optimal level of noise for behavioral improvement varies for each individual (Iliopoulos et al., 2014;Kitajo et al., 2003) likely given differences in the level of ongoing internal noise present in targeted cortical systems before the addition of electromagnetic noise. Indeed, participants with a high level of internal noise show limited facilitation via SR effects (Aihara et al., 2008). Hence, we cannot rule out if the noise levels induced by the three non frequency-specific TMS patterns tested in our study might have been either too low or too high to improve perception. Secondly, even if we did induce noise levels in a range susceptible to drive behavioral improvement, inter-individual variability in the levels of optimal noise could have extended the facilitating effect over all types of TMS patterns, cancelling off any significant perceptual improvements at the group level for each individual type of pattern (Groen & Wenderoth, 2016). In any case, this negative behavioral result should encourage further explorations to pinpoint and eventually customize the level of noise intensity necessary for improving cortical processing.

We here applied relatively brief TMS bursts (4 pulses, during an interval of 100 ms from the 1st to the 4th pulse) in order to facilitate a comparison between our results following left FEF stimulation and effects of right FEF stimulation reported previously (Chanes et al., 2013;Quentin et al., 2015;Vernet et al., 2019). However, short TMS bursts combined with technical limitations of TMS machines (at least 20 ms in between two pulses) strongly limit the inter-pulse intervals available to design non frequency-specific TMS patterns. The use of longer TMS bursts with more than 4 pulses (or, if behaviorally relevant, slower TMS frequencies) could provide additional freedom to modulate the structure of a burst within a continuum between complete random pulse onset timing and a pure perfectly regular rhythmic oscillation. With higher flexibility to titrate noise levels, one could individually customize non frequency-specific TMS patterns, hence reduce inter-subject variability and become more sensitive to stochastic resonance on visual performance effects at the group level.

Although we could not confirm previously reported improvement of conscious visual perception in response to either rhythmic or non frequency-specific TMS patterns, we did observe significant modulations of the subjective response criterion by active but not sham TMS. Indeed, response criterion was lowered, indicating the participants, when in doubt about the appearance of a visual target, provided less conservative responses (or, said otherwise, they took more liberal perceptual decisions), hence were more likely to respond that a target had been presented in active than in sham TMS trials. This effect, however, was not modulated by the temporal structure of the TMS bursts as no significant main effect of TMS pattern or interaction between TMS pattern and TMS condition (active or sham) on the response criterion were found.

To the best of our knowledge, such a TMS pattern-unspecific effect on subjective response bias has never been reported previously in response to the stimulation of left or the right FEF during a conscious visual detection task employing either single TMS pulses (Chanes et al., 2012) or rhythmic vs. non frequency-specific TMS patterns (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019) . However, the mentioned studies measured subjective bias with the likelihood ratio (b) instead of the decision criterion (c) we here present. Both of these measures are based on Signal Detection Theory to estimate the degree of subjective bias in perceptual detection tasks. Nonetheless, they differ in that the decision criterion (c) is completely independent from the objective perceptual sensitivity of the subject, whereas the likelihood ratio (b) is not (Ingham, 1970). The decision criterion (c) has been progressively established as a more sensitive measure of response bias (Macmillan & Creelman, 1990) than likelihood ratio (b).

Accordingly, previous studies which estimated the latter might not have been sensitive to any modulation of the subjective response bias by active TMS, that we here report for the first time.

Indeed, interestingly, we here measured both likelihood ratio (b) and decision criterion (c) and showed a specific effect of active vs. sham TMS only for the latter.

In order to explain this collateral finding, left frontal regions have been shown to be part of a left-lateralized network in charge of integrating sensory information in decision-making [START_REF] Heekeren | Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality[END_REF]. We here hypothesize that any of the tested active TMS pattern delivered to the left FEF have the capability to activate this network, leading to a modulation of decision criterion. Alternatively, reductions of response criterion (i.e., participants becoming less conservative or more liberal in their perceptual decisions) has been observed following the delivery of a loud warning signal preceding a visual target (Bolognini et al., 2005;Frassinetti et al., 2002) an effect which could be attributed to a general crossmodal increase of phasic alerting following a warning signal (Kusnir et al., 2011;Robertson et al., 1998). Although the impact of the loud clicking sound associated with TMS delivery is here controlled for by the use of sham TMS patterns, the somatosensory stimulation of the scalp generated by TMS on each pulse are only controlled for incompletely. Since the effect on decision criterion proved TMS patternindependent, hence not modulated by the temporal structure of the TMS bursts, this could have been caused by an unspecific effect of active TMS. The scalp tapping sensation tied primarily to active TMS pulses could have acted as a pre-target warning signal able to modulate the criterion of visual perception decisions (Shams & Kim, 2010).

Conclusion

In conclusion, this is the first study exploring the effects of several types of non frequencyspecific TMS patterns on cortical activity focusing in the left FEF as part of a bilaterally distributed dorsal attentional orienting network. Our study tested, with interleaved TMS-EEG recordings, speculative hypotheses as to the effect on EEG signals of non frequency-specific TMS bursts (Chanes et al., 2015). We brought evidence that non frequency-specific TMS patterns increase cortical oscillations in a broadband manner, and generated cortical signals of higher complexity in the targeted left frontal regions but also in inter-connected fronto-parietal sites of the left and the right hemispheres.

Our results are still preliminary but encourage further studies focusing on at least three Nous concluons que des sons uniques ou en rafale rythmique latéralisés à droite ne contribuent pas à des états, décrits ailleurs, d'activité neurale qui facilitent la perception visuelle.

Cependant, les effets que nous décrivons sur les processus de prise de décision et la réinitialisons de phase non spécifique en fréquence appellent à de nouvelles études afin de mieux comprendre les effets de la SMT placebo et d'améliorer les interprétations des études anciennes et actuelles utilisant des designs expérimentaux avec de la SMT placebo.

Introduction

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation method that has been widely used to causally explore and manipulate brain-behavior relationships (recent reviews in Polanía et al., 2018;Valero-Cabré et al., 2017). Each TMS pulse delivers an intense brief magnetic field which induces, intracranially, a current able to depolarize clusters of cortical neurons in circumscribed brain regions. The powerful electrical current circulating briefly through copper wire loops inside the TMS coil case produces vibrations resulting in a brief but loud clicking sound (Nikouline et al. 1999). The same phenomenon has been also linked to brief-lasting deformation of the coil's plastic case surface in contact with the scalp which, in synchrony with pulse delivery, results in slight scalp tapping that may induce vibration of the skull and the cochlea.

Additionally, the spread of magnetic field to superficial scalp muscles can induce their contraction and afferent proprioceptive stimulation (Ilmoniemi & Kičić, 2010).

In order to cancel out the potential influence of such sources of peripheral stimulation, exploratory and clinical TMS designs contrast the effects of active TMS with the those of a socalled sham TMS condition. This widely used approach aims to test participants (in trials interleaved with active TMS trials or in a separate experimental block) in the same tasks under the influence of the sensory confounding effects of TMS. This is done to ensure that the effects active TMS patterns might reveal are fundamentally linked to the modulatory effect of electromagnetic pulses, hence not significantly contributed to by the above mentioned peripheral sensory effects.

Most often, sham TMS consists in delivering active TMS pulses through a figure of eight coil placed at a 90 o angle between the coil surface and the scalp. This configuration reproduces the loud clicking noise of active TMS while the magnetic field is projected into the air hence not reaching the cortex. Custom-made sham TMS coils have also been designed that are equipped with magnetic shields so that, when they are positioned flat on the scalp, they prevent the magnetic field from reaching the brain, producing however a similar clicking noise as regular TMS coils (Duecker & Sack, 2015). Importantly, however, to date none of the available sham TMS approaches is in a opposition to accurately reproduce all the somatosensory sensations generated by the active TMS patterns [START_REF] Loo | Transcranial magnetic stimulation (TMS) in controlled treatment studies: Are some "sham" forms active?[END_REF][START_REF] Mennemeier | Sham Transcranial Magnetic Stimulation Using Electrical Stimulation of the Scalp[END_REF] such as scalp tapping, skull and cochlear vibration or muscle activation and ensuing proprioception.

Given the strong role played by cross-modal sensory interactions in brain systems, the confounding effects of loud clicking sounds associated with TMS delivery need to be carefully considered in venues exploring cognitive processes based or driven by any modality of sensory stimulation (Shams & Kim, 2010;[START_REF] Spence | Crossmodal correspondences: A tutorial review[END_REF]. In the domain of visuo-spatial attention and perception, visual detection can be improved when the onset of a visual target is accompanied by a salient sound (Frassinetti et al. 2002;Bolognini et al. 2005;[START_REF] Lippert | Improvement of visual contrast detection by a simultaneous sound[END_REF]. Furthermore, such effects can be observed even when the localization of the sound source in the extra-personal space does not carry any predictive information with regards to the position of visual target to be visually detected (Kusnir et al., 2011;[START_REF] Stein | Enhancement of Perceived Visual Intensity by Auditory Stimuli: A Psychophysical Analysis[END_REF]. Experimental evidence has also shown that sound acts as a warning signal triggering phasic alerting in a supra-modal manner, hence able to influence attentional or perceptual cognitive processes driven by other sensory modalities [START_REF] Sturm | On the Functional Neuroanatomy of Intrinsic and Phasic Alertness[END_REF]. Finally, it has been shown that lateralized sounds have the ability to orient spatial attention exogenously and hence improve the perception of ipsilateral visual targets [START_REF] Mcdonald | Involuntary orienting to sound improves visual perception[END_REF][START_REF] Spence | Audiovisual links in exogenous covert spatial orienting[END_REF].

In spite of all these psychophysical precedents, to our knowledge, only Sack and colleagues specifically analyzed in detail the effects of TMS sham stimulation on behavioral correlates of visual perception, likely mediated by attentional orienting processes. Their studies showed that the use of sham TMS delivered pre-target onset improved visual detection performance and that such effects were dependent on visual target location and sham TMS pulse timing (Duecker & Sack, 2013). On the basis of their findings, they concluded that the high specificity of active TMS peripheral sensory effects were challenging to deal with and hence demanded very careful planning of active TMS control conditions (Duecker & Sack, 2015).

The presence of auditory evoked potentials elicited by active TMS was already well documented two decades ago (Nikouline et al. 1999;[START_REF] Tiitinen | Separation of contamination caused by coil clicks from responses elicited by transcranial magnetic stimulation[END_REF]) when applications of noninvasive stimulation to study human cognition started to emerge. Nonetheless, for many years little was learned about the effects of sham stimulation on other types of brain activity, such as cortical oscillations.

Among recent findings in the time-frequency domain, studies documented the phaseresetting of cortical rhythms in auditory and also visual cortical regions following peripherally presented sounds [START_REF] Mercier | Auditory-driven phase reset in visual cortex: Human electrocorticography reveals mechanisms of early multisensory integration[END_REF]Romei et al. 2012). Additionally, it has long been observed that trains of clicking sounds (up to 100 Hz) give rise to an oscillatory response which follows the rhythm dictated by their spectral components in the auditory cortex (Galambos et al. 1981;Picton et al. 2003) and other cortical regions (Srinivasan et al. 1999;Srinivasan et al. 2006;Srinivasan et al. 2007). This very robust phenomenon known as an Auditory Steady-State Response (ASSR) has been employed to assess hearing capabilities in neurological patients (Picton et al., 2003) and shown to modulate several behaviors such as listening accuracy or illusory time perception [START_REF] Henry | Frequency modulation entrains slow neural oscillations and optimizes human listening behavior[END_REF]Herrmann et al., 2013;[START_REF] Lakatos | The Spectrotemporal Filter Mechanism of Auditory Selective Attention[END_REF]. There is a possibility that rhythmic sham patterns, which consist in a train of clicks delivered at a specific frequency, could evoke ASSR responses, in the primary auditory cortex and other cortical regions, and, by entraining frequency-specific oscillations, modulate several high level cognitive processes. In this context, we here entertain the hypothesis that sound-driven cortical oscillatory entrainment could interact with oscillatory entrainment achieved with active rhythmic (or repetitive) TMS delivered directly on the cortex (Thut et al., 2011). As a result, classical sham rhythmic or repetitive TMS bursts would not be suited to fully control for such phenomenon and isolate pure contributions of active rhythmic TMS patterns to the entrainment of cortical oscillations.

Aiming to explore the ability of clicking sound associated to sham TMS to entrain cortical oscillations and modulate visual detection performances, we recorded EEG signals from a group of healthy participants performing a conscious visual detection task with near-threshold lateralized visual targets consisting in low contrast Gabors. In half the trials, we tested the impact of either single pulses, short rhythmic or random patterns of sham TMS, delivered shortly before the onset of the visual target. In the remaining trials, no TMS of any sort was delivered (no sham stimulation trials). The three TMS patterns were tested in separate experimental blocks, whereas sham TMS and no TMS trials were embedded and randomized within the same block.

We predicted that sham stimulation pulses or bursts delivered shortly before visual target onset would improve visual perception by orienting spatial attention to the ipsilateral hemifield with respect to the TMS coil position (Duecker & Sack, 2013). This prediction would be substantiated in higher levels of perceptual sensitivity for visual targets for sham TMS compared to no sham TMS trials, and for right visual hemifield targets (ipsilateral to TMS coil position) compared to targets in the contralateral hemifield. On the basis of prior observations, we did not predict the entrainment of cortical oscillations by rhythmic sham bursts. Nonetheless, we hypothesized a transient phase-locking of cortical oscillations in the auditory cortex and other sensory cortices following the onset of sham clicking sound (Romei et al. 2012).

Lastly, TMS online experimental designs interleaving active and sham trials require two TMS devices attached to independent coils and placed on approximately the same area of the scalp.

Therefore, we also tested the use of an audio speaker attached to the TMS coil reproducing acoustical stimulation as a sham condition. This TMS-like sound setup would reduce needed TMS equipment and allow to conduct well controlled experiments with active/sham TMS being delivered from exactly the same location on the subject's scalp, making participants unaware of the presence of two TMS coils delivering two distinct TMS modalities [START_REF] Sommer | Transcranial magnetic stimulation-A sandwich coil design for a better sham[END_REF]. We hypothesized that these two sham TMS approaches (TMS coil placed perpendicular to the scalp or audio speaker) would produce similar effects on visual detection performance and EEG signals.

window showed two arrow-like signs (">>>" and "<<<") which were presented below and above the central fixation cross. Participants were asked to indicate which arrow pointed to the location of the placeholder where they saw the target. The location of the arrows (above or below the fixation cross) was randomized across trials and participants were prevented from preparing their motor response prior to the presentation of the response screen. Participants responded with their left hand by pressing the 'd' or 'c' letter key to signal the upper or lower arrow respectively. If they wanted to report that had not seen any target, they were asked to press the space bar.

The contrast of the Gabor stimulus was individually adjusted during a titration block completed immediately before the experimental sham TMS sessions. In such block, we aimed to determine the contrast threshold for which each participant achieved 50% correct detection performance (50% visibility). To this end, a one-up/one-down titration staircase procedure was applied (Cornsweet, 1962) for each participant. The procedure started with a Gabor stimulus of 1

Michelson contrast and a contrast step equal to the initial contrast level. Upon each response reversal the contrast step was divided by two. Throughout the titration procedure, the contrast of the target was always kept between 1 and 0.005 Michelson units. A final 50% detection threshold contrast was established when, for five consecutive trials, target contrast varied by less than 0.01

Michelson units. The threshold was measured twice using this exact same procedure. If the two thresholds differed by less than 0.01 Michelson units, the average of the two measures was used as the 50% visual detection threshold contrast for the experimental session. If this was not the case, the threshold was determined again until repeated titrations could yield two consecutive contrasts that fulfilled those criteria.

Eye movements recordings

To ensure fixation during the performance of the detection task, the position of both eyes was monitored with a remote eye tracking system (Eyelink 1000, SR Research, 1000 Hz sampling frequency). If at any point between the onset of the alerting cue and the target offset the position of participant's eyes was recorded more than 2° away from the center of the fixation cross, the trial was labeled as incorrectly fixated and excluded from further analyses. At the end of the trial the participants were alerted through a written message presented on the computer screen that fixation had been broken. All non-fixated trials were re-randomized and repeated during the remaining trials of the sub-block.

Sham TMS stimulation

Sham TMS was delivered with two different procedures. (Chanes et al., 2012(Chanes et al., , 2013(Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019).

We also tested (Sham Approach #2) the delivery of sham TMS-like clicking sound through a speaker (Mobi Wavemaster) taped on top of one of the loops of a 70 mm figure-of-eight TMS coil, which was positioned perpendicular to the skull of the participant and oriented exactly as described above for the classical sham procedure (Fig 1B). We created a template of the waveform generated by the TMS clicking sounds by recording 100 single TMS pulses (Aiwa CM-S32 microphone) and averaging their individual waveforms. Finally, we adjusted the envelope of the average waveform to emphasize high amplitude spikes present at the beginning of the waveform.

This was achieved so that once replayed through our speaker mounted on the TMS coil, the sound could not be distinguished from a click produced by a real active TMS pulse delivered with this same coil using an identical scalp configuration.

Mimicking online TMS paradigms tested in prior studies and which were proven effective for the modulation of conscious visual perception (Chanes et al., 2012(Chanes et al., , 2013(Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019), we tested the behavioral and EEG impact of three different sham TMS/TMS-like sound patterns (Fig. 1C): rhythmic sham TMS/TMS-like sounds bursts, random sham TMS/TMS-like sounds bursts and single pulses sham TMS/TMS-like sound.

Rhythmic sham TMS/TMS-like sounds consisted in bursts of 4 TMS/sound pulses regularly spaced in time with an inter-pulse interval of 1/30 secs at a 30 Hz frequency. The burst lasted 100 ms and the last sham pulse was delivered 33 ms before the visual target onset. To isolate the frequency-specific structure of the rhythmic sham TMS pattern, we also tested the impact of random sham TMS/TMS-like sounds patterns wherein the onset time of the 1 st and 4 th TMS/sound pulses were kept identical as in the rhythmic pattern but the onset times of the two middle pulses were randomly jittered (see Chanes et al., 2013;Quentin et al., 2015;Vernet et al., 2019). To be sure that the randomization would never reproduce a 30 Hz regular burst, the onset timings of the two jittered pulses were shifted at least 3 ms away from their 30 Hz rhythmic timings. Moreover, in order to leave enough time for TMS capacitors to fully recharge before delivering each active sham TMS pulse, inter-pulse interval was at least 20 ms long. In sum, both sham TMS/TMS-like sound random patterns were designed to produce a sound of the same duration as those delivered by the rhythmic pattern but avoiding the repetitive structure that could entrain pure high-beta cortical oscillations, as reported for active rhythmic TMS (Vernet et al., 2019).

We also tested the delivery of single pulses of sham TMS/TMS-like sound 133 ms before the target onset (the timing of the first pulse in the rhythmic and random sham bursts). Such a single false alarm rate was higher than 50%, participants were reminded to press the space bar if they had not seen a target. Additionally, participants received on the screen information on the percentage of incorrectly reported target positions and incorrectly fixated trials. Once the experimenter was sure that participants had understood the task and showed stable performance, the evaluation blocks started. The order of these three blocks within each session was counterbalanced across participants. Each of the evaluation blocks consisted in 10 sub-blocks (20 trials per sub-block, for a total of 200 trials) identical to those used for training, except that participants were provided feedback and were allowed to take a short break every two sub-blocks.

Once the two experimental sessions were completed, we systematically debriefed with the participants and inquired on their beliefs on the kind of stimulation they had received. To this regard, 6 out of 11 participants were able to correctly guess that they had received sham stimulation.

All of them were able to notice that the single pulse TMS/TMS-sound pattern was different than the 4-pulse rhythmic or random patterns, but the rhythmic and random patterns could not be identified as different. None of them declared to have been aware of the existence of two different sham procedures tested across two sessions.

Combined scalp EEG recordings

The EEG signals recorded during sham TMS/TMS like-sound trials and no-stimulation trials was continuously recorded from 60 scalp electrodes at a sampling rate of 5000 Hz with a TMS-compatible system (BrainAmp DC and BrainVision Recording Software, BrainProducts GmbH). The reference electrode was placed on the tip of the nose and the ground on the left earlobe. EOG signals were recorded from 4 additional electrodes positioned at the left and right temple and above and below the left eye. Throughout the experiment impedances were kept below 5 kOhms.

EEG data cleaning

EEG data processing was performed with the FieldTrip toolbox (Oostenveld et al., 2011) running on MATLAB R2017b. For analyses, EEG and EOG data were epoched in a [-2 2] s window centered on visual target onset. All trials were visually inspected and any trial containing eye blinks or muscle activity, trials on which fixation requirements had been violated or the very few trials in which sham TMS pulses had not been triggered with the exact expected timings, were excluded from further analyses.

The active magnetic pulses delivered away from the scalp during the Sham TMS session (i.e. sham procedure #1 delivering active pulses through a TMS coil in a classical 90 o sham configuration) induced short lasting high amplitude artifacts on EEG recordings. As done elsewhere (Thut et al., 2011;Vernet et al., 2019), stimulation artifacts were eliminated by cutting off artifacted EEG data within a [-4 12] ms window around the onset of each pulse and interpolating this period with a shape-preserving piecewise cubic interpolation.

For trials using TMS-like sound sham stimulation (i.e. sham procedure #2 delivering recorded clicking sounds, through a speaker mounted on a TMS coil placed in the same sham configuration as the former) no stimulation artifacts were generated on EEG signals. Nonetheless, in order to make the data of both sham procedures (#1 and #2) comparable, a similar period of EEG around TMS-like sound pulse onset was also removed and the signal in this same period was interpolated using the exact same procedure reported above for sham procedure #1.

In order to implement a data cleaning procedure identical to the one used for the two 4pulse patterns (rhythmic, random), for sham TMS single pulses and allow comparability, we removed and interpolated EEG signal using the same procedure describe above, not only around the TMS/TMS-like sound pulse onset, but also in three additional windows corresponding to the onset of pulses in the high-beta rhythmic condition. Finally, in no-sham TMS/TMS-like sound trials (in which no sham pulse was delivered), we implemented the same cleaning procedure described above for rhythmic and random sham TMS trials. In sum, to make sure artifact removal/interpolation procedures did not introduce additional artifacts into EEG signal and ensure a fair comparison between pairs of conditions, all sham TMS/TMS-like sound trials and also non TMS/TMS-like sound simulation trials underwent exactly the same artifact removal and data cleaning procedure.

EEG signals were down-sampled to 500 Hz and trials from all experimental blocks in each respective sessions (sham procedure #1 and #2) were gathered together. Two separate ICA were performed on the datasets of each experimental session. We removed independent components corresponding to residual artifacts from the active TMS magnetic field not removed during data interpolation as well as eye movement artifacts, electrode malfunction and 50 Hz land noise interferences. Artifactual components were identified following guidelines by Rogash et al. (2014).

On average 7±3 components were removed. After ICA cleaning, each dataset was separated into 6 conditions: sham rhythmic TMS/TMS-like sound trials, sham random TMS/TMS-like sound trials and sham single pulse TMS/TMS-like sound trials were paired with no sham TMS/TMS-like sound stimulation trials randomly interleaved within the same block. After the complete cleaning procedure, a mean of 85±10 trials (from a total of 100 recorded trials) remained for each given condition.

EEG data analyses

To estimate the magnitude and topographic distribution of cortical oscillatory activity

Behavioral data analysis

According to participant responses, trials from the visual detection task were classified in different categories. For trials in which a target was presented, participants could either correctly report the presence and the location of the target (trials classified as 'hits') or report not having seen the target (trials classified as 'misses'). There is also the possibility that participants could correctly report having seen a target but indicate the wrong location for it. These trials were classified as 'errors' and excluded from analysis because it was impossible to determine if participants had correctly seen the target but pressed the wrong button to report its location or had seen a target in a location where no visual target was displayed. On catch trials where no target was presented, participants could correctly report that no target was present ('correct rejection' trials)

or report to have seen a target that was not there ('false alarms' trials).

From the percentage of 'hits' and 'false alarms', the Signal Detection Theory (Green & Swets, 1966;Stanislaw & Todorov, 1999) extracted two complementary sets of measures. First, we computed the perceptual sensitivity (d') which is a bias free measure of how well the participant is able to detect the presence of the target from noise. Then, we computed the decision criterion (c) and the likelihood ratio (b), both measures of the subjective response biases shown by participants.

Participants can display either a 'liberal' behavior meaning they are more likely to report having seen a target in situation where they are unsure, or show a 'conservative' behavior, in which they will more often respond not having seen the visual target except in trials in which they are very confident about where a target was present on the screen. These outcome measures were calculated as follows:

# = '( () -'( (), = - where N is number of trials on which the percentage is calculated (Macmillan & Creelman, 2004).

Statistical analyses

We first analyzed data from the first experimental session, where sham TMS was delivered classically through a TMS coil placed perpendicular to the scalp (sham procedure #1). On the the sum of the T-statistic for all points in the cluster was used as the statistic of the cluster. A permutation test (10000 permutations, Montecarlo sampling method, alpha = 0.05) was performed on the clusters. Cluster-based permutation is a method of correction for multiple comparisons that is well suited to EEG because these signals are highly correlated in space and time (i.e. an effect spreads over adjacent sensors and lasts over several time points). Cluster-based permutations correct very well for false positives and ensure a minimal chance of false negatives (Maris & Oostenveld, 2007). Unfortunately, no agreement exists with regards to data permutation methods in the case of interaction effects in a factorial design (Edgington & Onghena, 2007;Suckling & Bullmore, 2004) therefore we chose to compute pairwise comparisons between our conditions.

Results

Modulation of conscious visual detection performance

Visual detection performance for near-threshold targets following a sham TMS coil placed perpendicular to the scalp (sham procedure #1) was evaluated with perceptual sensitivity (d') and No effects of the stimulation condition or stimulation pattern were found for perceptual sensitivity (main effect of sham condition: F(1,10)=0.12, MSE = 0.544, p = 0.73 ; main effect of sham pattern F(2,10)= 1.181, MSE=0.544, p=0.31 ; interaction F(2,10)=1.032, MSE=0.544, p=0.359) (Fig. 2A). However, the subjective decision criterion of our participants was modulated by sham stimulation. Our data reveal a significant main effect of sham condition on the response criterion (F(1,10)=4.254, MSE=0.177, p<0.05). The decision criterion was lowered when sham stimulation preceded a visual target, or, in other words, participants were more likely to respond that they saw a target (more liberal) when the apparition of the target was preceded by a loud clicking sound (Fig 2B). This effect was not specific to the sham pattern delivered as no effects of the sham pattern were found on decision criterion (c) (main effect: F(2,10)=0.1, MSE=0.177, p=0.905, interaction between sham condition and sham pattern: F(2,10)=0.012, MSE=0.177, p=0.988). The ANOVA on the likelihood ratio (b) revealed no effect of any of the factors (all p>0.1).

Our results suggest that sham stimulation does not modulate the perception of visual targets but affects subjective decision-making processes lowering the decision criterion of participants (i.e., participants became more liberal or less conservative).

Auditory event related potential

On the electrophysiological signal, all sham stimulation patterns delivered through a TMS coil placed perpendicular to the scalp (sham procedure #1) elicited clear auditory evoked potentials.

Comparisons of the EEG waveforms phase-locked to sham stimulation onset at the vertex electrode (Cz) revealed a significant positive deflection during sham trials compared to trials without stimulation for all three patterns tested (Fig. 3A). This significant positive deflection started about 140 ms post sham stimulation onset and lasted ~250 ms with two peaks at 150 and 300 ms post stimulation onset. The shape of this evoked potential showed no difference when comparing sham trials directly between the patterns (Fig. 3B). These results indicate that the loud clicking sound of the TMS produces a clear auditory evoked potential, which was modulated neither by the frequency content nor the length of the sham burst.

Auditory entrainment of high-beta oscillations

In the time-frequency domain, we investigated the impact of sham stimulation on cortical oscillatory signatures. More specifically, we explored signatures of auditory entrainment at the A direct comparison between rhythmic sham patterns and the two control patterns which either did not contain a specific frequency (random pattern) or had no rhythmic structure at all (single pulse pattern) showed that increases in high-beta phase-locking was more widespread over the scalp for single sham pulse pattern, extending over right parietal electrodes (Fig. 5B). This significant high-beta phase-locking in response to all patterns (irrespective of the rhythmic structure of the pattern) suggests that this was not driven by auditory entrainment at the frequency of the sham stimulation burst.

speaker, we found all the above-reported effects on the dataset generated by sham procedure #1, recorded TMS-like sounds played via a speaker (blue columns). Data is presented for each sham TMS pattern (rhythmic, random and single pulse TMS) and for left or right visual field targets. Note that the type of sham TMS approach had no effect on visual detection performance, leading to the conclusion that they could be used indistinctively.

The early negative deflection during sham pulses delivered through an audio speaker (sham procedure #2) showed an increased amplitude and later latency compared to trials with single sham pulses delivered through a TMS coil (sham procedure #1).

In the time-frequency domain, the sham procedure (#1 or #2) had no effect on high beta oscillations. Topographical maps for power or ITC at the high-beta frequency during the delivery of sham stimulation, showed no difference between the two sham procedures (not pictured).

However, a detailed comparison of ITC maps for electrode FCz between sham procedures revealed stronger phase-locking of high-theta to alpha band oscillations for sham procedure #2 for rhythmic and random sham patterns (Fig. 9). Note that recorded TMS-like sounds of rhythmic and random sham patterns delivered through a speaker phase-locked low-frequency (high theta and alpha oscillations) more strongly than TMS sounds delivered through a TMS coil.

Our audio recordings dubbing the clicking sound of TMS pulses with a speaker mounted on the TMS coil (sham procedure #2), appears to generally reproduce the effects of active pulses delivered with the TMS coil in a sham position (sham procedure #1). Indeed, both techniques showed identical effects on visual perception performance and subjective decision in a visual detection task with near-threshold Gabors. The auditory potentials evoked by both clicking sounds were of similar amplitude, except for the early negative deflection following single pulses of TMSlike sound (sham procedure #2) which showed increased amplitude compared to sham TMS pulses (sham procedure #1). The main difference between the two sham procedures tested in our study was that for sham bursts (either rhythmic or random), a train of TMS-like sounds delivered through a speaker phase-locked slow oscillations (theta to alpha band) in central scalp locations associated to signal from auditory regions more strongly than equivalent sham TMS bursts.

Discussion

Our study attempted for the first time to tease apart the influence of sham rhythmic and random TMS bursts and single sham TMS pulses on behavioral performance and electrophysiological correlates during a conscious visual detection task with near-threshold stimuli.

We show that sham stimulation delivered prior to a visual target shifts participant's decision criterion to report or not as 'seen' a target displayed at 50% visibility. When the target onset was preceded by the loud clicking associated with TMS delivery (produced either with a sham pulse or with recorded TMS-like sounds) participants became more liberal or, said otherwise, a lower level of visual saliency (lower evidence or lower information) was required for participants to acknowledge that a target had been present. Importantly, our outcomes did not show any significant effect of sham stimulation patterns on visual perception nor on the detection of ipsilateral vs.

contralateral targets with regards to stimulated hemisphere.

Scalp EEG recordings performed concurrently show that sham TMS elicited auditory evoked potential in central scalp electrodes. Nonetheless, surprisingly, those were highly unspecific, that is, they were not significantly modulated in latency or amplitude by patterns of sham TMS varying in length (single pulse vs. 4-pulse rhythmic or random trains) or frequency structure (rhythmic vs random 4-pulse bursts). In the time-frequency domain, all procedures of sham stimulation induced a phase-resetting of cortical oscillators. This phenomenon was observed for all sham patterns for the high-beta band. Nonetheless, it was more robust, wider spread over scalp electrodes and broader-band (from beta to low gamma) in response to sham single pulses than rhythmic or random sham stimulation bursts.

Modulation of visual detection response bias

Using a visual detection task very similar to ours, a prior study (Duecker & Sack, 2013) showed that single sham TMS pulses preceding a visual target decreased reaction times for correct target detection trials (vs. a no sham TMS condition). This finding converged with prior evidence study. Overall, decision criterion (c) is recommended as a better measure of response bias (Macmillan & Creelman, 1990).

Sham TMS and orientation of spatial attention

In addition to a general alerting effect facilitating visual detection accuracy, Duecker and Sack also reported a significant impact of coil position on detection reaction times, showing faster responses for ipsilateral visual targets (same hemifield as the sham TMS-stimulated hemisphere) compared to contralateral targets (Duecker & Sack, 2013). They concluded that the lateralized sham TMS sounds prompted the orientation of spatial attention towards the hemispace corresponding to the side of the head where sham TMS was delivered, speeding up the localization of sensory targets in visual modalities.

Surprisingly, our analyses failed to replicate this effect. This outcome speaks in favor of a rather weak or null influence of TMS sound on lateralized visual facilitation effects reported in prior studies by our group either with single pulses (Chanes et al., 2012) or with rhythmic active TMS bursts (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019). Indeed, we failed to find a significant interaction between sham condition (sham TMS either with sham procedure #1 or #2 and no sham stimulation) and visual field for detection sensitivity (d') or response bias measures such as the likelihood ratio (b) used in the above-cited studies.

Our results showed significant differences of visual sensitivity (d') and response criterion (c) between right and left targets, with better perceptual sensitivity and a more liberal decision criterion for right (ipsilateral to the stimulated hemisphere) than left targets (contralateral to the stimulated hemisphere). Nonetheless, these right vs left visual detection sensitivity differences, previously reported (Chanes et al. 2013, 2015, Vernet et al. 2019), are not related to sham stimulation since they were not only observed across sham stimulation patterns but were also found in no sham stimulation trials.

Prior literature on cross-modal attention orienting has argued that a sound can improve the processing of visual targets (Bolognini et al. 2005;Frassinetti et al. 2002;[START_REF] Lippert | Improvement of visual contrast detection by a simultaneous sound[END_REF]) and orient spatial attention [START_REF] Mcdonald | Involuntary orienting to sound improves visual perception[END_REF][START_REF] Spence | Audiovisual links in exogenous covert spatial orienting[END_REF]. However, such modulatory effects were shown only if the sound was spatially and/or temporally predictive of the visual target location and/or onset. This cross-modal modulatory effect has been shown to dissipate when the sound is redundant with another cue already alerting on target onset [START_REF] Lippert | Improvement of visual contrast detection by a simultaneous sound[END_REF]Kusnir et al. 2011). At difference with Duecker and Sack (2013), our visual detection paradigm showed an alerting central visual cue (fixation cross became larger) prior to sham stimulation (either sham TMS or TMS-like sound) and target onset. Hence it is possible that either procedure delivering TMS sound became redundant in predicting target onset, cancelling their potential modulatory effect on visual attention and visual detection. This difference, albeit apparently minor, could explain why the study by Duecker and Sack (2013) yielded a difference in ipsilateral vs.

contralateral detection performance, which our data did not find.

Impact of sham TMS on cortical oscillatory activity

As a novel contribution with regards to prior attempts to study sham TMS effects, we here recorded and analyzed scalp EEG activity tied to the different types of sham TMS patterns. We failed to find any evidence throughout scalp electrodes of entrainment due to the sound of rhythmic sham TMS bursts at high-beta (30 Hz) frequency compared to no stimulation or to random sham bursts. This outcome suggests that the contribution of rhythmic TMS sound on previously described entrainment effects of active 30 Hz TMS patterns on the right FEF and to improvements of conscious visual perception (see Chanes et al., 2013;Quentin et al., 2015;Vernet et al., 2019) is at best very limited and unspecific.

The delivery of sham stimulation did increase the phase-locking of high-beta oscillations.

Nonetheless, this increase was not unique to rhythmic sham TMS bursts or rhythmic TMS-like sounds, and similar phase-locking increases were observed for random sham TMS bursts or single TMS pulses (either in sham procedure #1 or sham procedure #2), which is surprising as none of the latter patterns contain any repeating high-beta rhythmic structure that could entrain high-beta oscillations. Further, it could be argued that high-beta phase-locking was stronger for sham single TMS pulses (or TMS-like single sounds) than sham rhythmic TMS bursts (or TMS-like sound bursts). Indeed, direct comparison of these two types of patterns showed wider spread scalp increases of high-beta phase-locking for single pulse than for rhythmic sham stimulation.

Moreover, significant increases of high-beta phase-locking during rhythmic sham patterns (compared to no sham stimulation) on EEG topographies cancelled out when analyses were performed on a larger time-frequency window at the scalp electrode showing the peak high-beta phase-locking (i.e., electrode FCz). This result casts doubt on the robustness of such an effect and leads us to conclude that, paradoxically, increases of high-beta phase-locking is possibly more robust in response to the sham single pulses than sham rhythmic stimulation.

The absence of auditory entrainment in scalp EEG recordings following short bursts of rhythmic sham TMS or TMS-like sound stimulation is surprising, as such rhythmic monaural or binaural sound patterns are being used for this purpose (Galambos et al., 1981;Picton et al., 2003).

Nonetheless, the auditory streams traditionally used to entrain oscillations are much longer (lasting a few seconds) than the very short (only 4 pulses, lasting 100 ms) trains delivered in our study to emulate prior active rhythmic TMS patterns (Chanes et al., 2013;Quentin et al., 2015;Vernet et al., 2019). Evidence indicates that steady-state auditory responses following rhythmic sounds develop at a latency of about 80 to 100 ms, and increases monotonically to reach maximum amplitude only ~200 ms after their onset [START_REF] Forss | Temporal integration and oscillatory responses of the human auditory cortex revealed by evoked magnetic fields to click trains[END_REF][START_REF] Roß | Temporal integration in the human auditory cortex as represented by the development of the steady-state magnetic field[END_REF]. Steady-state responses also vanished quickly a few cycles after the end of the auditory stimulus. Therefore, the duration of the sham stimulation bursts we applied in our study might have been too short to entrain cortical oscillations conducted by auditory afferent pathways. We cannot rule out if longer rhythmic sham TMS or TMS-like sound patterns lasting for several seconds, might entrain frequency-specific cortical oscillations.

Additionally, it has also been suggested that attention modulates the power of the frequency following steady-state responses, with stronger cortical oscillations at the frequency of the envelope of an attended auditory pattern and also weaker power at the frequency of the envelope of a distractor sound stream [START_REF] Bharadwaj | Measuring auditory selective attention using frequency tagging[END_REF][START_REF] Kim | Classification of selective attention to auditory stimuli: Toward vision-free brain-computer interfacing[END_REF]. This attention effect is intermodal, hence observed also when attention is directed to a visual task and diverted away from the auditory modality [START_REF] Saupe | Neural mechanisms of intermodal sustained selective attention with concurrently presented auditory and visual stimuli[END_REF]. Asking participants to ignore auditory stimulation to concentrate instead on the visual task, as we did in this and prior studies (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019), could reduce the probability to generate an auditory steady-state response to rhythmic sham stimulation.

Our phase-reset analyses revealed at least two sets of outcomes, which were unexpected or contradicted prior reports, and for which we cannot find a clear explanation. Indeed, sham stimulation did not entrain frequency-specific cortical oscillations reflecting the rhythmic structure of the bursts delivered. Nonetheless, clicking TMS sounds phase-locked cortical oscillations in a broad frequency band, spanning from high theta to low gamma. Such a broad-band phase-resetting of cortical oscillators was significant for single sham pulse but not for any of the periodical bursts, rhythmic or random. To explain such an unexpected outcome, we here hypothesize that repeated sham pulses within a burst could periodically phase-reset oscillators several times during a short time windows, and blur the phase-locking effect of the first sound pulse, especially at burst frequencies with a period lower than the inter-pulse interval. Nonetheless, this explanation remains purely speculative and should be further investigated with ad hoc experiments or computer models and simulations.

Second, the topography of the reported phase-resetting effects was maximal over central scalp EEG electrodes, hence congruent with activity emerging from the auditory cortex. Prior studies on the cross-modal effects of auditory stimulation on cortical rhythms have reported phaselocking also in occipital regions, with a subsequent modulation of visual cortex excitability [START_REF] Mercier | Auditory-driven phase reset in visual cortex: Human electrocorticography reveals mechanisms of early multisensory integration[END_REF]Romei et al. 2012) however we failed to show any increases of phase-locking over the occipital electrodes. Differences between the sound patterns used in these studies and the sham TMS-pulses or recorded TMS-like sounds used in the present study could explain such differences, and are a reminder of the dependence of phase-resetting effects on the length or structure of auditory stimuli which requires further investigation.

Recorded TMS-like sounds acting as a sham TMS condition

In the current study we tested two different sham procedures that could be used in embedded TMS designs, that is designs interleaving randomly active and sham TMS trials within the same experimental block. On the one hand, we tested an approach that requires the use of two separate TMS devices, one attached to an active coil targeting a brain region, and a second one attached to a second TMS coil positioned next to the active TMS coil and angled perpendicularly to the curvature of the scalp in a classical sham configuration (sham procedure #1). On the other hand, we developed and tested a less equipment-demanding sham approach (i.e., requiring only a single TMS machine and a single TMS coil) in which the sham TMS coil was replaced by a speaker mounted directly on the active coil delivering a recorded TMS-like sound (sham procedure #2).

Both sham procedures showed extremely similar effects on visual detection performance and scalp EEG correlates, supporting the idea that they can be used indistinctively, hence that sham procedure #2 requiring a single TMS machine and TMS coil could replace sham procedure #1 requiring two TMS devices. Nonetheless, our results also revealed increased phase-locking for slow oscillations (high theta to alpha band) for TMS-like sounds delivered through a speaker compared to classical sham TMS pulses. The waveform and volume of the recorded clicking TMS sound played through the audio speaker were carefully controlled to reproduce, as accurately as possible, the sound of the brief magnetic field flowing through the TMS coil. Nonetheless, minor discrepancies between the original and the recorded TMS sounds could have resulted in such EEG differences in phase-locking. Sham procedure #2 might need to be further refined to achieve exact same electrophysiological response than sham stimulation delivered with a TMS coil. Such discrepancy which would over-represent low frequency phase-locking in sham trials occurs at a spectral range (high-theta to alpha band) which is not directly modulated by high-beta rhythmic vs.

random patterns in active TMS high-beta entrainment studies previously conducted in our team (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019).

Nonetheless, sham TMS approaches have been recognized to not be optimal (Duecker & Sack, 2015) as they are not very successful at blinding participants from stimulation conditions, particularly when they are exposed to both active and sham TMS stimulation in block designs [START_REF] Mennemeier | Sham Transcranial Magnetic Stimulation Using Electrical Stimulation of the Scalp[END_REF][START_REF] Rossi | A real electro-magnetic placebo (REMP) device for sham transcranial magnetic stimulation (TMS)[END_REF]. The lack of efficient blinding has been mainly attributed to a failure of sham stimulation to correctly replicate somatosensory effects of TMS by direct scalp tapping or via the contraction of scalp muscles (Ilmoniemi & Kičić, 2010).

Additionally, participants may also differentiate sham and active stimulation by noticing changes in the location of the clicking sound [START_REF] Mennemeier | Sham Transcranial Magnetic Stimulation Using Electrical Stimulation of the Scalp[END_REF][START_REF] Sommer | Transcranial magnetic stimulation-A sandwich coil design for a better sham[END_REF]. Indeed, to avoid coil movement between active and sham TMS trials, double blind studies require an active and a sham coil to be placed both over the same area of the scalp. However, given the large surface of standard 70 mm figure-of-eight TMS coils (~14 cm long from loop edge-to-loop edge), their centers have to be placed several centimeters apart, generating a difference in sound location of that can be noticed by participants. The use of a speaker mounted on the active TMS coil that plays sham TMS-like sounds overcomes this limitation and makes the implementation of double-blind studies with active/sham trials less complex and more affordable.

Conclusion and future directions

Overall, on visual detection performances, we here show that the clicking sounds associated with TMS generate effects on response bias. Nonetheless, these effects were clearly non-specific as they were not modulated by visual target location (location relative to stimulated hemisphere)

or by sham TMS pattern type (number of pulses, length or temporal organization). Such an absence of main effects or interactions of sham TMS sounds with parameters tied to visual stimuli location or sham TMS patterns rules out major contributions of sham TMS sound to previously reported modulation of visual performance and brain activity correlates for active TMS modulatory (Chanes et al., 2012) and entrainment (Chanes et al., 2013(Chanes et al., , 2015;;Quentin et al., 2015;Vernet et al., 2019) studies in the field, which relied on the use of sham procedures as the ones tested here to control for the potential effects of TMS generated sound.

In spite of such conclusions, the potential impact of single or repetitive sounds on brain activity and perceptual performance should not be minimized. As indicated previously, a wellcrafted study using a slightly different experimental design demonstrated effects of sham stimulation on visual detection which were target onset time-and location-specific (Duecker & Sack, 2013). Moreover, our EEG analyses also suggest a significant impact of sound on the phaseresetting of cortical oscillators in auditory sites, which paradoxically, would be higher for single pulses than for longer sham bursts.

Unfortunately, studies on the effects of sham TMS on behavioral outcomes and brain activity are still too few to build an adequate understanding on the potential contributions of afferent auditory activity associated to TMS, which might strongly vary across experimental paradigms. Carefully designed experiments and sham interventions that do not take for granted the unspecific nature of auditory effects of TMS is paramount to produce solid results (Duecker & Sack, 2015).

The two sham procedures tested in our study addressed the contributions of auditory TMS effects on visual detection performances. Nonetheless, the same mechanism responsible for the generation of loud clicking sound associated to each TMS pulse (i.e., the vibration of copper loops inside the TMS coil when briefly passing electrical currents to generate a magnetic field) deforms the surface of the plastic case and generates a light tapping tactile sensation on the stimulated scalp region. It has been argued that TMS skull tapping can be transmitted by bone vibration directly into the internal ear, contributing to the nature of TMS-associated auditory sensations, which would not be solely transmitted via the ear canal (Nikouline et al., 1999). This bone conduction of TMS-associated clicks is difficult to accurately reproduce with sham stimulation. Additionally, somatosensory and proprioceptive afferent signals can be generated by TMS fields the stimulate nearby excitable fibers of the scalp muscles (Ilmoniemi & Kičić, 2010) and although some TMS sham coils integrate sophisticated mechanisms to induce scalp tactile sensations [START_REF] Mennemeier | Sham Transcranial Magnetic Stimulation Using Electrical Stimulation of the Scalp[END_REF][START_REF] Rossi | A real electro-magnetic placebo (REMP) device for sham transcranial magnetic stimulation (TMS)[END_REF], somatosensory effects have not been specifically controlled in a vast majority of TMS studies.

Such somatosensory effects cannot be disregarded, particularly since a recent study blocking sensory cutaneous receptors with local anaesthetics has shown that transcutaneous electrical stimulation of peripheral afferent nerves could explain the modulatory effects of transcranial alternating current stimulation (tACS) on motor systems (Asamoah et al. 2019).

Moreover, similarly to rhythmic auditory stimulation, rhythmic tactile or transcutaneous nerve stimulation might be able to entrain per se cortical oscillations at the rhythm paced by the stimulation patterns (Asamoah et al. 2019;Nangini et al. 2006) and explain currently controversial entrainment effects reported with tACS. Intracranial or interleaved TMS-EEG recordings leave little doubt on the ability of intracranial electrical pulses or transcranial magnetic pulses to entrain short-lasting cortical oscillations (Amengual et al., 2017;Thut et al., 2011;Vernet et al., 2019), nonetheless, the potential contributions of single pulse or rhythmic vs random scalp tapping to the impact of active TMS on cortical oscillations needs to be ruled-out in ad hoc experiments which,

given their complexity, were out of the scope of this study.

We conclude that TMS is a powerful tool for non-invasive brain stimulation which allows an effective modulation of cortical activity in a wide variety of cortical regions, cognitive processes and patient populations. However, the potential contribution of afferent peripheral effects (such as sounds or scalp tactile sensations associated with TMS) needs to be adequately ruled out.

Addressing this question, the effort of our study allows to better understand the influence of TMS sound. On the other hand, brain stimulation techniques free of sensory effects, for instance intracranial or deep brain stimulation in implanted patients (Amengual et al., 2017;Cleary et al.,

GENERAL DISCUSSION

I -Summary of the main results

The work presented in this thesis aimed to understand the causal contributions and neural coding strategies for attention orienting and conscious visual perception of two homotopic nodes of the bilaterally distributed fronto-parietal dorsal attention network, the left and the right FEFs. We used TMS to non-invasively manipulate cortical activity patterns in these regions in order to either entrain high-beta cortical oscillations or induce neural noise. While they received stimulation at specific time windows on a trial-by-trial basis, we asked participants to perform a visual detection task at threshold to probe the effects of the causal manipulation of cortical activity patterns within the left and the right FEF on conscious visual perception. We also recorded EEG signals concurrently to TMS to study the effect of our short stimulation patterns on brain activity in the stimulated cortex and other anatomically connected regions. Lastly, we conducted an experiment to characterize, on visual detection performances and EEG recordings, the confounding sensory side effects of magnetic stimulation. This last experiment was an attempt to verify that the effects of auditory stimulation associated with the delivery of TMS did not interact with the electrophysiological impact of active TMS bursts reported in our first two TMS-EEG experiments. Nonetheless, it also served to assess and compare the reliability of two sham TMS strategies and to gauge the ability of rhythmic or random series of sounds to entrain or desynchronize oscillations and impact perceptual performance.

In a first study (Project 1, Study I), we first confirmed that the delivery of 30 Hz rhythmic patterns of TMS on the right FEF does entrain local cortical oscillations at the frequency contained in the burst compared to random patterns made by an equal number of pulses delivered over the same time window but lacking a specific frequency signature. We then replicated previous findings (Chanes et al., 2013) demonstrating that 30 Hz rhythmic TMS delivered pre-target onset improved visual perception in the left visual hemifield. Taken together, EEG and behavioral results bring evidence for a causal role of high-beta pre-frontal oscillations in conscious visual perception.

In a second study (Project 1, Study II) performed on the same TMS-EEG dataset, we extended our analysis of local EEG signals to interconnected regions. A data re-analysis based on a new and independent data cleaning process confirmed prior evidence showing local entrainment of high-beta oscillations. Importantly, however, TMS-driven effects extended throughout an ipsilateral fronto-parietal network for attention orienting (Corbetta et al., 2008;Corbetta & Shulman, 2002). Indeed, rhythmic TMS, compared to random TMS, increased right fronto-parietal high-beta phase-synchronization. Moreover, high-beta oscillation power and trial-to-trial phase alignment also increased in response to rhythmic TMS in right and left parietal regions, distant from the stimulation site. On the basis of influential models showing a relation between increased high-beta phase-synchronization and improved inter-regional communication (Fries, 2005(Fries, , 2009)), we concluded that rhythmic TMS delivered over the right FEF resulted in a frequency-specific state of network synchronization across the dorsal attention network. We hypothesize that such effects might have been likely spread through the 1 st branch of the Superior Longitudinal Fasciculus (SLF I), a white matter tract linking the right FEF and right posterior parietal regions of the dorsal attention network, such as the Intra Parietal Sulcus (Thiebaut de Schotten et al., 2011). Interestingly, previous studies have shown that the microstructural characteristics of the SLF I correlated to the magnitude of improvement of visual perception driven by high-beta TMS entrainment in the FEF (Quentin et al. 2014(Quentin et al. , 2015)). and left parietal electrodes (compared to sham TMS). Taken together, these two results (broader oscillatory band and higher signal complexity) support a modulation of neural noise by non frequency-specific TMS patterns. However, at difference with a prior study of our lab (Chanes et al., 2015) such EEG correlates fail to translate into any significant modulation of visual sensitivity.

In our fourth and last study (Project 3), we investigated the contribution to behavior and electrophysiological recordings of single pulses and rhythmic (30 Hz) or random patterns of 4 TMS clicking sound present in sham stimulation modalities (either using active TMS pulses delivered in a 90 o sham configuration or TMS-like sounds recorded and played via a speaker mounted on a TMS coil). This topic holds interest as TMS pulses come always associated with brief but intense auditory stimulation, which could possibly contribute to the impacts of active electromagnetic pulses, such as entrained neural oscillations or improvement of conscious visual perception. We showed that, indeed, sham TMS sound patterns (compared to no stimulation), delivered with any of the two modalities, failed to show any sign of oscillatory entrainment or impact on visual sensitivity. However, irrespective of their specific temporal organization (i.e. equally for single pulse, rhythmic or random bursts) TMS clicking sounds lowered response bias in the visual detection task, making participants less conservative in their perceptual decisions. Loud TMS clicking sounds (in any pattern but most strongly for single pulses) also phase-locked oscillations in a broad set of frequencies in central scalp locations associated to the recording of auditory evoked potentials. We concluded that single or sound bursts associated to the delivery of active TMS did not contribute in any significant manner to local high-beta entrainment or to the improvement of perceptual outcomes reported in the 2 other active TMS studies presented in this dissertation. Nonetheless, for the design of suitable control conditions and accurate data interpretation, TMS researchers shall be aware of the power of single or periodic sounds to phase lock oscillatory activity and to influence response criterion for perceptual decision making.

II -Frontal and fronto-parietal contributions to the modulation of visual perception

The study of the anatomical basis of attentional orienting networks and the modular contributions of frontal and posterior parietal regions to such function has received substantial attention in the last two decades. In particular, it is well established that both top-down (i.e.

endogenous or voluntary) and bottom-up (i.e. exogenous or automatic) attentional orienting relies on components of a common bilaterally distributed dorsal network linking the FEFs and the Intraparietal Sulci (IPS) (Corbetta & Shulman, 2002). Studies in primate models (Bichot et al., 2005;Buschman & Miller, 2007;Fries et al., 2001;Saalmann et al., 2007) and healthy humans (Chanes et al., 2013;Gross et al., 2004;Hipp et al., 2011;Phillips & Takeda, 2009;Rodriguez et al., 1999) have highlighted the functional role of high-beta and gamma oscillations and fronto-parietal synchronization in spatial attention and the modulation of conscious visual perception for attended stimuli. More recently, correlational and causal evidence has revealed a multiplexing of high-beta and gamma frequencies in fronto-parietal systems to differentially encode for top-down (visual search task) vs. bottom up (pop-out task) spatial orienting (Buschman & Miller, 2007) or modulate stimulus salience (perceptual sensitivity, d') vs.

decision making (response criteria, beta) (Chanes et al., 2013). In line with this empirical evidence, a theoretical framework hypothesized that mechanisms of stimulus selection by topdown attention are subtended by inter-regional synchronization at a high-beta or gamma frequency across the dorsal attention network (Engel et al., 2001;Fries, 2005Fries, , 2009)).

Attempts to further extend knowledge in this domain in humans demanded concurrent TMS-EEG approaches combining the focal manipulation of specific network nodes to synchronize/desynchronize cortical activity and scalp EEG to monitor local and network-wide consequences. It is by simultaneously coupling brain manipulation, behavioral testing, and the recording of electrophysiological activity that challenging TMS-EEG experimental designs allowed us to progress in our understanding of frequency-based oscillatory strategies of frontoparietal systems for the modulation of conscious visual perception via attentional networks.

The reported association of improvements of visual sensitivity for left targets and proof of high-beta frontal entrainment and fronto-parietal phase-synchronization for rhythmic but not random TMS patterns, delivered prior to visual target onset, is compelling evidence informing on a causal top-down role for these type of activity to the modulation of conscious visual perception.

II.1 -Interhemispheric asymmetries in top-down systems for the facilitation of visual performance

By probing with identical causal approaches and task designs the role of right and left frontal regions (FEF) in the modulation of visual perception, we aimed to assess potential signs of hemispheric lateralization in attentional orienting, or interhemispheric asymmetries of coding strategies between left and right fronto-parietal systems, suggested previously (Chanes et al., 2013(Chanes et al., , 2015)).

Evidence in favor of a right hemisphere lateralization of attentional orienting systems has been solidly established. PET and MRI evidence showed stronger activations for right frontoparietal regions in tasks requiring the orienting of attention [START_REF] Corbetta | Voluntary orienting is dissociated from target detection in human posterior parietal cortex[END_REF][START_REF] Downar | A multimodal cortical network for the detection of changes in the sensory environment[END_REF]Shulman et al., 2010). In parallel, non-invasive stimulation studies (rTMS) interfering with the activity of parietal nodes strongly suggested a causal role for right but not left parietal nodes in the orientation of spatial attention and visual perception [START_REF] Bourgeois | Cortical control of Inhibition of Return: Exploring the causal contributions of the left parietal cortex[END_REF](Bourgeois et al., , 2013a;;Capotosto et al., 2012). Morphologically speaking, the third branch of the white matter tract linking dorsal frontal and parietal regions (the SLF III) was found to be larger in the right than the left hemisphere, and in the same population, the degree of right lateralization of the 2 nd branch of the SLF scaled with the magnitude of the left attentional bias observed in a line bisection task (Thiebaut de Schotten et al., 2011). As a further argument supporting a right lateralization of spatial attention, lesions in the right fronto-parietal system, either gray matter damage or white-matter disconnections, lead to deficits of attentional orienting towards left contralesional targets, referred to as hemineglect (Bartolomeo, 2007;Bartolomeo et al., 2012;Thiebaut de Schotten et al., 2014, 2005). In contrast, signs of hemineglect following damage of homotopic regions or tracts in the left hemisphere are rare and the symptoms of right and left lesions differ, hence suggesting they could be subtended by different mechanisms [START_REF] Bartolomeo | Laterally directed arm movements and right unilateral neglect after left hemisphere damage[END_REF]. A notable consequence of right lateralization of spatial attention is that the usual representation of sensory stimuli in the contralateral brain hemisphere does not apply to spatial representations linked to the orienting of attention. Indeed, right fronto-parietal regions seem to process orienting to visual targets in both visual hemifields whereas left hemisphere regions respond only to contralateral right targets [START_REF] Grosbras | Transcranial Magnetic Stimulation of the Human Frontal Eye Field: Effects on Visual Perception and Attention[END_REF], 2003;[START_REF] Kagan | Space representation for eye movements is more contralateral in monkeys than in humans[END_REF].

The first three studies of the current dissertation extend these known asymmetric right vs.

left hemisphere contributions to the domain of spatio-temporal coding strategies. On the one hand, we strengthen evidence in favor of right local and fronto-parietal high-beta synchrony for the top down modulation of conscious visual perception. On the other hand, in the left hemisphere, in spite of induced changes in left frontal and fronto-parietal EEG activity, we were unable to show significant improvements of conscious visual perception following 30 Hz rhythmic stimulation in the left FEF nor, as initially hypothesized (Chanes et al., 2015), following non-frequency specific stimulation patterns.

Although initially disappointing, this result reinforces the notion of a strongly right lateralized causal role of fronto-parietal systems in top-down modulation of perception, which will be easier to modulate from right than from left hemisphere regions. Nonetheless, a more nuanced interpretation of left FEF manipulations, taking into account EEG estimations of TMSinduced noise, might bring novel light to the right vs. left asymmetry debate. Indeed, in the left FEF, non-frequency specific TMS patterns which had previously been shown to facilitate conscious access for visual stimuli (Chanes et al. 2015), induced higher levels of noise than rhythmic patterns.

This finding provides indirect support for a role of neural noise in left frontal systems in the modulation of visual detection. Additionally, the Stochastic Resonance framework, based on well-established evidence that the addition of dosed levels of noise can enhance detection of weak signals (Moss et al., 2004) across sensory modalities (Collins et al., 1996;Groen & Wenderoth, 2016;Iliopoulos et al., 2014;Kitajo et al., 2003;Manjarrez et al., 2007;Simonotto et al., 1997;Zeng et al., 2000), provides an opportunity to better understand our findings.

Indeed, Stochastic Resonance precedents stress the difficulty to observe group mean perceptual improvement given a usually high level of inter-subject variability in optimal window of noise intensity required to strengthen, instead of degrade, cortical processing (Groen & Wenderoth, 2016;Iliopoulos et al., 2014;Kitajo et al., 2003). Integrating prior published evidence by our group and TMS-EEG evidence presented in the studies from this thesis, we put forward a speculative working model supporting a right vs. left hemisphere asymmetry in coding strategy subtending the orientation of spatial attention and conscious visual perception.

During the pre-target period, in right frontal regions of the dorsal attention network (right FEF), high-beta cortical oscillations (Project 1 Study I) spreading through the 1 st branch of the SLF (Quentin et al., 2014(Quentin et al., , 2015)), synchronizing right fronto-parietal systems at this same frequency, sets a state of general high-beta phase-locking (Project 1 Study II). Such state will favor causal improvements of visual sensitivity for right (Project 1 Study I) or possibly bilateral visual targets (Chanes et al., 2013;[START_REF] Grosbras | Transcranial Magnetic Stimulation of the Human Frontal Eye Field: Effects on Visual Perception and Attention[END_REF], 2003). In the left hemisphere, increases of internal neural noise levels in the frontal node (left FEF) of the left dorsal attention network, spreading through the fronto-parietal systems, increases neural noise in other network nodes (Project 2), improving perception of right visual targets (Chanes et al., 2015). These two sets of neural substrates and mechanisms could be activated independently to favor orienting to either unilateral left (or bilateral) or unilateral right stimuli, or be enabled jointly to achieve a balanced level of right high-beta synchronization and left neural noise that maximizes performance for either left (or bilateral) or right targets. We should not discard that such dynamic right vs left interhemispheric balance between high-beta synchronization and neural noise enabling strategies would also operate, as suggested by our left FEF datasets (Project 2), within fronto-parietal systems of both hemispheres, and that the final state of each system is set through a balance between these two competing forces.

To some extent, this currently speculative model resembles the alpha synchronization/ desynchronization push-pull interhemispheric dynamics between right and left occipito-parietal regions reported to subtend our ability to focus attention on an hemifield (contralateral alpha desynchronization) by removing attention from the opposite hemifield (contralateral alpha entrainment) (Marshall et al., 2015;Thut et al., 2006;Worden et al., 2000). Nonetheless, our concurrent TMS-EEG recordings in the left FEF does not support per se a desynchronization by non frequency-specific vs rhythmic TMS patterns, but rather an impact of local noise levels and signal complexity, which to date has never been hypothesized in attentional orienting mechanisms and needs to be better understood.

Hence, further studies would be absolutely necessary to solidify this model, particularly

II.2 -Methodological limitations of our datasets and experimental approaches

To the best of our efforts, during the planning of our studies we did care to design experimental paradigms that were as similar as possible to allow comparability. This was particularly relevant for Projects 1 and 2 which were planned to be able to compare right FEF and left FEF outcomes. Unfortunately for logistic and organizational reasons, the two experiments were still conducted on separate cohorts of healthy participants. Moreover, although the 30 Hz rhythmic TMS pattern aiming to entrain high-beta oscillations was present in both sets of studies, non frequency-specific TMS patterns tested in the right FEF with concurrent EEG were restricted to the random TMS pattern, and such choice at the time was not necessarily guided by the intention to manipulate local noise levels as a variable but rather as a control pattern to isolate the impact of stimulation frequency.

Nonetheless, left FEF TMS-EEG evidence pointing at a contribution of graded levels of noise sets the stage to reanalyze our right FEF TMS-EEG dataset (comparing active and sham rhythmic vs random TMS bursts) using measures of noise, such as signal-to-noise ratio, entropy and complexity. By doing so we will be able to build a complete picture of how stimulation patterns impact right FEF function and further characterize left vs right differences in impact of rhythmic or non frequency-specific TMS patterns. Indeed, this re-analysis would provide an opportunity to address the important question of whether the noise adding abilities of non frequency-specific TMS patterns emerge simply from the random temporal structure of their pulses or result from an interaction between the latter and specific properties (chemo-and cytoarchitectural, neurophysiological or anatomical) of the stimulated area.

Along the lines of what has been proposed in the prior section, the most elegant TMS-EEG experiment to address this point would consist in testing the impact of an identical set of TMS conditions (active and sham 30 Hz rhythmic vs different types of non frequency-specific TMS patterns) over the right and also the left FEF, perfectly counterbalancing the order of the interventions across participants. Unfortunately, these types of studies (as those suggested above testing unilateral right and left vs. bilateral FEF stimulation with rhythmic and nonfrequency specific patterns) could not be conducted since assuming an average of ~4h experiment to collect reliable TMS-EEG datasets for two TMS patterns over one cortical site, collecting data over more than 2 TMS patterns or over 2 cortical sites would have made for a very long, high-risk experiment. Moreover, stochastic resonance-like effects highlight the high dependence of neural facilitatory effects on the use of optimal levels of noise, and hence the testing of several non-frequency specific patterns is needed to demonstrate such effects, increasing the number of conditions to be compared across. We have therefore chosen to proceed by constraining the number of TMS patterns and cortical sites tested and conducted sequential experiments in the right and left hemisphere to identify, step-by-step, the relevant conditions to be tested in both hemispheres.

II.3 -Modulating visuo-spatial attention and recording conscious visual perception

Throughout this dissertation, we have been referring to our studies as assessing the contribution of right and left frontal systems to the top-down modulation of conscious visual perception via the manipulation of visuo-spatial neural networks. However, one possible criticism that could be easily argued is that none of our experimental paradigms directly manipulated the allocation of attention, for example by using central or peripheral predictive or unpredictive cues to endogenously or exogenously orient attention in space prior to target onset.

Instead, we relied on activations driven by TMS patterns tuned in frequency to experimentally engage top-down attentional networks, hence facilitate the detection of faint visual targets presented in the visual space.

We avoided the complexity of having to interpret combined contributions and possible interactions of TMS top-down modulation with cue-driven orientation of attention (Chanes et al. 2012) for several reasons. First, we prioritized a good understanding of isolated effects and EEG patterns associated to rhythmic TMS on attentional systems, a reasonable step before mixing processes with uncertain timings and scenarios requiring the evaluation of a very high number of conditions (2 TMS patterns (rhythmic, random), x 2 TMS modalities (sham, active)

x 3 types of cues (neutral, valid and invalid), x 2 visual target locations (ipsilateral and contralateral to right FEF stimulation). Second, the time interval in which we obtained modulatory effects (100 ms TMS bursts starting 133 ms before target onset) would be poorly adapted to test the impact of exogenous orientation of attention (with fast build-up times and decay between ~50-80 ms, see [START_REF] Shepherd | Movement versus focusing of visual attention[END_REF] limiting burst duration, number of cumulated phase locked cycles and time-frequency analyses for theta, alpha or even higher frequencies such as the high-beta rhythms probed in our studies. Such a time interval is also relatively short for a spatial cue to engage maximized endogenous attention effects (estimated to peak ~150 ms and lasting for 300-500 ms, post-cue see [START_REF] Shepherd | Movement versus focusing of visual attention[END_REF]or Carrasco, 2011 for a review), hence rendering the outcomes of a study combining exogenous orientation of attention and rhythmic TMS modulation uncertain. The difficulty to predict the onset timing of spatial cues, TMS bursts and visual target to allow synergistic interactions in combined trials (or avoid null effects or cancellations), would have required a chronometric approach testing several time intervals between events in combination with an already extremely high number of conditions.

Hence, as argued for several prior studies (Chanes et al., 2012(Chanes et al., , 2013(Chanes et al., , 2015;;Quentin et al., 2014Quentin et al., , 2015)), we rely on prior literature to confirm that the TMS-driven impacts on conscious visual perception that we report very likely relied on top-down modulatory processes mediated by the dorsal fronto-parietal attention network. First, widely reported improvements of visual perception and awareness following TMS over FEF (Chanes et al., 2012(Chanes et al., , 2013(Chanes et al., , 2015;;[START_REF] Grosbras | Transcranial Magnetic Stimulation of the Human Frontal Eye Field: Effects on Visual Perception and Attention[END_REF], 2003) are coherent with the enhancement of visual perception by of covert shifts of spatial attention [START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness[END_REF][START_REF] Moore | Selective gating of visual signals by microstimulation of frontal cortex[END_REF]Vernet et al., 2014). Second, correlational studies have shown a role for the right FEF in endogenous attention orienting tasks in humans (Corbetta et al., 2002;see Corbetta &Shulman, 2002 andVernet et al., 2014 for a review). Third, several sets of experiments have shown that TMS delivered over the FEF interacts with spatial attention. The combined manipulation, prior to target onset, of attention with spatial cues and single TMS pulses, showed that TMS improved perception only for validly cued targets, but had no effect or deteriorated the perception of invalidly cued targets (Chanes et al., 2012;[START_REF] Grosbras | Transcranial Magnetic Stimulation of the Human Frontal Eye Field: Effects on Visual Perception and Attention[END_REF]. Similarly, magnetic stimulation over the FEF has been shown to disrupt spatial cueing effects [START_REF] Smith | Transcranial magnetic stimulation of the left human frontal eye fields eliminates the cost of invalid endogenous cues[END_REF] and the process of inhibition of return [START_REF] Ro | Inhibition of return and the human frontal eye fields[END_REF] which prevents the orienting of attention to spatial locations that have already been explored. Taken together, these findings support a causal role for FEF in the modulation of spatial attention and for all these reasons, we believe that our rhythmic or non-frequency specific TMS patterns over the left and right FEF might have modulated spatial attention systems, and lead, via top-down processes, to enhancements of visual sensitivity in occipital regions through contrast gain mechanisms (Quentin et al., 2015).

III-Pending questions and some future directions

Given the complexity and the long duration of TMS-EEG experiments, the work presented in this thesis probed causal top-down contributions to conscious visual perception via attentional systems in a very small subset of cortical regions and frequency bands. Hence quite a lot is left to be causally explored, either in terms of frequency bands of interest, and their cross-frequency interactions, or in terms of network nodes. The model we built, on the basis of our results, of oscillatory contributions to top-down modulation of attention and visual perception focuses on the two frontal nodes, the right and left FEF, and we selectively probed the impact of a single frequency, high-beta, compared to a maximum of 4 non frequencyspecific patterns used to manipulate noise patterns. Hence, our model remains simplistic and incomplete, and most importantly, fails to consider the impact of combined cross frequency interactions.

III.1 -Towards an oscillatory model of attentional orienting and perceptual modulation

Guided by theories of Fries and colleagues for a role of high-frequency oscillations in top-down influences on perception (Engel et al., 2001;Fries, 2005Fries, , 2009) ) and findings by Buschman and Miller (2007) in monkeys that our team extended to humans (Chanes et al. 2013, Quentin et al. 2015), we focused on evaluating activity in the high-beta frequency band.

However, brain rhythms oscillating in many other frequencies, such as occipital or occipitoparietal alpha (8-12 Hz), occipital theta (5-7 Hz) and fronto-parietal low gamma (~50 Hz) bands have also been associated with the orienting or reorienting of spatial attention and/or the modulation of visual perception.

A role of gamma oscillations in visuospatial attentional networks has been reported but its specific function remains debated. On the one hand, non-human primate electrophysiology work established a correlational link between 47-52 Hz fronto-parietal (FEF to IPS) synchrony and performance in a pop-out task driven by exogenous, or bottom-up, attention (Buschman & Miller, 2007). Following up on this outcome, extending it to humans and adding causality to it, TMS pre-target entrainment of 50 Hz activity in the right FEF during a near threshold visual detection task did not act upon perceptual sensitivity (d') as high beta 30 Hz oscillations did, but instead reduced response criteria making participants engage in more liberal strategies for decision making (Chanes et al., 2013).

One of the most solidly studied oscillatory models accounting for attentional orienting, however, has relied on a spatially selective synchronization and desynchronization of alpha activity in occipito-parietal sites. Experimental EEG and MEG evidence has described a phenomenon of alpha desynchronization during a pre-target anticipatory period in occipitoparietal regions of the hemisphere contralateral to the visual hemifield where the target is expected (Sauseng et al., 2005;Thut et al., 2006;Worden et al., 2000). Concomitantly, opposite effects, i.e. increases of alpha synchronization, in the hemisphere ipsilateral to the hemifield from which attention is disengaged have also been well reported (Marshall et al., 2015).

Whereas contralateral alpha desynchronization signals the focusing of attention, the increases of alpha oscillations has been interpreted as a sort of a 'push-and-pull' mechanism needed to withdraw attention from the opposite hemifield and/or inhibit the processing of distractor stimuli (Foxe & Snyder, 2011;Klimesch et al., 2007). Strong pre-stimulus alpha occipital oscillations have also been found to be correlated with reduced visual detection performance for targets with the highest contrast, suggesting a role in scaling the regional response gain rather than modulating its sensitivity to perceptual inputs [START_REF] Chaumon | Prestimulus Neural Oscillations Inhibit Visual Perception via Modulation of Response Gain[END_REF]. Rhythmic TMS has been applied to modulate alpha oscillations in the parietal and occipital cortex, providing causality to the relationship between pre-stimulus alpha rhythms and the modulation of visual perception (Jaegle & Ro, 2014;Romei et al., 2010).

Most relevantly, visual perception has also been found to be modulated by the phase of alpha oscillations, with higher visual excitability and probability of visual detection for targets that reach occipital regions when alpha fluctuations are in a valley or trough, and opposite effects when such input coincides with the crest or peak of the alpha cycle [START_REF] Busch | The Phase of Ongoing EEG Oscillations Predicts Visual Perception[END_REF]Dugué et al., 2011;Mathewson et al., 2009). These observations have led to the model of attention as a cyclic process, at an alpha frequency, alternating periods of inhibition of inputs and periods where visual targets more easily reach awareness (Mathewson et al., 2011).

Occipital theta oscillations have also been involved in rhythmic sampling when attention alternates between several spatial locations (Huang et al., 2015;Landau & Fries, 2012;Landau et al., 2015). Similarly, a recent V1/V2 TMS experiment probed via phase reset the involvement of occipital theta rhythms in the periodical reorienting of attention at this specific frequency [START_REF] Dugué | Attention Reorients Periodically[END_REF]and see Dugué & VanRullen, 2017 for a review on the role of alpha and theta oscillations probed by TMS).

The correlational and causal evidence supporting roles of fronto-parietal gamma and beta and occipito-parietal alpha and theta rhythms in the orienting (or reorienting) of spatial attention and the modulation of visual perception highlight different and possibly complementary roles for each frequency band. To this regard, on the basis of experimental human and monkey work, a recent account has put forward an integrated oscillatory model for orientation and reorientation of attention which for the first time would reconcile a diversity of findings in several frequency bands [START_REF] Fiebelkorn | A Dynamic Interplay within the Frontoparietal Network Underlies Rhythmic Spatial Attention[END_REF], Fiebelkorn & Kastner, 2019 for a review).

To make a very long story short, this model posits a mechanism of fluctuation between two anatomically segregated brain states: a state associated to attention orientation and enhanced visual processing, subtended by frontal beta oscillations and parietal gamma oscillations in nodes of the attention network; and a state associated to attentional shifts subtended by parietal alpha oscillations, which could suppress the processing of visual stimuli currently attended, allowing attentional reorienting to other events in the visual field. Importantly, a theta rhythm would be in charge of mediating fluctuations between these two states, hence set the system in a state prone to orienting or reorienting.

In principle, the causal contribution of all these different frequency bands can be tested with rhythmic TMS patterns coupled to EEG recordings, provided that their operating gray matter nodes can be anatomically identified, spatially segregated and controllable from a restricted number of network locations. However, one should be careful when comparing the behavioral effects within the same cortical region of rhythmic TMS delivered at different frequencies. Indeed, beyond frequency, other features of rhythmic TMS bursts and the task epochs in which these are delivered can influence the observed impact on behavior. More specifically, hard-to-honor compromises between the number of TMS pulses delivered within a rhythmic burst (number of entrained cycles and total delivered energy), the pre-target onset time window covered by the pattern (chronometry of the impact) and the interval between the last pulse and a pre-target spatial cue or the visual target to be detected (phase at which the last generated cycle might interact with cue or target onset), make experimental designs aiming to compare frequencies very challenging.

When patterns are made equal in number of pulses, two bursts of rhythmic TMS at different frequencies will not only differ in the specific frequency they contain but also in the total duration of the burst, and hence the window of time during which the pattern is delivered.

Indeed, a 4-pulse pattern delivered at 10, 30 or 50 Hz will last for a period of 300, 100 and 60 ms, respectively. Thus it is impossible to tease apart if potential differences when contrasting their impact on behavior are causally related to differences in frequency, discrepancies in the period covered by the entrained episodic oscillation, or the onset time of its 1 st pulse entraining the 1 st cycle. Conversely, if the duration of the rhythmic TMS patterns is equalized by varying the number of pulses (4 pulses at 10 Hz, 10 pulses at 30 Hz or 15 pulses at 50 Hz, for an equal duration of 300 ms for all patterns), then it could be argued that bursts with a higher number of pulses might phase-lock cortical oscillators more strongly than those with less pulses (Thut et al., 2011a).

Finally, following the entrainment of episodic oscillations, the time interval (or Stimulus Onset Asynchrony, SOA) between the last pulse of the burst and the event that we aim to transiently modulate (either a spatial cue or a target) determines which phase of the last entrained oscillatory cycle will interact with the onset of that event. If the time interval used in experimental designs comparing across frequencies is kept the same to preserve an identical chronometry, event onset will interact with the entrained oscillations at different phases. If instead the interval is varied hence adapted to the cycle duration at each frequency (for example to reach target onset at the peak or the through of the oscillation), then it could always be argued that differences in chronometry could account for potential differences. To avoid these confounding effects, we restricted our experiments to a single frequency (30 Hz) of rhythmic TMS and compared it to a control condition consisting in random or non-frequency-specific TMS patterns with exact same number of pulses, total duration and time interval between the last pulse and the target onset.

III.2 -Contributions of parietal and occipital cortices to conscious perception

The studies of this dissertation used TMS-EEG approaches to highlight oscillatory activity recorded in response to frontal stimulation. Nonetheless, entrained or modulated activity by TMS bursts did not remain confined in the stimulated targets (in our case right or left FEF) but spread out to parietal cortices via increased fronto-parietal synchronization and parietal inter-trial coherence at the delivered frequency. This finding should not be surprising since, as presented in the introduction, repetitive TMS patterns are known to generate effects not only locally but also spread through networks in humans and animals (Chouinard et al., 2003;Paus et al., 1997;Valero-Cabré et al., 2005). Moreover, in attentional and perceptual modulation systems, the anatomical characteristics of fronto-parietal white-matter connections correlates significantly with the magnitude of visual facilitatory outputs following frontal stimulation (Quentin et al., 2014(Quentin et al., , 2015)). Lastly, direct stimulation of posterior parietal areas with alpha rhythms highlights the causal role of these regions in attention and conscious visual perception (Jaegle & Ro, 2014;Romei et al., 2010).

Nonetheless, from a hodological network perspective, given the role of the primary visual systems as the final receptor of attentional modulation, the missing link in our results remains the relationship between entrained oscillatory activity in the fronto-parietal attention network and occipital areas. None of our EEG analyses to this regard have been able to reveal EEG signs of occipital modulations (on visual evoked activity or alpha oscillations) temporally associated to top-down influences of the fronto-parietal attention systems manipulated with high-beta rhythmic TMS on the right and left FEF. were performed on this dataset at the time. Some years thereafter, studies by Capotosto and colleagues aimed to further bridge this gap and showed that brief rTMS disrupting activity in both the right FEF and the right IPS during a pre-stimulus anticipation period impaired the identification of visual targets (Capotosto et al. 2009;Capotosto et al. 2012). Additionally, EEG recordings monitoring anticipatory alpha rhythms revealed disruptions of the characteristic topography of alpha synchronization and desynchronization in the hemisphere ipsilateral and contralateral, respectively, to the expected target position (Capotosto et al. 2009;Capotosto et al. 2012). Importantly, the causal association between the disruption of occipital alpha rhythms and impaired visual perception performance was strengthened by positive inter-subject correlation between response reaction time in the visual discrimination task and the level of posterior alpha desynchronization. These authors concluded that fronto-parietal regions modulate visual perception by influencing alpha rhythms in parietal and/or occipital areas, but their model lacked a precise mechanism to explain the cross-frequency synchronization between posterior alpha rhythms and top-down fronto-parietal activity at a gamma or high-beta band.

Traditional approaches characterizing causal coding strategies in attention and perceptual networks have been based on interacting (disrupting, modulating or entraining) with known patterns of activity in single nodes of a network and measuring the local and network distributed impact of this manipulation. However, as our understanding of complex interactions across brain systems via oscillatory and synchrony processes evolves, multi-focal stimulation (i.e., simultaneous modulation of several targets) seems to be called to play a role. In the attentional and visual domain, paired pulse TMS protocols delivering with varying inter-pulse interval a pair of pulses to two cortical region have been used to establish top-down influences of FEFs and posterior parietal cortex (PPC) on the excitability of visual areas evaluated via phosphenes [START_REF] Silvanto | Stimulation of the Human Frontal Eye Fields Modulates Sensitivity of Extrastriate Visual Cortex[END_REF][START_REF] Silvanto | The Perceptual and Functional Consequences of Parietal Top-Down Modulation on the Visual Cortex[END_REF]. Similar operational principles could be extended to the use of brief periodical patterns tailored to prove the causal role of entrained local frequency-specific oscillations in fronto-parietal areas such as the FEF or the IPS on a physiological output (visual, motor or cognitive) evoked from stimulation of an interconnected region. Moreover, assuming the logistic feasibility and safety of the intervention, patterns of bifocal rhythmic TMS titrated in phase difference [START_REF] Plewnia | Enhancement of long-range EEG coherence by synchronous bifocal transcranial magnetic stimulation[END_REF] could be used in combination with EEG to pinpoint the specific role of interregional synchronization across a fronto-parietal network, without a need to do so via network effects of local entrainment. Even more complex experimental setups in which interregional connectivity between two distant cortical locations is probed before and following the influence of conventional rTMS or TBS protocol delivered on a third region have also been developed [START_REF] Davare | Causal Connectivity between the Human Anterior Intraparietal Area and Premotor Cortex during Grasp[END_REF]. Multi-coil TMS experiments under EEG monitoring

(2 or maximum 3 TMS coils given space restrictions) should be seriously considered to pursue a causal characterization of spatial attention and visual perception networks addressing synchrony processes and cross-frequency interaction between nodes.

IV-Further considerations

Compared to prior work (Chanes et al., 2012(Chanes et al., , 2013(Chanes et al., , 2015;;Quentin et al., 2014Quentin et al., , 2015) ) the most significant methodological achievement of this thesis is the recording of EEG activity concurrently with the delivery of magnetic stimulation and the evaluation of behavioral performance. Indeed, coupled TMS-EEG recordings granted access to novel and more detailed insight about the effects of non-invasive stimulation on local and interregional cortical activity.

It hence provided extremely valuable information on the organization of brain systems, even in those cases (see Projects 2 and 3) in which no significant TMS effects were found on behavior.

Globally, our outcomes attested to the high complexity of the TMS mediated modulation of EEG signals, particular for non frequency-specific patterns, with EEG impacts that were not always intuitive or predictable.

First, we showed that despite the highlighted high focality of TMS compared to other non-invasive brain stimulation technologies, the local TMS impact does not remain confined to the stimulated cortical regions but instead it spreads across network nodes likely conveyed and constrained by anatomical connectivity patterns. Secondly, our data strengthens prior evidence in the human brain at rest (i.e., not engaged in any task), showing that short rhythmic TMS bursts entrain cortical oscillations at the frequency carried by the burst [START_REF] Thut | Rhythmic TMS Causes Local Entrainment of Natural Oscillatory Signatures[END_REF].

Additionally, they also show that non frequency-specific bursts of TMS (lacking a regular rhythmic structure), can also modulate cortical oscillatory activity. Namely, these patterns increased the amplitude and phase-alignment of cortical oscillations in a wide frequency band.

Third and last, our data showed that the loud clicking sounds associated with the delivery of TMS pulses phase-lock cortical oscillations in a broad frequency band and that, unexpectedly, such phase-locking effects proved more intense following single pulses than sham TMS bursts.

In sum, our TMS-EEG datasets uncovered novel effects driven by both active and sham TMS on cortical activity which had not been considered before, particularly for non frequencyspecific active TMS bursts on the left FEF and sham TMS or TMS-like sound patterns. In light of these findings, it appears worthwhile to critically re-examine the design of TMS protocols and the conclusions that can be derived from such.

IV.1 -Unexpected impact of 'control' TMS patterns on EEG activity

As indicated above, some unexpected TMS-EEG findings highlight the need to understand in further detail the influence on brain activity of TMS conditions or patterns which are usually used as control conditions, i.e. contrasted to the main TMS patterns of interest. Both active non frequency-specific (or arrhythmic) TMS patterns and sham TMS have largely been used as control conditions in active rhythmic TMS experiments (Albouy et al., 2017;Chanes et al., 2013;[START_REF] Thut | Rhythmic TMS Causes Local Entrainment of Natural Oscillatory Signatures[END_REF]Vernet et al., 2019). Such experimental designs rely on the assumption that by contrasting active rhythmic TMS with such controls the specific effect on brain activity of direct cortical rhythmic stimulation will be isolated. However, this conclusion holds true only if such 'control' conditions carry effects which are independent and do not interact with the specific effects of active rhythmic TMS. Our findings suggest, unexpectedly, that this may not be the case. Indeed, in our study on the left FEF, rhythmic and non frequencyspecific TMS showed similar effects on cortical oscillations, consisting in enhancement of power and phase-locking of cortical oscillations in overlapping frequency bands (Project 2).

Less than a decade ago, rhythmic TMS at an alpha frequency (but not arrhythmic patterns) was shown to progressively phased-lock natural alpha oscillators in parieto-occipital areas [START_REF] Thut | Rhythmic TMS Causes Local Entrainment of Natural Oscillatory Signatures[END_REF]. It is reasonable to assume that rhythmic TMS at a higher frequency (30 Hz) and in a different cortical region (right or left FEF) entrains cortical oscillations through a similar mechanism [START_REF] Thut | Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions: A position paper[END_REF]Thut et al., 2011a).

The mechanism by which non frequency-specific TMS acts on cortical oscillations remains however less clear, particularly because, unexpectedly, some configurations of these TMS patterns ended up increasing high-beta power and phase alignment as strongly as pure rhythmic 30 Hz stimulation bursts did.

It could be argued that given the strong constraints for the design of TMS bursts (particularly the minimal time required by rTMS machines to recharge and discharge consecutive pulses at medium levels of intensity (e.g. at least 20 ms to recharge for 45-55% intensity pulses) precluding very brief inter-pulse intervals), non frequency-specific patterns (particularly the random pattern) resulted in sets of bursts very similar in mean frequency as those delivered by the rhythmic condition and therefore resulting in the same effects on EEG correlates. This phenomenon was probably not observed in the pioneering study by [START_REF] Thut | Rhythmic TMS Causes Local Entrainment of Natural Oscillatory Signatures[END_REF], since lower stimulation frequencies leave longer intervals (e.g. alpha 8-12 Hz used in [START_REF] Thut | Rhythmic TMS Causes Local Entrainment of Natural Oscillatory Signatures[END_REF], with ~100 ms inter-pulse period) for capacitors to recharge, allowing higher flexibility to randomize inter-pulse intervals and generate further differences between rhythmic and arrhythmic bursts. In contrast, the higher frequency of our patterns constrained to 30 Hz bursts (i.e., 33 ms inter-pulse intervals) severely curtailed our ability to produce very distinct rhythmic vs. non frequency-specific stimulation, reducing the magnitude of EEG and behavioral differences.

Nonetheless, if this explanation could apply to certain types of non frequency-specific patterns we designed such as the random pattern (all 3 inter-pulse intervals equal or higher than 20 ms but otherwise randomly jittered trial-to-trial), it is less likely for other types with fixed very unequal intervals such as the non-uniform rhythmic (24, 51, 24 ms intervals) or the irregular (40, 25, 35 ms) patterns. Hence alternatively, we also hypothesize that the ability shown by some specific non frequency-specific patterns to increase high-beta oscillatory activity to similar levels as rhythmic patterns could also be related to the addition of optimal levels of noise to a rhythmic signal (Mori & Kai, 2002;Srebro & Malladi, 1999). As a result, following Stochastic Resonance principles, electromagnetic noise would facilitate instead of prevent high-beta oscillations, making the net impact of rhythmic and non frequency-specific patterns on EEG measures similar (though likely mediated by different mechanisms), and curtailing our ability to observe behavioral differences between these two types of patterns.

Additionally, all our experiments include embedded sham conditions in which the sound structure of the tested active patterns is presented either by means of an active TMS pulse delivered with a TMS coil placed in a sham position on the scalp (i.e., preventing the magnetic field from reaching the cortex) or by playing the sound of a TMS pulse with a speaker mounted on the active TMS coil. To this regard, our data showed, surprisingly, that the sound of sham TMS pulses organized in 4 pulse bursts (rhythmic and random) or single sham TMS pulses, with respect to no-stimulation condition, phase-locked oscillations in central scalp regions, most likely reflecting activity from the auditory cortex (Project 3). Phase-locking effects were higher for single sham TMS pulse than for any type of bursts, including the rhythmic burst.

These observations are important as they emphasize the importance of ruling out TMS sound related phase locking when comparing sham and active control conditions. Evidently, phaseresetting properties by active TMS pulses, lying at the heart of TMS-driven entrainment (Rosanova et al., 2009;Thut et al., 2011a), could potentially interact with auditory stimulationdriven phase-resetting, making it very complex to tease out one from the other on EEG recordings.

In sum, any conclusions about cognition and brain function drawn from TMS experiments depend not solely on the effects of the active TMS patterns of interest but also on the effect of the control condition it is contrasted to. To this regard, our findings call for a more careful examination of specific and non-specific effects on brain activity by TMS patterns used in the so called 'control' conditions, either non-frequency specific patterns employed to isolate the impact of frequency in entrainment designs, or, most importantly, sham TMS which is widely used in research and clinical protocols (Duecker & Sack, 2013, 2015).

IV.2 -Network impact and state dependency of frequency-tailored TMS effects

One of the unique uses of TMS as a brain exploration technology is the manipulation of brain activity to probe its causal role on brain function. However, pinpointing causal inferences using this approach is not always as straightforward as presented. For the last two decades, the strategies behind uses of this method (which, technologically, has hardly changed) has evolved from 'virtual lesion' approaches probing individual regions by taking them transiently 'offline' and measuring impact on behavior, to more largely network distributed and physiologicallyinspired approaches [START_REF] Romei | Information-Based Approaches of Noninvasive Transcranial Brain Stimulation[END_REF]. The need for full hodological approaches have required concurrent whole brain recording methods such as fMRI or EEG and correlations of TMS outcomes with diffusion imaging approaches to better understand the extent of its impact on brain systems. Moreover, stimulation approaches inspired by neurophysiology have helped to move beyond the assumption that the brain is simply an ensemble of regions that can be efficiently controlled by any externally delivered TMS pattern, hence placing a focus on characterizing the spatiotemporal local and distributed coding related to the cognitive and behavioral processes that we aim to manipulate.

Additionally, two very powerful and related notions have emerged during this transition.

First, the state dependency of TMS effects, indicating that the impact of stimulation is extremely dependent on (hence should be respectful to) the levels and patterns of ongoing activity in the target region and its associated network (Silvanto et al., 2008). Second, the importance to develop TMS approaches that, rather than disrupt the activity of a cortical target, entrain activity mimicking local and network-distributed coding strategies for a more efficient manipulation of healthy and impaired behaviors [START_REF] Thut | Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions: A position paper[END_REF][START_REF] Thut | New insights into rhythmic brain activity from TMS-EEG studies[END_REF]Thut et al., 2011a).

Following these developments in the field, rhythmic TMS patterns used in the studies of this thesis achieve effective modulation of behavior by acting primarily on the natural frequency at which local or extended systems tend to operate and get synchronized [START_REF] Romei | Information-Based Approaches of Noninvasive Transcranial Brain Stimulation[END_REF].

However, as a result of such a stimulation approach that capitalizes on the ability to interact with network-wide activity patterns, TMS shows widely distributed effects which make it difficult to pinpoint the precise spatio-temporal coding pattern (i.e. oscillatory activity or the lack thereof on a specific region) that might be causally related to a cognitive function [START_REF] Thut | Modulating Brain Oscillations to Drive Brain Function[END_REF].

Indeed, our own datasets show that rhythmic TMS over the right FEF did not only entrain cortical oscillations in the right FEF, but it also increased phase-synchrony between frontal and parietal regions and entrained high-beta oscillations in the posterior parietal cortical regions.

Parallel behavioral measurements during TMS-EEG recordings, showed that high-beta rhythmic patterns enhanced conscious visual perception for near-threshold lateralized targets.

Nonetheless, the fronto-parietal distribution of TMS effects (during and immediately following stimulation) revealed by TMS-EEG recordings opens the major question of which specific nodes or groups of nodes other than the manipulated right FEF might contribute the most to such effects on visual perception. Moreover, although in our studies no cross-frequency modulations between high-beta and other frequencies were revealed neither locally nor distantly to the right FEF target, other studies and more complex models of oscillatory interactions for attentional systems have highlighted possible cross-frequency interactions, between parieto-occipital theta or alpha, frontal beta and parietal gamma rhythms in attentional systems (Capotosto et al., 2012;Capotosto et al., 2009;[START_REF] Fiebelkorn | A Dynamic Interplay within the Frontoparietal Network Underlies Rhythmic Spatial Attention[END_REF]Fiebelkorn & Kastner, 2019 for a review). Therefore, it could be argued that evidence supporting jointly the modulation of brain activity and a shift in behavior might not be conclusive to make a reliable inference of causality between these two events given oscillatory activity spread to other interconnected regions or cross-frequency activity at other frequency bands than the one contained in the stimulation pattern could also contribute to the effect.

In spite of the stated limitations, the causal role for high-beta right frontal oscillations and fronto-parietal synchronization for the top-down modulation of conscious visual perception should be considered strong given the support of prior literature that, for the last 20 years, with correlational or causal approaches in humans or animal models reported findings in this same direction (Buschman & Miller, 2007;Chanes et al., 2013;Fries et al., 2001;Gregoriou et al., 2009;Gross et al., 2004;Hipp et al., 2011;Phillips & Takeda, 2009;Quentin et al., 2014Quentin et al., , 2015)).

However, a technological strategy to overcome some of the stated limitations and tease out the contributions of network synchrony between several nodes from local synchronization in isolated nodes, and identify cross-frequency interactions, would be to combine traditional rhythmic mono-focal stimulation with multi-coil (2 or 3 sites simultaneously) TMS approaches.

To our knowledge, no commercially available, CE certified TMS equipment exists that allows the synchronized use of multiple sources of rhythmic or non frequency-specific TMS via small coils that can be fitted simultaneously on the scalp and are able to deliver repetitive pulses without warming up excessively. Hence, while awaiting technological solutions in this direction, the approaches are logistically complex as several interconnected rTMS machines and standard figure-of-eight-coils (70 or 45 mm diameter) are needed. Additionally, the safety of these intervention would need to be assessed and included in international guidelines, so that ethical committees can allow its uses in experimental procedures. An alternative approach worth trying that provides much more flexibility, an excellent safety profile and ease of use would be high density multichannel tACS stimulation. Nonetheless, its poor focality to modulate specific anatomical locations compared to TMS and its weak effects (unless used at intensities above 4-6 mA to overcome skin resistance), has lately instilled some controversy in the field of stimulation with regards to uses in exploratory applications (Lafon et al., 2017;Vöröslakos et al., 2018;reviewed in Liu et al., 2018).

Another area in which the domain has room for improvement, is state dependency. As indicated above, since 2008, well-established conceptual and experimental developments have warned about the need to consider the influence of ongoing cortical activity at the moment of stimulation (see reviews in Silvanto et al., 2008;[START_REF] Silvanto | State-Dependency of Transcranial Magnetic Stimulation[END_REF]. Such state dependency framework stated the importance of being aware of ongoing activity in the targeted cortical region and its associated network, most importantly it provided strategies (by priming the targeted region by means of non-invasive stimulation techniques or task adaptation approaches) to manipulate activity levels and maximize the effect of stimulation on excitability.

Only more recently this framework was extended to the manipulation of oscillations and synchrony [START_REF] Romei | Information-Based Approaches of Noninvasive Transcranial Brain Stimulation[END_REF] and emphasized the importance of monitoring via EEG recordings 'activity state'. A very influential study, of several published to this regard, showed that the same stimulation pattern could either strongly enhance cortical oscillations when delivered in phase with ongoing rhythms or show no effect when delivered out of phase [START_REF] Ngo | Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory[END_REF]. This finding provides a rather simple rule to be used in TMS protocols (single pulse or rhythmic TMS) to target specific cortical oscillators by tailoring stimulation to the optimal cycle phase to maximize intended effects, compared to other oscillators for which stimulation will be delivered in suboptimal phase, hence ineffective. The complexity of this approach lies in the fact that, to be effective, one needs to be able to put in place a temporally accurate system to detect and estimate oscillation phase with the least number of cycles possible to then synchronize the TMS pulse (or burst) delivery so that it hits the target at the right phase.

However, the more complex the EEG metrics that one relies on to drive stimulation, the more efficient the algorithm needs to be in real time. Eventually, closed-loop systems might be able to adjust several TMS parameters in real time to the dynamics of brain activity at local or network level to achieve a desired brain state much more precisely and with less intra-subject variability than current TMS strategies stimulating trial-by-trial at fixed parameters [START_REF] Bergmann | Brain State-Dependent Brain Stimulation[END_REF][START_REF] Zrenner | Closed-Loop Neuroscience and Non-Invasive Brain Stimulation: A Tale of Two Loops[END_REF]. However, as promising and sophisticated as such future developments for brain stimulation to boost function in the healthy brain or rehabilitate neurological deficits might be, their development will always rely on better anatomical and physiological knowledge of brain patterns subtending specific cognitive operations and behaviors [START_REF] Bergmann | Brain State-Dependent Brain Stimulation[END_REF]. The outcomes of this dissertation intend to be a modest contribution in this direction.

V-Conclusion and final remarks

The work presented in this thesis is in continuity with years of research developed in our lab on the causal basis of brain activity subtending the top-down modulation of conscious visual perception, via attentional orienting networks. With regards to prior achievements, the challenging implementation of TMS-EEG protocols has enabled us to progressively refine our methodological approaches, designs and knowledge in this cognitive domain and its manipulation with non-invasive brain stimulation (NIBS). We have progressed from an initial single pulse TMS study supporting a causal role for the right FEF in the modulation and conscious visual perception (Chanes et al., 2012), to the use of frequency-tailored TMS on FEF suggesting a functional role for local high-beta oscillations (Chanes et al., 2013) likely distinct for the left and right FEF (Chanes et al., 2015). The precious addition of EEG recordings to this work allows now to enrich this background via causation details on the local and network activity patterns subtending the top-down modulation of conscious visual perception and, for the first time, insight on the functional role of neural noise in such processes.

Our findings provide some answers, but as in every complex domain, it essentially opens new questions and generates new challenges. First, we highlight the need to develop new causal methods to dissect oscillatory contributions of network nodes as isolated or integrated systems.

Second, we contribute to the field a panoply of novel non frequency-specific stimulation patterns to induce rather unexplored patterns of cortical activity and neural coding (other than the well-studied local frequency-specific neural oscillations) to manipulate visually guided behaviors. Last but not least, we underline the importance of a refined characterization of the sensory accompanying effects carried by TMS to design better control conditions for active stimulation. We are confident that new approaches in the field of NIBS, in particular those considering multifocal network approaches, the brain state dependency nature of stimulation and the likely integration of close loop real time monitoring systems, will be able to increase the specificity of our interventions and address these new challenges in the years to come.

  II -Transcranial Magnetic Stimulation ............................................................................. II.1 -Stimulation parameters ................................................................................................... II.2 -Design of rhythmic and random TMS patterns ................................................................ II.3 -Cortical target selection and MRI-based frameless neuronavigation ................................ III -Concurrent TMS-EEG recordings of brain activity ................................................... III.1 -Electromagnetic TMS-EEG artifact removal and data cleaning procedures .................... III.2 -Concurrent TMS-EEG recordings and EEG data pre-processing .................................... III.3 -Control analysis on the TMS-EEG artifact removal and data cleaning procedures .......... III.4 -Outcome measures to assess the impact of TMS on oscillatory activity .......................... III.4.1 -Outcome measures for local oscillatory activity.................................................................... III.4.2 -Outcome measures for inter-regional network synchronization ............................................ III.5 Outcome measures to quantify and characterize noise in EEG datasets ........................... III.5.1. Measures to characterize noise in the time-frequency domain ................................................ III.5.2. Measures to characterize noise in the time domain ................................................................ III.6 -Cluster-based permutation tests for the correction of multiple comparisons .................. REFERENCES ............................................................................................................... PROJECT 1: Causal role of high-beta oscillations in the right fronto-parietal network for conscious visual detection ........................................................................................... I -Entrainment of local synchrony reveals a causal role for high-beta right frontal oscillations in human visual consciousness ..................................................................... II -Causal role of high-beta right fronto-parietal synchrony in the modulation of human conscious visual perception ............................................................................................ PROJECT 2: Exploring unexpected contributions of left frontal neural noise to the modulation of conscious visual perception in the human brain: a combined TMS-EEG study .................................................................................................................................. PROJECT 3: Non-specific effects of auditory stimulation generated by transcranial magnetic stimulation (TMS) on cortical oscillations and visual detection performances ........................................................................................................................................... GENERAL DISCUSSION ............................................................................................... I -Summary of the main results ...................................................................................... II -Frontal and fronto-parietal contributions to the modulation of visual perception ....... II.1 -Interhemispheric asymmetries in top-down systems for the facilitation of visual performance .......................................................................................................................... II.2 -Methodological limitations of our datasets and experimental approaches ...................... II.3 -Modulating visuo-spatial attention and recording conscious visual perception ............... III-Pending questions and some future directions ........................................................... III.1 -Towards an oscillatory model of attentional orienting and perceptual modulation ........ III.2 -Contributions of parietal and occipital cortices to conscious perception ....................... IV-Further considerations .............................................................................................. IV.1 -Unexpected impact of 'control' TMS patterns on EEG activity .................................... IV.2 -Network impact and state dependency of frequency-tailored TMS effects ................... V-Conclusion and final remarks .................................................................................... REFERENCES ...............................................................................................................

Figure 1 .

 1 Figure 1. Communication through coherence. (A) Feedforward Coincidence Detection mechanism. Neuronal clusters 'A' and 'B' have converging connections to neuronal cluster 'C'. Through the Feedforward Coincidence Detection mechanism, neurons in cluster A synchronize in phase with neurons in cluster C. (B) Input Gain Modulation mechanism. Schematic representation of high (good)

Figure 2 .

 2 Figure 2. Grey and white matter components of the attentional orienting network. (A) Cortical regions synchronized at high-beta frequency during attentional orientating. In the frontal lobe (1), the Frontal Eye Fields (FEF); in the posterior parietal Lobe (2), the Intraparietal Sulcus (IPS); in the lateral occipito-temporal region (3), the middle temporal cortex (MT/V5); in the medial occipital region (4), the primary visual cortex (V1) (Adapted from Hipp et al. 2011). (B) Anatomical distribution of dorsal and ventral attention networks. (Adapted from Corbetta et Shulman, 2002). (C) Fronto-parietal structural white matter connections: the three branches (SLF I, II and III) of the Superior Longitudinal Fasciculus. (Adapted from Thiebaut de Schotten et al. 2011).

  interventions to manipulate brain activity have required animals or human to be engaged in specific behavioral tasks, while relying on non-invasive technologies to record their neural activity (fMRI, surface EEG, MEG). Direct causal manipulations of cortical activity, by means of epidural/intracranially implanted electrodes to perturb brain activity, could exclusively be performed in animal models or in a very limited set of human patient populations (such as Parkinson's, epilepsy, obsessive compulsive disorder, or brain tumor patients for which the implantation of epidural/intracranial electrodes is justified for diagnostic or therapeutic purposes). In this context, the 21 st century has seen the development of technological innovations able to manipulate brain activity in humans without the need of invasive surgery. Currently, cortical rhythms can be entrained or manipulated experimentally by means of pulsed or fluctuating sensory stimuli which can influence activity along sensory pathways and reach the cortex. Alternatively, more recently, the field has seen the development of non-invasive transcranial brain stimulation techniques: Transcranial Magnetic Stimulation (TMS) and Transcranial Current Stimulation (tCS) using, respectively, electromagnetic pulses or electrical current delivered on the scalp that penetrates the skull and can reach the cortical surface to modulate neural activity.

Figure 3 .

 3 Figure 3. Technical equipment for the delivery of Transcranial Current Stimulation (tCS) and Transcranial Magnetic Stimulation (TMS). (A) tCS is delivered through a small light and portable rechargeable battery system and controlled wirelessly from a computer or portable device. Current is conveyed by short and light physical wires to a montage (at least two, an anode and a cathode) of leads (either sponge contacts, or solid ferromagnetic leads, see both in the figure) placed in specific scalp locations. Systems also often integrate independent channels to record EEG signals. (B) The wireless tCS device is mounted directly on a lycra cap worn by participants while performing a task on a computer screen. A very mild current will flow between at least two electrodes (active and return) placed in separate locations of the scalp generating on the brain surface a large polarization gradient able to modulate the resting membrane potential of exposed neurons. The wireless control of tCS allows full head and possibly body motion. (C)TMS requires heavy non-portable equipment that charges current in a series of capacitors. From the central unit, accumulated current is then circulated through a stimulation coil (in the picture a double ¨butterfly¨ 70 mm coil) to generate a brief magnetic field, called a pulse, capable of penetrating through skull tissue layers and induce a focally distributed electrical current inside of the brain powerful enough to depolarize neurons.(D)The stimulation coil is placed lying flat on a subject's head and held manually by an operator, or with help from a mechanic arm, while the subject is performing a task on a computer screen. TMS can be delivered in single pulse, short bursts (4 or 5 pulses) or long patterns of repetitive (rTMS) stimulation to modulate activity in a focal, targeted cortical region. Targeting is monitored throughout the session by means of an MRI-based neuronavigation system. Note in (D) TMS is delivered while EEG activity is being monitored through an independent equipment.

Figure 4 .

 4 Figure 4. Mechanisms of oscillatory entrainment by periodical TMS pulses. (A) Schematic drawing of an independent neural oscillator fluctuating naturally at the so called 'natural frequency'. labels the phase of the oscillation. (B) Schematic representation of three oscillators operating with a similar frequency. In physiological conditions these oscillators each have their own temporal dynamics and are not phase synchronized. A periodic external force, exerted by series of single TMS pulses (n=11 pulses in the figure) repeated rhythmically at a given frequency phase resets the cycles of the different units within each oscillator type, hence progressively phase-locking (i.e., synchronizing) their rhythms, making them fluctuate jointly. (C) As result of such a progressive synchronization of local oscillators,

Figure 5 .

 5 Figure 5. Threshold Stochastic Resonance. (A) The addition of noise facilitates the detection of a subthreshold signal, in our case a fluctuating level of voltage difference between the inner and outer cellular environment know as transmembrane resting potential. Random noise (light grey vertical lines) added to a subthreshold signal (bold black fluctuating line) will help such signal to cross the firing or depolarization threshold and manifest electrophysiologically as a single spike or a train of spikes generated by the same neuron. (Adapted from Moss et al. 2004). (B) Optimal window of noise levels to enable stochastic facilitation. Schematic drawing of a classical inversed U-shape curve for Stochastic

(

  dendrites spines, axons or terminal boutons) (White et al. 2000); (2) variability in the amplitude of the postsynaptic currents generated by stereotyped action potentials; (3) variation in the neurotransmitter concentration released by the terminal bouton; (4) the neurotransmitters' diffusion across the synaptic cleft; and (5) the fluctuations in the density of post-synaptic receptors associated to ion channels are all phenomena affected by stochastic variability and susceptible to generate noise. Moreover, the sources of noise generated by stochastic dynamics

Figure 1 .

 1 Figure 1. Conscious visual detection task. After a period of fixation, a central fixation cross becomes slightly larger to alert participants of an upcoming event; then different types of active or sham TMS pattern are delivered prior to the presentation of a visual target, a 50% visibility Gabor that may appear at the center of a right or a left placeholder for a brief period of time (33 ms). Participants were requested to indicate, with the middle, index and thumb fingers of the left hand, whether they did perceive a targetor not (no/yes), and, if they saw it, to signal where it appeared (right/left placeholder) by selecting the position (upper or lower) of the arrow pointing to the location of the target. Note that catch trials (20% of the trials) in which no target was presented in any of the placeholders were randomly interleaved on each block.

Figure 2 .

 2 Figure 2. Changes in detection performances across experimental blocks. Barplots of detection rates for each dataset analyzed in this thesis (respectively datasets 1, 2 and 3 associated to results of projects 1, 2 and 3 presented in the results section of this dissertation). Colored barplots represent detection rates for individual experimental blocks. Grey barplots represent the average detection rates across all experimental blocks. Colored barplots represent individual experimental blocks on each experimental session for datasets 1 (in red hues), dataset 2 (in green hues) and dataset 3 (in blue hues). Notice that, for datasets 2 and 3 (corresponding to projects 2 and 3), data was acquired in two separate sessions (performed 72h to 7 days apart), hence individual blocks (3 or 4 blocks per session) are presented separately. Mean barplots show that average correct conscious visual detection performance was centered at ~50% detection rate. Mean performance drifted with the accrual of blocks. Nonetheless none of the drops in performance observed in boxplots achieved statistical significance.

  trials. If needed, target contrast was manually adjusted. Only after the experimenter verified that: (1) participants had a good understanding of what the task required, (2) they became used to the sensory sensations triggered by active TMS bursts (sound and tapping), being able to neglect them to concentrate on the visual detection paradigm, and, (3) they showed consistent detection performance, experimental blocks started. Participants performed an experimental block for each of the TMS pattern tested on each of the three studies (see below for full details on TMS procedures).

Figure 3 .

 3 Figure 3. Signal Detection Theory (SDT). Distributions of information along the decision axis for the presentation of a signal or noise. Colors and vertical or horizontal filling patterns represent the categories in which the trials are classified according to what was presented on the screen (signal or noise) and the response of the participant (seen/not seen). Perceptual sensitivity (d') is the distance between the mean of the two distributions. The decision criterion (c) is the threshold on the decision axis above which

Figure 4 .

 4 Figure 4. Schematic representation of the TMS patterns employed in active and sham stimulation. Frequency-specific 30 Hz rhythmic pattern designed to entrain oscillatory activity at the input frequency in the cortex and 3 non-frequency specific patterns designed to induce different levels of noise in the cortex.

  should be noted that other TMS-EEG cleaning methods have not yet been able to consistently and satisfactorily clean the early component of the TMS artifact either. Hence, whether one uses the average TMS-template approach, PCA or ICA to subtract the TMS artifact from the EEG signals, or employs a 'sample-and-hold' circuit to prevent the recording of the TMS artifact on EEG systems, EEG signals directly following the delivery of each TMS pulse will be lost anyway.The independent components extracted from the signal by the ICA are visually inspected and four types of artifact are identified: (1) Eye blinks and eye movements. These components are identified on the basis of their topography as located on the most frontal scalp electrodes. They are also characterized by time-courses that show bell-shaped artifacts when the subject blinks; (2) Electrode malfunctioning (transient disconnection or 'bad' impedances). These components are characterized by topographies concentrated on a single electrode and high signal amplitudes but only present in isolated time periods or trials. The removal of these components have little impact on the average across trials(Rogasch et al., 2014); (3) Power line (50 Hz/60 Hz) artifacts or land noise. In our recordings made in France, these components are identified by a 50 Hz peak in their Fourier spectral analyses; Last but not least, (4), residual TMS artifacts. Components are identified as TMS artifacts if their topography is mainly found

Figure 5 .

 5 Figure 5. Control analyses to rule out an impact of TMS artifact cleaning procedures applied to our EEG data. The figure shows data from representative trials of EEG datasets associated to the following conditions: (A, left & right) Eyes-Open resting state EEG and active rhythmic TMS-EEG. (B) Addition of TMS-artifacts from randomly selected active rhythmic TMS-EEG trials (right) to our original eyes-open resting-state EEG dataset (left). (C to E) TMS-EEG artifact removal steps applied to resting-state EEG data with added artifacts (C-D-E, Left) and active TMS-EEG data (C-D-E, Right). (C) Removal of EEG TMS artifacted data within a [-4 +12] ms window around each TMS pulse onset to eliminate high-amplitude portion of TMS artefacts. (D) Interpolation of blank [-4 +12] ms periods left after artifact removal with a shape-preserving piecewise cubic interpolation. (E) TMS artifact-free EEG time series with interpolated data following the removal of ICA components corresponding to eye movements, electrode malfunctions, 50 Hz powerline artifact and residual TMS artifacts.

  during eyes-open resting-state recordings (see Fig.5A& 5B left panel). The templates of TMSartifacts added to resting state EEG data were obtained from 150 randomly selected trials from an active 30 Hz rhythmic TMS-EEG dataset (Fig.5Aright graph). Individual TMS artifacts

Figure 6 .

 6 Figure 6. Effect of TMS-EEG cleaning procedure on signal power spectrum. Top row shows power spectra for frequencies within a [6 80] Hz window. Shaded colored areas represent 95% confidence intervals for median power. Bottom row shows boxplots for log power between the compared conditions at 30 Hz frequency. (A) Comparison of artifact size on power spectrum for real TMS-EEG artifacted data (blue line) and resting state EEG data with artificially added TMS 'artifacts' (red line). (B) Comparison of power spectra for resting-state data before the addition of TMS artifacts to resting state EEG data (black line) and after the removal of artificially added TMS 'artifacts' to the same resting state EEG data (red line) by means of our TMS-EEG artifact removal and cleaning procedure. (C) Power spectra for cleaned resting-state EEG data with artificially added TMS artifacts (blue line) and TMS-EEG signals recorded during the delivery of active 30 Hz rhythmic TMS (red line), on both cases after applying the same artifact removal and data cleaning procedure. TMS artifact cleaning procedure did not introduce 30 Hz power in our dataset (panel B bottom, n.s = non-significant) therefore any significant differences in power spectrum (after TMS artifact cleaning procedure) between active TMS and resting state data (panel C, ***: p<0.001) cannot be artifactual signal from our data cleaning procedure.

Figure 7 .

 7 Figure 7. Outcome measures to identify quantify and characterize oscillatory activity in EEG signals. (A) Oscillation power expressed in decibels (dB) relative to a baseline taken in the pre-cue period, i.e. prior to the increase in size of the central alerting cue (to indicate to participants a TMS burst was about to be delivered) prior (-233 ms) to visual target onset. (B) Inter-trial coherence calculated as the module of the complex average vector of signal phase across trials. (C) Imaginary phase-locking value calculated as the projection on the imaginary axis of the complex average vector across trials of phase-difference between two signals.

Figure 8 .

 8 Figure 8. Outcome measures to identify quantify and characterize noise levels in EEG signals. (A) Width and number of peaks in the power spectrum of EEG time series within a time window of interest including TMS stimulation patterns. Peaks are local maxima in the power spectrum. Peak width is calculated at the half-prominence. Peak prominence is the difference between the local maxima and the smallest local minima between this local maxima and the next higher local maxima. (B) Computation of 'Multi-scale entropy', which is the ensemble of 'Sample entropy' values calculated at several time scales. 'Time scales' are estimated through the process of coarse graining. Sample entropy counts in EEG time series repeating patterns in signal of length 'm' and 'm+1' and computes a ratio of probability of repeating patterns of length 'm' and repeating patterns of length 'm+1'. (Adapted from Costa et al. 2005).

  -based permutation tests for the correction of multiple comparisons EEG data is characterized by high dimensionality, over both the sensor space (in our datasets, 60 scalp EEG electrodes) and also in the time and the frequency space. Consequently, the statistical comparison of EEG topographies, time courses or time-frequency maps leads to the Multiple Comparison Problem (MCP). When a large number of statistical comparisons are performed (for example for each electrode or each time-frequency point) the family-wise error rate, that is the probability to have a false positive result to a statistical test, is not well corrected when simply looking at p-values. The Multiple Comparison Problem requires p-values to be corrected to account for multiple comparisons. Although several methods are available to correct p-values, the most commonly used to evaluate behavioral or imaging large datasets are the Bonferroni correction method or the slightly less conservative False Discovery Rate (FDR) correction. Additionally, Nonparametric Permutation Tests are another straightforward methodto correct for multiple comparisons(Maris & Oostenveld, 2007) which have become increasingly popular to process EEG data. This approach compares a test statistic obtained when comparing two conditions 'A' and 'B' to the null distribution of the test statistic computed by the systematic permutation of data labels across individual samples in groups 'A' and 'B'. The p-value of nonparametric permutations tests is the proportion of random data permutations that results in a higher test statistic than the test statistic for comparison between 'A' and 'B'. If this proportion is smaller than a threshold alpha=0.05, conditions 'A' and 'B' are considered significantly different.

  sont-elles simplement corrélées ou bien causalement liées à nos capacités à rapporter consciemment la présence d'une cible dans notre champ visuel ? Au cours de cette étude, nous avons enregistré les signaux EEG d'un groupe de sujets sains réalisant une tâche de détection visuelle alors qu'ils recevaient dans le même temps de courtes rafales de Stimulation Transcranienne Magnétique (SMT) rythmiques ou aléatoires au niveau du champ oculomoteur frontal droit juste avant la présentation d'une cible latéralisée. Nous démontrons que la SMT entraîne des oscillations, c'est-à-dire amplifie la puissance et l'alignement de phase des oscillations beta-hautes (et ce, pour cette dernière mesure, de manière plus importante pour les rafales rythmiques que aléatoires) et parallèlement augmente la sensitivité visuelle pour la détection. En considérant, dans une analyse post hoc, seulement les participants pour qui la stimulation rythmique a amélioré la détection visuelle, nous montrons que l'amplitude de l'entrainement d'oscillations beta-hautes corrèle avec les augmentations de performances visuelles dans le champ oculaire gauche. Notre étude apporte des preuves en faveur d'un lien causal entre l'activité oscillatoire beta-haute dans le champ oculomoteur frontal et la détection visuelle. De plus, ces résultats sont favorables à une application future de la stimulation cérébrale pour manipuler la synchronie oscillatoire locale et améliorer ou restaurer les fonctions visuelles lésées dans des populations de patients. II -Causal role of high-beta right fronto-parietal synchrony in the modulation of human conscious visual perception Résumé (Français) Des études corrélationnelles chez les primates non-humains ont mis en évidence une augmentation de la synchronisation fronto-pariétale dans la bande de fréquence beta-haute (22-30 Hz) pendant l'orientation endogène de l'attention visuo-spatiale. Le recrutement de la synchronie inter-régionale à cette même bande de fréquence pourrait-il constituer le mécanisme causal par lequel l'attention est engagée chez l'homme et facilite, de façon descendante, la perception visuelle ? De surcroît, la manipulation de ces processus amènerait-elle à une amélioration des capacités de détection visuelle consciente ? Dans cette étude, nous avons reanalysé les signaux encéphalographiques (EEG) d'un groupe de sujets sains (n=14) réalisant une tâche de détection visuelle consciente, sous l'influence de brèves rafales rythmiques (30 Hz) ou aléatoires de Stimulation Magnétique Transcrânienne (SMT), constituées d'un nombre identique d'impulsions magnétiques et étalées sur la même durée, délivrées sur le champ oculomoteur frontal droit juste avant l'apparition d'une cible visuelle latéralisée au seuil de détection. Nous rapportons une augmentation de la synchronie inter-régionale dans la bande beta-haute (25-35 Hz) entre la région corticale stimulée (champs oculomoteur frontal droit) et un groupe d'électrodes pariétales, induite par la SMT rythmique et pas aléatoire. Surtout, ces augmentations étaient accompagnées d'améliorations des performances de détection visuelle consciente pour les cibles visuelles gauches (controlatérales à la stimulation) dans la condition de SMT rythmiques et pas aléatoire au niveau du groupe. Ces résultats démontrent que la synchronisation beta-haute entre les régions frontales et pariétales chez l'homme peut être manipulée de façon non invasive et que l'activité oscillatoire beta-haute dans la totalité du réseau attentionnel dorsal droit pourrait contribuer à la facilitation de la détection visuelle consciente. De plus, nos résultats sont favorables à l'application future des méthodes de stimulation cérébrale non invasives pour manipuler la synchronie inter-régionale, une technique qui pourrait être utilisée pour améliorer les comportements visuels de sujets sains ou de patients souffrant de troubles neurologiques.

  below and above the fixation cross signaling the location of the right and left rectangular placeholders. Participants were requested to indicate which arrow pointed to the location of the placeholder where he/she had seen the target. The location of each arrow was randomized across trials to prevent the preparation of a motor response prior to the appearance of the response window and to make sure that visual processing activities in the FEF, located very close to the motor areas, were temporally separated from motor decisions and responses. Participants provided a response using three keyboard keys: an upper key to indicate the upper arrow (corresponding to the 'd' letter key), a lower key to indicate the lower arrow (corresponding to the 'c' letter key) and the space bar to indicate that no target had been consciously perceived. Each participant performed 6 blocks: 1 calibration block, 1 training block and 4 experimental blocks (2 blocks for each TMS pattern: random or rhythmic, see details on TMS patterns below). The order of the experimental blocks was counterbalanced across participants. Each block was divided into sub-blocks of 20 trials. The length of the calibration and training blocks was variable, as the termination of these two blocks was decided by the experimenter on the basis of individual performance. Experimental blocks consisted of 7 sub-blocks and lasted approximately 20 minutes each.

Figure 1 .

 1 Figure 1. Experimental design, stimulation patterns and targeted cortical regions. (A) Visual detection task performed by participants. After a period of fixation, a central cross became slightly larger to alert participants of an upcoming event; then active and sham rhythmic or random TMS patterns were delivered to the right FEF region prior to the presentation of a target that could appear at the center of a right or left placeholder for a brief period of time. Participants were requested to indicate whether they did perceive a target or not (no/yes), and, if they saw it, where it appeared (right/left). Notice that in 20% of the trials, no target was presented in any of the placeholders. (B) Coronal, axial and sagittal MRI sections from the frameless stereotaxic neuronavigation system showing the localization of the targeted cortical right FEF (Talairach coordinates X=31, Y=-2, Z=47) in a T1-3D MRI of a representative participant. (C) Schematic representation of the TMS patterns employed in active and sham 30 Hz rhythmic stimulation to entrain oscillatory activity at the input frequency in the right FEF, and the random stimulation used as a control to isolate the effect of stimulation frequency. (D) Schematic representation of the calculation of the phase locking value. The phase of signals between each two sets of electrodes was extracted from the

  procedureTMS was triggered in synchronization with the presentation of the visual stimuli via a high temporal resolution multichannel synchronization device (Master 8, A.M.P.I.) connected to two biphasic repetitive stimulators (SuperRapid, Magstim) each attached to a standard 70 mm diameter figure-of-eight TMS coil. The coil in charge of delivering active TMS patterns was held tangentially to the skull above the location of the right FEF, with its handle oriented ~ parallel to the central sulcus, at a ~45° angle in a rostral to caudal, lateral to medial direction. The other coil, which delivered sham TMS stimulation was placed close the stimulation site but positioned perpendicular to the skull directing the magnetic field away from the brain. The sham coil produced the same clicking noise characterizing the delivery of an active TMS pulse but did not deliver active and effective stimulation on the targeted right frontal cortex.During the whole experiment, the position of the active TMS coil was tracked using a neuronavigation system (Brainsight, Rogue Research). A T1-weighted MRI scan (3T Siemens MPRAGE, flip angle=9, TR=2300 ms, TE=4.18 ms, slice thickness=1mm) was acquired for each participant and the right FEF was localized on each individual scan as a spherical region of interest of radius 0.5 cm centered on the Talairach coordinates x=31, y=-2, z=47(Paus, 1996) (Fig 1B).

Figure 2

 2 Figure 2 illustrates the topographic representation of the imaginary PLV in the high-beta

Figure 2 .

 2 Figure 2. Topographical maps of the imaginary PLV in the time window [-0.133 0] ms for frequencies [25 35] Hz. The map represents the imaginary synchrony values for all scalp electrodes compared to the signal in the electrode closest to the stimulation coil (FC2). The maps are arranged following our 2x2 cross-design. We compared sham vs. active TMS condition (in the columns) and random vs. rhythmic TMS patterns (in the rows). Margin maps show the results of the statistical permutation tests. Bolded electrodes represent clusters of electrodes that reached significance (p<0.05). Imaginary synchrony significantly increased in a group of right parietal electrodes in the active rhythmic TMS condition compared to both the sham rhythmic and active random controls.

Figure

  Figure 3. Evolution in time of frontoparietal oscillatory synchronization. (A) Imaginary synchrony between the FC2 electrode and right parietal electrodes. (B) Imaginary synchrony between the FC2 electrode and left parietal electrodes. Time is centered on the onset of the target (dotted gray line). Dotted red lines underline the time window between first (-133 ms) and the last (-33 ms) TMS pulse. Dotted black line indicates frequency of stimulation (30Hz). Margin maps show the results of the statistical permutation tests, with black points indicating clusters that reached significance (p<0.05). Imaginary fronto-parietal synchrony increases ipsilaterally (right hemisphere) during 30Hz rhythmic stimulation compared to both random and sham TMS controls.

Figure 4 .

 4 Figure 4. Distant local high beta entrainment. (A) Time frequency plot of power over right parietal electrodes. (B) Time frequency plot of power over left parietal electrodes. Time is centered on the onset of the target (dotted gray line). Dotted red lines underline the time window between first (-133 ms) and the last (-33 ms) TMS pulse. Dotted black line indicates frequency of stimulation (30Hz). Margin maps show the results of the statistical permutation tests, with black points indicating clusters that reached significance (p<0.05). There is no difference in the time course of 30Hz power between trials with rhythmic and random patterns of stimulation, although the noise of the TMS coil increases alpha oscillations throughout the parietal region.

Figure 5 .

 5 Figure 5. Distant local phase-locking of high beta oscillations. (A) Time frequency plot of inter-trial coherence over right parietal electrodes. (B) Time frequency plot of inter-trial coherence over left parietal electrodes. Time is centered on the onset of the target (dotted gray line). Dotted red lines underline the time window between first (-133 ms) and the last (-33 ms) TMS pulse. Dotted black line indicates frequency of stimulation (30Hz). Margin maps show the results of the statistical permutation tests, with black points indicating clusters that reached significance (p<0.05). Rhythmic stimulation of the FEF increases inter-trial coherence in the high-beta range distantly over the whole parietal cortex, whereas random stimulation patterns increase phase-locking transiently in alpha frequencies contralateral to the stimulation.

Figure 6 .

 6 Figure 6. Modulation of perceptual sensitivity (d') by TMS for targets presented in the left and right visual field (extracted from Vernet et al., 2019). Results of the post-hoc t-tests are indicated as follows: ** p < 0.01. Notice that perceptual sensitivity is increased only for targets contralateral to the stimulation site (left) in active compared to sham rhythmic TMS.

  Des résultats antérieurs chez le primate non-humain ont mis en évidence un rôle crucial des oscillations à une fréquence beta-haute, opérant dans l'ensemble du réseau fronto-pariétal dorsal latéralisé à droite, pour l'orientation de l'attention endogène et la modulation de la perception visuelle consciente. Cependant, une étude parallèle faisant usage de la Stimulation Magnétique Transcrânienne (SMT) pour examiner les contributions des régions homotopes dans l'hémisphère gauche a reporté, paradoxalement, que dans ces régions (le champ oculomoteur frontal gauche) une activité non spécifique en fréquence ou arythmique, générée par la stimulation cérébrale non invasive focale, et non des oscillations à une fréquence beta-haute conduisaient à des améliorations de la perception visuelle consciente. Dans l'intention de mieux comprendre ce résultat, nous avons combiné la stimulation cérébrale non invasive et les enregistrements EEG avec pour objectif de caractériser quels motifs d'activité cérébrale, entrainés avec des courtes rafales de stimulation cérébrale non spécifiques en fréquence, permettent une facilitation de la perception visuelle consciente. Nous avons aussi voulu dévoiler les stratégies de codage du cortex frontal gauche permettant un engagement des fonctions d'orientation de l'attention et de modulation de la perception visuelle consciente. A cette fin, nous avons enregistré les signaux cérébraux d'une cohorte de sujet sains effectuant une tâche de détection visuelle consciente simultanément à la stimulation de leur champ oculomoteur frontal gauche avec trois types de rafales non spécifiques en fréquence (4 impulsions magnétiques, réparties de façon irrégulières dans le temps), et une rafale de stimulation rythmiques (4 impulsions, à une fréquence de 30 Hz) utilisée pour la comparaison. Notre intervention n'a pas permis de moduler significativement les performances de détection visuelle consciente. Néanmoins, nous démontrons que les différentes rafales de stimulation non spécifiques en fréquence amplifient les oscillations corticales dans une large bande de fréquence et induisent des niveaux de bruits plus élevés dans l'activité corticale. Nous concluons que la modulation du niveau de bruit neural par la stimulation non invasive offre une stratégie prometteuse pour l'amélioration de l'activité neurale et offre un outil pour étudier et mieux comprendre le rôle du bruit dans le traitement de signal dans le cortex, ainsi que conceptualisé par le phénomène de la Résonnance Stochastique.

A

  group of 15 right-handed participants (9 women, and 6 males) aged between 21 and 45 years old (30 ± 7) took part in the sessions of the current study. Participants reported no history of neurological disorders and had normal or corrected-to-normal vision. All of them voluntarily consented to participate in the study and signed a consent form. The research protocol including all the interventions of this study was sponsored by the INSERM (Institut National de la Santé et la Recherche Médicale) and approved by an Institutional Review Board, the Comité de Protection des Personnes (CPP), Ile de France V.

  for a schematic representation of the sequence of events during a trial). Participants were seated with their eyes positioned 57 cm away from the center of a computer screen. Trials started with a fixation screen that displayed a central fixation cross (size 0.5x0.5 o ) and a right and left rectangular placeholders (6.0 x5.5 o , drawn 8.5° away from the center of the screen) indicating the potential location of a visual target later in the trial. The fixation screen was presented for an interval randomly jittered between 1000 and 1500 ms, to avoid predictability with regards to upcoming events and ensure sustained central fixation. Then the fixation cross became slightly larger (size 0.7x0.7°) during 66 ms to alert participants that the target would soon appear on the screen. After an inter-stimulus-interval of 233 ms, in 80% of the trials a low-contrast Gabor stimulus (0.5 o /cycle sinusoidal spatial frequency, 0.6 o exponential standard deviation) with vertical lines appeared for 33 ms in the center of the left or the right placeholder with equal probability. The remaining 20% of the trials were catch trials in which no target was displayed on the screen. Prior to the beginning of the experimental session, Gabor contrast was adjusted for each participant to reach 50% detection rate during a calibration block.

Figure 1 .

 1 Figure 1. Visual detection task, targeted cortical regions and TMS patterns. (A) Visual detection task performed by participants. After a period of fixation, a central cross became slightly larger to alert participants of an upcoming event; then active or sham patterns of rhythmic or non frequency-specific TMS were delivered to the left FEF prior to the presentation of a target, a near threshold 50% visibility Gabor, that could appear for a brief period of time at the center of a right or left placeholder. Participants were requested to indicate whether they did perceive a target or not (no/yes), and, if they saw it, where it appeared (right/left). Notice that in 20% of the trials, no target was presented in any of the placeholders. (B) Coronal, axial and sagittal MRI sections from the frameless stereotaxic neuronavigation system showing the localization of the targeted left FEF (Talairach coordinates X=-32, Y=-2, Z=46) in a T1-3D MRI of a representative participant. (C) Schematic representation of the TMS patterns employed in active and sham stimulation. 30 Hz rhythmic pattern (designed to entrain oscillatory activity at the input frequency) in the left FEF and the 3 non frequency-specific TMS patterns (designed to induce different levels of cortical local noise in the left FEF).

  fixation cross. Participants were asked to indicate which arrow pointed towards the placeholder (right or left) where they consciously detected the target. The location of the arrows (above or below the fixation cross) was randomized across trials to prevent participants from preparing their motor response prior to the onset of the response window. This precaution was taken to be able to separate in different time epochs motor decisions from visual processing activities occurring in frontal and central sensors associated to the right and left Frontal Eye Fields (FEF), two regions located very close to the motor areas. Participants responded with the index/middle left hand finger by pressing the 'd' letter key to indicate the upper arrow or 'c' letter key to indicate the lower arrow. They were requested to press the space bar with their left thumb to indicate they had not seen any target. The response of the participant ended the trial.

  procedureTMS was triggered via a high temporal resolution multichannel synchronization device (Master 8, A.M.P.I.). Stimulation was delivered with a biphasic repetitive stimulator (SuperRapid, Magstim) and a standard 70 mm diameter figure-of-eight TMS coil, held tangentially to the skull.The position of the coil was tracked throughout the experiment with a neuronavigation system (Brainsight, Rogue Research) and the left Frontal Eye Field (FEF) was localized on individual T1-weighted MRI scans (3T Siemens MPRAGE, flip angle=9, TR=2300 ms, TE=4.18 ms, slice thickness=1mm) as a spherical region of interest of 5 mm radius centered on the Talairach coordinates x=-32, y=-2, z=46(Paus, 1996) (Fig 1B). The coil was angled tangentially to the skull and held in a position ensuring the shortest possible distance between the center of the stimulation coil and the cortical region of interest. The coil handle was oriented ~parallel to the central sulcus, at a ~45° angle in a rostral to caudal, lateral to medial direction. The neuronavigation system allowed to keep the TMS coil within a ± 3 mm radius from the center of the targeted site throughout the experiment. Sham stimulation was delivered through an audio speaker (Mobi wavemaster) attached to the TMS coil. To simulate a TMS pulse, the speaker played a recording of the clicking noise characteristic of the delivery of active TMS. The precise timing of the onset of the sham audio pulses was handled by the in-house MATLAB script using the Psychtoolbox extensions(Brainard, 1997) which also controlled the presentation of the visual detection task. The audio file of a TMS pulse was generated by recording the sound of 100 single TMS pulses (Aiwa CM-S32 stereo microphone) and averaging their individual waveforms. The envelope of the average waveform was then adjusted to emphasize the high amplitude spikes at the beginning of the pulse so that, once replayed through our speaker, the clicking sound became indistinguishable from the loud click produced by the TMS coil. The volume of the speaker was also adjusted to reproduce the volume of the active TMS pulse.We delivered four different TMS patterns(Fig 1C), all comprised of a burst of 4 TMS pulses lasting 100 ms (between the onset of the first pulse and the onset of the last pulse): a rhythmic pattern with pulses regularly spaced in time at a frequency of 30 Hz, i.e. with a fixed interpulse interval of 33 ms, designed to entrain high-beta cortical oscillations and 3 additional non frequency-specific patterns (non-uniform rhythmic, random and irregular patterns) tailored to induce different levels of neuronal noise in the stimulated cortex. In the non frequency-specific

  signals were transformed into the time-frequency domain with a 3-cycle Morlet wavelet analysis on a [-500 +500] ms window (centered on target onset) and for frequencies between 6 and 50 Hz. In the time-frequency domain, we calculated measures of power and Inter-Trial Coherence (ITC). ITC measures oscillatory phase alignment across trials by averaging signal phase at each time-frequency point over all trials. Power was expressed in decibels relative to a baseline period of 2 oscillation cycles prior to the alert cue onset (i.e., the central cross becoming larger, preceding Gabor visual target onset by 233 ms). First, we concentrated our analysis on a frequency ([25 35] Hz) and time ([-133 0], 0 being the target onset) window of interest and looked at the topographies of power and ITC across all electrodes of our montage. The frequency window of interest reflected the 30 Hz frequency of our rhythmic TMS pattern and the time window of interest corresponded to the time window during

  tailed paired Student's t-test. Comparisons between topographical maps were performed for each electrode and comparisons between time-frequency maps were performed for each timefrequency point in the frequency window [6 45] Hz and time window[-300 200] ms. We corrected for multiple comparisons with cluster-based permutation tests. Clusters were formed by neighboring electrodes or time-frequency points that exceeded the significance threshold (alpha = 0.01) in the paired t-tests and assigned as a statistic the sum of T-statistics of each point in the cluster.

  performed 2x2x4 repeated measure ANOVAs with factors Visual Field (left, right), TMS Condition (active, sham) and TMS Pattern (rhythmic, non-uniform rhythmic, random, irregular) on values of d', c and b. Post-hoc, in order to replicate exactly the analyses performed in an earlier study, which tested a subset of the stimulation patterns delivered here (Chanes et al., 2015), we performed two other repeated-measure ANOVAs on measures of d'. First, a 2x2x2 ANOVA with within-subjects factors Visual Field (left, right), TMS Condition (active sham) and TMS Pattern (rhythmic, non-uniform rhythmic). Second, a 2x2 ANOVA on measures of d' during stimulation with random TMS with within subject factors Visual Field (left, right) and TMS Condition (active sham).

Figure 2 .

 2 Figure 2. Impact of rhythmic and non frequency-specific TMS patterns on local high-beta oscillations. Topographical maps representing data in the [25 35] Hz frequency band during sham stimulation (time window [-133 0] ms centered on visual target onset). Comparisons of high-beta (A) power and (B) ITC between active (first row) and sham (second row) TMS for each TMS pattern (30 Hz rhythmic TMS, and 3 non frequency-specific patterns: non-uniform rhythmic TMS, random TMS and irregular TMS). Bottom row shows the results of the pairwise (active vs. sham TMS) cluster-based statistical permutation tests. Bolded electrodes represent clusters of electrodes that reached statistical significance (p<0.05). (C) Direct two-by-two comparisons between active TMS patterns. Colored maps represent distribution of ITC over the scalp for all four TMS patterns (30 Hz rhythmic TMS, non-uniform rhythmic TMS, random TMS and irregular TMS). Uncolored maps show the results of the cluster-based statistical permutation tests for the pairwise comparison of active trials in the two ITC topographical maps represented at the top of the column and the left of the row. Bolded EEG electrodes represent clusters of sensors that reached significance (p<0.05).

FC1

  and FC3) located the nearest to the left FEF, over a broader frequency ([6 45] Hz) and time window ([-500 500] ms centered on Gabor target onset) (Fig 3). Active-sham comparisons revealed that during the delivery of the bursts, rhythmic and non frequency-specific patterns increased oscillation power (Fig 3A) and ITC (Fig 3B) in a wide frequency band, not limited to 30 Hz. For ITC, direct two-by-two comparisons showed that, compared to the active rhythmic TMS condition, active irregular TMS increased oscillations phase-locking in a frequency band extending to the low-beta (12 to 20 Hz) band (Fig 3C). Additionally, similarly to what was observed on the topographical maps of ITC (Fig 2C), direct two-by-two comparisons of time-frequency maps also showed that active random TMS bursts increased high-beta phase-locking less strongly than active rhythmic, non-uniform rhythmic or irregular TMS patterns (Fig 3C).

  ) = 2,68, p<0.01, corrected for reduced error variance by jackknife procedure) or random TMS bursts (T(14) = 2,016, p<0.05, corrected for reduced error variance by jackknife procedure) (Fig 4).

Figure 3 .

 3 Figure 3. Frequency-specific modulation of cortical oscillations by rhythmic and non frequency-specific TMS patterns. Time-frequency maps in a cluster of left frontal electrodes (F1, F3, FC1, FC3) closest to the center of the stimulation coil. Time is centered on the onset of the visual target (dotted gray vertical line). Red dotted vertical lines signal the first (-133 ms) and last (-33 ms) TMS pulses in the burst. Black dotted horizontal line indicates frequency (30 Hz) of rhythmic TMS pattern. Comparisons of high-beta (A) power and (B) ITC between active (first row) and sham (second row) TMS for each TMS pattern (30 Hz rhythmic TMS, and three non frequencyspecific patterns: non-uniform rhythmic, random and irregular TMS). Bottom row shows the results of the pairwise (active vs. sham TMS) cluster-based statistical permutation tests. Black dots indicate clusters that reached statistical significance (p<0.05). Right marginal graphs in the first row for power time-frequency maps (A) show the average power spectrum over the whole window of TMS stimulation. Colored lines show the width of the peaks of oscillations power as detected with the jackknife procedure. (C) Direct two-by-two comparisons across the 4 types of active TMS patterns. Colored maps represent time-frequency maps of ITC for all TMS patterns (30 Hz rhythmic TMS, non-uniform rhythmic TMS, random TMS and irregular TMS). Black and white maps show the results of the cluster-based statistical permutation tests for the pairwise comparison of active TMS trials in the two ITC maps represented at the top of the column and the left of the row. Black dots indicate clusters that reached significance (p<0.05). Both 30 rhythmic TMS and the 3 non frequency-specific TMS patterns increased amplitude and phase-alignment of cortical oscillations over a wide frequency band during active compared to sham stimulation. However, direct pairwise comparisons of ITC between the 4 active TMS patterns show that the irregular TMS pattern achieved higher increases of oscillatory phase-locking in the high-beta band than the 30 Hz rhythmic TMS pattern. Also note that the random TMS pattern phase-locked cortical oscillations trial-to-trial significantly less than the 3 other active TMS patterns.

Figure 4 .

 4 Figure 4. Width of peaks of power increase during rhythmic or non frequency-specific stimulation.The non-uniform rhythmic TMS pattern is not represented because the power spectrum of oscillations during active TMS stimulation showed two different peaks and therefore was not comparable to the others, displaying only a single peak (in the high-beta range). Notice that error bars represent the standard error corrected for the reduced error variance obtained from the jackknife procedure. One-way ANOVA showed a significant main effect of TMS pattern. Results of the post-hoc t-tests are indicated as follows: ** p < 0.01, * p < 0.05. Notice that irregular TMS patterns increased cortical oscillations in a significantly wider frequency band than rhythmic or random TMS patterns.

Figure 5 .

 5 Figure 5. Modulation of signal complexity by rhythmic vs. non frequency-specific stimulation. (A) Bar plot of Sample Entropy (SE) across time scales for the left frontal electrodes (F1, F3, FC1, FC3) closest to the center of the stimulation coil. Color signals each of the 4 TMS pattern tested in the study (30 Hz rhythmic TMS, and three non frequency-specific patterns: non-uniform rhythmic TMS, random TMS and irregular TMS). Full colors indicate bar plots for active TMS condition, whereas 'pastel' colors signal bar plots for sham TMS condition. Notice that the estimated values of SE increased across time scales for all TMS patterns for both, active and sham TMS trials. This suggests that EEG time series contain a measure of unpredictability and noise at several time scales, which is the hallmark of a complex signal. (B) Comparisons between active TMS (1st row) and sham TMS (2nd row) topographical maps of the areas under the curve of SE across time scales, a measured entitled MSE (Multi-scale entropy), are shown for each TMS pattern (30 Hz rhythmic TMS, and the 3 non frequency-specific patterns: nonuniform rhythmic TMS, random TMS and irregular TMS). The bottom row shows the results of pairwise (active vs. sham TMS) cluster-based statistical permutation tests. Bolded EEG electrodes represent clusters of sensors that reached significance (p<0.05). Non frequency-specific TMS increased MSE in clusters of left frontal and bilateral parietal electrodes. Comparison for the rhythmic TMS pattern shows only sporadic significant differences in isolated electrodes.

  d') and also response bias (decision criterion noted c and likelihood ratio noted b) which is influenced by late decision-making stages.Repeated-measure 2x2x3 ANOVAs with factors Visual Field (left, right), TMS Condition (active, sham) and TMS Pattern (rhythmic, non-uniform rhythmic, random, irregular) did not show any significant effect for any of the factors nor any significant interactions between factors on perceptual sensitivity (d') or likelihood ratio (b) (p>0.05). Indeed our analyses, were only able to show a main effect of TMS Condition on decision criterion (c) (F(1,14)=9.154, MSE=0.136, p<0.01) suggesting that when active stimulation was delivered participants lowered their decision criterion and became more liberal (i.e. they were more likely to respond that a target was presented in case of doubt), compared to sham stimulation. No main effect of TMS Pattern or interaction between TMS Condition and Pattern were found for c values, indicating that decision criterion was not modulated by the temporal distribution of pulses within active TMS bursts.

Figure 6 .

 6 Figure 6. Impact of rhythmic and non frequency-specific TMS on conscious visual detection performance. Modulation of perceptual sensitivity (A) and decision criterion 'c' (B) by active TMS patterns (red bars) compared to sham TMS patterns (blue bars). Data is presented for each TMS pattern (30 Hz rhythmic TMS and the 3 non frequency-specific TMS patterns: non-uniform rhythmic TMS, random TMS and irregular TMS) and separated for left visual field (LVF) and right visual field (RVF)targets. Note that the delivery of any active TMS pattern, regardless of their temporal structure lowered decision criterion (significant main effect of TMS condition) rendering participants less conservative (hence more liberal) when deciding if a near threshold target had been presented or not. No specific effects of rhythmic or non frequency-specific TMS patterns of TMS were revealed by our analyses.

  areas: First, the role of neural noise in cognitive coding tied to the modulation of visual perception via spatial attention mechanisms; Second, the ability of non frequency-specific patterns inducing several levels of noise and complexity to interact and modulate brain activity and visually guided behaviors; Third and last, the need for individualized customization of non frequency-specific patterns to account for the variability of individual ongoing neural noise levels. Transcrânienne (SMT) est une méthode de stimulation cérébrale non invasive largement utilisée dans la recherche fondamentale en neurosciences cognitives. Cependant, simultanément au bref champ électromagnétique délivré sur le cortex, chaque impulsion de SMT génère un bref mais intense clic sonore. Afin d'annuler l'influence de cet effet, les designs expérimentaux contrastent une stimulation active avec une SMT placebo qui imite la stimulation auditive associée à la SMT. Néanmoins, l'influence des sons de la SMT sur l'activité cérébrale et les performances comportementales doit être mieux caractérisée, en particulier en ce qui concerne l'utilisation de sons répétés pour l'entrainement d'oscillations corticales et la modulation du comportement. Dans ce but, nous avons enregistré les signaux EEG d'une cohorte de sujets sains réalisant une tâche de détection visuelle consciente au seuil sous l'influence d'impulsions uniques ou des courtes rafales rythmiques ou aléatoires de SMT placebo ou de sons imitant la SMT délivrés avant l'apparition d'une cible visuelle au niveau d'un site frontal droit préalablement impliqué dans la facilitation de la détection visuelle consciente. Nos résultats démontrent que ni la SMT placebo ni les sons imitant la SMT ne modulent la sensibilité visuelle. De plus, aucun indice d'entrainement oscillatoire à une fréquence spécifique n'a été trouvé dans les signaux EEG enregistrés pendant la stimulation placebo. Ces résultats renforcent la légitimité de la SMT placebo comme condition contrôle adéquate dans les designs expérimentaux avec SMT active. Néanmoins, la stimulation placebo a réinitialisé la phase des oscillations corticales dans une large bande de fréquences dans le cortex auditif et a amené les sujets à être plus libéraux dans leurs réponses à la tâche de détection (c'est-à-dire plus promptes à signaler la présence d'une cible quand ils avaient un doute sur sa présence).

Figure 1 .

 1 Figure 1. Experimental task, setup and sham stimulation patterns. (A) Visual detection task. After a period of fixation, a central cross became slightly larger to alert participants of an upcoming event, then, in 50% of the trials, sham rhythmic, random or single pulse TMS patterns were delivered prior to the presentation of a lateralized visual target that could appear inside a right or a left placeholder (80% of total trials; 40% right 40% left) for a brief period of time (in the remaining 20% of trials no target was presented). Participants were requested to indicate whether they did perceive or not the presence of a target (yes/no), and, when they saw it, to report where the target had appeared (right/left) selecting the arrow pointing at the placeholder in which the target had appeared. (B) Right-lateral, front and top schematic drawings of a participant's head presenting the two tested sham procedures, placed above the right FEF on the FC2 electrode (EEG 10-20 coordinates). Sham procedure #1: active TMS pulses delivered with the TMS coil (in grey) in a 90 o sham configuration. Sham procedure #2: TMS-like sound played by a speaker (in black) mounted on the TMS coil. (C) Schematic representation of the 3 sham TMS patterns tested in our study: 30 Hz rhythmic TMS aiming to entrain cortical oscillatory activity at the input frequency, and 2 control patterns, random TMS and single pulse TMS, to isolate the effect of stimulation frequency.

  figure-of-eight TMS coil placed in a right frontal location, overlying the right Frontal Eye Field

  elicited by sham stimulation, EEG signal in a [-500 500] time window centered on visual target onset were transformed into the time-frequency domain using a 3-cycle Morlet wavelet analysis for frequencies between 6 and 50 Hz. We assessed local oscillatory synchronization with measures of Power and Inter-Trial Coherence (ITC), a measure of trial-to-trial phase alignment. Power was expressed in decibels (dB) relative to a baseline window of 3 oscillation cycles prior to cue onset (-233 ms before visual target onset).We also measured the auditory Event-Related Potentials (ERPs) evoked by sham TMS and TMS-like sound stimulation. To this end, EEG signals were re-referenced to the averaged mastoid, low-pass filtered below 30 Hz and baseline corrected with the [-1 -0.3] second window before visual target onset, before being averaged over trials. Our analyses focused on ERPs time-locked to the onset of sham TMS/TMS-like sounds recorded by the Cz (vertex) EEG electrode.

  is the inverse of the normal cumulative distribution function, H is the percentage of 'hit' trials and FA is the percentage of 'false alarms' trials. To avoid infinite values,

  behavioral outcome measures (d', c and b), we performed three 2x2x3 repeated-measure ANOVA with factors Visual Field (left, right target), Sham Pattern (rhythmic, random, single pulse) and Stimulation Condition (sham stimulation, no stimulation).For all outcome measures on EEG data, we first compared, for each pattern separately, sham stimulation trials to no stimulation trials. Then, we compared the sham stimulation trials directly between patterns, by pairs. ERP waveforms were compared with two-tailed paired Student's t-test computed for each time point in the[-200 400] ms window centered on target onset.On time-frequency data, we first compared topographical maps of power and ITC for high-beta frequencies([25 35] Hz) for the duration of sham stimulation ([-133 0] ms, centered on target onset), then we compared time-frequency maps across frequencies [6 45] Hz and[-300 200] ms time window, at the electrode FCz, which was selected as electrode of interest because it exhibited maximal high-beta power and ITC in the topographical maps. All two-by-two comparisons were performed with two-tailed paired Student's t-test computed for each electrode on topographical maps and for each time-frequency point on time-frequency maps.

  subjective decision-making (decision criterion, c and likelihood ratio, b). The 2x2x3 repeatedmeasure ANOVAs with within-subject factors Visual Field (left, right), Stimulation Condition (sham stimulation, no stimulation) and Stimulation Pattern (rhythmic, random, single pulse) performed on the above-mentioned outcome measures revealed a main effect of Visual Field for perceptual sensitivity (d') (F(1,10)=9.781, MSE=0.544, p<0.005) and decision criterion (c) (F(1,10)=16.706, MSE =0.177, p<0.001). This result indicates that, independently of stimulation condition, participants perceived right visual field targets better that left ones and they also showed a more liberal behavior for right right lateralized Gabors (Fig 2A and B).

Figure 2 .

 2 Figure 2. Impact of sham stimulation on measures of conscious visual detection performances. Group averages (± standard errors) for (A) perceptual sensitivity (d') and (B) decision criterion (c) corresponding to trials delivering sham stimulation over the FC2 electrode with a 90 o sham coil configuration (sham procedure #1) (red columns) and trials with no sham stimulation (blue columns). Data is presented for each sham TMS pattern (rhythmic, random and single pulse TMS) and for left or right visual field targets. For all sham TMS patterns, sham stimulation delivered prior to target onset lowered decision criterion (c) (significant main effect of stimulation condition) but had no effect on perceptual sensitivity (d').

Figure 3 .

 3 Figure 3. Auditory evoked potentials elicited by sham TMS patterns. (A) Evoked potentials elicited by rhythmic, random and single pulse sham TMS (sham procedure #1: TMS pulses delivered with a 90 o sham configuration) (red solid line) compared to no-stimulation trials (blue solid line). (B) Two-by-two comparisons of auditory evoked potentials across sham TMS patterns: rhythmic (red solid line), random (blue solid line) and single pulse (green solid line). Evoked potentials correspond to recordings from electrode Cz, referenced to both mastoids. Time is centered on visual target onset (dotted gray vertical line). Red dotted vertical lines signal the onset of sham stimulation patterns (the 1 st pulse of rhythmic and random TMS patterns or single pulse). The width of the vertical red dotted lines indicates the duration of the sham stimulation (rhythmic and random patterns lasted for 100 ms). Colored shaded areas indicate 95% confidence intervals for evoked potential amplitude. Grey shaded areas represent time points showing significant differences in evoked potential amplitude between compared conditions or patterns (clusterbased permutation tests, a=0.05). Notice in panel A (top) the significantly more positive deflection for evoked potentials in sham compared to no stimulation trials. The shape of this positive deflection was (panel B) identical for the 3 tested sham patterns.

Figure 4 .

 4 Figure 4. Influence of sham stimulation on high-beta power EEG activity. Topographical maps displaying the distribution of spectral power in the high-beta [25 35] Hz band during the delivery of the sham stimulation patterns (sham procedure #1: TMS pulses delivered with a 90 o sham configuration) (time window [-133 0] ms centered on visual target onset). The bottom row shows pairwise (Sham TMS vs. No stimulation) cluster-based statistical permutation tests for each tested sham TMS pattern, however, notice that none of the electrodes reached significance (p<0.05) in any of the comparisons. None of the sham patterns (including the sham 30 Hz rhythmic patterns) showed increases of high-beta power on scalp EEG electrodes.

  frequency of the burst by 30 Hz rhythmic patterns of sham stimulation. None of the sham patterns tested increased high beta ([25 35] Hz) power during the delivery of sham stimulation compared to trials without stimulation (Fig.4). However, the degree of trial-to-trial phase alignment in the high-beta band, computed with Inter-Trial Coherence (ITC), was significantly increased during the delivery of sham stimulation compared to trials without stimulation. This effect is observed for the three sham stimulation patterns and was maximal at electrode FCz (Fig5A).

Figure 5 .

 5 Figure 5. Phase-locking of high-beta oscillations during sham stimulation. Topographical maps displaying the distribution of inter-trial coherence (ITC) within the [25 35] Hz frequency band during sham stimulation (sham procedure #1: TMS pulses delivered with a 90 o sham configuration) (time window [-133 0] ms centered on visual target onset). (A) Sham TMS and no sham stimulation ITC topographies for rhythmic, random and single pulse sham patterns. The bottom row displays the results of the cluster-based statistical permutation tests. Bolded dots represent clusters of EEG electrodes showing significant statistical differences between sham TMS vs. no sham stimulation for each pattern (p<0.05). (B) Direct comparisons of ITC topographies between sham rhythmic pattern and the random and single pulse patterns. Black and white topographic maps display the outcomes of cluster-based statistical permutation tests. Bolded electrodes identify clusters of electrodes that reached significance (p<0.05). Notice that the three sham patterns significantly increased high-beta ITC. This increase was distributed more widely over the scalp during single pulses of sham TMS stimulation compared to rhythmic sham high-beta (30 Hz) stimulation.

  namely a significant main effect of Visual Field for d' (F(1,10)=4.621, MSE=0.648, p<0.05) and c (F(1,10)=15.053, MSE=0.181, p<0.001) as well as a significant main effect of Sham Condition on decision criterion (c) (F(1,10)=5.408, MSE=0.181, p<0.05).

Figure 7 .

 7 Figure 7. Specific impact of sham procedure #1 (TMS pulses with a 90 o coil) vs. sham procedure #2 (TMS-like sounds via a speaker) on conscious visual detection outcomes. Group averages (± standard errors) of perceptual sensitivity (A) and decision criterion (B) corresponding to trials delivering sham stimulation over the right FEF (EEG coordinate FC2) using sham procedure #1: active TMS pulses delivered with a sham coil angled perpendicular to the curvature of the scalp (red columns) or sham procedure #2:

Figure 9 .

 9 Figure 9. Impact of sham procedure #1 (TMS with a 90 o coil) vs. sham procedure #2 (TMS-like sound via speaker) on oscillatory phase-locking. Time-frequency maps representing inter-trial coherence (ITC) at EEG electrode FCz (in which high-beta ITC was the strongest). Comparison across sham patterns (rhythmic, random and single pulse) delivered by sham procedure #1 (active TMS pulses with a 90 o sham coil configuration) and sham procedure #2 (recorded TMS-like sound played via a speaker). The time line is centered on the onset of the visual target (dotted gray vertical line). Red dotted vertical lines signal the 1 st pulse of sham stimulation patterns. The width of the vertical red dotted lines indicates the length of the sham burst (sham rhythmic and random patterns span 100 ms between first and last pulse). Horizontal black dotted lines indicate frequency of stimulation for rhythmic sham TMS pattern (30 Hz). The bottom row shows outcomes of cluster-based statistical permutation tests in which black areas label time-frequency clusters for which ITC difference between Sham Procedures #1 and #2 reached statistical significance (p<0.05).

  In a third study (Project 2), we explored the impact of arrhythmic or noisy patterns of activity in the left FEF by comparing rhythmic high-beta stimulation to non frequency-specific TMS patterns designed to induce different levels of neural noise. This study was inspired by evidence suggesting different coding strategies in the right and left nodes of a bilaterally distributed dorsal attentional network in charge of a top-down modulation of conscious perception. Indeed, we aimed to add electrophysiological recordings to prior evidence showing that arrhythmic or non frequency-specific left FEF TMS patterns (and not the rhythmic TMS pattern which improved visual perception over the right FEF) enhanced conscious visual perception(Chanes et al., 2015). To this end, we replicated the prior paradigm ofChanes et al. (2015) by stimulating the left FEF with 30 Hz rhythmic TMS bursts compared to three different types of non frequency-specific TMS patterns (non-uniform rhythmic, random and irregular patterns), while participants performed a near threshold detection task. Importantly, to characterize the patterns of cortical activity induced by the latter TMS patterns, we recorded EEG signals all along the trials. We showed that non frequency-specific TMS patterns increased neural noise levels over bilateral fronto-parietal areas. More specifically, non-uniform rhythmic and irregular TMS patterns increased oscillation power over the left FEF within a broader frequency range (not confined to the high-beta but extending to low-beta band), than 30 Hz rhythmic TMS (whose effects impacted selectively the high-beta band). A broadband power increase indicates lower signal regularity than a frequency-specific narrow-band high-beta oscillations recorded during rhythmic 30 Hz TMS. Additionally, non frequency-specific TMS patterns significantly increased EEG signal complexity over clusters of right frontal or right

  concerning coding strategies for left dorsal attentional network based on the neural consequences of noise induction on EEG oscillatory activity. To this end, it would be interesting to compare within the same population in two separate sets of experiments, and under EEG monitoring, unilateral right FEF or unilateral left FEF high-beta or arrhythmic stimulation patterns with the concurrent application of two stimulation patterns in the right FEF and left FEF simultaneously.

  [START_REF] Taylor | FEF TMS Affects Visual Cortical Activity[END_REF] provided the first causal electrophysiological characterization of distant fronto-occipital influences by showing a modulation of event related activity (ERP) evoked by a target following 10 Hz TMS bursts delivered to the right FEF during the allocation of attention period. Nonetheless no time-frequency analyses of frontal or occipital EEG activity

TABLE OF ABBREVIATIONS

 OF

		INTRODUCTION
	ASSR I -	Auditory Steady-State Response
	cTBS	continuous Theta Burst Stimulation

Brain oscillations, local and network synchronization and orienting of spatial attention

  Thut et al. (2011b) stimulated participant's brain at rest, i.e., not engaged in any specific task,

SPECIFIC AIM 3: We will use sham TMS-EEG to explore the influence of rhythmic and arrhythmic patterns of periodical sounds associated to the delivery of TMS on brain evoked and oscillatory activity and conscious visual performance in healthy participants.

	We hypothesize that TMS patterns with different degrees of randomness in their temporal structure
	delivered to the left FEF would induce different levels of neural noise in cortical activity and
	differently facilitate conscious visual detection performance. Such outcomes would support
	'Stochastic Resonance-like' effects, which will be finely dependent on the levels of induced noise.
	They would also support an asymmetry of coding strategies for left vs right hemisphere
	contributions to the top-down modulation of visual perception.
	The discharge of periodical TMS patterns is accompanied by the delivery of trains of pulsed high
	intensity clicking sounds associated to each individual pulse. Drawing conclusions about the
	neurophysiological and behavioral impact of active TMS patterns requires comparison with a sham
	condition that, among other factors, reproduces the sounds emitted by the TMS coil. However, the
	impact of lateralized sound patterns on physiological activity and cognitive (attention or
	perception) behaviors is far from neutral. Moreover, since single pulses or rhythmic active TMS
	patterns are being used to entrain or manipulate oscillatory activity, it is plausible that sound
	patterns present during active rhythmic TMS delivery could also contribute to oscillatory
	entrainment and to its behavioral impact. Using concurrent sham TMS-EEG recordings, we aim to
	assess the impact of single pulses and rhythmic or random patterns of TMS-generated sounds
	(delivered via sham TMS pulses or TMS-like sounds played by a speaker mounted on the TMS
	coil) on cortical oscillations and conscious visual detection performance by comparing them to a
	no-sound stimulation condition. Detailed insight on the impact on EEG activity of what is thought
	of as a control condition could enable more nuanced conclusions on the causal effect of magnetic
	as also involved in attentional orienting stimulation on cortical entrainment and visual modulations.
	and top-down facilitation of conscious visual perception. However, the lack of concurrent EEG
	recordings rendered this explanation purely speculative and precluded any neurophysiological
	insights on potential subtending mechanisms. Using concurrent TMS-EEG recordings, we here aim
	to evaluate and compare the impact on local and network EEG activity of 4 types of periodical
	TMS patterns (from high-beta regular rhythmic bursts to random patterns) engineered to induce
	different levels of neural noise in the left FEF, and to investigate differential effects on conscious
	visual detection. Collaterally, we also aim to provide insight supporting a potential asymmetry of
	coding strategies for left and right fronto-parietal systems in attentional orienting and conscious
	visual perception.

ACKNOWLEDGEMENTS

The work presented in this thesis is far too extensive to be the work of a single person. I would like to thank all who have contributed or who have been alongside me for this adventure.

Acknowledgement

Chloé Stengel was supported by a PhD fellowship from the University Pierre and Marie Curie. Marine Vernet was supported by fellowships from the Fondation pour la Recherche Medicale. Julià L. Amengual was supported by a fellowship from the Fondation Fyssen. The activities of the laboratory of Dr. Valero-Cabré are supported by research grants IHU-A-ICM-Translationnel, ANR projet Générique OSCILOSCOPUS and Flag-era-JTC-2017 CAUSALTOMICS. The authors would also like to thank Romain Quentin, Juliette Godard and Laura Fernandez for providing help during data acquisition and the Naturalia & Biologia Foundation for financial assistance for traveling and attending meetings. Acknowledgement Chloé Stengel was supported by a PhD fellowship from the University Pierre and Marie Curie. Julià L. Amengual was supported by a fellowship from the Fondation Fyssen. The activities of the laboratory of Dr. Valero-Cabré are supported by research grants IHU-A-ICM-Translationnel, ANR projet Générique OSCILOSCOPUS and Flag-era-JTC-2017 CAUSALTOMICS. The authors would also like to thank Marine Vernet and Clara Sanchez for providing help during data acquisition and the Naturalia & Biologia Foundation for financial assistance for traveling and attending meetings. Acknowledgement Chloé Stengel was supported by a PhD fellowship from the University Pierre and Marie Curie. Julià L. Amengual was supported by a fellowship from the Fondation Fyssen. The activities of the laboratory of Dr. Valero-Cabré are supported by research grants IHU-A-ICM-Translationnel, ANR projet Générique OSCILOSCOPUS and Flag-era-JTC-2017 CAUSALTOMICS. The authors would also like to thank Clara Sanchez for providing help during data acquisition and the Naturalia & Biologia Foundation for financial assistance for traveling and attending meetings.

Data availability statement

Data are available from the corresponding author upon request.

Data availability statement

Data are available from the corresponding author upon request.

Data availability statement

Data are available from the corresponding author upon request.

Abstract

Correlational studies in non-human primates have reported evidence of increased frontoparietal high-beta band (22-30 Hz) synchrony during the endogenous capture of visuospatial attention. But may the engagement of inter-regional synchrony at this same frequency band provide the causal mechanism by which top-down attention is engaged and facilitates visual perception in humans? Moreover, would the manipulation of such processes lead to increases of conscious visual detection capabilities? Here we re-analyzed electroencephalographic (EEG) signals from a group of healthy human participants (n=14) who performed a conscious visual detection task, under the influence of brief rhythmic (30 Hz) or random bursts of Transcranial Magnetic Stimulation (TMS), with an identical number of pulses and duration, delivered to the right Frontal Eye Field (FEF) prior to the onset of a lateralized near-threshold target. We report an increase of inter-regional synchronization in the high-beta band (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) between the stimulated region (right FEF) and a cluster of parietal electrodes, and increases of local inter-trial coherence in the same frequency band over parietal electrodes, driven by rhythmic but not random TMS patterns. Importantly, such increases were accompained by improvements of conscious visual detection performance for left visual targets (contralateral to the stimulation) in the rhythmic but not the random TMS condition at the group level. These outcomes show that human high-beta synchrony between parietal and frontal regions can be manipulated non-invasively and that high-beta oscillatory activity across the right dorsal fronto-parietal attention network could contribute to the facilitation of conscious visual detection. Furthermore, our results support future applications of non-invasive brain stimulation technologies for the manipulation of inter-regional synchrony, which could be applied to improve visual behaviors in healthy humans or neurological patients.

Material and Methods

Participants

The TMS-EEG dataset analyzed in the present study is the same used in a recent publication to demonstrate local high-beta entrainment during stimulation of the right FEF and its association with improvements of conscious visual performance (see Vernet et al. 2019 for details). A group of right-handed 14 healthy participants (9 women) aged between 20 and 34 years old (24 ± 4) took part in the original experiment. Participants had normal or corrected-to-normal vision. They all took part voluntarily after having signed a consent form and were naïve as to the purpose of the experiment. All the experimental procedures were performed according the Declaration of Helsinki. A research protocol including all the interventions of this study was sponsored by the INSERM and approved by an Institutional Review Board known in France as Comité de Protection de Personnes (CPP Ile-de-France IV).

Conscious visual detection paradigm

An in-house MATLAB (Mathworks, version R2012b) script using the Psychtoolbox extensions (Brainard, 1997) was used to control presentation of visual stimuli synchronized with the delivery of the TMS pulses. During the task, participants were seated with their heads resting on a chin-rest set so that their eyes stayed 57 cm away from the center of the screen.

Each trial started with a gray resting screen that stayed for 2.5 secs, followed by a fixation screen that displayed a central fixation cross (size 0.5x0.5 o ) and a right and left rectangular placeholders (6.0x5.5 o ) drawn 8.5° away from the center (Fig. 1A). These placeholders indicated the potential right or left lateralized locations of the target during the trial. The duration of the fixation screen was jittered between 1 and 1.5 secs to avoid predictability with regards to upcoming events and to ensure sustained central fixation. A brief-lasting (66 ms) increase of size (0.7 x 0.7 o ) for the central fixation cross alerted participants of the presentation of an upcoming

Mechanistic explanation for the causal role of inter-regional synchrony in improvements of visual perception

A detailed mechanistic explanation for our electrophysiological and behavioral findings remains open. Nonetheless in their original non-human primate study, Buschman and Miller (2007) hypothesized that synchronization of neuronal activity may increase the efficiency of interareal coordination and communication, enabling to process a single object and to suppress the processing of distractors. This hypothesis is consistent with the explanatory model developed in 2009 by Fries in which he proposes that inter-regional synchronization in the gamma-band provides an exclusive and effective communication link between two areas which is selective to one stimulus and invariant even in the presence of distractors (Fries, 2009).

Unfortunately, although our data show that entrained high-beta neural activity in the right FEF (modulated by focal rhythmic TMS bursts) increased synchronization between the stimulated site and electrodes positioned over right parietal regions, the limited spatial resolution of our EEG montage (60 electrodes) does not enable us to pinpoint which specific parietal regions got synchronized during right FEF rhythmic stimulation patterns. However, previous results employing diffusion imaging with participants who underwent similar non-invasive stimulation patterns and behavioral tasks (Quentin et al., 2014(Quentin et al., , 2015) ) suggest that fronto-parietal synchronization occurs along the 1st branch of the Superior Longitudinal Fasciculus (SLF I), which is part of the white matter connections of the dorsal attention network linking the FEF and the posterior intraparietal sulcus (IPS). According to rich and solid correlational (Corbetta et al., 2005(Corbetta et al., , 2008) ) and causal evidence (Chanes et al., 2013;[START_REF] Chica | Dorsal and ventral parietal contributions to spatial orienting in the human brain[END_REF] the network defined by the areas linked by this white matter system has been shown to play a major role in the allocation of visuo-spatial attention and the modulation of conscious perception.

We here propose that the entrainment of oscillations within a high beta frequency (around 30 Hz) in the right FEF could spread, presumably through white matter projections contained within the SLF I, from the FEF to right parietal regions. This specific tract of white matter

Conflict of interest

The authors declare no competing interests.

Author contributions

Conceptualization

Abstract

Prior evidence in non-human and human primates has pinpointed a crucial role for highbeta oscillations, operating throughout the right lateralized fronto-parietal dorsal network, subserving endogenous attentional orienting and the modulation of conscious visual perception.

However, a parallel study using transcranial magnetic stimulation (TMS) to address the contribution of homotopic regions within the left hemisphere paradoxically reported that in such location (the left Frontal Eye Field, FEF) either non frequency-specific or arrhythmic patterns of activity, generated with focal noninvasive brain stimulation, but not high-beta rhythms, were able to drive improvements of conscious visual perception.

In an attempt to further understand this finding, we aimed to combine focal non-invasive stimulation with coupled EEG recordings and characterize which patterns of neural activity entrained with episodic non frequency-specific brain stimulation enabled a facilitation of conscious visual perception. We also aimed to uncover left frontal coding strategies allowing the engagement of attentional orientating and the modulation of conscious visual perception. To this end, we recorded brain signals from a cohort of healthy participants performing a conscious visual detection task, while having their left FEF activity manipulated with three non frequency-specific TMS bursts (4 pulses, irregularly spaced in time), whereas a perfectly rhythmic pattern (4 pulses at 30 Hz) was also employed to compare.

Our interventions failed to significantly modulate conscious visual detection. Nontheless, we here show that different non frequency-specific activity patterns enhanced cortical oscillations in a wide frequency range by inducing higher levels of noise in local cortical signals. We conclude that the modulation of neural noise levels by noninvasive stimulation can be a promising strategy to enhance neural activity and further understand the role of noise in cortical processing, within the framework of the Stochatic Resonance phenomenon. by more than 0.01 Michelson contrast unit, then threshold was determined again until repeated titrations yielded two consecutive contrasts that varied by less than 0.01 Michelson contrast unit.

During the calibration block, the participant received only sham TMS (see below for details on the TMS procedure).

Participants performed two experimental sessions, each made of 6 blocks and performed in this order: 1 calibration block to adjust the contrast of the visual target, 1 training block to introduce subjects to active TMS trials and ensure stable performance and 4 experimental blocks (1 block for each TMS pattern, see below for details on the patterns tested). The two experimental sessions were performed on two separate days, with an interval of a at least 48-72 hours and a maximum of 7 days between sessions to avoid carry over effects. The experimental procedure was identical during both sessions.

The order of experimental blocks for the two experimental sessions was counterbalanced across participants. Each block was divided into short sub-blocks of 20 trials. The order of trials (leftward target, rightward target, or catch trial with no target) and the stimulation condition (sham and active TMS) was randomized for each sub-block. During the training block, at the end of each sub-block participants received some feedback. They were alerted if their false alarm rate was higher than 50% and the percentage of incorrectly reported target positions and trials with incorrect fixations was displayed on the computer screen. At the end of each sub-block participants took a short break (~1-2 minutes) to limit the effects of fatigue. Between sub-blocks, the experimenter could decide to manually adjust the contrast if the conscious visual detection performance was away from the 50% detection rate established during Gabor contrast titration.

The duration of the calibration and training blocks was variable, as the termination of these two blocks was decided by the experimenter on the basis of individual performance. Experimental blocks consisted of 7 sub-blocks (140 trials total) and lasted approximately 20 minutes each.

Experimental blocks were identical to training blocks (with the same feedback for the participant)

. . = √ -1 × Where s.e. is the corrected standard error and std corresponds to the standard deviation of the jackknife subsampled measures. To test for significance, t-and F-statistics must also be corrected for the reduced error variance in the following way (Ulrich & Miller, 2001):

Lastly, in the time-domain, we computed the measure of Multi-Scale Entropy (MSE) (Costa et al., 2002(Costa et al., , 2005)). MSE evaluates the complexity of a signal. A measure of complexity differs from a measure of noise or entropy in that neither a completely regular signal (with an entropy of 0) nor a completely random signal (with a maximal entropy) exhibit a lot of complexity.

Indeed, both types of signals contain very poor information because they are governed by very simple laws, for example a sine wave at a single frequency for a completely predictable signal or a random draw from a uniform distribution for random noise signal. A complex signal is more meaningful biologically than a completely random signal (Costa et al., 2005).

MSE is based on the calculation of Sample Entropy (SE) at several time scales. Sample

Entropy identifies repeating patterns within a time series or signal. Two time points are considered similar if they are within a distance of r of each other and thus for any pattern of m consecutive time points u1, u2 … um in the signal, another sequence v1, v2 … vm is considered a repetition of the first pattern if ½v1 -u1½ ≤ r , ½v2 -u2½ ≤ r …½vm -um½ ≤ r respectively. In other words, a sequence of consecutive time points is considered a repetition of a pattern if each time-point in the sequence is within a distance of r from the corresponding time point in the pattern. The search for repeating patterns in the signal is done for each sequence of m consecutive time points in the data (excluding self-matches) and yields the probability U m (r) that two sequences of m time points seen on any EEG signal is likely to last over several time points and spread over adjacent electrodes, hence cluster-based permutations is a highly sensitive method to correct for multiple comparisons in this data because it is adapted to high degree of correlation in time and space (Maris & Oostenveld, 2007). However, there is currently no consensus on how to apply clusterbased permutations to interaction effects for ANOVA analyses (Edgington & Onghena, 2007;Suckling & Bullmore, 2004) and therefore we chose to carry out pairwise comparisons between our TMS experimental conditions.

We compared the width of peak of power increase between active TMS experimental patterns by means of a one-way repeated measure ANOVA with factor TMS pattern (rhythmic, random and irregular). The non-uniform rhythmic pattern was excluded from this analysis because it was the only pattern that exhibited two peaks in its power spectrum during stimulation and therefore could not be compared in terms of the noise level of EEG signals with the 3 other patterns which exhibited a single peak in their power spectrum during stimulation.

Behavioral data analysis

Behavioral performance in the conscious visual detection task with near-threshold Gabor stimuli was assessed following the approach of Signal Detection Theory (SDT) (Green & Swets, 1966;Stanislaw & Todorov, 1999). SDT separates perception of the target and late-stage decisionmaking processes when delivering a response and provides two sets of outcome measures:

perceptual sensitivity (d') which is a bias free measure of a participant's ability to distinguish the presence of a target from noise, and decision criterion (c) and likelihood ratio (b) which are both measures of the response bias of the participants. Indeed, participants might be biased in case of doubt to respond more likely that they saw a target (liberal participants) or on the contrary to respond more likely they did not see a target (conservative participants) independently of how well they perceived the target.

Such a non-statistically significant result on the d' is at odds with the outcomes of a previous study by our team that reported significant improvement of perceptual sensitivity following active stimulation with both non-uniform rhythmic and random TMS bursts (Chanes et al., 2015). In this earlier study a first group of healthy participants as the ones of the current study, In a further attempt to replicate the analyses of this earlier study (which compared less TMS patterns together) we conducted, post-hoc, two repeated-measure ANOVAs identical to those in Chanes et al. (2015). Unfortunately, we were not able to replicate such results and neither of the two ANOVAs on d' yielded any main effect or interactions for any of its factors (p>0.05).

Discussion

In this study, we aimed to better characterize the coding contributions of the left FEF to frontal activity and the modulation of conscious visual perception. We did so by assessing the neurophysiological impact of non frequency-specific short bursts of TMS on scalp EEG signals from participants performing a visual detection task on near-threshold stimuli. We followed up on previously published results (Chanes et al., 2015) Short bursts of arrhythmic or random TMS had been employed in multiple TMS experiments as control patterns to isolate the impact of frequency for rhythmic TMS bursts (Albouy et al., 2017;Chanes et al., 2013Chanes et al., , 2015;;Thut, Veniero, et al., 2011;Vernet et al., 2019).

However, the specific effect of such TMS 'noise' patterns on neuronal signals remained to be characterized and understood in further detail. To fulfil this experimental and theoretical knowledge gap, we here designed three patterns of non frequency-specific short bursts of 4 TMS pulses, with different degrees of irregularity, plus a 30 Hz frequency-specific rhythmic TMS pattern and, concurrently with the delivery of stimulation, we recorded EEG signals. We were

Conflict of interest

The authors declare no competing interests.

Author contributions

Conceptualization

Abstract

Transcranial Magnetic Stimulation (TMS) is a method of non-invasive brain stimulation widely used in fundamental research studies in cognitive neuroanatomy. However, simultaneously with the brief electromagnetic field delivered to the cortex, each TMS pulse generates a brief but intense clicking sound. In order to cancel the influence of this effect, experimental designs contrast active stimulation with a sham TMS pattern mimicking auditory stimulation associated with TMS.

Nonetheless, the influence of TMS sounds on brain activity and its relation with behavioral performance needs to be better characterized. The issue is particularly relevant with regards to applications using repetitive sound patterns to entrain cortical oscillations and modulate behavior.

To this end, we recorded scalp EEG signals from a cohort of healthy participants performing a near-threshold conscious visual detection task under the influence of either single pulses or short bursts of sham TMS or TMS like-sounds (with a rhythmic or random configuration) delivered pretarget onset around a right fontal location previously shown to facilitate conscious visual detection.

Our results show that neither sham TMS nor TMS-like sound stimulation were able to significantly modulate visual sensitivity. In parallel, no signs of frequency-specific entrainment were found in EEG recordings performed along with episodic sound bursts. These results strengthen the reported ability of active rhythmic TMS patterns to entrain cortical oscillations and boost conscious visual perception and the use of sham control designs in TMS entrainment applications. Nonetheless, sham stimulation induced broadband phase-locking in the auditory cortex and also led participants to become more liberal in the decision making (i.e., more prone to report to have seen a target when in doubt about its presence).

We conclude that single or rhythmic patterns of right lateralized sound have no significant contribution to specific states of neural activity leading to the facilitation of visual perception reported elsewhere. Nonetheless, effects on decision-making processes and non frequency-specific phase-locking call for new studies that allow a better understanding of sham TMS effects, improving interpretations of past and current studies and designs for sham conditions. The response window was presented 1000 ms after the offset of the visual target.

Material and Methods

Participants

Participants were asked to report whether they saw a target and if they did ('yes') to indicate where the visual target appeared (in the left/right placeholder). In order to provide a response, the response TMS/TMS-like sound pulse was added to our experiment to evaluate the perceptual and EEG effects of a single sound, this time completely devoid of any time-frequency components, mimicking previous active TMS studies which used single active TMS pulses to modulate conscious visual perception (Chanes et al., 2012).

Experimental sessions

Participants performed 2 experimental sessions separated by at least 48 hours. On the 1 st experimental session, sham TMS patterns were delivered using a classical sham approach with active TMS pulses delivered with the coil placed perpendicular to the curvature of the skull (sham procedure #1). During the 2 nd session, sham TMS-like sound patterns were delivered using a speaker taped to the TMS coil (sham procedure #2) playing an average audio recording of TMS clicking noises. Importantly, participants were kept naïve as to the purpose of the experiment and they were told that they would receive active TMS.

On On EEG data, we first compared ERP waveforms data from the sham procedure #2 session between sham stimulation (TMS-like sound) and no stimulation (no TMS-like sound) trials for each sham TMS pattern. We then compared directly, again for each sham TMS pattern separately, ERP waveforms for sham stimulation trials from sham procedure #1 session (sham TMS pulses delivered via a 90° TMS coil) to sham stimulation trials in the sham procedure #2 session (TMSlike sounds delivered with a speaker). ERP waveforms were compared with two-tailed paired Student's t-test computed for each time point in the [-200 400] ms window centered on target onset.

For time-frequency spectral analyses, we compared sham stimulation trials for each TMS pattern between the Sham procedures #1 and #2 sessions. For measures of power and ITC, we No significant differences in ITC were found for direct comparisons of time-frequency maps between sham patterns. It is interesting to notice, however, that the significant high-beta phase-locking observed on ITC topographical maps (Fig. 5A) for the rhythmic and random stimulation patterns did not reach significance on the time-frequency maps (Fig. 6).

In sum, time-frequency analyses yielded significant oscillatory phase-locking over central electrodes following sham stimulation. This phase-locking is non-specific, broad-band and not driven by the rhythmic structure of the different sham stimulation patterns tested.

Impact of Sham Procedure

An experimental session was performed on the same group of participants with sham stimulation delivered through a speaker taped on a TMS coil placed above the scalp playing a recorded TMS sound (sham procedure #2: TMS-like sounds). Behavioral performance during trials with sham stimulation delivered through a speaker was compared to performance in the experimental session with sham TMS delivered through a TMS coil placed perpendicular to the scalp (sham procedure #1: Sham TMS pulses). On electrophysiological recordings, sham stimulation delivered through a speaker (sham procedure #2) elicited a clear auditory evoked potential (Fig. 8A) with a similar amplitude as the potentials recorded following sham stimulation delivered through a TMS coil (sham procedure #1) (Fig. 8B). Only for single sham pulses, the early negative deflection of the evoked potential showed significant differences between the two sham procedures (Fig. 8B, graph on the far right). Grey shaded areas represent time points showing significant differences in evoked potential amplitude between compared conditions for each sham patterns tested (cluster-based permutation tests, a=0.05). Note that evoked potentials by sham procedure #2 were very similar to those of sham procedure #1. The single noted difference is the higher amplitude and longer latency of the early negative deflection for the former than the latter.

from the cross-modal sensory modulation literature showing that a concurrent sound, preceding or following a visual target, leads to higher performance and faster reaction times acknowledging the presence of visual targets [START_REF] Bernstein | Intermodal effects in choice reaction time[END_REF][START_REF] Spence | Audiovisual links in exogenous covert spatial orienting[END_REF]Kusnir et al. 2011).

These authors concluded that single sham TMS pulses acted as a warning signal facilitating visual detection. Nonetheless, increases of performance and/or decreases in reaction times cannot be taken as proof of improved perception and might be caused by changes in decision criterion (Bolognini et al. 2005;[START_REF] Lippert | Improvement of visual contrast detection by a simultaneous sound[END_REF]). In our study, participants were not asked to provide answers as fast as possible, but instead to prioritize accuracy over speed. Moreover, we employed Signal Detection Theory (SDT) outcome measures to dissociate influences of sham stimulation on visual salience and sensitivity (d') vs. later processes tied to decision-making (c or b).

Thanks to such differences, our study is now able to nuance and extend the prior reports by Duecker and Sack (2013) and show that the loud clicking sounds tied to lateralized TMS (generated either with sham TMS or with TMS-like sounds recorded and played) do not modulate perceptual sensitivity outcomes (d') but affect decision-making criterion (c) rendering participants less conservative. Cross-modal influences of response biases in absence of any effect on visual performance correlates (in our case d') has been extensively reported in prior studies (Frassinetti et al. 2002;Bolognini et al. 2005;[START_REF] Odgaard | Cross-modal enhancement of perceived brightness: Sensory interaction versus response bias[END_REF]) and attributed to general phasic alerting in response to a non-spatially predictive (hence not informative) sound that acts as a warning signal (Kusnir et al., 2011;Robertson et al., 1998).

To evaluate subjective response bias, two measures of the Signal Detection Theory framework were used: decision criterion (c) and likelihood ratio (b). While we demonstrated a modulation of the decision criterion by sham stimulation patterns, we were unable to show any modulation of the likelihood ratio. These two measures differ in their relationship to the measure of perceptual sensitivity (d'). While the decision criterion is independent from d', likelihood ratio is not (Ingham, 1970), making the former a more sensitive measure to detect changes in subjective response bias, particularly in the absence of changes of perceptual sensitivity, as is the case in our

Conflict of interest

The authors declare no competing interests.

Author contributions

Conceptualization