Mathilde Par

Lapoix

Volker Bormuth

Professor, Tatiana Deliagina

PU Claire Legay

DR Véronique Marchand-Pauvert

MCU Olivier Thouvenin

Dimitri Ryczko

DR Claire Wyart
email: claire.wyart@icm-institute.org

Dr. Volker Bormuth Pr Claire Legay

neurons underlying spontaneous locomotion in zebrafish

Keywords: V2a neurons, reticulospinal neurons, brainstem, MLR, locomotion, motor control, zebrafish, light-sheet microscope, oblique light-sheet microscope, mesencephalic locomotor region, PPN Lateral paragigantiocellular nucleus MLR: Mesencephalic Locomotor Region Pedunculopontine nucleus PSF: Point Spread Function rGi: rostral Gigantocellular nucleus Rh: Rhombomere RP: retropontine RSN: reticulospinal neuron SNR: signal-to-noise ratio vi Mesencephalic locomotor region (MLR), locomotion, reticulospinal neurons (RSNs), V2a neurons, vsx2 transcription factor, brainstem, zebrafish

In the absence of direct sensory stimuli, animals still need to locomote to explore their environment in order to fulfil internal needs. The circuits underlying spontaneous locomotion are highly conserved in vertebrates. In the brainstem, the mesencephalic locomotor region (MLR) recruits the descending reticulospinal neurons (RSNs) to initiate and control spontaneous locomotion. However, the organization of brainstem circuits remains obscure due to the heterogeneity and difficulty to access these systems in actively moving animals. We leverage the optically and genetically accessible larval zebrafish to investigate the recruitment of a genetically-defined population of RSNs conserved across vertebrates, referred to as V2a RSNs, during MLR-induced and spontaneous locomotion. As the MLR remained to be defined in zebrafish, we first combined multiple approaches to functionally and anatomically define the MLR in larval zebrafish as a small region medial and dorsal to the locus coeruleus. We found that the MLR specifically drives and modulates the duration and frequency of forward locomotion by recruiting a spatially defined cluster of V2a RSNs in the medulla that act as "maintain and speed control" neurons. To investigate how the V2a neurons encode kinematics of locomotion at the population scale, we built a tilted light-sheet microscope allowing the recording of all V2a neurons in the brainstem simultaneously during a single locomotor event. Using high-speed volumetric calcium imaging recorded on this titled light-sheet microscope, we revealed a similar cluster of V2a neurons in the medulla is recruited during spontaneous forward locomotion. Our results unravel, for the first time in a vertebrate, the genetic identity and activity pattern of RSNs recruited by the MLR to execute maintenance and speed control of forward, spontaneous locomotion.

Titre:

Etude in vivo de la région mésencéphalique locomotrice et des neurones réticulospinaux vsx2+ sous-tendant la locomotion spontanée chez le poisson-zèbre

Résumé :

En l'absence de stimuli sensoriels directs, les animaux doivent continuer de se déplacer dans leur environnement afin de satisfaire leurs besoins vitaux. Les vertébrés utilisent des modalités de locomotion très différents, mais le substrat neuronal qui génère le rythme et la coordination de ces comportements moteurs est très conservé. Dans le tronc cérébral des vertébrés, ces commandes sont envoyées par la région mésencéphalique locomotrice (MLR), qui contrôle la locomotion par ses projections sur les neurones réticulospinaux (RSNs), qui, en retour, contacte et commande directement les circuits spinaux effecteurs de la locomotion. Tandis que ces circuits spinaux générant le rythme et le schéma moteur produisant la locomotion sont largement identifiés, la nature des RSNs recrutés par le MLR pour initier et moduler la locomotion spontanée reste imprécise, en partie dû à la difficulté d'accès et l'hétérogénéité de ces systèmes. Dans cette étude, nous prenons avantage du modèle de la larve du poisson zèbre, optiquement et génétiquement accessible, pour examiner le recrutement d'une population de RSNs exprimant le facteur de transcription vsx2, aussi appelés V2a RSNs, durant la nage spontanée et induite par la stimulation du MLR. Comme la MLR restait à définir chez le poisson zèbre, nous avons d'abord combiné plusieurs approches pour définir fonctionnellement et anatomiquement la MLR chez le poisson zèbre. Nous avons défini la MLR comme une petite région située médianement et dorsalement du locus coeruleus. Nous avons découvert que la MLR dirige et module spécifiquement la durée ainsi que la fréquence de la locomotion vers l'avant, en recrutant un groupe spatialement défini de V2a RSNs, situé dans la médulla. Ces V2a agissent comme des neurones de maintien de la locomotion et de contrôle de la vitesse. Pour étudier comment la population complète des neurones V2a encodent la cinétique de la locomotion à, nous avons construit un microscope à feuille de lumière oblique, permettant l'enregistrement de tous les neurones V2a dans le tronc cérébral simultanément pendant un seul et même événement locomoteur. En utilisant l'imagerie calcique volumétrique à haute vitesse enregistrée sur ce microscope à feuille de lumière oblique, nous avons révélé qu'un groupe similaire de neurones V2a dans la médulla est recruté pendant la locomotion spontanée vers l'avant. Nos résultats révèlent, pour la première fois chez un vertébré, l'identité génétique et le motif d'activité des RSNs recrutés par le MLR pour exécuter le maintien et le contrôle de la vitesse de la locomotion spontanée vers l'avant.

Mots clefs:

neurones V2a; neurones reticulospinaux; tronc cérébral; MLR; locomotion; contrôle moteur; poisson-zèbre; microscope à feuillet de lumière; microscope à feuillet de lumière oblique; region mésecencéphalique locomotrice ; PPN i for review, see [START_REF] Arber | Connecting neuronal circuits for movement[END_REF]Kiehn, 2016). Locomotion emerges from the coordinated contraction of muscles, which is directly initiated by motoneurons (MNs) located in the spinal cord. MNs concerted activity is controlled by neural circuits of local interneurons, called Central Pattern Generators (CPGs). CPGs can intrinsically generate both the rhythmi.e., the timing of the neural activity oscillation and ultimately the motor output, and the pattern -i.e., the coordinated recruitment of motoneurons in sequence, the alternation between the left and right MNs pools as well as the flexor and extensor MNs pools. Even though the spinal CPGs can self-sustain rhythmic activity, they are under the necessary control of descending pathways that start and modulate their activity. Amongst those, the reticulospinal neurons (RSNs) in the brainstem integrate external sensory inputs and commands from higher brain regions to duly recruit the spinal CPGs. RSNs are directly driven by the mesencephalic locomotor region (MLR), which conveys the command to start and maintain the appropriate locomotor program planned and selected by the basal ganglia. Other notable motor centers important for locomotion are the cerebellum, which contributes to sensorimotor coordination and motor learning, the vestibulospinal neurons adjusting posture and balance, and in species with a cortex, the motor and premotor cortex planning motor action (for review, see [START_REF] Grillner | Current Principles of Motor Control, with Special Reference to Vertebrate Locomotion[END_REF]). It is important to note that, although the common representation is a top-to-bottom flow of information, most often for simplification, there are numerous crucial cross-talks between all these centers that flow in both directions [START_REF] Grillner | The motor infrastructure: from ion channels to neuronal networks[END_REF]. Moreover, locomotion can be initiated at different levels of this hierarchical structure.

Table of content

In this part, I will describe how the production of movement of varying strength and speed is produced by motor centers at different levels, from the motoneurons to the mesencephalic locomotor region.

Footnote:

In terms of nomenclature, we can still find in neuroscience textbooks or reviews the use of upper and lower motor neurons [START_REF] Purves | Neurosciences[END_REF][START_REF] Zayia | Neuroanatomy, Motor Neuron[END_REF]. The term "Upper motor neurons" is used to refer to all neurons involved in the production of motor action controlling "lower motor neurons" (= motoneurons), i.e., neurons directly contacting muscle fibers through a neuromuscular junction. The group of "upper motor neurons" englobes very distinct neuronal types from locally projecting interneurons to long-range projection neurons, with very different functions. Moreover, the term "lower motor neurons" refers to motoneurons, a term widely used and accepted by the community. For this reason, I will not use the nomenclature of "upper and lower motor neurons" in this thesis. I propose that we stick to describing the distinct structures and neurons composing them, distinguishing them by their projection types (projection neurons or interneurons) or the specific structure they belong to (reticulospinal neurons, spinal cord neurons…). I will use the term "motoneurons" to describe neurons establishing a neuromuscular junction (NMJ) with muscle fibers. I will only use the specific term "premotor neuron" to describe an interneuron projecting to motoneurons. In the forebrain, the basal ganglia and the cortex, directly and indirectly interplay to select the locomotor action to initiate. In the brainstem, the initiation of locomotion is mediated by the mesencephalic locomotor region (MLR). The MLR projects to neurons located in the reticular formation of the brainstem, called reticulospinal neurons (RSNs), which also integrate various sensory inputs. The RSNs send descending projections to locomotor networks in the spinal cord that execute locomotion. These locomotor networks are composed of local circuits of interneurons that generate the rhythm and pattern of excitation necessary to activate in coordination the MNs, which directly contact muscle fibers. The spatiotemporal coordination of the MNs activity produces the coordinated contraction of muscles underlying locomotion. To ease visualization, not all neural circuits controlling locomotion are depicted here. Adapted from [START_REF] Arber | Connecting neuronal circuits for movement[END_REF].

Spinal circuits generating locomotor rhythms and patterns

The core of the locomotor system consists of neural circuits in the spinal cord [START_REF] Hochman | Whole cell recordings of lumbar motoneurons during locomotor-like activity in the in vitro neonatal rat spinal cord[END_REF]. While MNs receive crucial proprioceptive feedback that can generate recurrent loops leading to their rhythmic recruitment, the rhythm and pattern of locomotion are generated by neural circuits in the spinal cord (Figure I.4) [START_REF] Grillner | Central pattern generators for locomotion, with special reference to vertebrates[END_REF]. These circuits referred to as central pattern generators (CPGs) are composed of interconnected excitatory and inhibitory neurons that coordinate body segments, limbs, and body sides. Developmental investigations revealed the genetic code that underlies the generation of classes of interneurons building the spinal CPGs and that is conserved in vertebrates. This genetic code represented a breakthrough enabling the precise description of the CPGs into molecularly-defined classes of interneurons with a precise contribution to locomotion [START_REF] Goulding | Circuits controlling vertebrate locomotion: moving in a new direction[END_REF][START_REF] Jessell | Neuronal specification in the spinal cord: inductive signals and transcriptional codes[END_REF]. Twenty years of studies describing which neuronal classes build up the CPGs, as well as their properties and their connectivity, brought to light the organization of these spinal sets of neurons modulating speed, amplitude, and coordination between motor pools to drive locomotion [START_REF] Bikoff | Interneuron diversity and function in the spinal motor system[END_REF][START_REF] Deska-Gauthier | The functional diversity of spinal interneurons and locomotor control[END_REF]. Given the level of conservation of the transcription factors defining classes of interneurons [START_REF] Briscoe | A Homeodomain Protein Code Specifies Progenitor Cell Identity and Neuronal Fate in the Ventral Neural Tube[END_REF] for review, see [START_REF] Jessell | Neuronal specification in the spinal cord: inductive signals and transcriptional codes[END_REF], this approach also enabled comparison of their role across vertebrate species. The periods of inactivity of the limb's flexor muscles (extension, stance phase, the limb touches the ground) are depicted in blue. During walking, the flexors of each limb are recruited sequentially and none at the same time. During the trotting gait, the flexors of diagonal limbs are active at the same time. A3, Schematic and electromyography (EMG) recordings of a given limb during locomotion, showing the alternating swing (flexor muscles active) and stance (extensors muscles active, the limb touches the ground). B, Swimming in lamprey where the body propulsion is brought by the undulation of the body from the rostral to caudal part. B1, Schematic of sequential lamprey position during swimming (from left to right), showing the body undulations enabling forward propulsion. B2, Schematic showing the sequential activation of Hemi-segments, from rostral to caudal and from left to right. The periods of activity of the muscles in the hemi-segment are depicted in red. The periods of inactivity of the muscles in the hemi-segment are depicted in blue. Adapted from [START_REF] Purves | Neurosciences[END_REF]

Modular organization of motoneurons

Motoneurons (MNs) are the final output neurons of any motor command as they directly activate groups of muscle fibers and lead to their contraction. A given MN and its target muscle fibers are referred to as a motor unit (for review, see [START_REF] Heckman | Physiology of the motor neuron and the motor unit[END_REF]. A given muscle belongs to multiple motor units that are grouped according to the speed at which they operate as well as their resistance (for review, see [START_REF] Close | Dynamic properties of mammalian skeletal muscles[END_REF][START_REF] Manuel | Alpha, beta and gamma motoneurons: functional diversity in the motor system's final pathway[END_REF]. The different types of motor units are associated with the amplitude and the speed of the movement they generate upon activation. Within a muscle, the slow and fast motor units are recruited to contract the muscle for different purposes: while MNs driving slow-twitch muscle fibers are recruited for posture maintenance, MNs driving fast-twitch muscle fibers are recruited for fast and transient efforts such as sprint (for review, see [START_REF] Purves | Neurosciences[END_REF]).

What explains the differential recruitment of MNs necessary to produce movements at distinct locomotor speed and strength? The recruitment of motoneurons follows the size principle proposed by Henneman, enuncing that the soma size of a given MN is inversely correlated to its resistance [START_REF] Henneman | Relation between size of neurons and their susceptibility to discharge[END_REF]. Consequently, small resistive MNs are highly excitable and therefore recruited at a low level of excitation for slow movements while large MNs are in contrast recruited for fast movements requiring a high level of excitation [START_REF] Henneman | Excitability and inhibitibility of motoneurons of different sizes[END_REF][START_REF] Liu | Function of identified motoneurones and co-ordination of primary and secondary motor systems during zebra fish swimming[END_REF][START_REF] Somjen | Responses of motoneurons of different sizes to graded stimulation of supraspinal centers of the brain[END_REF]. This rule is of utmost importance across species so that the system doesn't start large and fast movements with very little input from the descending commands.

In light of this organization, how can MNs and the muscle fibers they recruit produce rhythmic movements of varying speed and amplitude underlying locomotion? During locomotion, MNs are periodically active in bursts to sustain the oscillatory motor activity at a given frequency defining locomotor speed (Figure I.3B) [START_REF] Hochman | Whole cell recordings of lumbar motoneurons during locomotor-like activity in the in vitro neonatal rat spinal cord[END_REF].

Therefore, the periodicity of locomotion depends on the oscillation frequency of MN bursting activity. The strength of movement depends on 1) the type of motor units active, as large motor units produce large movements while small motor units produce small amplitude movements [START_REF] Burke | Selective recruitment of motor units[END_REF][START_REF] Nardone | Selective recruitment of high-threshold human motor units during voluntary isotonic lengthening of active muscles[END_REF]; 2) the number of motor units active that affect the number of muscle fibers recruited; 3) The instantaneous firing rate of individual MNs (rate coding). MNs are active in bursts during a locomotor cycle and an increase in their firing frequency within these bursts leads to more recruitment of muscle fibers, therefore a larger amplitude of the movements [START_REF] Fuglevand | Models of recruitment and rate coding organization in motor-unit pools[END_REF] for review, see [START_REF] Enoka | Rate Coding and the Control of Muscle Force[END_REF]. 25 and 46 (R-25, R-46). During a swimming cycle, the muscles are segmentally recruited from the rostral to the caudal segments. The left and right hemi-segments myotomes are recruited alternatively at each mid-cycle. The locomotor frequency is given by the period of time necessary to execute a full swimming cycle, i.e., between two bursts of activity in the same hemi-segment. A2, EMG recordings showing the electrical activity of hindlimb muscles. During a walking cycle, there is a wave activation from the R-Tr to the L-VI which produces alternating the limb movement underlying the walking gait. The locomotor frequency is given by the period between the beginning of a burst activity in a muscle and the beginning of the next burst. B, Ventral nerve roots recording showing the motoneurons activity at distinct body segments during fictive locomotion in the lamprey spinal cord (B1), and in the mouse spinal cord (B2). B1, Ventral nerve root recording at 3 Hemi-segments of the lamprey isolated spinal cord, showing the wave of MNs activity along the rostrocaudal axis. B2, Transmitter induced (NMDA/5-HT) locomotor activity in mouse isolated spinal cord shows the alternating activity of left-right (R-L2 and L-L2) and flexor-extensor (R-L2 and R-L5). In these lumbar segments, the excitation is propagated from rostral to caudal, and from left to right, as it is in swimming species. Note that in B1 and B2, the period of the locomotor cycle is increased by a factor 10, which means that the locomotor frequency is decreased by a factor 10. Rebuilt from [START_REF] Goulding | Circuits controlling vertebrate locomotion: moving in a new direction[END_REF]. Recordings in lampreys from [START_REF] Wallén | Fictive locomotion in the lamprey spinal cord in vitro compared with swimming in the intact and spinal animal[END_REF].

Initial description of the minimal neural circuits necessary for generating the motor pattern underlying locomotion.

What is the mechanism that generates the oscillatory activity of MNs underlying locomotion? A great amount of research described circuits underlying spinal reflexes where sensory feedback from muscle contraction and the stretch of the Golgi tendon modulates the activity of MNs. This led to the hypothesis that the MN oscillatory activity could come from the periphery [START_REF] Gray | THE EFFECT OF DEAFFERENTATION UPON THE LOCOMOTORY ACTIVITY OF AMPHIBIAN LIMBS[END_REF][START_REF] Gray | Further Observations on the Effect of De-Afferentation on the Locomotory Activity of Amphibian Limbs[END_REF], for review see [START_REF] Delcomyn | Neural basis of rhythmic behavior in animals[END_REF]. However, the existence of a central circuit in the spinal cord inherently able to generate the rhythm and pattern of repetitive motor behavior was also proposed early on [START_REF] Brown | The intrinsic factors in the act of progression in the mammal[END_REF]. This latest was validated by experiments showing that, upon pharmacological induction of locomotion, the motor rhythmicity remained in absence of the rest of the CNS and of any sensory inputs [START_REF] Bullock | The origins of patterned nervous discharge[END_REF][START_REF] Holst | Von der Mathematik der nervösen Ordnungsleistung[END_REF][START_REF] Delcomyn | Neural basis of rhythmic behavior in animals[END_REF][START_REF] Grillner | Locomotion in vertebrates: central mechanisms and reflex interaction[END_REF][START_REF] Grillner | Central pattern generators for locomotion, with special reference to vertebrates[END_REF]. For instance, recordings in lampreys showed that oscillations recalling the general pattern of activity observed on freely swimming animals with R-C propagation and L/R alternation (Figure I.3A) can be observed during fictive locomotion on the spinal cord in vitro when applying drugs such as Glu, DOPA or 5HT (Figure I.3B) [START_REF] Grillner | Central pattern generators for locomotion, with special reference to vertebrates[END_REF][START_REF] Wallén | Fictive locomotion in the lamprey spinal cord in vitro compared with swimming in the intact and spinal animal[END_REF]. If the oscillations are generated centrally, the mechanosensory feedback from the periphery is critical to modulating the kinematics of locomotion. Removing sensory feedback in fictive preparation drastically slows down locomotion (Figure I.3A1 and B1, in lampreys by a factor 10) [START_REF] Wallén | Fictive locomotion in the lamprey spinal cord in vitro compared with swimming in the intact and spinal animal[END_REF]. Accordingly, mechanosensory feedback was later found in fish to increase locomotor frequency through different sensory pathways (Dorsal Root Ganglia & Trigeminal neurons: [START_REF] Knafo | Mechanosensory neurons control the timing of spinal microcircuit selection during locomotion[END_REF]CSF-contacting Neurons: Böhm et al., 2016;[START_REF] Wu | Spinal sensory neurons project onto the hindbrain to stabilize posture and enhance locomotor speed[END_REF]. Under natural conditions, proprioceptive inputs also modulate the CPGs activity to compensate for perturbation in the environment, such as walking a slope. [START_REF] Pratt | Ia inhibitory interneurons and Renshaw cells as contributors to the spinal mechanisms of fictive locomotion[END_REF]. Altogether, the inhibitory neurons pattern the activity of both excitatory INs and MNs to define locomotor frequency and the pool of MNs active [START_REF] Baldissera | Integration in Spinal Neuronal Systems[END_REF][START_REF] Buchanan | Identification of interneurons with contralateral, caudal axons in the lamprey spinal cord: synaptic interactions and morphology[END_REF][START_REF] Butt | Functional identification of interneurons responsible for left-right coordination of hindlimbs in mammals[END_REF][START_REF] Jankowska | Interneuronal relay in spinal pathways from proprioceptors[END_REF][START_REF] Kjaerulff | Distribution of networks generating and coordinating locomotor activity in the neonatal rat spinal cord in vitro: a lesion study[END_REF]. The multiple experiments that built this model revealed that the spinal CPGs are composed of multiple interneuron types which makes them inherently able to generate the pattern and rhythm of motor activity underlying locomotion. One population of excitatory neurons (in red, lateral) rhythmically activates MNs and another population of excitatory Ins. The latest activate MN and inhibitory commissural INs that project onto the other hemicenter. A-B were pasted from [START_REF] Mccrea | Organization of mammalian locomotor rhythm and pattern generation[END_REF]. C was pasted from [START_REF] Grillner | The CPGs for Limbed Locomotion-Facts and Fiction[END_REF].

Insight into the diversity of spinal interneurons as genetically defined cell classes

In the 2000s, developmental investigations highlighted a transcription factor code underlying the organization of the spinal cord in classes of interneurons, conserved across vertebrates [START_REF] Briscoe | A Homeodomain Protein Code Specifies Progenitor Cell Identity and Neuronal Fate in the Ventral Neural Tube[END_REF] for review, see [START_REF] Alaynick | SnapShot: Spinal Cord Development[END_REF][START_REF] Jessell | Neuronal specification in the spinal cord: inductive signals and transcriptional codes[END_REF][START_REF] Lee | Transcriptional networks regulating neuronal identity in the developing spinal cord[END_REF]. During development, the transcription factors define progenitor domains that give rise to 3 sets of motor-related neurons (INs involved in the production of movement) located in the ventral spinal cord (named V0 to V3) and sensory-related neurons in the dorsal spinal cord (named dI1-dI6) that each share a set of molecular markers (Figure I.5A). Although it is now clear that each class contains numerous molecularly distinct cell types [START_REF] Bikoff | Spinal Inhibitory Interneuron Diversity Delineates Variant Motor Microcircuits[END_REF] for review, see [START_REF] Bikoff | Interneuron diversity and function in the spinal motor system[END_REF], this classification enabled a genetic targeting that allowed precise identification and manipulation of these circuits and enabled the identification of neurons classes playing complementary roles in the generation of the rhythmic and coordinated motor activity underlying locomotion (for review, see [START_REF] Gosgnach | Delineating the Diversity of Spinal Interneurons in Locomotor Circuits[END_REF][START_REF] Goulding | Circuits controlling vertebrate locomotion: moving in a new direction[END_REF][START_REF] Grillner | Measured motion: searching for simplicity in spinal locomotor networks[END_REF].

The main excitatory premotor neurons were identified as genetically defined V2a INs which contribute to generating the locomotor rhythm (Eklöf- [START_REF] Eklöf-Ljunggren | Origin of excitation underlying locomotion in the spinal circuit of zebrafish[END_REF] alongside the bursting shox2+ INs [START_REF] Dougherty | Locomotor rhythm generation linked to the output of spinal shox2 excitatory interneurons[END_REF] and glutamatergic hb9+ INs [START_REF] Caldeira | Spinal Hb9::Cre-derived excitatory interneurons contribute to rhythm generation in the mouse[END_REF] (for review, see [START_REF] Rancic | Recent Insights into the Rhythmogenic Core of the Locomotor CPG[END_REF]. [START_REF] Lanuza | Genetic identification of spinal interneurons that coordinate left-right locomotor activity necessary for walking movements[END_REF][START_REF] Talpalar | Dual-mode operation of neuronal networks involved in left-right alternation[END_REF]. In limbed animals, the Ia, ipsilateral inhibitory neurons that are rhythmically active within the locomotor cycle to coordinate the opposite activation of flexor and extensor groups represent a subset of the V1 and V2b classes (Figure I.5) [START_REF] Benito-Gonzalez | Renshaw cells and Ia inhibitory interneurons are generated at different times from p1 progenitors and differentiate shortly after exiting the cell cycle[END_REF][START_REF] Falgairolle | V1 interneurons regulate the pattern and frequency of locomotor-like activity in the neonatal mouse spinal cord[END_REF][START_REF] Zhang | V1 and v2b interneurons secure the alternating flexor-extensor motor activity mice require for limbed locomotion[END_REF]. How does this organization support locomotion at varying speeds and amplitude? [START_REF] Goulding | Circuits controlling vertebrate locomotion: moving in a new direction[END_REF]. B, Non-exhaustive connectivity map between IN classes building up the vertebrates' spinal CPGs. A circle represents a population of neurons. The colors represent the progenitor domain the neurons emerge from. Lines ending with arrows represent excitatory input, and lines ending with blocks represent inhibitory inputs. Dotted lines represent putative connectivity. V2a interneurons provide rhythmic excitatory drive to MNs and other CPG neurons (Kimura et al., 2006;[START_REF] Mclean | Continuous shifts in the active set of spinal interneurons during changes in locomotor speed[END_REF]. V0 neurons project to neurons on the other side of the spinal cord: excitatory V0e project to inhibitory Ia INs; while inhibitory V0i directly inhibit MNs (for review, see (Kiehn, 2016)). Ia inhibitory INs provide reciprocal inhibition between flexors and extensors and represent a subset of V1 and V2b classes [START_REF] Britz | A genetically defined asymmetry underlies the inhibitory control of flexor-extensor locomotor movements[END_REF][START_REF] Zhang | V1 and v2b interneurons secure the alternating flexor-extensor motor activity mice require for limbed locomotion[END_REF]. A subset of the V1 class differentiates into Renshaw cells (R), known to mediate the recurrent inhibition of MNs [START_REF] Sapir | Pax6 and engrailed 1 regulate two distinct aspects of renshaw cell development[END_REF]. Other V1 inhibitory INs provide inhibition to ventrally-located V2a INs, while other V2b inhibitory INs provide inhibition to dorsally-located V2a INs (see 1.1.5). V3 neurons directly project onto MNs on both sides to control the number of motoneurons pools recruited (see 1.1.5) [START_REF] Böhm | Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish[END_REF][START_REF] Wiggin | V3 Interneurons are Active and Recruit Spinal Motor Neurons During In Vivo Fictive Swimming in Larval Zebrafish[END_REF][START_REF] Zhang | V3 spinal neurons establish a robust and balanced locomotor rhythm during walking[END_REF]. To ease visualization, the position of the neurons in the dorso-caudal and medial-lateral axis is not always respected (for more faithful representation, see Bikoff, 2019).

V2a excitatory interneurons and locomotor speed

The excitatory ipsilateral premotor neurons in the spinal CPGs provide the excitatory drive to generate locomotor activity [START_REF] Grillner | The motor infrastructure: from ion channels to neuronal networks[END_REF]Kimura et al., 2006;[START_REF] Roberts | Origin of excitatory drive to a spinal locomotor network[END_REF]. Amongst those, V2a interneurons, emerging from the V2 domain, are glutamatergic, characterized by the expression of the transcription factor vsx2 (also referred to as chx10 and alx in other species) and core components of the locomotor CPGs (Cepeda-Nieto et al., 2005;Kimura et al., 2006). During locomotion, spinal V2a INs are rhythmically active and provide direct excitatory drive to MNs (Figure I.5B) (Kimura et al., 2006). Further investigations revealed that the V2a class of INs is organized in functionally and morphologically distinct subtypes [START_REF] Menelaou | Hierarchical control of locomotion by distinct types of spinal V2a interneurons in zebrafish[END_REF][START_REF] Zhong | Neuronal activity in the isolated mouse spinal cord during spontaneous deletions in fictive locomotion: insights into locomotor central pattern generator organization[END_REF], making V2a INs both necessary and sufficient to the generation of the rhythm (Eklöf- [START_REF] Eklöf-Ljunggren | Origin of excitation underlying locomotion in the spinal circuit of zebrafish[END_REF][START_REF] Ljunggren | Optogenetic activation of excitatory premotor interneurons is sufficient to generate coordinated locomotor activity in larval zebrafish[END_REF] and crucial for pattern generation [START_REF] Crone | Genetic ablation of V2a ipsilateral interneurons disrupts left-right locomotor coordination in mammalian spinal cord[END_REF][START_REF] Dougherty | Functional organization of V2a-related locomotor circuits in the rodent spinal cord[END_REF].

Several studies in zebrafish revealed that the V2a INs are topographically organized in speed modules recruiting matching MN pools to generate distinct locomotor speed (Figure [START_REF] Ampatzis | Separate microcircuit modules of distinct v2a interneurons and motoneurons control the speed of locomotion[END_REF][START_REF] Song | V2a interneuron diversity tailors spinal circuit organization to control the vigor of locomotor movements[END_REF] [START_REF] Ampatzis | Separate microcircuit modules of distinct v2a interneurons and motoneurons control the speed of locomotion[END_REF]Kimura et al., 2006;[START_REF] Song | Multiple Rhythm-Generating Circuits Act in Tandem with Pacemaker Properties to Control the Start and Speed of Locomotion[END_REF]. This organization holds true in both adult zebrafish [START_REF] Ampatzis | Separate microcircuit modules of distinct v2a interneurons and motoneurons control the speed of locomotion[END_REF] and mice [START_REF] Zhong | Electrophysiological characterization of V2a interneurons and their locomotor-related activity in the neonatal mouse spinal cord[END_REF].

I.6)

Other INs classes contributing to the control of locomotor speed and strength

In order to recruit the right set of excitatory premotor INs, ipsilateral inhibitory interneurons from both V1 and V2b classes disengage premotor sets inapt to produce the desired locomotor speed. Ablation of V1 interneurons in mice using either conditional knockout (KO) of a common transcription factor or the selective expression of diphtheria toxin A led to an overall decrease in fictive locomotor speed [START_REF] Gosgnach | V1 spinal neurons regulate the speed of vertebrate locomotor outputs[END_REF]. The direct mechanism through which V1 INs regulate speed was highlighted in the larval zebrafish model, in which, during fast swimming, V1 INs inhibit the slow module of premotor excitatory INs [START_REF] Kimura | Regulation of locomotor speed and selection of active sets of neurons by V1 neurons[END_REF][START_REF] Menelaou | Hierarchical control of locomotion by distinct types of spinal V2a interneurons in zebrafish[END_REF]. Interestingly, the V2b INs exhibit a complementary role in the regulation of speed in larval zebrafish. In a recent study, genetically ablating V2b interneurons resulted in an overall increase in the locomotor speed, suggesting this class of INs rather acts as a brake reducing the locomotor speed [START_REF] Callahan | Spinal V2b neurons reveal a role for ipsilateral inhibition in speed control[END_REF].

Commissural interneurons (CINs), i.e., neurons with an axon crossing the midline and reaching neurons on the other side of the spinal cord, are essential to control left/right alternation at a specific pace. The V0 INs were identified as a key class of CINs essential for left/right alternation during stepping [START_REF] Lanuza | Genetic identification of spinal interneurons that coordinate left-right locomotor activity necessary for walking movements[END_REF][START_REF] Talpalar | Dual-mode operation of neuronal networks involved in left-right alternation[END_REF]. Remarkably, this class is composed of two speed-dependent subtypes: dorsally-located V0 (V0d) are inhibitory CINs recruited at low speed of locomotion that establishes the left/right alternation during walking, while ventrally-located V0 (V0v) are excitatory CINs recruited at a higher locomotor speed that are crucial to establish the alternation of left and right limbs during the trotting [START_REF] Lanuza | Genetic identification of spinal interneurons that coordinate left-right locomotor activity necessary for walking movements[END_REF][START_REF] Talpalar | Dual-mode operation of neuronal networks involved in left-right alternation[END_REF]. In aquatic animals, this organization is conserved to ensure the left/right the proper alternation of body segments differently according to the locomotor speed [START_REF] Kawano | Long descending commissural V0v neurons ensure coordinated swimming movements along the body axis in larval zebrafish[END_REF][START_REF] Satou | Functional role of a specialized class of spinal commissural inhibitory neurons during fast escapes in zebrafish[END_REF][START_REF] Satou | Generation of multiple classes of V0 neurons in zebrafish spinal cord: progenitor heterogeneity and temporal control of neuronal diversity[END_REF]. Zebrafish V0v are long-descending excitatory interneurons crucial for the diagonal intersegment coordination, contributing in fish to the generation of the S shape of the body contributing to the maintenance of the head position during slow swimming [START_REF] Kawano | Long descending commissural V0v neurons ensure coordinated swimming movements along the body axis in larval zebrafish[END_REF]. In a nutshell, molecularly distinct CINs have an evolutionary-conserved role to set proper left-right alternation in a speed-dependent manner, ultimately defining the gait at which the animal locomotes.

In larval zebrafish, V3 interneurons are glutamatergic INs essentially controlling the amplitude/strength of the locomotor output through direct projections to MNs [START_REF] Böhm | Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish[END_REF][START_REF] Wiggin | V3 Interneurons are Active and Recruit Spinal Motor Neurons During In Vivo Fictive Swimming in Larval Zebrafish[END_REF]. Two recent studies revealed that more V3 INs are active upon increasing swimming strength, that optogenetic activation of these neurons increases bout strength [START_REF] Böhm | Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish[END_REF], and that ablating these neurons reduces the number of MN pools active [START_REF] Wiggin | V3 Interneurons are Active and Recruit Spinal Motor Neurons During In Vivo Fictive Swimming in Larval Zebrafish[END_REF]. These studies suggest the direct mechanism by which they control the amplitude is by setting the number of MNs pools recruited. In mice, where V3 INs make direct connections with MNs on both sides of the spinal cord, they are coherently associated with the amplitude of movement as well as left/right alternations [START_REF] Chopek | Sub-populations of Spinal V3 Interneurons Form Focal Modules of Layered Pre-motor Microcircuits[END_REF][START_REF] Zhang | V3 spinal neurons establish a robust and balanced locomotor rhythm during walking[END_REF].

The spinal CPGs are now relatively very well described circuits built of functional modules of molecularly-defined cell types whose organization enables the generation of locomotion at a given speed and strength. However, the spinal CPGs are under obligatory control of descending inputs to initiate and modulate their activity to command the appropriate locomotor action. What are the upstream structures commanding the spinal CPGs?

Reticulospinal neurons

In the brainstem of vertebrates, the reticulospinal neurons (RSNs) send descending inputs to spinal CPGs to execute locomotion. RSNs are localized in the reticular formation which is a highly complex structure of intermingled neurons from neuromodulatory systems and command neurons, usually described in the ascending group (role in cognitive states and wakefulness) and descending group, which projects to the spinal cord to control repetitive motor behaviors and autonomic functions (posture, locomotion, micturition, breathing) [START_REF] Brownstone | Reticulospinal Systems for Tuning Motor Commands[END_REF]. Particularly, the RSNs are of utmost importance for starting, steering, and stopping locomotion (for review, see [START_REF] Brownstone | Reticulospinal Systems for Tuning Motor Commands[END_REF][START_REF] Ferreira-Pinto | Connecting Circuits for Supraspinal Control of Locomotion[END_REF][START_REF] Takakusaki | Brainstem control of locomotion and muscle tone with special reference to the role of the mesopontine tegmentum and medullary reticulospinal systems[END_REF] in all vertebrates. However, unlike the spinal locomotor circuits, the molecular and cellular identity of RSNs underlying these functions, as well as their anatomical location and functional organization are not fully understood. Although the brainstem can be classified in rhombomeres defined by the differential expression of Hox genes [START_REF] Parker | A Hox gene regulatory network for hindbrain segmentation[END_REF], there are no obvious boundaries defining the anatomical organization of the diffusive population of RSNs extending throughout the brainstem [START_REF] Brownstone | Reticulospinal Systems for Tuning Motor Commands[END_REF]. In order to precisely understand which RSNs control the distinct kinematics of locomotion, and how this is conserved across species, we need molecular markers that allow a reproducible segmentation of the RSNs. During my PhD, I focused on V2a RSNs defined by the expression of the transcription factor vsx2 that appear across species essential to elicit motor output and steer locomotion [START_REF] Bouvier | Descending Command Neurons in the Brainstem that Halt Locomotion[END_REF][START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF][START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]Usseglio et al., 2020).

Regarding the brainstem nomenclature used in my thesis: Given the inconsistencies between the nomenclature across and within species, I chose to follow a recently published paper in which Watson et al. proposed a nomenclature that can be used for all mammals, based on developmental gene expression (Watson et al., 2019). In this nomenclature, the hindbrain is divided into 4 regions from caudal to rostral: medulla, retro-pontine region, pontine region, and prepontine region. The following figure represents the boundaries between these regions as defined by the publication, in a generic vertebrate brain ( (Watson et al., 2019), is based on developmental gene expression, progeny analysis, and fate mapping. A, Proposed nomenclature of the hindbrain in a generic brain. The hindbrain is divided into 4 regions, from caudal to rostral, which are segmented given the rhombomeres limits: medulla oblongata (Rh7-Rh11), retropontine (Rh5-Rh6), pontine region (Rh2-Rh4), prepontine (isthmus and Rh1). Anatomical landmarks are denoted as well, such as motor nuclei (3N, 4N, 5N, 7N), and the inferior olive (IO). B, Para-sagittal scheme of a mouse brain, where regions of the hindbrain are color-coded according to the proposed nomenclature.

Reticulospinal neurons in the brainstem command locomotion by projecting onto spinal circuits

RSNs are crucial for the production of all aspects of the locomotor output, such as its episodic nature, its directionality, and speed, through projections to spinal CPGs (Bretzner and Brownstone, 2013;[START_REF] Davies | Inputs to group II-activated midlumbar interneurones from descending motor pathways in the cat[END_REF]Noga et al., 2003;[START_REF] Szokol | Imaging synaptically mediated responses produced by brainstem inputs onto identified spinal neurons in the neonatal mouse[END_REF] and MNs (Buchanan et al., 1987;[START_REF] Grillner | The origin of a descending pathway with monosynaptic action on flexor motoneurones[END_REF][START_REF] Peterson | Reticulospinal connections with limb and axial motoneurons[END_REF][START_REF] Szokol | Imaging synaptically mediated responses produced by brainstem inputs onto identified spinal neurons in the neonatal mouse[END_REF]. The RSNs that carry these different functions are spread across the brainstem (for review, see [START_REF] Brownstone | Reticulospinal Systems for Tuning Motor Commands[END_REF][START_REF] Ferreira-Pinto | Connecting Circuits for Supraspinal Control of Locomotion[END_REF][START_REF] Takakusaki | Brainstem control of locomotion and muscle tone with special reference to the role of the mesopontine tegmentum and medullary reticulospinal systems[END_REF].

A subset of RSNs control locomotor initiation and termination. It was clear from early studies electrically stimulating RSNs across the brainstem that RSNs could initiate locomotion and modify muscular tone but the recruitment of the RSNs conveying the start command of locomotion has only been identified recently [START_REF] Juvin | A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion[END_REF]Li and Soffe, 2019). In lampreys, a subset of RSNs in the pontine and retropontine region is specifically active only at the beginning of the locomotor episode (Figure I.8A, top trace "start") [START_REF] Juvin | A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion[END_REF].

The ability to start locomotion is also shared by RSNs in the medulla in mice, tadpoles, and larval zebrafish (mice: Capelli et al., 2017, tadpoles: Li and Soffe, 2019, zebrafish: Kimura et al., 2013). Moreover, excitatory and inhibitory subsets of RSNs can also halt locomotion in lampreys, tadpoles, and mice (lamprey: [START_REF] Juvin | A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion[END_REF], tadpoles: Perrins et al., 2002, mice: Bouvier et al., 2015;Capelli et al., 2017). In lampreys, RSNs of unknown nature that fire at the beginning of the locomotor episode and reliably emit a burst of spikes at the end, are necessary for the prompt termination of the locomotor episode (Figure I.8A bottom trace "stop") [START_REF] Juvin | A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion[END_REF]. Altogether, subsets of RSNs of different nature and locations in the brainstem determine the initiation and termination of locomotor episodes.

Within the locomotor episode, the RSNs also modulate the locomotor output.

Glutamatergic RSNs located in the mouse medulla (in a nucleus called LPGi) modulate the locomotor speed (Figure I.8B) (Capelli et al., 2017). Moreover, RSNs are important for posture adaptation and direction of locomotion in both limbed and non-limbed animals (Deliagina et al., 2000;[START_REF] Musienko | Spinal and supraspinal control of the direction of stepping during locomotion[END_REF][START_REF] Oueghlani | Brainstem Steering of Locomotor Activity in the Newborn Rat[END_REF] for review, see [START_REF] Deliagina | Comparative neurobiology of postural control[END_REF][START_REF] Deliagina | Spinal and supraspinal postural networks[END_REF]. More recently, glutamatergic RSNs expressing the transcription factor vsx2+ were shown to convey this steering command to spinal CPGs in mice and larval zebrafish (see next section and Figure I.9) [START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF]Huang et al., 2013;Usseglio et al., 2020). [START_REF] Juvin | A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion[END_REF]. In this study, the authors show that stop neurons are critical for prompt termination of the locomotor episode. B, Glutamatergic RSNs in the LPGi nucleus are important for speed control commanded by glutamatergic MLR neurons (Capelli et al., 2017). Pasted from [START_REF] Ferreira-Pinto | Connecting Circuits for Supraspinal Control of Locomotion[END_REF].

Altogether, RSNs clearly have conserved functions across vertebrates to control the timing and kinematics of locomotion. However, assigning a function to a given nucleus or region has proven difficult due to the heterogeneity of nuclei or subregions and the fact that a given function is often spread out in the hindbrain. These limitations have prevented the clear establishment and comparisons of RSNs' functional maps. In order to build a comprehensive model of the RSNs modules underlying locomotion, we need molecular markers to identify cell types.

V2a RSNs form a genetically-defined population of neurons controlling multiple aspects of locomotion in mice and zebrafish

V2a RSNs are glutamatergic, ipsilateral descending neurons that span throughout the hindbrain of vertebrates, characterized by the expression of the transcription factor chx10/vsx2 [START_REF] Bouvier | Descending Command Neurons in the Brainstem that Halt Locomotion[END_REF]Bretzner and Brownstone, 2013;Cepeda-Nieto et al., 2005;[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]. While not all vsx2+ neurons in the brainstem project to the spinal cord [START_REF] Bouvier | Descending Command Neurons in the Brainstem that Halt Locomotion[END_REF][START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF], the V2a neurons that project to the spinal cord, as other RSNs, play a critical role in the control of locomotion. Initial investigations in zebrafish identified that V2a RSNs were rhythmically active during locomotion and could promote locomotion upon activation (Figure I.9A) [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]. In mice, a small subset of V2a RSN co-expressing lhx3 in the medulla is recruited during locomotion (Bretzner and Brownstone, 2013). The ability to genetically target these neurons allowed the description, in larval zebrafish, of the mediolateral organization of V2a RSNs by birthdate and function (Pujala and Koyama, 2019). More specifically, the authors showed that medially located, late-born V2a RSNs are recruited during fictive spontaneous locomotion, amongst which a large number are located in the medulla.

Prof. Ole Kiehn, Julien Bouvier, and collaborators investigated a subset of V2a RSNs, located at the boundary between the retropontine region and the medulla, in the mouse Gigantocellular nucleus (Gi), and found that they are involved in steering locomotion [START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF]Usseglio et al., 2020). Initially thought to mediate locomotor arrest, as bilateral activation stopped ongoing movement [START_REF] Bouvier | Descending Command Neurons in the Brainstem that Halt Locomotion[END_REF], both these groups later proposed that V2a RSNs from the Gi mediate locomotor steering by halting hindlimb movements and steering the upper part of the body [START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF]Usseglio et al., 2020). These RSNs are built up of at least two subsets in V2a RSNs in the Gi, with distinct projection patterns and functions that altogether promote a change in direction (Figure I.9B2). Activating V2a neurons projecting to the cervical level mediate steering of the head, likely by projecting onto MNs (Figure I.9B2) (Usseglio et al., 2020), while activating lumbar-projecting V2a neurons halts ipsilateral hindlimb movement (Figure I.9B1) [START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF]Usseglio et al., 2020).

Interestingly, a subset of RSNs located as well in the V2a stripe in the retropontine and pontine regions were also involved with eliciting turns and steering in larval zebrafish (Huang et al., 2013;[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]Orger et al., 2008). Altogether, these studies highlighted the critical role of V2a RSNs in controlling both the episodic nature and the direction of locomotion. [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]. A1, V2a neurons in the brainstem of 3dpf larval zebrafish and the different regions where the light was shined in the transgenic Tg(vsx2:Gal4)(UAS:ChR2) larval zebrafish. A2. Example experiments in which blue light was shined over the entire hindbrain of a transgenic larval zebrafish, showing that the larva starts to swim upon light application. A3, Proportion of experimental trials during which illumination elicited swim-like activity, as a function of the region where the light was patterned and the power of light. Note that the most effective site to elicit swim-like activity is the caudal hindbrain (medulla). B. V2a RSNs located at the boundary between the retropontine and the medulla (Gi nuclei) steer locomotion by braking hindlimbs activity and steering forelimbs and head in mice. B1. In ex-vivo preparation of mouse spinal cord and brainstem, optogenetic activation of V2a RSNs on one side stops chemically induced activity of ipsilateral lumbar ventral nerve root (Pasted from [START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF]. B2. Cervically-projecting V2a neurons of the Gi mediate headturning on the ipsilateral side upon optogenetic activation, while lumbar-projecting V2a neurons of the Gi stop hindlimbs upon optogenetic activation (copied Usseglio et al., 2020) indicating that these neurons can control the steering of locomotion.

How are RSNs recruited to elicit the appropriate locomotor output? [START_REF] Buhl | The role of a trigeminal sensory nucleus in the initiation of locomotion[END_REF]. This allows either direct activation/inhibition of RSNs (auditory/visual/trigeminal inputs trigger startle response in fish (Dunn et al., 2016a;[START_REF] Fetcho | Spinal Network of the Mauthner Cell (Part 1 of 2)[END_REF][START_REF] Kimmel | The development and behavioral characteristics of the startle response in the zebra fish[END_REF]) or the modulation of their ongoing activity [START_REF] Buhl | The role of a trigeminal sensory nucleus in the initiation of locomotion[END_REF][START_REF] Di Prisco | The trigeminal sensory relay to reticulospinal neurones in lampreys[END_REF] to start, stop or modulate ongoing locomotion. As a mechanistic example, in mice, visual information is integrated by the superior colliculus that projects to the V2a RSNs in the Gi mediating steering (described in 1.2.2) [START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF]Usseglio et al., 2020). This connectivity explains how, when a mouse encounters an obstacle in its environment, the visual information can mediate the appropriate avoidance of the obstacle.

Not only do RSNs integrate external sensory information, but their activity is also constantly modulated by sensory feedback and afferent copies from the spinal locomotor CPGs. During fictive locomotion, the central nervous system doesn't receive any sensory inputs from movement, and the RSN activity is modulated by an afferent copy of the motor command sent by the spinal CPGs [START_REF] Dubuc | The role of spinal cord inputs in modulating the activity of reticulospinal neurons during fictive locomotion in the lamprey[END_REF][START_REF] Einum | Reticulospinal neurons receive direct spinobulbar inputs during locomotor activity in lamprey[END_REF][START_REF] Kasicki | Müller cells and other reticulospinal neurones are phasically active during fictive locomotion in the isolated nervous system of the lamprey[END_REF][START_REF] Perreault | Activity of medullary reticulospinal neurons during fictive locomotion[END_REF][START_REF] Vinay | Spinal inputs from lateral columns to reticulospinal neurons in lampreys[END_REF]. During active locomotion, RSN activity is also largely modulated by proprioceptive feedback from mechanosensory neurons (Dubuc et al., 1993a(Dubuc et al., , 1993b;;[START_REF] Wu | Spinal sensory neurons project onto the hindbrain to stabilize posture and enhance locomotor speed[END_REF]. This feedback is crucial to reinforce the locomotor command, in order to sustain locomotion and increase the locomotor speed [START_REF] Fidelin | State-Dependent Modulation of Locomotion by GABAergic Spinal Sensory Neurons[END_REF] for review, see [START_REF] Rossignol | Dynamic sensorimotor interactions in locomotion[END_REF].

Upstream recruitment by locomotor regions such as the MLR conveys the command for internally-generated locomotion

In the absence of any salient cues in the environment, animals still need to locomote in order to fulfill internal needs. The term "internally-driven locomotion", also named "spontaneous locomotion", refers to any type of locomotor output that was not initiated by a direct external sensory stimulus (for review, see [START_REF] Grillner | Locomotion in vertebrates: central mechanisms and reflex interaction[END_REF]. One key area driving spontaneous locomotion is the mesencephalic locomotor region (MLR), a multifunctional region located in the brainstem (for review, see Ryczko and Dubuc, 2013). This region conveys the locomotor program selected by the basal ganglia to RSNs via direct inputs (Brocard and Dubuc, 2003;for review, see Garcia-Rill and Skinner, 1987). Moreover, the RSNs receive multiple other inputs from cortical regions [START_REF] Aravamuthan | Cortical and subcortical connections within the pedunculopontine nucleus of the primate Macaca mulatta determined using probabilistic diffusion tractography[END_REF][START_REF] Keizer | Distribution of corticospinal neurons with collaterals to the lower brain stem reticular formation in monkey (Macaca fascicularis)[END_REF][START_REF] Matsuyama | Vestibulospinal and reticulospinal neuronal activity during locomotion in the intact cat. I. Walking on a level surface[END_REF], the cerebellum [START_REF] Eccles | Reticulospinal neurons with and without monosynaptic inputs from cerebellar nuclei[END_REF][START_REF] Homma | Fastigiofugal projection to the brainstem nuclei in the cat: an anterograde PHA-L tracing study[END_REF], and the hypothalamus (for review, see [START_REF] Sinnamon | Preoptic and hypothalamic neurons and the initiation of locomotion in the anesthetized rat[END_REF][START_REF] Sinnamon | Locomotion elicited by lateral hypothalamic stimulation in the anesthetized rat does not require the dorsal midbrain[END_REF] that shape the command to locomote according to the animal's internal state and needs.

The mesencephalic locomotor region

In the absence of direct sensory stimuli, animals still need to navigate their environment in search of optimal conditions to survive. The mesencephalic locomotor region is a critical locomotor center involved in the generation of spontaneous locomotion, i.e., internally driven according to the animal's needs, by integrating direct inputs from decision centers in the basal ganglia [START_REF] Grillner | The Basal Ganglia Over 500 Million Years[END_REF]; for review, see [START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF] and neuromodulatory systems such as multiple dopaminergic pathways [START_REF] Fougère | Descending dopaminergic control of brainstem locomotor circuits[END_REF][START_REF] Ryczko | Nigral Glutamatergic Neurons Control the Speed of Locomotion[END_REF]. The MLR is a mesopontine region functionally defined in cats by its ability to initiate forward locomotion whose strength linearly scales with the stimulation of the MLR applied, through activation of the reticulospinal neurons [START_REF] Shik | Control of walking and running by means of electrical stimulation of the midbrain[END_REF]. Even though this region is highly conserved across vertebrate species, it is a diffuse and heterogeneous region built of different neuron populations (for review, see (Ryczko and Dubuc, 2013)). From its early description in cats in the 1960s, it has been a long path to decipher which population of this heterogeneous set of neurons was exerting the different functions assigned to the region, and in which context. While it is now clear that the glutamatergic neurons in the pedunculopontine nucleus (PPN) drive spontaneous forms of locomotion by recruiting glutamatergic RSNs [START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF][START_REF] Josset | Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse[END_REF][START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF], the exact nature of the RSNs neurons the MLR recruit and how they are coupled to produce and modulate exploratory-type of locomotion remains elusive. There is no clear map of recruitment from MLR neurons to RSNs controlling locomotion at various strengths, speeds, and patterns. In ex vivo preparations where the lamprey tail is kept intact, the MLR is electrically stimulated at 1.5 uA (A), 2uA (B), and 2.5 uA (C) at a fixed frequency (1-ms pulses at 10Hz). MLR stimulation elicits a wave of lateral displacement of the tail in the rostrocaudal direction. The vigor of the tail displacement (visible by tail snapshot superimposed A2-B2-C2) increases upon increasing the intensity of MLR stimulation. Both the muscle activity (represented in the EMG recording in A1-B1-C1) and the locomotor frequency increase upon the increasing intensity of the MLR stimulation. Pasted from (Sirota et al., 2000).

1.3.1

The MLR is a mesopontine region functionally defined by its ability to elicit graded forward locomotion upon rising stimulation strength Initially defined in cats as the most effective region to elicit forward locomotion in the brain by [START_REF] Shik | Control of walking and running by means of electrical stimulation of the midbrain[END_REF][START_REF] Shik | Control of walking and running by means of electrical stimulation of the midbrain[END_REF], the conservation of the MLR across vertebrate species was later functionally assessed lampreys (Sirota et al., 2000), salamanders (Cabelguen et al., 2003), rats (Skinner and Garcia-Rill, 1984), mice [START_REF] Lee | Identification of a brainstem circuit regulating visual cortical state in parallel with locomotion[END_REF], rabbits [START_REF] Musienko | Spinal and supraspinal control of the direction of stepping during locomotion[END_REF], guinea pigs (Marlinsky and Voitenko, 1991), pigs [START_REF] Chang | Deep brain stimulation of midbrain locomotor circuits in the freely moving pig[END_REF], and non-human primates (Eidelberg et al., 1981). An important characteristic of the role of the MLR that emerged from these studies is that unilateral stimulation of the MLR induces symmetrical forward locomotion, indicating that this region has the ability to convert a unilateral input into a bilateral command [START_REF] Brocard | The Transformation of a Unilateral Locomotor Command into a Symmetrical Bilateral Activation in the Brainstem[END_REF]. More importantly, the MLR controls locomotion, as the intensity of the stimulation applied directly sets the strength of the locomotor output. In all vertebrate species investigated, the frequency and/or the intensity at which the MLR was stimulated linearly scaled with the power of the locomotor output reflected either in the amplitude or in the locomotor frequency (Figure I.11) (Sirota et al., 2000;Skinner and Garcia-Rill, 1984, for review see (Ryczko and Dubuc, 2013)).

The MLR controls locomotion via projections onto RSNs

How does the MLR promote and control locomotion? The MLR heavily projects to the reticular formation (Brocard and Dubuc, 2003;Edwards, 1975;Steeves and Jordan, 1984) via glutamatergic mono or disynpatic inputs (Garcia-Rill et al., 2001;[START_REF] Iwakiri | Stimulus effects of the medial pontine reticular formation and the mesencephalic locomotor region upon medullary reticulospinal neurons in acute decerebrate cats[END_REF]Ryczko et al., 2016). The MLR projections to the RSN are necessary for the locomotor promoting effect of the MLR, the RSNs are thus the substrate relaying the MLR command down to the spinal cord (Noga et al., 2003;Ryczko et al., 2016). In lampreys and salamanders, RSNs in the pontine and retropontine regions receive monosynaptic EPSP from the MLR (Brocard and Dubuc, 2003), and their activation amplitude linearly scales with the intensity of the MLR stimulation applied (Brocard and Dubuc, 2003;Ryczko et al., 2016). Therefore, this increase in the activity of the RSNs could modulate the intensity of the locomotor output. Alternatively, the increase in the strength of locomotion could be mediated by the recruitment of an additional set of RSNs. Indeed, RSNs in the medulla are recruited at higher intensities of MLR stimulation compared to the ones in the retropontine and pontine areas (Brocard and Dubuc, 2003), through a mechanism that was later described in lampreys: a complementary cholinergic path recruited at high intensities of MLR stimulation only that amplifies the signal and in turn recruits RSNs located in the medulla [START_REF] Smetana | A parallel cholinergic brainstem pathway for enhancing locomotor drive[END_REF].

I.3.4 The MLR is a diffuse region composed of diverse cell types whose functions are not trivial to investigate

Throughout vertebrate species, the anatomical and histological investigations of the MLR showed that this high motor center is located around the mesopontine cholinergic group of neurons, also referred to as Ch5 cholinergic nuclei spanning from the caudal border of the substantia nigra to the locus coeruleus (Cabelguen et al., 2003;Garcia-Rill et al., 1987;[START_REF] Karachi | Cholinergic mesencephalic neurons are involved in gait and postural disorders in Parkinson disease[END_REF][START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF]Sirota et al., 2000 for review, see (Ryczko and Dubuc, 2013). The MLR always contains local GABAergic interneurons, which likely modulate the activity of MLR glutamatergic neurons sending the excitatory drive to activate the RSNs (Figure II.12A) (Brocard and Dubuc, 2003;Ryczko et al., 2016). Due to the heterogeneity of neuron types in the dense MLR, the precise organization of the MLR has been a matter of debate for multiple decades (for review, see [START_REF] Jordan | Descending command systems for the initiation of locomotion in mammals[END_REF]Ryczko and Dubuc, 2013). In mammals, it is now generally accepted that the MLR is composed of both the pedunculopontine nucleus (PPN) and the cuneiform nucleus (CuN) (Figure II.12A) [START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF][START_REF] Josset | Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse[END_REF]. Recent optogenetic manipulation of neurotransmitter-specific cell types indicated that these two nuclei are responsible for driving locomotion in distinct contexts: while the PPN drives the slow forms of locomotion underlying exploration [START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF][START_REF] Josset | Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse[END_REF], the CuN drives fast locomotion in the context of fear [START_REF] Dielenberg | When a rat smells a cat": the distribution of Fos immunoreactivity in rat brain following exposure to a predatory odor[END_REF][START_REF] Sinnamon | Preoptic and hypothalamic neurons and the initiation of locomotion in the anesthetized rat[END_REF]. Accordingly, the CuN projects to and receives inputs from defensive system centers in mammals (for review see Ryczko and Dubuc, 2013).

In terms of anatomy, the MLR in lampreys overlaps with the LDT and the putative PPN and can upon stimulation elicit the full range of locomotor speeds that the animal naturally exhibits (Sirota et al., 2000 for review see Ryczko and Dubuc, 2013).

Recent use of modern optical techniques to manipulate the MLR allowed insights into the role of subpopulations of neurons.

The collection of studies using pharmacology, electrophysiology, and geneticallytargeted optogenetic stimulation combined with behavior in genetically-accessible animal models shed light on the identity of neurons in the MLR controlling locomotion.

Pharmacological investigations in aquatic species had already established that the MLR contained both glutamatergic and cholinergic neurons and proposed that both neurotransmitters could directly or indirectly activate RSNs and in turn initiate and modulate locomotion (Brocard and Dubuc, 2003;Le Ray et al., 2003;Ryczko et al., 2016) Optogenetics stimulations performed in mice specified the role of the glutamatergic, cholinergic and GABAergic neurons in the CuN and PPN [START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF][START_REF] Josset | Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse[END_REF][START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF]. By stimulating glutamatergic neurons in the MLR, Roseberry et al revealed that these neurons are necessary and sufficient to induce locomotion and relay the command to start or stop locomotion from the basal ganglia. Interestingly, activating the cholinergic neurons in the same locus did not successfully induce locomotion, but could only modulate the speed of ongoing locomotion (Figure II.12B) [START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF]. Moreover, Roseberry et al. described that stimulating local GABAergic neurons resulted in inhibition of the surroundings glutamatergic neurons and an arrest of ongoing locomotion. Later on, Caggiano and colleagues used the same experimental design to further describe two functionally-distinct populations of glutamatergic neurons, respectively located in the PPN and CuN in mice (Figure II.12B) [START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF]. This study indicated that glutamatergic neurons in the MLR initiate locomotion at the appropriate speed based on the behavioral context: while the PPN glutamatergic neurons support locomotion during exploratory behavior and therefore promote slow locomotion, CuN glutamatergic neurons support fast locomotion during aversive behavior. By doing such, this study also validated the dichotomic involvement of the PPN and CuN for driving respectively exploratory and aversive forms of locomotion. [START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF][START_REF] Josset | Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse[END_REF][START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF]. Pasted from [START_REF] Lau | Cellular-level understanding of supraspinal control: what can be learned from zebrafish?[END_REF].

Altogether, these studies set the ground for a better characterization of this compact, multifunctional region. There is yet a need to link this functional organization to the projection patterns of the MLR neurons onto RSNs in order to build models explaining how MLR neurons appropriately recruit RSNs to transmit the motor commands to the spinal effector circuits.

Linking neuronal populations to motor function by leveraging the optical and genetic accessibility of larval zebrafish

In the brainstem, the MLR and the RSNs work in concert to integrate multiple internal and external inputs and then convey the command for the appropriate locomotor action to the spinal CPGs. Reticulospinal neurons at different locations in the brainstem have been shown to contribute to starting, stopping, steering, modulating the kinematics of locomotion or controlling posture [START_REF] Arber | Connecting neuronal circuits for movement[END_REF]Bretzner and Brownstone, 2013;[START_REF] Deliagina | Spinal and supraspinal postural networks[END_REF][START_REF] Takakusaki | Brainstem control of locomotion and muscle tone with special reference to the role of the mesopontine tegmentum and medullary reticulospinal systems[END_REF]). Yet, the precise organization and genetic identity of RSNs involved in each aspect of locomotor control, and the inputs they receive from the MLR to initiate and modulate locomotion are unclear. This is mainly due to the difficulty of accessing deep brainstem circuits in mammals and the limited use of genetic targeting in Xenopus, lampreys, or salamanders.

For my PhD, I thus took advantage of the zebrafish model, which seemed to be a tailormade model to decipher this organization using a combination of optics and genetics. This small vertebrate is indeed both genetically and optically accessible at the larval stage, which makes it possible, in an animal that has already a wide motor repertoire, to both record and manipulate each and all neurons from a genetically-defined cell type, in vivo. Multiple studies previously linked neuronal activity to a specific behavior in larval zebrafish (for review, see [START_REF] Mcarthur | Chapter 46 -Zebrafish as a Model for Revealing the Neuronal Basis of Behavior *[END_REF][START_REF] Vanwalleghem | Integrative whole-brain neuroscience in larval zebrafish[END_REF]. However, most of these studies did not target a given cell type and investigated the neural circuits underlying sensory-driven locomotion, and very few studies investigated the circuits underlying spontaneous locomotion [START_REF] Dunn | Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion[END_REF]. V2a RSNs, which are conserved in zebrafish, are key to zebrafish locomotion and could be conveying the command of internally-driven locomotion that is transmitted from the basal ganglia to the MLR. However, to understand which V2a RSNs drive and modulate internally-driven locomotion, we face two challenges: 1) the common imaging techniques (two-photon laser scanning microscopy, confocal spinning disk) do not allow the simultaneous recording of the full volume of V2a RSNs at sufficiently high frame rate during a single locomotor event, 2) There is no MLR description in zebrafish, preventing us to investigate how V2a RSNs can be recruited to initiate and modulate spontaneous forms of locomotion.

Investigation of motor circuits in larval zebrafish

A powerful model organism for neurophysiological investigations underlying locomotion

Larval zebrafish is a well-suited model to investigate the function and organization of any neural circuits. Indeed, this small animal has key features that enable recording, manipulation, and investigation of the morphology of specific neural populations to investigate their function and how they participate in a given behavior. The transparency of the mitfa -/larvae lacking skin pigments, routinely used in zebrafish labs, makes it prone to fluorescence microscopy techniques. Moreover, its genetic accessibility allowed the generation of multiple transgenic lines (referenced in multiple brain browsers, [START_REF] Randlett | Whole-brain activity mapping onto a zebrafish brain atlas[END_REF], interchangeable using the UAS/GAL4 system. These powerful tools allowed researchers to develop stable transgenic lines expressing calcium indicators, enabling indirect simultaneous recording of neuronal activity in multiple of a specific type genetically defined [START_REF] Higashijima | Imaging Neuronal Activity During Zebrafish Behavior With a Genetically Encoded Calcium Indicator[END_REF]. This use of genetically encoded calcium indicators (GECIs) offers great advantages compared to previous techniques requiring acute manipulation that were potentially damaging and only transients, such as injections to load calcium indicators [START_REF] O'malley | Imaging the functional organization of zebrafish hindbrain segments during escape behaviors[END_REF]. GECIs enable the indirect measure of the activity of large genetically-defined neuronal population. The external development of the larvae enables easy injections of genetic material constructs for mosaic single cell labeling to investigate the morphology [START_REF] Menelaou | Differences in the morphology of spinal V2a neurons reflect their recruitment order during swimming in larval zebrafish[END_REF], but other techniques such as electroporation [START_REF] Tawk | Focal electroporation in zebrafish embryos and larvae[END_REF][START_REF] Zou | Targeted Electroporation in Embryonic, Larval, and Adult Zebrafish[END_REF] of genetic constructs or dye can also achieve this goal. Moreover, as early as 5 days post fertilization (dpf), larval zebrafish already exhibit a wide range of stereotyped motor behaviors such as forward swimming, turns, escape behaviors and prey capture (Budick and O'Malley, 2000). At this stage, larval zebrafish explore their environment typically by producing low speed swim bouts: forward swims-characterized by small and symmetrical tail oscillations, and "routine" turns, characterized by a large initial bend in the first cycle followed by swimming in the direction imposed by the first bend (Budick and O'Malley, 2000).

Organization of RSNs in larval zebrafish

In the 1980s, Kimmel and colleagues initially described the zebrafish reticulospinal system by chemically backfilling these neurons from the spinal cord (Kimmel et al., 1982;[START_REF] Metcalfe | Segmental homologies among reticulospinal neurons in the hindbrain of the zebrafish larva[END_REF]. In this technique, axonal tracts in the spinal cord are lesioned and a dye is injected that will enter lesioned axons and can be retrogradely transported to the soma of the cells that projected an axon at the lesion site. Kimmel and colleagues injected dyes at the level of spinal cord segment ~14 and described the reticulospinal system as composed of around 200 canonical neurons in the brainstem (Figure II.13A) (Kimmel et al., 1982;[START_REF] Metcalfe | Segmental homologies among reticulospinal neurons in the hindbrain of the zebrafish larva[END_REF], which can be individually identified and whose overall organization is conserved up to adulthood [START_REF] Lee | Identifiable reticulospinal neurons of the adult zebrafish, Brachydanio rerio[END_REF]. These results paved the way for numerous studies investigating the role of these spinal-projecting neurons to control posture and locomotion (Huang et al., 2013;[START_REF] O'malley | Imaging the functional organization of zebrafish hindbrain segments during escape behaviors[END_REF]Orger et al., 2008;Thiele et al., 2014).

Recently, Kimura et al used optical backfills to identify neurons projecting to the spinal cord, using the photoconvertible protein Kaede changing emission from green to red upon UV excitation [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]. In this approach, the Kaede protein is expressed in geneticallydefined neurons and photoconverted by UV light localized to the spinal cord to label only spinal-projecting neurons. The soma of spinal-projecting neurons then exhibits a higher ratio of red to green after diffusion of the protein to the soma. By performing this optical backfill in transgenic larvae where Kaede protein is expressed in V2a neurons (Figure II.13B, Tg(vsx2:Kaede)), Kimura showed that RSNs had to be more numerous than initially thought in larval zebrafish as V2a RSNs alone were more than 200 neurons. Interestingly, most of V2a RSNs located in the medulla had never been identified in previous studies using retrograde labeling with dyes injected aaround segment 14 (Figure II.13B) (Kimmel et al., 1982;[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]. [START_REF] Metcalfe | Segmental homologies among reticulospinal neurons in the hindbrain of the zebrafish larva[END_REF]. B, Using optical backfills in Tg(vsx2:Kaede) larvae, Kimura and colleagues revealed that numerous V2a neurons in the brainstem project to the spinal cord, including most of V2a Neurons in the medulla: these later neurons were not revealed by classical techniques of spinal backfill relying on a chemical dye injected in segment 14. Right panel, all V2a photoconverted by spinal UV illumination, correspond to all V2a neurons that project an axon in the spinal cord. Copied from [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF].

Recording neural activity during active locomotion

The investigation of the role of RSNs in controlling locomotion critically relies on the ability to record neural activity and manipulate neural circuits during active locomotion (Deliagina et al., 2000). As stated above (Part 1.2), the proprioceptive feedback and change in position during movement contribute to shaping the locomotor output. Thus, the recruitment and activity of command neurons may be distorted in preparations in which movement is prohibited, where locomotion is "fictive" and the body position stable. In the 2000s, the first use of awake head-fixed, tail-freed preparations, also referred to as "tethered preparations", represented a breakthrough in the use of larval zebrafish as a model for neuroscience. In this preparation, the head of larval zebrafish is constrained in an agarose gel and the tail is left free to beat [START_REF] Ritter | In Vivo Imaging of Zebrafish Reveals Differences in the Spinal Networks for Escape and Swimming Movements[END_REF]. This allows one to monitor the motor behavior of larval zebrafish by recording the tail beating with a camera, while also being able to record or manipulate neural circuits in the steady head of the larval zebrafish [START_REF] Orger | Correlating Whole Brain Neural Activity with Behavior in Head-Fixed Larval Zebrafish[END_REF]. In order to monitor the neural activity of multiple neurons at the same time, we can use population calcium imaging where multiple neurons express fluorescent calcium indicator [START_REF] Akerboom | Optimization of a GCaMP calcium indicator for neural activity imaging[END_REF][START_REF] Chen | Ultrasensitive fluorescent proteins for imaging neuronal activity[END_REF][START_REF] Orger | Correlating Whole Brain Neural Activity with Behavior in Head-Fixed Larval Zebrafish[END_REF][START_REF] Tian | Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators[END_REF].

Using the typical calcium sensor GCaMP, the fluorescence emitted by neurons expressing the sensor changes according to the intracellular calcium concentration, which promptly rises for each action potential due to the opening of voltage-gated calcium channels at the plasma membrane and slowly decays according to buffering and extrusion from the cytoplasm [START_REF] Tian | Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators[END_REF]. We can therefore estimate the firing rate of the neuron from the relative change of fluorescence in time, using deconvolution methods [START_REF] Berens | Community-based benchmarking improves spike rate inference from two-photon calcium imaging data[END_REF][START_REF] Yaksi | Reconstruction of firing rate changes across neuronal populations by temporally deconvolved Ca2+ imaging[END_REF]. Combined with the latest advances in microscopy, genetic engineering, and optics, scientists successfully used larval zebrafish to monitor the activity of the whole brain during sensorimotor processes, which helped to decipher the neural circuits underlying sensorimotor processes [START_REF] Migault | Whole-Brain Calcium Imaging during Physiological Vestibular Stimulation in Larval Zebrafish[END_REF][START_REF] Naumann | From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response[END_REF][START_REF] Privat | Sensorimotor Transformations in the Zebrafish Auditory System[END_REF][START_REF] Vanwalleghem | Integrative whole-brain neuroscience in larval zebrafish[END_REF]. However, most of these studies lacked the genetic targeting necessary to decipher the precise nature of the neurons controlling these functions, and/or did not investigate the neural circuits underlying spontaneous forms of locomotion.

V2a

RSNs in larval zebrafish are interesting candidates to elicit spontaneous locomotion

Initiation and steering of locomotion

Both optogenetic experiments and population imaging showed that V2a RSNs are sufficient and necessary for starting and steering locomotion in larval zebrafish (Huang et al., 2013;[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]Orger et al., 2008). In 2008, Orger and collaborators identified a ventral group of RSNs, later revealed to be vsx2+ [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF], recruited either for forward swim bouts, for ipsilateral turns, or for both (Orger et al., 2008). A few years later, Huang and collaborators combined targeted two-photon laser ablation and precise description of the tail kinematics to reveal that specific V2a neurons generate asymmetric tail deflection during the first cycle of turns on the ipsilateral side relative to their soma (Figure I.14B) (Huang et al., 2013). In 2013, Kimura and colleagues used optogenetic stimulation to investigate the effect of activation or inhibition of the population of V2a neurons in the brainstem [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]. The authors found that whole hindbrain activation of V2a neurons was efficient at generating locomotion, while silencing when the larvae were spontaneously swimming was sufficient to stop ongoing locomotion.

Late-born V2a RSNs are recruited during slow, exploratory-type of locomotion

While the primary studies described above focused on V2a RSNs located in the pontine and retropontine regions, a recent study established that the medullary V2a RSNs were also recruited during distinct types of locomotion (Pujala and Koyama, 2019). In this study, the authors identified that the V2a neurons were organized according to their birth date during development and that they were recruited accordingly for specific functions related to Pujala described that late-born V2a neurons localize laterally and ventrally in the stripe of brainstem V2a, while early-born V2a neurons localize more medially and dorsally (Figure

I.14C1).

This study highlighted a fundamental principle of topographical organization, with a different topography than the simple ventrodorsal organization described in the spinal cord [START_REF] Mclean | Continuous shifts in the active set of spinal interneurons during changes in locomotor speed[END_REF]. This study also shed light on the numerous V2a RSNs in the middle medulla that are recruited during slow, spontaneous locomotion. B2, Laser ablation of these RoV3, MiV1 and MiV2. B3, Example behavior response to light illumination on left or right side of the larval zebrafish, before and after ablating these V2a RSNs. Note that, in response to illumination on the non-ablated side, the tail and heading angles are unchanged after ablation. On the ablation side, the fish still swims in response to the illumination but the initial large bend towards the side of the illumination is not present after ablation. Pasted from (Huang et al., 2013)C1, V2a neurons born before 24 hpf are located on the lateral side of the V2a neurons. Example experiment in which transgenic Tg(vsx2:Kaede) larvae, in which V2a neurons express the photoconvertible protein Kaede, were illuminated with UV light at 24 hpf. The Kaede protein expressed in the neurons born at that time was photoconverted and turned from green to red. In the image, the neurons that appear purple were thus born before 24hpf, the neurons born after appearing green. Pasted (Pujala and Koyama, 2019) C2, V2a RSNs born the earliest are recruited during crude maneuvers that appear at early stages of the larval zebrafish development. V2a neurons that appear later on during development are positioned more medially in the stripe and sustain exploratory forms of locomotion that appear concomitantly. Pasted from [START_REF] Wyart | Building behaviors, one layer at a time[END_REF], commented from (Pujala and Koyama, 2019) 2.3 Classical imaging techniques prohibit the fast recording of the whole population of V2a RSNs during locomotion

The recording of the activity of V2a RSN population in free-tailed animals requires fast volumetric recording of neurons across the entire hindbrain of transgenic larval zebrafish expressing calcium indicators in V2a RSNs. To capture the activity of neurons during swimming events, the volumetric sampling rate needs: 1) to match the time-scale of genetically encoded available calcium indicators [START_REF] Chen | Ultrasensitive fluorescent proteins for imaging neuronal activity[END_REF] 2) to be in the range of the duration of a typical swimming event of larval zebrafish (~250ms in freely swimming conditions) (Mirat et al., 2013). Laser point-scanning one photon, two-photon, or spinning disk confocal microscopy is often used to image specific brain regions in larval zebrafish with single-cell resolution (Orger et al., 2008;Pujala and Koyama, 2019;[START_REF] Wee | Zebrafish oxytocin neurons drive nocifensive behavior via brainstem premotor targets[END_REF]. These approaches do not provide sufficient scanning speed for volumetric imaging. Light-sheet microscopy offers a great alternative and has recently been used for whole-brain imaging [START_REF] Ahrens | Whole-brain functional imaging at cellular resolution using light-sheet microscopy[END_REF][START_REF] Migault | Whole-Brain Calcium Imaging during Physiological Vestibular Stimulation in Larval Zebrafish[END_REF][START_REF] Panier | Fast functional imaging of multiple brain regions in intact zebrafish larvae using selective plane illumination microscopy[END_REF]. While most of these techniques rely on multiple objectives to build the light sheet and collect the signal, the SCAPE microscope developed by the Hillman lab [START_REF] Bouchard | Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms[END_REF][START_REF] Voleti | Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0[END_REF] provides the advantages of building light-sheet and collecting the emitted photons with a single-fixed objective at single cell resolution and high volumetric rate for the entire zebrafish hindbrain.

Laser point-scanning microscopy

Laser-point scanning microscopy relies on focusing illumination light on a single point and sequentially moving to scan through the sample. This type of fluorescent microscopy is used in confocal microscopes using visible lasers or two photon microscopes (2P) using infrared lasers. Unlike widefield imaging, confocal microscopes using a visible laser provide 3D optical sectioning brought by the funneling of the detected light through a pin-hole to remove out-of-focus light [START_REF] Conchello | Optical sectioning microscopy[END_REF]. The emitted light is then collected by the same objective and brought through a dichroïc mirror to the photomultiplier tube that collects photons [START_REF] Conchello | Optical sectioning microscopy[END_REF]. This method provides the necessary spatial resolution to investigate the dynamic processes of single-cell individually, so it is preferred to image a given region of interest at single-cell resolution. In zebrafish, fluorescent signals from

RSNs loaded with a calcium indicator were first recorded with 2-photon laser scanning microscopes (2P) (Orger et al., 2008), where optical sectioning is enabled by the two-photon absorption of the sensor in the infrared range. The necessary excitation is brought to the fluorescent probe by a pulsed infrared laser, which is of interest to image deep structures in the brain.

Can we use laser scanning microscopy for fast volumetric imaging? Increasing imaging speed can be achieved by either moving the focus point or by parallelizing the number of focus points. When I started in the Wyart lab, two commercial microscopes were available that use these two techniques respectively to increase the speed of scanning in the axial dimensions.

First, a 2P laser scanning microscope in which two galvanometer mirrors change the focus point in the transverse dimension, and a piezo changes the focal plane in the vertical dimension (commercial system from 3i). This system provides a great trade-off between axial resolution and scanning speed when imaging a single 2D plane (up to 10 Hz for 100 x 400 um) but is not optimized for volumetric imaging as scanning in the vertical dimension is slower.

Second, we have a spinning disk confocal microscope in which a wheel composed of multiple holes is spinned to focus the illumination light at different positions at the same time while the image is collected using a camera (commercial system from 3i, for review on the method, see [START_REF] Gräf | Live cell spinning disk microscopy[END_REF]). The illumination time is thus reduced by increasing the number of points scanned at the same time. Although the spinning disk confocal is fast (up to 30 Hz on a single plane), the issue of out-of-focus light due to the collections of diffracted photons in the neighboring pinholes of the wheel forbids the recording of deeply-located V2a RSNs.

Moreover, any increase in speed is made at the expense of a decrease in the integration time available to collect signals from a pixel and form the image.

Light-sheet microscopy enables volumetric imaging

Light-sheet microscopy is a form of microscopy where the illumination light is focused on a single plane rather than a single point, and the image of the illuminated sample is collected on a camera (for review, see [START_REF] Adams | Light Sheet Fluorescence Microscopy (LSFM)[END_REF][START_REF] Hillman | Light-Sheet Microscopy in Neuroscience[END_REF]). Although this type of microscopy is not necessarily faster than spinning-disk microscopes, it offers a better optical sectioning and a better photon budget. The sample is illuminated by the thin sheet of light created by a cylindrical objective lens, traditionally located on the side of the sample, and the emitted light is collected by an orthogonal detection objective, above or below the sample. The image formed is collected by a camera placed on a fixed plane. In order to scan through the volume, the sample or the light-sheet is translated. This system has been successfully used for whole-brain functional imaging in larval zebrafish [START_REF] Ahrens | Whole-brain functional imaging at cellular resolution using light-sheet microscopy[END_REF][START_REF] Panier | Fast functional imaging of multiple brain regions in intact zebrafish larvae using selective plane illumination microscopy[END_REF], mostly to investigate sensory and sensorimotor processing [START_REF] Migault | Whole-Brain Calcium Imaging during Physiological Vestibular Stimulation in Larval Zebrafish[END_REF][START_REF] Privat | Sensorimotor Transformations in the Zebrafish Auditory System[END_REF][START_REF] Wee | Zebrafish oxytocin neurons drive nocifensive behavior via brainstem premotor targets[END_REF]. Although this technique enables fast volumetric imaging, it still requires mechanical translation of the sample or both the illumination and detection arm to scan through the sample. This is not so appropriate to record from the entire hindbrain of actively moving larval zebrafish as not only is it difficult to perform at high speed, but the mechanical translation can create water flow and vibrations at which the larval zebrafish can respond to.

SCAPE microscope enables fast volumetric imaging with a single fixed objective

The lab of Elizabeth Hillman exploited the concept of titled light-sheet microscopy to develop an innovative type of light-sheet microscope with a single fixed objective, called Swept Confocally Aligned Planar Excitation (SCAPE) microscope [START_REF] Bouchard | Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms[END_REF]. In this system, the illumination is brought to the sample by a titled sheet of light formed by a fixed II-Functional coupling of the mesencephalic locomotor region and V2a reticulospinal neurons driving forward locomotion

As we presented in the introduction, the MLR is a key locomotor center that controls locomotion by recruiting RSNs that, in turn, recruit locomotor CPGs in the spinal cord. Studies in lampreys and mice established that glutamatergic neurons in the MLR provide the excitatory drive to RSNs, while cholinergic neurons in the MLR modulate locomotor speed, and GABAergic neurons in the MLR provide local inhibitions (Brocard and Dubuc, 2003;[START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF]Capelli et al., 2017;[START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF][START_REF] Smetana | A parallel cholinergic brainstem pathway for enhancing locomotor drive[END_REF]. However, the cellular and molecular identity of the RSNs recruited by the MLR to initiate locomotion and modulate the kinematics of movement remains unclear. Unraveling this organization relies on our ability to record genetically-identified RSNs while stimulating the MLR. RSNs expressing the transcription factor vsx2+ were shown in multiple vertebrate species to be key for initiation, steering, and halt of locomotion [START_REF] Bouvier | Descending Command Neurons in the Brainstem that Halt Locomotion[END_REF][START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF][START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]Li and Soffe, 2019;Usseglio et al., 2020). In the optically and genetically accessible larval zebrafish, it is possible to identify and monitor the activity of V2a RSNs as shown by optical population recordings coupled to fictive locomotion that revealed V2a RSNs in the medulla recruited during spontaneous slow locomotion typically occurring during exploration (Pujala and Koyama, 2019). Larval zebrafish thus offers a great opportunity to investigate the coupling between the MLR and V2a RSNs and how this coupling produces adapted exploratory locomotion. However, when Dr. Martin Carbo-Tano and I started this investigation in 2019, the MLR had yet to be described in zebrafish. This chapter will cover three goals:

1) The functional and anatomical identification of the MLR in larval zebrafish

2) The functional coupling between V2a RSNs and the MLR by describing which V2a

RSNs are recruited upon MLR electrical stimulation

3) The recruitment of medullary V2a RSNs during MLR-induced locomotion and how they encode kinematics of movement

History of the project and authors' contribution

This project was designed by Pr. Réjean Dubuc and Dr. Claire Wyart during conversations at SfN in 2017 and initiated by Dr. Martin Carbo Tano who started performing the experiments to functionally and anatomically identify the MLR. I joined the project in 2019 to help Martin analyze the complex behavioral and calcium imaging data he had collected. I had previously developed a pipeline to analyze calcium imaging data that I adapted for this project. Together, we worked on extracting meaningful results from the data, interpreting the results, building the figures, and writing the manuscript presented here, with the input of Pr. Réjean Dubuc and Dr. Claire Wyart. In this project, I suggest a new approach to tackling complex, often mixed locomotor events by quantifying swimming bouts in larval zebrafish given instantaneous kinematic parameters. Moreover, my contribution brings a new perspective to interpret the recruitment of neurons commanding locomotion, using machine learning to model the activity of neurons with kinematic parameters of the locomotor output.

Affiliations

Introduction

Locomotion is the fundamental behavior enabling animals to move in their environment, and thereby to rapidly adapt to the challenges of their environment while satisfying their inner needs. Locomotion can be sensory-evoked to avoid a threatening stimulus for example during escape responses, or goal-directed to explore the environment in search of resources. Motor circuits in vertebrate species are conserved at the anatomical, physiological, and molecular levels 1-3 . The production of locomotor movements relies on the recruitment of command neurons in the brainstem, referred to as reticulospinal neurons (RSNs) in the reticular formation. RSNs integrate synaptic inputs from higher brain areas and from the periphery, and in turn, instruct the spinal circuits that produce movements 4,5 (for reviews, see 6-8 ). The RSNs have been shown to play a critical role in starting, maintaining and stopping locomotion 5,9-11 , as well as in adjusting posture and steering 12-14 .

A critical region involved in the production of locomotion upstream the reticular formation is the mesencephalic locomotor region (MLR), first identified in decorticated cats 15 . The MLR has since been functionally defined in numerous other vertebrate species including lampreys 16 , salamanders 17 , rats 18 , mice 19 , rabbits 20 , guinea pigs 21 , pigs 22 and monkeys 23 .

Electrical stimulation of the MLR triggers forward locomotion in a graded fashion as a function of the stimulation intensity 15,20 (for review, see 24 ). The conservation of MLR properties across vertebrate species suggests that this brainstem structure is essential to vertebrate locomotion.

Anatomical studies indicate that MLR is localized in the vicinity of the mesopontine cholinergic nuclei (for review, see 24 ) and corresponds in mammals to the pedunculopontine nucleus 25 (PPN) and the cuneiform nucleus 15 (CuN). In all vertebrate species investigated, the MLR induces locomotion via the activation of RSNs 26-33 . However, the MLR-dependent recruitment pattern of RSNs that elicits forward locomotion has not yet been resolved due to the complexity and difficulty to access the entire reticular formation in vivo. An interesting subclass of RSNs downstream of the MLR expresses the transcription factor vsx2+ and corresponds to V2a neurons 34-36 . While V2a RSNs have been shown to be necessary for the initiation of locomotion 37 , some studies have highlighted the role of pontine and retropontine V2a RSNs in turning 38-40 and stopping locomotion 41 . Yet, the nature of V2a RSNs recruited by the MLR to trigger forward movement at various locomotor speeds is unknown.

Here we leverage the transparency and genetic accessibility of larval zebrafish to unravel how genetically identified RSNs are recruited by the MLR to produce forward locomotion.

We first identified functionally and anatomically the location of the MLR in larval zebrafish.

We then combined behavioral recordings and population calcium imaging to investigate the recruitment of V2a RSNs across the brainstem upon MLR stimulation. We found that subsets of brainstem V2a RSNs are gradually recruited upon MLR stimulation at increasing intensities. Our study uncovers that a population of V2a reticulospinal neurons in the medulla is sustainably recruited during MLR-evoked forward locomotion and encodes key parameters controlling speed of forward locomotion.

Results

Functional identification of the MLR in larval zebrafish

Previous studies in fish found that electrical stimulation of the dorsocaudal tegmentum in the midbrain that contains the nucleus of the medial lateral fasciculus (nMLF) induced locomotion 42-46 , leading some authors to suggest this region corresponds to the MLR 42,43,46,47 . However, the nMLF is adjacent to the oculomotor nuclei (N3) and not to the mesopontine cholinergic nuclei where the MLR has been identified in all other vertebrates 24 (Fig. 1a-b). Based on previous anatomical and physiological experiments performed in other vertebrate species (summarized in Fig. 1a), including mice 48 , lampreys 16,49 and salamanders 17 , we localized the homologous mesopontine cholinergic nuclei of larval zebrafish in the prepontine region, medial to the locus coeruleus (Fig. 1b).

Fig. 1 | The putative location of the MLR in zebrafish was inferred from previous studies in mammals, amphibians and lampreys showing overlap with mesopontine cholinergic nuclei. a, Schematic illustration of the MLR location in mammals (dashed line) in relation to the mesopontine cholinergic cells (magenta). The structures classically defined as part of the MLR are the pedunculopontine nucleus (PPN), cuneiform nucleus (CuN) and laterodorsal tegmental nucleus (LDT). The characteristics of the structures were taken from the Allen Mouse Brain Atlas (accessible at https://atlas.brain-map.org) and the location and extent of the mesopontine cholinergic nucleus was approximated from 48 . b, Larval zebrafish brain image depicting the probable MLR location (dashed white line) in relation to the locus coeruleus (LC) and the mesopontine cholinergic cells labeled in the Tg(chata:GAL4) transgenic line. The hindbrain reticulospinal neurons and the nMLF are labeled by spinal backfills. All images were taken from the web-interface of mapzebrain (https://fishatlas.neuro.mpg.de/, download: 12 Apr. 2020). Sale bar is 50 µm.

In order to narrow down the locus of MLR, we next investigated the behavioral responses elicited by electrical stimulation applied using monopolar tungsten microelectrode inserted in different locations within the mesopontine cholinergic nuclei of larval zebrafish (Fig. 1b). Using a high speed camera, we monitored the behavioral responses of 6 days post fertilization (dpf) head-embedded tail-free larvae to 2 to 40 s-long stimulation trains of 2 ms duration pulses occurring at variable frequency [START_REF] Grätsch | A brainstem neural substrate for stopping locomotion[END_REF][START_REF] Grillner | Neural bases of goaldirected locomotion in vertebrates--an overview[END_REF][START_REF] Kiehn | Decoding the organization of spinal circuits that control locomotion[END_REF][START_REF] Ruder | Brainstem Circuits Controlling Action Diversification[END_REF](9)[START_REF] Zelenin | Reticulospinal neurons controlling forward and backward swimming in the lamprey[END_REF][START_REF] Juvin | A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion[END_REF][START_REF] Orlovsky | Vestibular control of swimming in lamprey. I. Responses of reticulospinal neurons to roll and pitch[END_REF][START_REF] Deliagina | Vestibular control of swimming in lamprey -III. Activity of vestibular afferents: convergence of vestibular inputs on reticulospinal neurons[END_REF][START_REF] Karayannidou | Responses of reticulospinal neurons in the lamprey to lateral turns[END_REF][START_REF] Shik | Control of walking and running by means of electric stimulation of the midbrain[END_REF][START_REF] Sirota | Stimulation of the mesencephalic locomotor region elicits controlled swimming in semi-intact lampreys[END_REF][START_REF] Cabelguen | Bimodal locomotion elicited by electrical stimulation of the midbrain in the salamander Notophthalmus viridescens[END_REF][START_REF] Skinner | The mesencephalic locomotor region (MLR) in the rat[END_REF][START_REF] Lee | Identification of a brainstem circuit regulating visual cortical state in parallel with locomotion[END_REF][START_REF] Musienko | Postural performance in decerebrated rabbit[END_REF] and intensity (0.1 -2 µA) (Fig. 2a, see Methods). The behavioral responses consisted of locomotor bouts corresponding either to coordinated symmetric forward swims (Fig. 2b, blue trace), uncoordinated asymmetric tail bends resembling struggle (Fig. 2b, magenta trace), or a mix of both occurring sequentially as distinct motor episodes within the same bout (Fig. 2c, left). We manually segmented and classified each episode as either forward swim or struggle (see Methods).

The maximal tail bend amplitude and the median tail beat frequency best differentiated forward swimming from struggle episodes (Fig. 2c, right). For each stimulation, we defined a forward index as the normalized ratio of forward over struggle elicited episodes (see Methods, Fig. 2d). For each microelectrode position, we calculated the median forward index over all stimulations and then mapped each 3D position to a reference brain space based on the mapzebrain atlas 50 (see Methods, Fig. 2e). We defined the MLR as the brainstem region comprising all stimulation sites with a positive median forward index. The MLR locus corresponded to a confined area of ~30 µm in radius in rhombomere 1, which was medial and dorsal to the locus coeruleus (LC) (Fig. 2e, and Supplementary Video 1).

The stimulation of the MLR at 10 Hz was the most effective at inducing forward swimming, while 5 Hz or 20 Hz stimulation frequency often resulted in struggle-like behaviors (Extended Data Fig. 1). This observation is consistent with previous results in lampreys and salamanders showing an optimal frequency of stimulation between 2 and 10 Hz 16,17,51 .

Altogether, our approach functionally identified the locus of the MLR of larval zebrafish in a prepontine area that is medial to the locus coeruleus in the vicinity of the mesopontine cholinergic nuclei.

Parameters of the MLR stimulation determine the latency, duration, and frequency of locomotion

In previously investigated vertebrate species, the intensity of the MLR stimulation controlled the duration and power of locomotion 16,17,22,27 . We investigated whether the intensity, train duration and pulse frequency defining the MLR stimulation modulated the duration and kinematics parameters of forward swims. In head-fixed tail-free larvae, stimulating the MLR for 2 or 4 s elicited sustained episodes lasting the stimulation train (Fig. 3a-b, 10 Hz pulse train at 1 µA; mean ± sd for 2 s long train: 1.91 ± 0.44 s, for 4 s long train: 3.65 ± 0.31 s). In comparison, spontaneous forward swims recorded between stimulation trials only lasted for a few hundreds of milliseconds (Fig. 3a-b, spontaneous: 0.41 ± 0.15 s). Furthermore, as previously observed in lampreys and salamanders 16,17 , the delay to locomotor onset was inversely proportional to the stimulation intensity (Fig. 3c, 0.1 µA: 1.22 ± 1.97 s; 1 µA: 0.15 ± 0.11 s). We noticed that MLR-induced forward swims exhibited larger median tail bend amplitude (TBA) than spontaneous swims (Fig. 3d, spontaneous: median TBA = 5.9 ± 3.16°, MLR-induced: median TBA = 8.5 ± 3.8°), but similar median tail beat frequencies (TBFs) (Fig. 3e

Neurons in the MLR locus project to the reticular formation

The locomotor output elicited in response to electrical stimulation of the MLR is due to projections onto hindbrain reticulospinal neurons (RSNs) 4,31,32 . To test whether the MLR region contains neurons that project to RSNs in larval zebrafish, we performed retrograde tracing experiments via a unilateral injection of biocytin in the reticular formation between rhombomeres 2 and 6 (Fig. 4a-b, Extended Data Fig. 2). The injection sites and the retrogradely-labeled neurons found inside the MLR locus defined in Fig. 2 were registered to a common reference brain space 50 . Such retrograde tracing revealed neurons located in the MLR locus whose axonal projections descended both ipsi-and contra-laterally (Fig. 4c1-2, 11 larvae). We observed no difference in the number of cells labeled at either side of the injection site. To investigate the possible projection pattern of individual neurons located in the MLR region we employed "virtual single cell tracing" interrogating the cellularresolution brain atlas of mapzebrain 50 . We searched for neurons with soma located in a region of 30 µm in radius medial and dorsal to the LC. Most of the neurons retrieved had ipsilateral descending axons to the reticular formation, with some of them projecting down to the most caudal medulla (Fig. 4d, see also Extended Data Fig. 3). Interestingly, we found that none of the axons reached the dorsal portions of the pontine and retropontine reticular formation.

In lamprey as in mouse, the main synaptic output from the MLR that drives locomotion is glutamatergic 27,49,53 . To investigate whether some MLR neurons were glutamatergic in zebrafish, we performed backfill experiments targeting the reticular formation in the Tg(vglut2:GFP) transgenic line that labels numerous glutamatergic neurons 54 . We found that some of the retrogradely-labeled neurons in the MLR region were vglut2-positive (Fig. 4e-g). As glutamatergic neurons are intermingled with cholinergic neurons in the MLR of lampreys 49 , salamanders 17 and mice 53,55 , we investigated in retrograde labeling experiments whether cholinergic neurons were located within the zebrafish MLR locus using immunohistochemistry against ChAT and Dbh. As seen on cross sections at the level of the MLR locus (see Fig. 4c3), retrogradely-labeled neurons were medial to Dbh-positive cells from the locus coeruleus and in close proximity to ChAT-positive cells, presumably belonging to the zebrafish homolog of the laterodorsal tegmental nucleus 29,29 (LDT) (Fig. 4h). In agreement with this observation, mapping all the results obtained so far in the same reference brain space revealed that glutamatergic and cholinergic cells were in the vicinity of the loci whose stimulation elicited forward swimming (Fig. 4i). We found that the cholinergic cells expressing the chata gene in the tegmentum within the nucleus isthmi 56 do not colocalize with the MLR, suggesting that the immunoreactive ChAT-positive neurons found centered on the MLR belong to the nucleus expressing chatb and vachtb genes 56,57 . Altogether, our anatomical investigations confirm that MLR neurons in zebrafish project onto the reticular formation and include glutamatergic cells in the vicinity of cholinergic neurons. ). i, Based on the mapzebrain zebrafish space 50 , schematic representation of the location of the retrogradely-labeled glutamatergic cells (cyan, 6 fish, 15 cells), chata + neurons (orange), chatb + cells whose location was estimated from 56,57 (yellow), locus coeruleus (white), and the MLR locus defined by the delimitation of stimulation sites that effectively triggered forward swimming (dashed line) with all neighboring retrogradely-labeled neurons (red). For panels b, e, h: maximum Z projection stacks (schematized in the bottom right corner with squares including multiple lines); f: single optical sections (schematized in the bottom right corner with squares including a single line). PP, prepontine; P, pontine; RP, retropontine; Med, medulla.

Extended Data Fig. 2 | Brainstem nomenclature applied in this study according to 58 . The anatomical landmarks were defined based on the projecting neurons labeled by spinal backfills, the expression patterns of Tg(chata:GAL4) and Tg(vmat2:GFP) transgenic lines. All images were taken from the web-interface of mapzebrain (https://fishatlas.neuro.mpg.de/, download: 12 Apr. 2020). The prepontine region contains the isthmus and rhombomere 1 (r1). The trochlear nucleus (4N) lies in the rostral border of the isthmus and, amongst other structures, r1 contains the locus coeruleus (LC). The pontine region is defined rostrally by rhombomere 2 (r2), containing the rostral trigeminal motor nucleus (5Nr), the caudal part of the trigeminal motor nucleus (5Nc), and caudally by rhombomere 4 (r4) including the Mauthner cell (M cell). The retropontine region is composed of rhombomeres 5 and 6 (r5, r6) containing the abducens (6N) and facial nucleus (7N) respectively. The medulla is delimited rostrally by rhombomere 7 (r7) and caudally by rostral spinal cord. Under this nomenclature, the oculomotor nucleus (3N) is a midbrain structure, but the nucleus of the medial longitudinal fasciculus (nMLF) should be considered as a diencephalic structure.

In contrast, a vast majority of V2a cells were RSNs in the ventral pontine nucleus (vP: 95.6% ± 5.1%, 4 fish), ventral retropontine (vRP: 84.1% ± 9.5%), and medulla (Med: 96.6% ± 1.8%).

Interestingly, the medulla contained 60% of all V2a RSNs. For our analysis on the recruitment of V2a RSNs upon MLR stimulation, only the ventral pontine, ventral retropontine and medullary regions were therefore included.

In lampreys and salamanders, a unilateral single pulse stimulation of the MLR elicits bilateral excitatory postsynaptic potentials in hindbrain RSNs 29,51,60 . In order to test whether the MLR drives ipsi-and contra-lateral RSNs in larval zebrafish, we investigated in Tg(vsx2:GAL4;UAS:GCaMP6s) transgenic larvae the response of V2a RSNs to unilateral single pulse (1-5 µA, 2 ms long) MLR stimulations (Fig. 5b1-b2). We found that unilateral stimulation of the MLR evoked bilateral excitatory responses in V2a neurons throughout the hindbrain from the pontine region to the medulla (Extended Data Fig. 4a).

We confirmed the specific MLR coupling to RSNs by analyzing the amplitude of the calcium transients in large identifiable ventral pontine and retropontine V2a RSNs named RoM2, RoM3, MiV1, MiD2i, MiD3i 37 (Fig. 5c1). In agreement with previous reports 29,51,60 , these large ventrally located RSNs were reliably recruited starting at low stimulation intensity and similarly responded with an amplitude modulation scaling with stimulation intensity (Fig. 5c2-c3), further supporting our identification of the MLR in larval zebrafish.

Upon stimulation of graded intensities (Fig. 5b2), a subset of brainstem V2a neurons reliably responded above a given threshold ("reliably recruited neurons'" in color, 22%, Extended Data Fig. 4b, Extended Data Fig. 5a), others either did not consistently respond ("unreliably recruited" in gray, 31%) or never responded ("non recruited", 47%). We focused our analysis on reliably recruited cells as they likely receive excitatory inputs from the MLR and contribute to the motor output (5 fish, n = 391 out of 1781 recorded neurons). Half of V2a

RSNs were reliably recruited in the ventral pontine and retropontine areas (Fig. 5d, 5 fish, vP: 42.8 %, vRP: 50.0 %, Extended Data Fig. 5b) and their recruitment occurred at the lowest stimulation intensity used (Fig. 5e, Extended Data Fig. 4b, Extended Data Fig. 5c). We investigated whether calcium responses of reliably recruited V2a RSNs exhibited different rising slopes upon MLR stimulation, as the firing rate of neurons correlates with the rising slope of their calcium transients 61,62 . Responses of V2a RSNs in the ventral pontine region and ventral retropontine (subset marked by arrowhead, Fig. 5f1) showed consistently large rising slopes, larger to the ones observed in any of the other regions (Fig. 5f2, Extended Data Fig. 5d), suggesting an effective and prompt coupling between MLR and ventrally located V2a RSNs.

In the medulla, a small proportion of V2a RSNs were reliably recruited (Fig. 5e, 5 fish, 17%).

In contrast to RSNs in the pontine and retropontine areas, the recruitment of medullary V2a RSNs occurred in a graded manner as a function of MLR stimulation intensity (Fig. 5e, Extended Data Fig. 4b), suggesting that these cells could be involved in eliciting graded locomotion.

Subsets of V2a medial medullary neurons encode number of oscillations and locomotor frequency during forward locomotion

In order to test whether medullary V2a RSNs translate the MLR command into graded forward locomotion, we investigated whether these cells could encode locomotor frequency or amplitude of forward swimming. We performed functional imaging in headfixed tail-free zebrafish larvae during long lasting forward episodes elicited by long MLR train stimulations (0.1 µA; 10 Hz; 40 s-long) (Fig. 6a). Such stimulation elicited unusually long symmetric forward episodes lasting up to 20 s and containing up to hundreds of oscillations (Extended Data Fig. 6a-b). We found that half of the medullary V2a neurons were active during locomotion (60.1 ± 19 %, 133 ± 37.2 out of 228 ± 35.9 neurons per fish, a total of 1140 neurons out of 5 fish recorded). As medullary V2a RSNs exhibited diverse patterns of activity, we clustered them based on their calcium signal (Fig. 6b, see Methods, Supplementary Video 2). We identified a cluster of medullary V2a RSNs that were specifically recruited during forward swims (Fig. 6b, referred to as "forward cluster") and another specifically recruited during struggles (Extended Data Fig. 6c). Medullary V2a RSNs in the forward cluster were medially located (Fig. 6c) whereas the ones active during struggles were laterally located (Extended Data Fig. 6d). The proportion of V2a medullary neurons of the forward cluster was similar to the proportion of medullary neurons reliably recruited by the MLR (forward cluster: 18.32 ± 7.32%, 26.4 ± 12.6 out of 228 ± 35.9 neurons per fish, a total of 1140 neurons out of 5 fish recorded; Fig. 6, reliably recruited V2a neurons: 22% of medullary V2a neurons).

We then focused our analysis on the activity of V2a RSNs associated with forward swimming. We noticed that the calcium signals of V2a RSNs in the forward cluster increased throughout the forward episode (Fig. 6b), suggesting that these V2a RSNs could encode the number of oscillations of forward swims. Accordingly, we found a strong correlation between the maximal amplitude of the calcium transient and the number of oscillations during a forward episode (Fig. 6d1) for numerous medial V2a RSNs dorsally located in the caudal medulla (Fig. 6d2). Using a regression-based approach to model the activity of these cells, we investigated which kinematic parameters the medullary V2a RSNs encode among tail beat frequency, amplitude of the tail beat and their modulation (see equations used to build the linear model on the calcium activity in Methods; Fig. 6e,f1,f2, Extended Data 6d-e). Notably, the activity of most medullary V2a RSNs in the forward cluster encoded the instantaneous tail beat frequency (Fig. 6f1, neurons whose coefficient was above 0 and significantly relevant: n = 59/89 neurons; see Methods). Interestingly, a group of V2a RSNs in the caudal and dorsal medulla both encoded the instantaneous tail beat frequency and the number of oscillations (Fig. 6f2). In contrast, the activity of other V2a RSNs in the ventromedial medulla encoded both instantaneous tail beat frequency and the rise in tail bend amplitude during forward swims (Fig. 6f2). Altogether, our results highlight that medullary V2a RSNs have a sustained activity during forward locomotion and mainly encode the instantaneous locomotor frequency. a, Schematic illustration of the experiment investigating medullary V2a RSN recruitment during MLR-induced forward swimming. b, Tail angle trace and calcium activity of a functional cluster of V2a RSNs active during forward swimming. For ease of visualization, only the tail angle for forward episodes is represented here. Mean neural activity of the group is plotted in blue, individual traces in the clusters in black (z-score). c, V2a RSNs whose activity is represented in (b) are in the medial medulla (filled circles in blue). Empty circles represent neurons that did not show any activity during the experiment or corresponded to different clusters than the forward one (see also Extended Data Fig. 4 c andd). d, The amplitude of the response of medullary V2a RSNs in the forward cluster correlated with the number of oscillations during a forward episode. d1, The plot shows a linear regression between the max ΔF/F of individual V2a RSN and the number of oscillations of the forward episodes (n = 132 neurons in n=5 fish, n = 8 trials). d2, Dorsal view showing the spatial distribution of neurons whose response amplitude correlated the most with the number of oscillations (blue filled circles, n =38/132 neurons in n = 5 fish, n = 6/8 trials, p < 0.12 and correlation coefficient >= 0.8). Empty circles correspond to other V2a RSNs from the forward cluster whose response amplitude was less correlated with the number of oscillations during a forward episode. e, Individual neurons' activity reflect 3 kinematic parameters: tail beat frequency, tail beat amplitude and binary positive derivative of the tail amplitude. Top: traces from 3 example neurons that were recruited during forward episodes and whose calcium activity differed during the episode Middle: 3 motor regressors that best recapitulated the calcium activity of the 3 neurons above (matching color codes) with high correlation for instantaneous tail beat frequency (red), instantaneous tail amplitude (green) or the increase in tail amplitude (yellow). Bottom corresponding tail angle trace. f1, Subsets of V2a medullary RSNs encode the instantaneous tail beat frequency. The circle size encodes their location along the dorsoventral axis (n=5 fish, n=6 trials, n= 59/89 neurons); f2, dorsal view showing the spatial distribution of V2a RSNs whose activity encoded tail beat frequency and either increase in tail beat amplitude (yellow outline) or number of oscillations (blue outline, from panel d2). Non-filled dots represent neurons recorded which did not show any activity during the experiment. e, Medullary V2a RSNs in the forward clusters were more recruited during forward swimming than during struggle behavior. Forward activity index calculated from the calcium activity as (forward activity index = (average (max ΔF/F during forward episodes) -average (max ΔF/F during struggle) / average (max ΔF/F during forward episodes) + average (max ΔF/F during struggle)). A positive index indicates an average max ΔF/F higher during forward than during struggle behavior (5 fish; forward cluster: 1438 cells (mean ± sd): 0.71 ± 0.37, one sample t-test against mu = 0; t(1437) = 72 p < 0.001; rest of the clusters: 2603 cells: -0.17 ± 0.72, one sample ttest against mu = 0, t(6626)=-12.6, p < 0.001). f, Mean activity of an example forward cluster and the 4 motor regressors encoding distinct kinematic parameters: the tail beat frequency, the tail bending amplitude, and their binary positive derivative. Each motor regressor simulates the calcium trace of a neuron whose spiking activity would perfectly recapitulate a motor feature. Top trace, mean activity of an example forward cluster. Middle traces, raw signal (black) and corresponding regressor (color) of each motor feature represented. Bottom trace, corresponding tail angle. g, Distribution and correlation between the different instantaneous swimming parameters. Instantaneous tail beat frequency and tail amplitude are co-dependent, but not linearly correlated. Distribution of the computed instantaneous tail beat frequency (red, top) and instantaneous tail amplitude (green, right) (2176 time steps, in 4 fish, 8 trials). h, Single neurons are explained by different motor regressors. h1, Neural activity of single neurons shown in Fig. 6e overlaid with the motor regressor that best explains it. h2, At each time step, the value of the motor regressor (x axis) is represented as a function of the mean activity of the forward cluster (y axis). i, Correlation coefficient of each motor regressor tested in the multiple linear model and illustrated for one example plane in one fish.

Extended Data

Discussion

Our study provides the first identification of the locus of the zebrafish MLR in the prepontine region of the hindbrain. We found that electrical stimulation of the MLR induces sustained forward locomotion lasting for the duration of the stimulation and mimicking bouts deployed by larval zebrafish during spontaneous navigation. Here, we leveraged the genetic toolkit of larval zebrafish to investigate the functional coupling of the MLR to a key population of RSNs, the V2a RSNs, labeled by the transcription factor vsx2. We show that the MLR sequentially recruits a subset of V2a RSNs throughout the brainstem: while pontine and retropontine V2a RSNs are recruited at once at low intensity, medullary V2a RSNs are progressively recruited upon increasing intensity of the MLR stimulation. By taking advantage of the exceptionally long forward swims induced by MLR stimulation, we were able to uncover a previously overlooked group of medullary V2a reticulospinal neurons whose activity is maintained during forward locomotion. In contrast to lateral medullary V2a RSNs recruited during struggles, medial V2a RSNs in the medulla encode essential parameters determining the duration and speed of forward locomotion: the number of oscillations, the locomotor frequency and the increase in movement amplitude.

Identification of the MLR in larval zebrafish

In all vertebrate species, the MLR sits at the mesopontine border along the cholinergic nuclei spanning from the caudal border of the substantia nigra to the locus coeruleus 16,17,53,63,64 . In mammals, the MLR comprises the pedunculopontine nucleus (PPN) and cuneiform nucleus (CuN) in the rostral mesopontine cholinergic stripe 15,19,24 . In salamanders and lampreys, the MLR is located in the caudal mesopontine cholinergic stripe corresponding to the laterodorsal tegmental nucleus 16,17 (LDT). Here, we identified, based on electrical stimulations and anatomical investigations, the MLR of larval zebrafish as a small region of approximately ~30 µm in radius, which is medial and dorsal to the locus coeruleus and extends rostrocaudally in the prepontine area. Similarly to the location found in lamprey and salamanders, the zebrafish MLR colocalizes with cholinergic cells in the isthmus 56,57 , likely corresponding to the LDT 49 .

Even though stimulation in the region of the nMLF in fish induces locomotion 42-46,65,66 , the nMLF does not correspond to the MLR since it is not located within the mesopontine border 67 . The locomotor output elicited by nMLF stimulation is consistent with the motor responses observed upon activation of reticulospinal neurons 26,68-70 . Instead the nMLF should be considered primarily as a sensorimotor coordination center 71 since it receives sensory inputs from the visual 72,73 and vestibular systems 74 and in turn sends descending projections 67,75 to control head-tail positioning 66 and spinal motor neurons 76,77 . The activity of the nMLF correlates with spontaneous 66,71 and sensory induced locomotion 65,66,71,78 , probably due to the fact that eyes and tail movements are tightly coupled 79 . In zebrafish, the term "nMLF" was adopted in the first descriptions of the spinal projecting neurons 67,80 .

Nevertheless, the nMLF corresponds to a structure called across vertebrate species the interstitial nucleus of Cajal (INC) (for review, see 81 ). Indeed, the INC projects to the spinal cord 82 and coordinates eye and head movements assisting the body in adjusting posture 83,84 (for review, see 85 ). Furthermore, INC stimulation can elicit locomotion in cats and rats 86 . Based on its anatomical and functional similarities, we propose that the nMLF should be renamed as the INC, as formulated for other vertebrate species (for review, see 87,88 ).

Control of locomotor output

Animals move using different types of gaits. Quadrupedal mammals walk at low speeds, trot at intermediate speeds and gallop at high speeds (for review, see 89 ). The MLR controls the locomotor speed and its stimulation at different strength evokes the different gaits naturally exhibited by a given animal (for review, see 90 ). For instance, stimulating the MLR in salamanders at low intensity produces a terrestrial stepping (walking), but the animal transitions to swimming (a faster gait) at higher stimulation strength 17 . In larval zebrafish, evidence for two different forward gaits have been reported in freely swimming conditions during optomotor response at different grating speeds 65 (~27 and ~34 Hz each). Upon MLR stimulation we could elicit two clear locomotor regimes, one at 17 Hz for MLR stimulation below 15 Hz, and another at 23 Hz for stimulation at 20 Hz. We propose that these two locomotor regimes elicited correspond to the adaptation, in the head-embedded condition, of the two gaits described in freely swimming larval zebrafish.

Larval zebrafish swims in a beat-and-glide fashion, where short periods of activity of typically few hundreds of milliseconds 91,92 are followed by long interbout intervals where the animal is not actively swimming 93 . One of the most notable findings of this work is that by stimulating the MLR we were able to drastically prolong the duration of their swimming bouts, reaching up to 20 seconds.

Functional coupling of the mesencephalic locomotor region to V2a reticulospinal neurons

In line with previous studies identifying the MLR (for review, see 24,94 ), we demonstrate using retrograde tracing that the zebrafish MLR neurons send descending projections to the reticular formation. Experiments done in lampreys, mice and cats showed that only a small proportion of RSNs are active during spontaneous or MLR induced forward locomotion (lampreys 10 , cats: 95 , mice 35 ). In agreement with these observations, we found that the MLR recruited a low proportion of brainstem V2a RSNs. More specifically, we show that the MLR recruits ventral V2a RSNs in the pontine and retropontine regions in an all-or-none fashion, consistent with large excitatory postsynaptic potentials previously recorded in these cells at low MLR stimulation intensity 29,51,60 . In contrast, the MLR gradually recruited, upon increasing intensities of stimulation, medial V2a RSNs in the medulla, similarly to what had been observed in lampreys and salamanders in unidentified RSNs 51,60 that may include V2a neurons.

Medullary V2a RSNs act as maintain cells encoding the instantaneous locomotor frequency

We find that a subset of medial medullary V2a RSNs sustain their activity during long episodes of forward locomotion elicited by MLR stimulation. In zebrafish, the recruitment of the large V2a RSNs in the ventral pontine and retropontine region had been well documented 37,40,78,96 . Yet, less was known on the role of medullary V2a RSNs found active during brief episodes of spontaneous fictive locomotion 59 . Here, we show that the calcium activity of a subset of V2a RSNs located in the caudal medulla scales with the number of oscillations within a bout, suggesting that this subset of V2a RSNs could operate as "maintain V2a RSNs". Consistently, the ablation of medial V2a RSNs in the medulla in larval zebrafish resulted in shorter forward bouts 59 . Zebrafish medullary V2a RSNs project their descending axon ipsilaterally 36 and resemble medullary RSNs previously recorded in xenopus tadpoles 97 , where they have been found to spike in phase and preceding each motor burst. Remarkably, zebrafish "maintain" V2a RSNs we highlight here are located in rhombomere 8, in the caudal most medulla at the boundary with the spinal cord. This region in lamprey has been found to generate the locomotor rhythm in isolation 98 . Multiple evidence indicate that this area may act as a critical hub to gate information from brainstem to spinal cord as its targeted electrical and optogenetic stimulation robustly induced locomotion 99-101 . By modeling the neuronal calcium activity with motor regressors for amplitude and frequency modulation, we uncovered that the activity of most medial V2a RSNs in the medulla encodes the locomotor frequency and for a subset, the increase in movement amplitude. Altogether these findings indicate that medial V2a RSNs in the medulla act as maintain cells setting the speed of forward motion, likely by recruiting spinal CPG interneurons controlling locomotor frequency 102,103 and motoneurons to increase movement amplitude 59 . The previously overlooked V2a medullary RSNs we unveiled here may act in concert with other glutamatergic RSNs in the LPGi nuclei previously involved in mice in the control of locomotor speed 26 .

Here, we identified the MLR in larval zebrafish and mapped the downstream command circuits involved in forward locomotion. Altogether, this study represents a breakthrough for future investigations of supraspinal motor control, as virtually each and all reticulospinal neurons can be identified in the brain atlas, recorded and manipulated during active locomotion in this transparent and genetic model organism.

MLR electrical stimulation

Homemade glass-coated tungsten microelectrodes (0.7-3.1 MΩ, 10-35 µm exposed tip) were used to perform monopolar electrical stimulations. The electrodes positioned with a 35° angle penetrated the skin at different sites in the prepontine region using a motorized micromanipulator (MP-285A, Sutter Instruments, California, USA). In order to guide the electrode position to the MLR, we used the locus coeruleus as a landmark by labeling it with GFP in the Tg(vmat2:GFP) zf710Tg transgenic larvae expressing GFP under the vmat2 promoter 106 . Stimulation was delivered using an isolated stimulation unit (2100, AM systems, USA) controlled with a Digidata series 1440A Digitizer (Axon Instruments, Molecular Devices, USA) to synchronize the electrical stimulation and behavioral recording. The timing of the electrical stimulations was triggered and recorded using Clampex 10.3 software (Axon Instruments, Molecular Devices, USA). When searching for the MLR, the behavioral response to stimulations in the locus of the MLR was typically recorded for multiple loci by applying continuous stimulation at 10 Hz and 1 µA not exceeding 30 s. If after 5 loci tested, no clear behavioral response was observed, the larva was discarded. To ensure success, a rest period of at least 3 min was allowed between successive trials. Once the proper site for the MLR was found, we implemented experimental trials lasting each about one minute. The stimulation protocol consisted of negative pulses of 2 ms duration, 5-20 Hz frequency at 0.1-3 µA intensity for 1-4s. To allow recovery and avoid synaptic habituation, the resting period between trials was at least 2 minutes. For experiments on paralyzed Tg(vsx2:GAL4; UAS:GCaMP6s) transgenic zebrafish, the stimulation protocol consisted of single 2 ms-long negative pulses with increasing intensities (1 to 5 µA) with steps of 1 µA and inter trial intervals of 20 s. For the same fish, a rest period of at least 3 min was allowed between trials performed at different imaging planes. All electrical stimulation experiments were carried out in a confocal microscope combining an upright microscope (Examiner Z1, Zeiss, Germany), using a 20X/1.0 DIC D = 0.17 M27 75mm (Zeiss, no:421452-9880-000) objective, a spinning disk head (CSU-X1, Yokogawa, Japan) and a laser light source (LaserStack, 3i Intelligent Imaging Innovations, USA).

Behavior recording and analysis

Behavioral recording 6-7 dpf transgenic Tg(vmat2:GFP) or Tg(vsx2:GAL4;UAS:GCaMP6s) larvae were embedded in 2.5% low melting point agarose (Invitrogen, Thermo Fisher Scientific, USA) in a 35 mm Petri dish. The dish was then filled with external bath solution (134 mM NaCl, 2.9 mM KCl, 2.1 mM CaCl2-H2O, 1.2 mM MgCl2, 10 mM glucose, and 10 mM HEPES, with the pH adjusted to 7.4 and osmolarity to 290 mOsm). The agarose was removed caudally to the swim bladder in order to leave the tail free to move. Head-embedded larval zebrafish were illuminated from the side with a 45° angle using an 890 nm LED (ILH-IW01-85SL-SC211-WIR200, Intelligent LED Solutions, Berkshire, UK) and behavior was recorded at 300 Hz from below through a 5X 20X/0.25 microscope objective 12.5mm (Zeiss, no:440125-0000-000) and imaged on a high-speed camera (acA640-750um, Basler, Ahrensburg, Germany) with a 50 mm lens (MVL50M23, Navitar, New York, USA). When the behavioral recording was coupled with functional calcium imaging, the 488 nm imaging laser was blocked with a 600 nm high pass filter located in front of the camera. The high-speed camera was controlled using the Hiris software (RD Vision, https://www.rd-vision.com/r-d-vision-eng, Saint-Maur-des-Fossés, France).

Tail tracking and automated segmentation of bouts

We used the open-source software ZebraZoom (https://github.com/oliviermirat/ZebraZoom) to track the position of the tail for each frame and extract bouts defined as discrete events when the tail was continuously moving 91,109,110 .

The tail angle was defined as the angle between the body axis of the fish and the tip of the tail 91 . The quality of the tail tracking was carefully assessed by visual inspection. Electricallyinduced bouts were defined as all bouts starting during the electrical train stimulation.

Swim bouts starting just before the stimulation were excluded from analysis.

"Spontaneous" bouts were defined as bouts starting and ending before the first electrical stimulation of the trial. Note that bouts occurring in the 1 min long time window after a stimulation train were excluded from the analysis as their properties could have been impacted by the prior electrical stimulation.

Segmentation of episodes within a bout

In the behavioral responses to electrical stimulations, bouts sometimes occurred as episodes of forward swims and struggles (see Fig. 1,2,6). We therefore manually segmented all bouts into episodes of either forward swims or struggles. The procedure was blindly implemented by an investigator who ignored the electrode position and the stimulation protocol used. Three rules were followed to maximize consistency: i) forward swims were defined by symmetric tail angle traces below 25 degrees with a minimum of three oscillations; ii) long forward episodes with rare tail bends above 25 degrees were kept as single forward episodes; iii) struggle episodes were defined as series of very large bends (tail angle > 25°) assigned as one single episode, but segmented into multiple episodes when bends were separated by more than 100 ms.

Analysis of kinematics

From the tail angle, the ZebraZoom algorithm extracted the timing and amplitude of each tail bend that we then used to calculate instantaneous tail beat frequency and instantaneous tail bend amplitude 91,109,110 . For each episode (Fig. 1c, 2), we estimated the corresponding duration, number of oscillations, and maximal tail bending amplitude as previously done for bouts in freely-swimming animals 91,109,110 , together with median bend amplitude and median instantaneous tail beat frequency (https://github.com/mathildelpx/Carbo-Tano_Lapoix_2022/).

Behavioral forward index

In order to estimate the efficiency of a stimulation site to elicit forward swimming, we computed for each stimulation trial a forward index defined as:

Forward index_stim = (number of forward swim episodes elicited -number of struggles episodes elicited) / (number of struggle episodes elicited +number of forward swim episodes elicited)

As we describe in the results section and in Extended Data Fig. 1, electrical stimulation at frequencies lower or above 10 Hz, or with intensities above 2 µA elicited mainly struggle behaviors. For this reason, we computed the median forward index of each stimulation site (Fig. 1e) only using the stimulation trials with optimal stimulation parameters, i.e.trains of pulses at 10Hz with current amplitudes of 1-2uA.

Anatomy

Registration of electrical stimulation sites in the brain atlas

To assess the position of the stimulation electrode, Z projection confocal stacks were acquired combining the GFP signal and the differential interference contrast (DIC) channel using a 20X/1.0 DIC D = 0.17 M27 75mm (Zeiss, no:421452-9880-000) objective. The z-stacks were acquired with a step of 1 µm depth using Slidebook software 6.0 (3i Intelligent Imaging Innovations, Inc., Denver, CO, USA). From each image volume, we annotated manually in Fiji 111 (https://fiji.sc/Fiji) the coordinates of the electrode position from the DIC channel and three anatomical reference coordinates from the GFP pattern of expression of the Tg(vmat2:GFP) transgenic line (medial boundary of the locus coeruleus on each side, anterior limit of the superior raphe and anterior limit of the inferior raphe). Using a customwritten python script, the coordinates of the anatomical landmarks were transformed to the mapzebrain fish space for the Tg(vmat2:GFP) transgenic line (https://fishatlas.neuro.mpg.de/, download: 12 Apr. 2020) and the resulting transformation matrix was then applied to the electrode coordinates.

Retrograde-labelling experiments using biocytin

6 dpf larvae were anesthetized with 0.02% MS-222 (A5040, Sigma-Aldrich) and embedded dorsally in 1.5% low melting point agarose prepared in external solution (see above). A small portion of the agarose was removed on top of the hindbrain and all the liquid was then extracted from the dish. A few crystals of biocytin Alexa Fluor 594 (Thermo Fisher, A12922)

were diluted in 5 µL of dH2O, and then allowed to recrystallized on the tip of insect pins (stainless steel Minutiens, 0.2 mm diameter, Austerlitz). Under a stereo dissecting microscope, a unilateral single lesion in the hindbrain between rhombomere 2 and 6 was done using the dye-soaked insect pins. After the procedure, the dish was filled with external solution, the larvae were unmounted and allowed to recover for 2 hrs in oxygenated external solution. After larvae were again anesthetized with 0.02% MS-222 and mounted in 1.5% low melting point agarose. The retrogradely-labeled neurons were then imaged in a confocal microscope as described above. From each image volume, the coordinates of the backfilled neurons in the region of the MLR were annotated using Fiji. The positive backfilled neurons selected for this analysis were taken from the MLR area defined by electrical stimulations (with a diameter of approximately ~30 µm). For the experiment in Fig. 3a-d, the injections were done in WT fish. Since the dye injection forms a background labeling in the entire nervous system, we chose four anatomical landmarks also easily distinguishable in the mapzebrain DAPI fish space. For the experiments in Fig. 3e-g the four anatomical landmarks were selected on the basis of the DsRed fluorescence pattern in each transgenic Tg(vglut2:DsRed-GFP) larva (referred to as Tg(vglut2:lox-DsRed-lox-GFP))

and from the mapzebrain Tg(vglut2a:DsRed) fish space. The script used and the registration steps applied were the same as described in the precedent section.

Immunohistochemistry

6 dpf larvae were processed as described above in order to label MLR neurons projecting to the reticular formation. After the injection, the larvae were allowed to recover for 2 hrs in oxygenated external solution. The larvae were then euthanized using 0. The primary antibodies used in combination here have been tested separately resulting in similar labeling of the same neuronal structures. Removing the primary antibodies from the procedure while keeping everything else the same did not produce any labeling of neuronal structures in our material. The rabbit anti-DBH from this study was successfully used in zebrafish by other authors to label locus coeruleus neurons specifically 112,113 . In our material, only locus coeruleus neurons were labeled with this antibody.

Alternatively, the rabbit anti-DBH from this study was replaced with a rabbit anti-tyrosine hydroxylase (RRID: AB_390204, cat. number AB152, lot: 3031639, diluted 1:400, Millipore). The resulting labeling of the locus coeruleus neurons was similar in every aspect to the one obtained with the DBH antibody. The goat anti-ChAT used in this study has been used with success on a wide range of vertebrate species, including zebrafish, to label cholinergic neurons 49,113-115 .

Optical backfills using spatially-confined photoconversion of Kaede

Transgenic Tg(vsx2:Kaede) zebrafish larvae expressing Kaede protein in V2a neurons were raised in darkness. At 4 dpf, larval zebrafish were anesthetized in 0.02% MS-222 and mounted dorsal side up in 1.5% in low melting point agarose. The Kaede protein in the rostral spinal cord (segments 4-9) was photoconverted by using a UV LED (405nm, Thorlabs, SOLIS-405C) for 2 minute-long illumination (500ms on and 500ms off pulsed illumination) through a 40X NA = 0.8 water immersion objective (power : 1.02 mW/mm2).

This photoconversion procedure was repeated five times with intervals of 40 minutes.

Digital mirror devices 116-118 were used to confine the illumination in 2 dimensions from segment 4 to 9. Upon completion, the larvae were kept in the dark for 8 to 12 hrs to allow the photoconverted Kaede protein in the rostral spinal cord to travel back up to the hindbrain to label the soma of spinal projecting V2a neurons. The larvae were next imaged using a confocal microscope using a spinning disk head (CSU-W1, Yokogawa) on an upright microscope (Examiner Z1, Zeiss), using the 488 nm and 561 nm laser light sources (LaserStack, 3i Intelligent Imaging Innovations, Denver, USA). Images were acquired using a 20X N.A. = 1.0 DIC D=0.17 M27 75mm (Zeiss, no:421452-9880-000), using Slidebook software 6.0 (3i, Intelligent Imaging Innovations).

Image analysis

All image analysis was performed using Fiji 111 .

Calcium imaging experiments

For in vivo recording of calcium activity in V2a brainstem neurons (Fig. 4, 5), we screened the brightest fluorescent Tg(vsx2:Gal4,UAS:GCaMP6s) transgenic larvae at 3 dpf. On the day of the experiments, 6 dpf larvae were dorsally mounted as described above, with or without the tail left free to move according to the experiment's needs. For experiments relying on MLR stimulations via single pulses, larvae were first paralysed by bath application of 1 mM α-Bungarotoxin solution (Tocris) diluted E3 medium for 3-6 min. Calcium imaging activity was recorded using a confocal spinning microscope (CSU-W1, Yokogawa) on an upright microscope (Examiner Z1, Zeiss; LaserStack, 3i Intelligent Imaging Innovations). Images were acquired using a 20X N.A. = 1.0 objective (Zeiss, no:421452-9880-000), using the Slidebook software 6.0 (3i, Intelligent Imaging Innovations). Images were acquired at either 16 Hz (Fig. 4) or 10 Hz (Fig. 5) with a field of view of 512 × 512 px and 0.666 μm/px pixel size.

We choose to record planes representative of the different anatomical regions we imaged from, corresponding to planes at depth 182, 225, 257 and 288 µm in the mapzebrain brain atlas. After imaging calcium activity in each plane, we systematically recorded an anatomical z-stack of the entire hindbrain in order to localize the site of stimulation.

Calcium imaging data analysis

Calcium imaging movie pre-processing

For all analysis of the fluorescence movies, we used the software suite2p 119 (https://github.com/MouseLand/suite2p, v.0.8.0). The pipeline first corrected the movies for motion artifacts in 2D using rigid and nonrigid registration. Most regions of interest (ROIs)

were automatically identified by the pipeline, and we manually added ROIs on cells that were not detected, including the soma of neurons that did and did not show activity during the recording. Then, using a custom-built Python script, for each ROI: 1) we corrected the raw fluorescence trace by subtracting the neuropil signal (i.e., sum of surrounding pixels' fluorescence, with a factor n = 0.7); 2) we calculated the DF/F(t) = (F(t) -F0) / F0, where F(t)

is the fluorescence at time t and F0 is the cell baseline fluorescence (median fluorescence value selected on a 3 s-long period of inactivity); 3) we estimated the noise, as the standard deviation of the signal during the same 3-s long period of inactivity, which was further used to set the recruitment threshold of the cell; 4) the DF/F was filtered using an average running filter with 3 time steps. For visualization purposes and regression analysis, a low pass filter was applied instead. In the tail-free experiments, when the motion artifact was larger and the 2D registration was not satisfactory, we removed the artifactual values by adding non existent values for frames in which the correlation to the mean image was low (we used 3 lowest percentile of correlation in the suite2p ops['corrXY'] output). In order to run correlation and regression analysis, we linearly interpolated the missing values to process continuous time series. Neurons were assigned to anatomical groups defined in Extended Data Fig. 2 based on their rostro-caudal position in the hindbrain and the manually-assigned boundaries between pontine, retropontine and medullary regions. For Fig. 4, each neuron was assigned to either ventral or dorsal group based on its normalized depth location in the mapzebrain atlas, using a dorso-ventral limit depth of 100 µm for pontine and retropontine regions and 80 µm for the medulla.

Registration of all calcium imaging planes across experiments

To compare recruitment of V2a neurons across fish, we registered ROIs from the calcium imaging data of all fish to a shared brain space (corresponding to one reference fish illustrated in Fig. 4 and Fig. 5), as such: 1) anatomical boundaries, including the midline and the boundary between medulla and retropontine region-between r6 and r7, were visually identified (see Extended Data Fig. 2), and shifted to match the boundaries of the reference fish; 2) The same shift was then applied to each ROI's position in order to extract a normalized X and Y position in the reference fish; 3) We estimated for each plane its normalized depth by manually comparing to which depth the plane corresponded the most to the mapzebrain chx10:GAL4 fish space. The normalized coordinates were subsequently used for mapping neurons position in 3D.

Recruitment analysis in paralyzed transgenic larvae

In order to define whether a cell was recruited following an electrical stimulation in Fig. 4, we tested whether its calcium trace significantly increased above baseline. To do so, we checked whether the maximal DF/F value between the stimulation start and 3 seconds after was above an arbitrary threshold defined as the baseline plus 3 times the noise (standard deviation of the signal during the baseline). The baseline, for each cell at each stimulation, was defined as the mean signal over 1.5 seconds before the stimulation started.

We defined 'unresponsive cells' as cells whose calcium trace did not reach threshold in the 3 s for any stimulation. Reliably-responding cells were the ones that reached the recruitment threshold systematically once a stimulation intensity was reached (Fig. 4b).

Cells were classified as not reliably responding to the stimulation otherwise. For all cells that exhibited a calcium transient in response to the MLR stimulation, we computed the rising slope of the calcium trace as:

rising slope = (DF/Fpeak -DF/Fonset) / (timepeak -timeonset) in which timeonset is the time when DF/F starts rising after the stimulation and timepeak is the time where the calcium transient peaks. As filtering calcium traces would induce a lag in the signal, we computed these time points on the raw DF/F. These time points were semiautomatically defined for all exhibited responses (see https://github.com/mathildelpx/Carbo-Tano_Lapoix_2022/notebooks/fig5_kinematicsAnalysisAuto.ipynb for detailed algorithm).

The canonical V2a RSNs (RoM2, RoM3, MiV1, MiD2i, MiD3i) were named based on previous identifications [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF][START_REF] Kimmel | Brain neurons which project to the spinal cord in young larvae of the zebrafish[END_REF] .

Recruitment analysis in tail-free larvae

We investigated which V2a neurons were specifically recruited during forward swimming episodes by comparing their activity during forward versus struggle episodes. To do so we extracted the maximum DF/F between 0.5 seconds before the start and 2 seconds after the end of the episode. As the max DF/F during a given episode could be influenced by the residual increase from a previous swimming episode, we corrected the max DF/F value by subtracting a normalized baseline (median DF/F of the 300 ms preceding the beginning of the episode). In these conditions, a cell was recruited if its corrected maximum DF/F reached 5 times the noise (defined as above in the preprocessing section).

Forward activity index and identification of a forward cluster based on calcium activity

In order to identify neurons recruited during forward episodes elicited by MLR stimulation, we clustered neurons using the calcium activity using an agglomerative clustering based on the python package AgglomerativeClustering from scipy.cluster library. We manually selected an arbitrary number of clusters (3 to 6) such that at least one cluster was specifically recruited during forward swimming only. To quantify that a group of neurons was indeed more active during forward swims, we computed for each neuron a forward activity index as :

forward activity index = (mean(max DF/F during forward swims) -mean(max DF/F during struggles) ) / (mean(max DF/F during forward swims)+ mean(max DF/F during struggles))

We verified for each trial that the cluster of neurons assigned as "forward" had the highest forward activity index.

Linear regression between max DF/F and number of oscillations

We used the package linregress from scipy.stats to model the max DF/F using the natural log of the number of oscillations. Here, we used a logarithmic transformation to get rid of the effects of the outliers in the distribution of the number of oscillations.

Building motor regressors

In order to compare neuron's activity to motor activity, we built multiple motor regressors defined as fictive calcium traces modeling distinct kinematics parameters. Specifically, we first built four time series describing the tail beat amplitude, the tail beat frequency and for each of them, the binary value (0,1) based on their positive differential reflecting their increase over time. These were built as follow -

• Instantaneous tail amplitude: we resampled the tail angle trace (acquired at 300Hz) to the corresponding sampling rate of calcium imaging (10Hz), taking for each calcium imaging frame the maximal (in absolute) tail angle value.

• Instantaneous tail beat frequency: we calculated the mean tail beat frequency of all the cycles happening during each calcium imaging frame.

• binary value of the positive derivative of the kinematic variables: for each parameter computed above, we computed a binary variable equal to 1 when the parameter was increasing over time and equal to 0 the rest of the time. To do so, we used a Heaviside step function on the derivative of the kinematic parameter after filtering with a running average filter using 5 frames.

We then built the motor regressor concolving these time series with a GCaMP6s kernel modeled as negative exponential signal of decay 1.5 s.

Multiple linear regression to explain calcium trace with motor regressors.

As each neuron could drive multiple kinematic parameters, we examined whether calcium activity could be explained by the four motor regressors described above, using multiple linear regression to model each neuron's calcium trace DF/F, at each time step t, as:

with x1 the regressor for the tail beat amplitude ; x2 the regressor for the binary value of the increase in tail beat amplitude; x3 the regressor for the tail beat frequency; x4 the regressor for the binary value of the increase in tail beat frequency.

To fit the model on the data, we used the z-score of each kinematic parameter. We used a linear model optimized using ordinary least squares. The model aims to predict the calcium activity using a linear combination of the coupling coefficients (α1, α2, α3, α4) minimizing the error between the data and the model. We performed permutation tests to assess significance of a kinematic parameter in predicting the DF/F values (for significance, α needed to be positive and the p-value below 0.05). Overall, the kinematic parameters significantly explained respectively: iTBF, 59/89 neurons, iTA, 17/89 neurons; positive derivative iTBF, 6/89 neurons; positive derivative iTA, 32/89 neurons.

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Discussion and perspectives

Altogether we have discovered the locus of the MLR in larval zebrafish and identified a population of V2a RSNs in the caudal medulla that is recruited upon MLR stimulation and whose activity encodes key kinematic parameters controlling the speed of forward swimming.

MLR modulation of swimming kinematics

In our study, we found that stimulating the MLR at different frequencies modulated the tail beat frequency in a non-linear fashion (Figure 3). However, our observation in zebrafish contrasts with previous findings in other vertebrate species (for instance, lampreys, salamanders, pig) for which the locomotor frequency linearly scaled with the frequency of the MLR stimulation (Cabelguen et al., 2003;[START_REF] Chang | Deep brain stimulation of midbrain locomotor circuits in the freely moving pig[END_REF]Sirota et al., 2000). As discussed in the manuscript, this could be related to the organization of the forward locomotor gaits in larval zebrafish into two restricted speed regimes [START_REF] Severi | Neural Control and Modulation of Swimming Speed in the Larval Zebrafish[END_REF]. Furthermore, we did not identify in forward bouts of larval zebrafish a modulation of the tail bend amplitude given the frequency or the intensity at which we stimulated the MLR. This could be due to limitations of our protocol of electrical stimulation. In contrast, we found during prolonged and lowintensity stimulation of the MLR (Figure 6), that the tail bending amplitude could vary within the long-lasting forward episodes (Figure 6 and Extended Data Figure 6), indicating that the modulation of forward movement amplitude during MLR-induced locomotion may be possible.

Further MLR stimulations at varying intensities and a fixed frequency should be applied using either electrical stimulation or optogenetics in order to conclude the ability of the MLR to modulate movement amplitude in larval zebrafish.

Medullary V2a RSNs relay the MLR command to initiate forward locomotion

We identified that a subset of medullary V2a RSNs was recruited upon electrical stimulation of the MLR (Figure 5) and was recruited during MLR-induced forward swims (Figure 6). As we discussed in our manuscript, these V2a RSNs located at the boundary with the spinal cord are forward-specific and encode locomotor frequency (Figure 6). Based on previous work in Xenopus from Roberts, Soffe, Li, and collaborators, we suggest that these V2a RSNs likely correspond to descending INs and could form recurrent connections among themselves in order to build up excitation to initiate and maintain locomotion (Li and Soffe, 2019;[START_REF] Soffe | Defining the excitatory neurons that drive the locomotor rhythm in a simple vertebrate: insights into the origin of reticulospinal control[END_REF]. We can hypothesize that these medullary V2a RSNs recruited during forward swims amplify the MLR command and convey it to spinal CPGs in order to initiate forward locomotion. Whether the recruitment of these V2a RSNs corresponds to monosynaptic connectivity between glutamatergic MLR neurons and these medullary V2a

RSNs remains to be investigated. To my knowledge, previous evidence of monosynaptic connectivity between MLR neurons and RSNs comes from work on RSNs located more rostrally in the pontine and retropontine regions in lampreys (Brocard and Dubuc, 2003). This is compatible with our observations, in larval zebrafish, that the MLR command is relayed by V2a RSNs located in the pontine and retropontine regions, which were recruited at low MLR stimulation intensity and whose response's amplitude linearly scaled with the intensity of the MLR stimulation (Figure 5).

How does the MLR control locomotor speed?

Locomotor speed is a function of both movement amplitude and locomotor frequency.

Thus, in larval zebrafish, locomotor speed depends on both the tail bending amplitude and beating frequency. Our data provide evidence that the MLR can control the locomotor speed by modulating the tail beat frequency (Figure 3). Moreover, we found that a subset of the medullary V2a RSNs, the ones recruited during MLR-induced forward locomotion and located in the caudal-most medulla, encoded instantaneous tail beat frequency (Figure 6), suggesting that the MLR could control locomotor frequency by modulating the activity of the caudal medullary V2a RSNs.

In mice, MLR cholinergic cells have been shown to modulate the speed of the MLRinduced locomotion [START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF]. In lampreys, the MLR cholinergic cells convey the command to modulate locomotor speed to a parallel group of muscarinoceptive glutamatergic neurons, located just medially to MNs of the motor nuclei IX, which amplify the MLR command and project back to RSNs [START_REF] Smetana | A parallel cholinergic brainstem pathway for enhancing locomotor drive[END_REF]. Our results show that the activity of a small subset of V2a RSNs, located in the ventral and rostral medulla, encodes the increase in tail bending amplitude (Figure 6). We propose that these V2a RSNs could be recruited by a similar cholinergic-dependent parallel pathway as described for RSNs in lampreys and be added to the pool of recruited V2a RSNs to modulate movement amplitude.

In the future, it will be interesting to test whether muscarinoceptive, glutamatergic neurons are also present in zebrafish using pharmacology or targeted optogenetic stimulations.

Remarkably, recent investigations in mice revealed as well that glutamatergic RSNs elicit high speed locomotion upon recruitment of CuN glutamatergic neurons (Capelli et al., 2017).

However, these glutamatergic RSNs are located in the LPGi and are not V2a RSNs. It is possible that the LPGi (located ventral and lateral to the Gi, in rhombomeres 6 to ~9, [START_REF] Paxinos | Organization of brainstem nuclei[END_REF]) in mice exert similar functions than the muscarinoceptive glutamatergic neurons described by Smetana and colleagues in lampreys (located just medial to MNs of cranial nerve IX, in rhombomere 6, [START_REF] Murakami | Segmental development of reticulospinal and branchiomotor neurons in lamprey: insights into the evolution of the vertebrate hindbrain[END_REF][START_REF] Smetana | A parallel cholinergic brainstem pathway for enhancing locomotor drive[END_REF]). In larval zebrafish, non-V2a glutamatergic neurons can be found from rhombomere 5 to 8, in the glutamatergic stripes lateral to vsx2 that expresses the transcription factor dbx1 from the V0d domain [START_REF] Kinkhabwala | A structural and functional ground plan for neurons in the hindbrain of zebrafish[END_REF]. However, we currently do not know whether glutamatergic dbx1+ brainstem neurons in zebrafish project to the spinal cord. Even less understood are their recruitment during locomotion, and whether these neurons would be recruited by the activation of cholinergic neurons in the MLR. It would be of interest to find out whether dbx1+ brainstem neurons could share properties with the LPGi glutamatergic neurons of mice and act in concert with medial V2a RSNs to enhance the speed of locomotion in different contexts.

Downstream targets to modulate locomotor speed

In our study, we did not investigate how downstream the motor command is conveyed from the brainstem centers (MLR & RSNs) to the locomotor CPGs in the spinal cord. Based on our results, we can build hypotheses of such coupling that could be further tested.

As presented in the introduction, INs within the spinal CPGs are organized in modules recruited at distinct locomotor speeds [START_REF] Fetcho | Some principles of organization of spinal neurons underlying locomotion in zebrafish and their implications[END_REF][START_REF] Mclean | A topographic map of recruitment in spinal cord[END_REF][START_REF] Mclean | Continuous shifts in the active set of spinal interneurons during changes in locomotor speed[END_REF] and numerous INs are involved to enable the switch of MNs and premotor INs between low and high speeds. Forward slow locomotion relies on the recruitment of ventral V2a INs and V0v neurons, which project onto ventral secondary MNs in the ipsilateral and contralateral side respectively [START_REF] Fetcho | Some principles of organization of spinal neurons underlying locomotion in zebrafish and their implications[END_REF][START_REF] Kawano | Long descending commissural V0v neurons ensure coordinated swimming movements along the body axis in larval zebrafish[END_REF]. To increase locomotor speed, more dorsal V2a INs and MNs shall be recruited, which require a large and coherent synaptic drive to get recruited (for review, see [START_REF] Fetcho | Some principles of organization of spinal neurons underlying locomotion in zebrafish and their implications[END_REF])) (see 1. Based on our observations that V2a RSNs activity encodes both the speed and amplitude of slow forward locomotion, we can hypothesize that V2a RSNs can increase the recruitment of more premotor excitatory neurons (V2a or V0v) for rising speed, and MNs for rising amplitude. To perform such a change in recruitment, the V2a RSNs could rise their firing rate (leading to larger calcium transients) to send stronger and more coherent excitatory inputs to recruit the dorsal early-born V2a INs. In parallel, upon increasing locomotor speed, the V2a

RSNs we highlight here could project and recruit ipsilateral ascending V1 INs that silence slow premotor pools [START_REF] Kimura | Regulation of locomotor speed and selection of active sets of neurons by V1 neurons[END_REF][START_REF] Menelaou | Hierarchical control of locomotion by distinct types of spinal V2a interneurons in zebrafish[END_REF]. It is however likely that other types of brainstem neurons participate in this modulation, either by projecting to the spinal circuits or to local RSNs. For instance, non-V2a glutamatergic RSNs that could be homologous to the glutamatergic neurons in the mice LPGi (Capelli et al., 2017), and V1 neurons that have an ipsilateral ascending axon and whose activity is tightly correlated to locomotion [START_REF] Severi | Investigation of hindbrain activity during active locomotion reveals inhibitory neurons involved in sensorimotor processing[END_REF].

V2a RSNs and halt of locomotion

Our protocol of MLR stimulation produced remarkably-long locomotor episodes compared to the short swim bouts exhibited by the larval zebrafish spontaneously (Marques et al., 2018;Mirat et al., 2013). We took advantage of these long episodes to investigate using calcium imaging the dynamical recruitment of V2a RSNs during locomotion. Although some V2a RSNs in the rostral part of the Gi nucleus (rGi) have been associated with halting locomotion (located at the boundary between rhombomeres 6 and 7, [START_REF] Bouvier | Descending Command Neurons in the Brainstem that Halt Locomotion[END_REF][START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF])), we did not detect in our experiments any of the V2a cells reliably recruited at the end of locomotor events. This could be explained by the fact that we did not sample through all V2a RSNs along the dorsoventral axis, in particular in the retropontine and rostral medullary regions (rhombomeres 6 and 7). Recent challenging recordings of a small subset of V2a RSNs located in the rGi in mice showed that V2a RSNs recruited at the end of the locomotor event were not reliably encoding a locomotor stop [START_REF] Schwenkgrub | Deep imaging in the brainstem reveals functional heterogeneity in V2a neurons controlling locomotion[END_REF].

Based on the large variability of recruitment of "stop" cells, the authors suggested that a locomotor halt is likely encoded at the population level in the V2a RSNs within the Gi. In larval zebrafish, we only investigated a small number of prolonged forward swims for which we could not sort out stop from start cells. Thus, the data presented here did not enable us to decipher the presence of "stop" V2a RSNs in larval zebrafish.

To investigate in the future the existence of "stop" V2a RSNs in the medulla and retropontine region, we shall repeat MLR stimulations while imaging the entire population of V2a RSNs, in particular in the rhombomeres 5, 6, and 7. While exploring the naturalistic circuits of stops in larval zebrafish still remains challenging, the emerging animal model danionella cerebrum, a closely related species that swims at the larval stage in very long bouts [START_REF] Rajan | Evolutionary divergence of locomotion in two related vertebrate species[END_REF], represents a good alternative model species to tackle this question. As the Del Bene lab has shown in collaboration with our lab, danionella cerebrum and zebrafish share a very similar organization of the brainstem and the reticulospinal system at the larval stage [START_REF] Rajan | Evolutionary divergence of locomotion in two related vertebrate species[END_REF]. Furthermore, whole-brain imaging in a swimming larval danionella cerebrum expressing GCaMP throughout the brain successfully identified neurons recruited at the end of locomotor periods, although their cellular and molecular identity remains unknown [START_REF] Rajan | Evolutionary divergence of locomotion in two related vertebrate species[END_REF].

Next

Our identification of the locus of the MLR in larval zebrafish represents a breakthrough to understand how the MLR-RSNs axis initiates locomotion and controls the kinematics of movement. Here, we took the first step in this direction by describing that V2a RSNs in the caudal medulla encode locomotor frequency during forward locomotion. Our results also show, in coherence with previous reports from (Bretzner and Brownstone, 2013;[START_REF] Schwenkgrub | Deep imaging in the brainstem reveals functional heterogeneity in V2a neurons controlling locomotion[END_REF], that the population of V2a RSNs is highly heterogeneous, as very few V2a RSNs are recruited during MLR-induced forward locomotion and that their activity is distinctly encoded by the kinematics of movement.

To solve the problem of heterogeneity of the V2a neurons, one issue lies in the fact that, with the confocal spinning disk we used for population calcium imaging of V2a neurons, we could not record at a sufficient frame rate all V2a neurons across the dorsoventral column.

In order to get a comprehensive view of the roles of V2a RSNs and how, as a population, they can control locomotion, we need to be able to record all of them synchronously during locomotor events.

III -High-speed volumetric imaging of V2a neurons in the hindbrain and the rostral spinal cord during active spontaneous locomotion

Context

I presented in the introduction that the RSNs are numerous, spread in the hindbrain, and have diverse functions for the generation of locomotion (for review, see [START_REF] Brownstone | Reticulospinal Systems for Tuning Motor Commands[END_REF][START_REF] Takakusaki | Brainstem control of locomotion and muscle tone with special reference to the role of the mesopontine tegmentum and medullary reticulospinal systems[END_REF], even within the genetically-defined population of V2a RSNs [START_REF] Schwenkgrub | Deep imaging in the brainstem reveals functional heterogeneity in V2a neurons controlling locomotion[END_REF]. Therefore, to identify the neurons recruited to produce a given locomotor output, explore their putative functional connectivity, and build working models of how these neurons can shape locomotor output, we need to be able to record from all of them at the same time. In the previous chapter, we used spinning-disk confocal microscopy combined with population imaging to record from a sample of V2a RSNs. This technique does not allow volumetric imaging at sufficient speed and resolution to map the recruitment of all V2a neurons in the hindbrain during a single locomotor episode, which typically lasts 250ms (Mirat et al., 2013).

Light-sheet microscopy techniques offer a great alternative to performing volumetric imaging at high speed with better optical sectioning and reduced photo-bleaching compared to confocal spinning-disk microscopes [START_REF] Hillman | Light-Sheet Microscopy in Neuroscience[END_REF]. However, typical light-sheet microscopes operate with a system of 2 objectives to illuminate the sample and collect the emitted light, which requires either moving the objectives or the sample producing disturbances for the larval zebrafish. Tilted light-sheet microscopes, such as the SCAPE (for Swept confocally aligned planar excitation) microscope developed by the Hillman Lab, use an oblique light-sheet illumination, a single fixed objective, and rely on angular scanning to scan a sample in 3D at single-cell resolution and high imaging speed without moving the objective [START_REF] Bouchard | Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms[END_REF][START_REF] Voleti | Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0[END_REF]. Thus, the SCAPE microscope is particularly appropriate to investigate the recruitment of the entire population of V2a RSNs in the hindbrain during spontaneous locomotion. This chapter will cover 3 goals:

1) The reproduction and optimization of the SCAPE microscope developed by the Hillman lab.

2) The development of an open-source pipeline to analyze large, volumetric SCAPE data.

3) The investigation of the collective recruitment of V2a RSNs in the hindbrain and V2a

INs in the rostral spinal cord during spontaneous locomotion

Context of the study and authors' contribution

The SCAPE microscope was first assembled in 2019 by Paul Bivas and Dr. Olivier Thouvenin following the guidelines provided by the Hillman lab. Dr. Olivier Thouvenin and I aligned and optimized the system to adapt it to whole-hindbrain recordings of head-embedded, tail-free larval zebrafish. I developed a protocol to enucleate the larvae and performed all the experiments. Together with Dr. Marco Pascucci, we assembled an open-source pipeline to pre-process the large data generated by such recordings, i.e., with deconvolution, motioncorrection, and reshaping of the volumetric images. Then, I performed all the analyses and built the figures with inputs from Dr. Claire Wyart and Dr. Martin Carbo-Tano.

Results

Development and optimization of the SCAPE microscope

We built the SCAPE microscope following a step-by-step guide provided by the Hillman lab.

We added a few components to 1) hold the sample and automate its translation 2) record the tail movement, and 3) build a transmitted light image of the larvae brain to be able to insert pipettes or electrodes in the brain to manipulate neural circuits (Figure 1) (see Material and Methods).

After initial assembly, we aligned and calibrated the system using images of fluorescent beads (see Material and Methods). The calibration parameters we measured corresponded to the ones of the Hillman lab system (y:1.4um/pixel, z:1.1um/pixel). However, the depth of field, corresponding here to the maximum axial range with the optimal resolution, was smaller by a factor of 2 in our system than what the Hillman lab described (see Material and Methods). The extension of the field of view (FOV) where the image is resolved depends on how well aligned the image plane and the camera plane are, which is set by moving the camera block or the height of the camera. This step is particularly challenging as these components are heavy and hard to move with the precision required. One solution we found was to add an extension to the support breadboard, but we did not manage to further improve the size of the FOV resolved. In short, the SCAPE microscope is composed of 3 main parts: 1) A 2D illumination system composed of a Powell lens to shape the laser beam from a circle to an extended line, an illumination telescope, and a 1D galvo system to scan the imaging plane by rotating the beam in the back focal plane of the objective. 2) A high NA 20x microscope objective that forms a thin light-sheet and collects fluorescence photons in the orthogonal direction.

3) A detection system that descans the beam, magnifies it using 2 telescopes, and corrects the light sheet angle using a system of 2 objectives mounted in a titled geometry to form a 2D image on a CMOS sensor orthogonal to the light beam propagation. T: (xyz) translation, M1 and M2: mirrors to collect IR image of the fish tail, C1: camera used to record the fish tail movement. C2: camera used to record a transmitted image of the fish brain. A close-up of the objective and the sample holder in A2. B, Example image of a 6dpf Tg(vsx2:GAL4)(UAS:GCaMP6s) larval zebrafish. B1, Schematic representing how the larval hindbrain is scanned by the illumination light-sheet (in blue) along the x' axis. The angle of the illumination sheet is fixed, and the sheet is shifted along the scan-axis to scan through the volume. B2, Uncorrected image of a single plane (in the x' axis) as recorded by the camera. As the image is not corrected for the tilt of the light sheet, it is represented in the (Lz',Ly) dimension.

Development of an open-source pipeline in collaboration with

Marco Pascucci

Once we had a functioning and aligned system, we aimed to build an analysis pipeline to extract from the recordings the positions and fluorescence traces of neurons monitored during the experiment. In short, the output images of an experiment, as given by the program written by the Hillman lab, are Nt 3D images of dimension (Lz',Lx',Ly), where Nt is the number of time steps (Figure 2). We combined functions given by the Hillman lab, open-source algorithms, and homemade transformation scripts to transform these images into meaningful fluorescence traces. The output images from the recording are given as T volumes of dimension (Lz',Lx',Ly) where T is the number of time steps recorded. These images are deconvolved using a Point Spread Function (PSF) measured experimentally from the 3D images of fluorescent beads. Then, the volumes are registered to the mean volume to correct for the eventual motion of the sample during the recording. The volumes are then transformed to a regular (zxy) space by applying skew correction [START_REF] Bouchard | Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms[END_REF]. We reshape the Nt volumes of (Lz,Lx,Ly) dimension into Nz volumes of (Lx,Ly,Nt) dimension, where Nz is the number of planes, each spaced by 7um, and apply on these stacks a segmentation and extraction algorithm to detect neurons as ROIs and extract their fluorescent traces over time.

Deconvolution with point spread function

Imaging green fluorophores like GCaMP with 1 photon illumination requires using a laser around 488 nm, which is particularly prone to light scattering compared to higher wavelengths. This issue leads to 1) loss of resolution, i.e., the ability to distinguish two objects in space, which can prevent a proper segmentation of the image when trying to detect the position of the neurons in the image; 2) contamination of neurons' fluorescence to surrounding pixels, which creates "parasite" signals in other neurons' traces, i.e., a signal that does not come from the neuron itself but from neighbor fluorescent structures (in our recordings, mostly surrounding neurons' somas or axons).

This phenomenon artificially increases the level of correlation between neurons' signals and can prevail in a fine resolution of differences and similarities between neurons' true signals.

This problem is directly related to how much the image of an object spreads in space, which corresponds to the optical response of the microscope. In fluorescent microscopy, using the function that defines the spatial extension of the image of a single point in space, named Point Spread Function (PSF), we can theoretically restore the image of the original object using deconvolution [START_REF] Sibarita | Deconvolution Microscopy[END_REF]. With deconvolution, we aim to retrieve the exact object distribution by correcting the PSF.

We computed an experimental PSF from the 3D image of fluorescent beads smaller than the system resolution [START_REF] Shaw | The point-spread function of a confocal microscope: its measurement and use in deconvolution of 3-D data[END_REF] (see Material and Methods), and deconvolved the output images (Figure 3). The deconvolution significantly improved the contrast in the images and sharpened the borders between neurons (Figure 3), which became much easier to distinguish visually and were better detected by our segmentation algorithm (see 2. and single plane (A2, zoomed-in A3) acquired with the SCAPE microscope by imaging a 6dpf Tg(vsx2:GAL4)(UAS:GCaMP6s) larval zebrafish. B, The same example images after a deconvolution step using the experimental PSF computed with the same acquisition parameters. Note how the contrast is improved in B3, and neurons that were previously hard to segment from are now more clearly separated in space. In the center is a 2D representation of the 3D PSF.

We asked whether the deconvolution was also effective at reducing the levels of correlation between neurons' fluorescence signals. Using the same pixel masks, we extracted the fluorescent traces of neurons with and without the deconvolution step with the PSF. We found that the correlation values were overall lower and more centered around zero when the deconvolution was applied (Figure 4).

Altogether, these results show that deconvolution of functional calcium imaging data using experimental PSF is efficient at improving contrast in the image, improving the segmentation performances, and reducing signal contamination between surrounding neurons, therefore, reducing correlations between fluorescent signals.

3D motion correction using ANTsPy

In head-embedded, tail-free preparations, the larval zebrafish's head is never completely stable. Indeed, large tail movements can create brain movement, resulting in motion artifacts in the recordings. Such motion artifacts are even more likely in head-embedded preparations, as the animal reacts to the agarose surrounding its head by performing large tail bending resembling struggle behavior (see in part II, example tail angle traces). This is a problem as a neuron's signal is extracted from the intensity of a given set of pixels in the image, and if the neuron changes positions, we lose its signal during the motion artifact. One can compensate for such motion artifacts via translation by shifting the image at which the motion happened until it looks the most similar to a reference image, a process called registration [START_REF] Pnevmatikakis | Analysis pipelines for calcium imaging data[END_REF]. To perform the registration, we used in ANTsPy a 3D registration algorithm (https://github.com/ANTsX/ANTsPy, [START_REF] Tustison | The ANTsX ecosystem for quantitative biological and medical imaging[END_REF] (see Material and Methods). The major source of motion artifacts in our recordings comes from brain movement when the fish contracts its musculature in the head and/or tail, during which the brain can change volume and the resulting brain image gets warped. Therefore, we chose to apply a non-rigid, deformable transformation, which we found gave better results than rigid or affine transformations (see Material and Methods).

Skew correction

Up to now, we worked on the output images in the SCAPE referential with dimension (Lz',Lx',Ly). These images need to be corrected for the angle at which the sample was scanned, due to the tilted light-sheet geometry, to reconstruct the volume into a regular referential with dimension (Lz,Lx,Ly) (Lz: length of the image in depth, Lx: length of the image from left to right, Ly: length of the image from rostral to caudal). This step was performed using a built-in function provided by the Hillman lab [START_REF] Bouchard | Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms[END_REF]. Briefly, a transformation matrix computed using the angle at which the sample is scanned is applied to each volume (see Material and Methods). At the end of this step, we had Nt volumes of (Lz,Lx,Ly) dimension (Figure 5). We then asked whether enucleated, head-embedded larval zebrafish exhibited forward and turn swim bouts that recall locomotor bouts typically exhibited by freely swimming larvae during exploration (Marques et al., 2018;Mirat et al., 2013;[START_REF] Quan | Somatostatin 1.1 contributes to the innate exploration of zebrafish larva[END_REF]. To do so, we manually classified the swim bouts by applying a threshold on the tail angle as previously used in freelyswimming larval zebrafish ( [START_REF] Quan | Somatostatin 1.1 contributes to the innate exploration of zebrafish larva[END_REF], (see Material and Methods). To exclude locomotor episodes that contained struggle-like behavior that is not naturally exhibited by the larvae during exploration, we excluded all swim bouts with a maximal tail amplitude higher than 60°. We could successfully identify forward, left and right turns (Figure 7A1). At the level of a single experiment, the swim bouts classified as belonging to the same type were highly similar (Figure 7A, 7B). We also noticed that, except for a characteristic initial bend of large amplitude on one side during the turn that steers the tail for the duration of the locomotor episode (Figure 7B3 and B4), the kinematics of the tail curvature were strikingly similar between forward, left turns and right turns. In all cases, a wave of oscillations goes from the head to the tip of the tail of similar amplitude and propagation speed during forward, left and right turns (Figure 7B). 2.4. V2a RSNs encode the direction, frequency, and amplitude of tail bends during slow spontaneous locomotion I used the SCAPE microscope to perform population calcium imaging of 12 enucleated 6 dpf Tg(vsx2:GAL4)(UAS:GCaMP6s) larval zebrafish. In the following part, I investigate the recruitment of V2a RSNs in the hindbrain and V2a INs in the rostral spinal cord during spontaneous locomotion in one example fish as a proof of concept. In the near future, I will analyze the subsequent 11 fish left to confirm whether my observations were robust in the first animal analyzed below.

Recruitment of V2a neurons during forward swims and turns

Usual approaches to detect recruitment based on calcium signals rely on the establishment of a statistical threshold of calcium increase above which a neuron is considered active, typically 3

times the standard deviation of the signal [START_REF] Böhm | CSF-contacting neurons regulate locomotion by relaying mechanical stimuli to spinal circuits[END_REF]. Such approaches can successfully detect when neurons are active if the signal contains a long period of inactivity that can be used to establish a baseline and a noise level, and when events of interest are sparse. In the data I collected, the high frequency of swimming events, sometimes very close to one another, coupled with the slow decay of calcium transients of GCaMP6s [START_REF] Chen | Ultrasensitive fluorescent proteins for imaging neuronal activity[END_REF] make this approach unreliable due to two issues :

1) We could detect a high calcium signal during a swim bout that could correspond to the tail of the calcium kernel from the previous activation of the neurons;

2) The proper establishment of a baseline can be difficult as most neurons are active all along the recording hampering the identification of a common time window for which all neurons did not show any calcium transients.

I chose to address these two issues with the following approaches:

1) First, I used a deconvolution algorithm to interpolate the spiking rate of the neuron given the instantaneous increase of calcium over time [START_REF] Friedrich | Fast online deconvolution of calcium imaging data[END_REF] (see Material and Methods) (Figure 8A2, gray traces);

2) Second, I defined a neuron as active during a given type of swim bout (forward, left turn or right turn) if the distribution of spiking rates during all swim bouts of this type was significantly greater than the distribution of spike rates when the fish was not swimming (Figure 8A1, 8A2 and 8A3) (see Material and Methods). With this approach, I aim to identify neurons that were significantly more active during a type of swim bout than when the larva was not swimming. The periods during which the larva swam forward are pictured as the orange rectangles, the periods during which the larva was not swimming are pictured as gray rectangles. To ease visualization, the DF/F and spike rate traces are represented as their z score (in arbitrary units). B, Distribution of spike rates for all example neurons during forward episodes ("during forward", orange), or when the larva was not swimming ("quiet time", gray). To ease visualization, the density is represented in a logarithmic scale. To identify if a neuron was active during forward swims, we performed a Wilcoxon rank-sum statistical test to assess whether the distribution of spike rates when the fish was swimming forward was significantly greater than the distribution of spike rates when the larva was not swimming. Neurons 1 to 6 had a greater distribution of spike rate during forward swims (p < 0.01). Neurons 7, 8, and 9 did not have a greater distribution of spike rate during forward periods (p > 0.01).

V2a RSNs in the caudal medial medulla and V2a INs in the rostral spinal cord are recruited during forward swimming component

We first asked which neurons were active during each type of swim bout. We found that only a few V2a neurons were active during forward swims (Figure 9A, n = 131/844 neurons), located in small clusters in the spinal cord (26 neurons) and the caudal medial medulla (37 neurons), the pontine region (14 neurons), and a small cluster at the boundary between the rostral medulla and the retropontine region (52 neurons) (Figure 9A). Interestingly, a lot more V2a neurons were active upon left and right turns compared to forward swims (Figure 9B and 9C, left turns: 327/844 neurons, right turns: 223/844 neurons), with an obvious preference for neurons whose soma was ipsilateral to the turn side (left turns: 72% of neurons active were located left to the midline, right turns: 77% of neurons active were located right to the midline). We previously described that during a turn, the tail typically bends on one side and then oscillates at a frequency similar to those of forward swims (Figure 7B). We asked which neurons could drive the common forward component. Neurons involved in initiating the forward rhythmic rostrocaudal oscillations should 1) be active during both forward swims and turns, regardless of the side of the first bend, and 2) show bilaterally symmetrical responses, i.e., there should be similar neurons located on both sides of the midline. Interestingly, only a few neurons were active during both forward bouts, left turns, and right turns (N=75 neurons;

Figure 9D

). A vast majority of these neurons were located in the medial part of the caudal medulla, and in the rostral spinal cord (56/75 neurons, Figure 9D). More importantly, this cluster of neurons is spatially organized as a symmetrical stripe extending from the rostral spinal cord to the medulla.

Our results point towards V2a neurons in the medial part of the rostral spinal cord and caudal medulla being recruited to elicit the forward oscillatory component of slow spontaneous locomotion. We further named them "forward component" neurons.

V2a RSNs encoding directionality

We identified a cluster of neurons active during the forward oscillatory component of slow locomotion. We then asked which neurons could be driving the steering of the tail during turns, that is added to the common oscillatory component. To tackle this question, we looked for neurons that were not active when the fish was swimming forward and active when the fish was turning on one side. In contrast to the "forward component" neurons, we found that the neurons active only when the fish was turning were mostly located in the pontine and retropontine regions (Figures 10A and10B). Amongst those, we could identify two anatomical clusters of neurons that we further named "steering cluster RP", located in the retropontine region, and "steering cluster vP", located in the ventral pontine region (steering cluster RP: 120 neurons, steering cluster vP: 68 neurons). In contrast, we also identified a small group of neurons in the rostral medulla that which were active during left turns but not during forward swim bouts, but were located on both sides of the midline (pink dots, Figure 10A1). As they are located in close vicinity with the forward component cluster (Figure 9D), it is possible that these neurons were recruited during the forward component of the numerous left turns, but that our method to detect their recruitment failed to reveal them active during forward swim bouts. Overall, these results point towards V2a neurons in the retropontine and ventral pontine regions recruited for ipsilateral turning swim bouts and therefore behaving as "steering neurons". Active neurons are represented as filled dots and inactive neurons as unfilled dots. Two clusters of neurons, in the retropontine region ("steering cluster RP", n=120 neurons) and ventral pontine region ("steering cluster vP", n = 68 neurons), are recruited when the fish turns towards the ipsilateral side relative to their soma location, and not when the fish swims forward. The limits of the anatomical regions are depicted on the top.

V2a RSNs activity encodes the tail beat frequency and ipsilateral tail amplitude

We then asked how V2a RSNs in the hindbrain and V2a INs encode the kinematics of locomotion. To test this, we modeled the neurons' spiking rate using the kinematics of the tail movement: the instantaneous tail beat frequency, the tail amplitude on the left side, the tail amplitude on the right side, and the increase in tail amplitude (see Material and Methods). We found that the tail beat frequency (TBF) was the most efficient to model the neurons' spiking activity (Figure 11A), and the increase in tail angle amplitude was the least effective (Figure 11D). More than half of V2a In comparison, only half of the neurons in the "steering" clusters encoded instantaneous tail beat frequency ("steering RP": 62/120 neurons, "steering_vP": 31/68 neurons). Fewer neurons were explained by the tail angle (n=276/844 neurons, Figure 11B and11C). Remarkably, the "steering clusters" in retropontine and pontine regions (Figure 10) encoded the ipsilateral tail amplitude, but not the contralateral tail amplitude (Figures 11B and11C). We also noticed that the activity of a few neurons located in the medial and lateral part of the medulla was best explained by the ipsilateral tail bend amplitude (Figures 11B and11C). These results confirm the concept that V2a RSNs located in distinct anatomical regions differently encode amplitude and speed of locomotion, with a "steering cluster" of V2a neurons in the retropontine and pontine regions whose activity encodes ipsilateral tail amplitude for turns that is distinct from the "forward component" cluster of V2a RSNs in the medulla and V2a INs rostral spinal cord that encode the tail beat frequency of all slow swimming episodes.

Discussion

We implemented a titled light-sheet microscope to record the entire population of V2a RSNs in head-embedded, tail-free larval zebrafish to investigate the collective recruitment of V2a RSNs during active locomotion. Using this system, we were able to highlight that distinct subpopulation of V2a drive the forward oscillatory and the steering aspects of low-speed, spontaneous locomotion. Our results indicate that to elicit and sustain forward locomotion, a continuous cluster of ipsilateral descending V2a neurons in the rostral spinal cord and caudal medulla drive symmetrical left-right oscillations leading to muscle contractions in the caudal tail (Figure 9, Figure 7). In our recording conditions when swimming occurs spontaneously as short forward swimming bouts of few oscillations only, the activity of these V2a neurons mainly encodes tail beat frequency. Interestingly, to elicit turns, which are composed of an initial asymmetrical tail bend with subsequent forward locomotion, the same continuous cluster of V2a neurons is recruited to drive the forward component of the turn. In addition, turn-specific V2a neurons located mainly on the same side that the turn in the retropontine and ventral pontine regions encode tail beat amplitude, suggesting that these cells are recruited to steer the movement towards the ipsilateral side (Figure 10, Figure 11).

V2a ipsilateral descending neurons in the caudal medulla and the rostral spinal cord likely generate the forward component of slow locomotion.

Consistently with results from Chapter 1, our observations highlighted a cluster of V2a neurons in the caudal medulla whose activity strongly encodes tail beat frequency (Figure 11). In addition, we reveal here that these V2a RSNs form a continuous cluster with ipsilateral descending interneurons in the rostral spinal cord that are recruited for all bout types elicited at the low locomotor frequency where tail oscillations occur, regardless of the directionality of locomotion (Figure 9). As discussed in Chapter 1, these results are in line with a converging set of evidence showing that V2a RSNs in the caudal medulla form an efficient center for eliciting locomotion (Arrenberg et al., 2009;Buchanan, 2018;[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]Li and Soffe, 2019). Recordings in Xenopus tadpoles showed that excitatory RSNs in the caudal medulla with the same typical morphology of V2a RSNs act as a recurrent neuronal network building up activity to induce and maintain locomotion (Li and Soffe, 2019;[START_REF] Soffe | Defining the excitatory neurons that drive the locomotor rhythm in a simple vertebrate: insights into the origin of reticulospinal control[END_REF]. The fact that the V2a neurons we highlight here encode tail beat frequency recalls the function of V2a INs in the spinal cord setting the locomotor rhythm (Eklöf- [START_REF] Eklöf-Ljunggren | Origin of excitation underlying locomotion in the spinal circuit of zebrafish[END_REF]. The additional set of evidence we brought with volumetric imaging data indicates that ipsilateral descending V2a RSNs in the caudal medulla together with V2a interneurons in the rostral the spinal cord form a functional hub to elicit forward locomotion and set the locomotor frequency. This observation questions the relevance of the partition applied to V2a neurons (RSNs versus INs) between these two anatomical regions.

While previous studies showed that V2a RSNs in the medial medulla were selectively active for weak fictive forward swim bouts (Pujala and Koyama, 2019), our regression analysis provides evidence for these neurons to drive the forward oscillatory component of all bout types within the low-speed locomotion occurring during exploration. Together with our observations from Chapter 1, our results in the present study indicate that V2a RSNs in the medial caudal medulla elicit both spontaneous and MLR-induced forward locomotion for which these command neurons encode tail beat frequency. During spontaneous locomotion recorded here, these neurons form a "forward component" cluster that was also active to elicit turns, regardless of the side. These observations, combined with recent report in rodents that mice can turn during MLR-induced locomotion (van der Zouwen et al., 2021), is consistent with the hypothesis that in zebrafish, the "forward component" neurons revealed in our study could correspond to "forward specific" neurons we previously found recruited during MLR-induced spontaneous locomotion. More specifically, we propose that the "forward component" neurons receive inputs from the MLR to drive the basic forward motor pattern of symmetrical tail undulation propagating from head to tail that propels the larval zebrafish forward during spontaneous locomotion, and that other types of inputs directed to other RSNs add an additional control to steer the movement. Although we did not investigate sensory-driven locomotion, we can hypothesize that the "forward component" neurons may also produce the basic forward motor pattern during visually induced locomotion. As such, they could receive inputs from the diencephalic sensorimotor center, the nMLF or interstitial nucleus of Cajal (see Manuscript), which has been implicated in sensory-induced forward swimming in larval zebrafish (Orger et al., 2008;[START_REF] Severi | Neural Control and Modulation of Swimming Speed in the Larval Zebrafish[END_REF]Thiele et al., 2014). Similarly, it will be of interest to investigate whether the same cluster is recruited to drive burst swim bouts (Marques et al., 2018) occurring at a higher tail beat frequency in order to induce fast forward swim bouts.

Our approach detected few V2a RSNs active during forward swimming (Figure 9A). In contrast, almost all V2a RSNs located in the medial medulla were recruited during spontaneous swimming recorded in fictive preparation (Pujala and Koyama, 2019). This noticeable difference may be due to a lower signal-to-noise ratio in our system compared to the 2 photons scanning microscope used in the study cited, an effect possibly enhanced by the fact that during active swimming motion artifacts raise the threshold of detection of active cells.

V2a RSNs encode directionality of locomotor output

Altogether, our results reveal that many V2a RSNs in two clusters of the retropontine and pontine area encode the directionality of movements during slow turns (Figure 10 and Figure 11). These results are in line with previous findings that a subset of retropontine and pontine, retrogradely-labeled, V2a

RSNs were recruited during turns in zebrafish (RoV3/MiV1, MiV2 in (Huang et al., 2013)). Here, by genetically targeting the entire population of V2a neurons, we reveal that the neurons are more numerous than the small cluster highlighted in this previous study. However, RoV3, MiV1, and MiV2

were also shown to be active during forward locomotion occurring during optomotor response recorded using 2P laser scanning microscopy on retrogradely labeled RSNs (Orger et al., 2008). This difference may be explained by the fact that we did not sample ventrally enough using SCAPE or that, as mentioned above, we failed to detect activity during forward swimming in these cells due to the low SNR of SCAPE compared to two-photon laser scanning microscopy. Indeed, the activation of ventral most V2a neurons may be difficult to decipher from noise, especially in the ventral-most planes where the signal-to-noise ratio is low using 1 photon microscopy. The analysis of the available 11 other recordings from different fish with larger volumes acquired should help us to solve this question.

Our observation of "steering" V2a neurons in larval zebrafish, active during ipsilateral turns but not contralateral turns no forward swims, is coherent with previous observations in lampreys that RSNs are preferentially active on the side of the steering (Deliagina et al., 2000). Our investigation in larval zebrafish revealed that the "steering" V2a neurons build up two clusters located in 1) the ventral retropontine area corresponding to Rh5 and Rh6, and 2) the ventral pontine regions corresponding to Rh3 and Rh4. (Figure 11, Figure 12). Interestingly, the recruitment pattern and location of V2a neurons in the retropontine area recall the location of cervically-projecting V2a RSNs in the mice rGi located in the Rh6, that orient locomotion most likely by projections onto MNs controlling neck and forelimbs musculature (Usseglio et al., 2020). In larval zebrafish, some of the V2a RSNs were shown to directly project to MNs in the spinal cord (Pujala and Koyama, 2019). We propose that, upon unilateral activation, the V2a RSNs from the retropontine and pontine clusters steer locomotion by providing an ipsilateral and asymmetric drive to MNs in the spinal cord at the beginning of the locomotor episode that would result in the large tail bend on the ipsilateral side (Figure 7).

Limits and further improvements of SCAPE microscope and analysis pipeline

We discussed that the SCAPE microscope we built here exhibits a smaller depth of field than the system built in the Hillman Lab [START_REF] Voleti | Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0[END_REF]) by a factor of 2. Neurons can be detected within an axial range (z' axis) of about 440um but the signal and resolution are only optimal in a region of 220um around the center of the field. Although we should be able to increase the depth of field by further optimization of the system's alignment, the accessible optimal axial range depends on the optical properties of the sample and is not necessarily compatible with a 3% agarose, which is necessary for head-embedded preparation. Furthermore, the optimization of the resolution of the system is difficult in practice as it requires a perfect alignment to avoid aberrations and would rely on both a fine-tuning of the light sheet angle and an addition of a slit aperture in the illumination path. Both a slit aperture and a further increase of light-sheet angle would lead to a decrease in photons efficiency, i.e. a decrease in the SNR that would need to be compensated by increasing the laser power and/or decreasing the speed of acquisition.

activity of the V2a neuronal population in the brainstem and rostral spinal cord during the exceptionallylong locomotor episodes induced by MLR stimulation (see Manuscript). In the experimental paradigm of MLR stimulation we developed, the zebrafish larvae performed exceptionally long forward swims of varying tail beat amplitude and frequency. Using a similar modeling approach as I developed in these two studies, we could ask in further depth which V2a RSNs encode tail amplitude, tail beat frequency, initiation, maintenance, and halting of locomotion at the population scale.

The data we collected additionally provides a unique opportunity to investigate the functional coupling between V2a RSNs distributed in the brainstem, across the dorsoventral and rostrocaudal axes, and together with V2a INs in the rostral spinal cord that provide the excitatory drive leading to the generation of the locomotor rhythm [START_REF] Ljunggren | Optogenetic activation of excitatory premotor interneurons is sufficient to generate coordinated locomotor activity in larval zebrafish[END_REF]. These investigations will be necessary to build a comprehensive model of how V2a RSNs can act in a coordinated manner to control spontaneous locomotion.

Furthermore, our work sets the ground to investigate the contribution of other genetically-defined populations of neurons in the brainstem to produce locomotion. Some of the key transcription factors defining IN classes in the spinal locomotor CPGs are also conserved at the level of the hindbrain (Kimura et al., 2006[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF][START_REF] Kinkhabwala | A structural and functional ground plan for neurons in the hindbrain of zebrafish[END_REF], such as the vsx2 transcription factor labeling V2a neurons that we investigated here. For instance, the eng1b transcription factor labels ipsilateralprojecting, inhibitory V1 neurons forming a stripe extending from the spinal cord to the hindbrain [START_REF] Higashijima | Engrailed-1 expression marks a primitive class of inhibitory spinal interneuron[END_REF][START_REF] Kinkhabwala | A structural and functional ground plan for neurons in the hindbrain of zebrafish[END_REF]. In the spinal locomotor CPGs, V1 INs control swimming speed by inhibiting slow premotor circuits during fast locomotion [START_REF] Kimura | Regulation of locomotor speed and selection of active sets of neurons by V1 neurons[END_REF][START_REF] Menelaou | Hierarchical control of locomotion by distinct types of spinal V2a interneurons in zebrafish[END_REF]. In the hindbrain, V1 neurons project an ipsilateral ascending axon ascending and are reliably recruited during locomotion in a paradigm of optomotor response [START_REF] Severi | Investigation of hindbrain activity during active locomotion reveals inhibitory neurons involved in sensorimotor processing[END_REF]. Using SCAPE combined with MLR stimulation and regression analysis, we could further investigate the function of V1 INs in the brainstem and rostral spinal cord. By adding a parallel red line available in the SCAPE with the red calcium indicator jRGECO [START_REF] Dana | Sensitive red protein calcium indicators for imaging neural activity[END_REF], it could be possible to simultaneously record from the two populations of V1 and V2a neurons in order to uncover their functional coupling during low-speed locomotion. MNs are involved in slow locomotion in larval zebrafish [START_REF] Kawano | Long descending commissural V0v neurons ensure coordinated swimming movements along the body axis in larval zebrafish[END_REF][START_REF] Liu | Function of identified motoneurones and co-ordination of primary and secondary motor systems during zebra fish swimming[END_REF][START_REF] Menelaou | Hierarchical control of locomotion by distinct types of spinal V2a interneurons in zebrafish[END_REF]. To elicit low-frequency tail oscillations, the V2a RSNs could therefore send excitatory inputs to the V0v and ventrally-located V2a INs. To test this hypothesis, we could use targeted electroporation or single-cell stage low-concentration injection of (UAS:synaptophysin-mCherry) (unpublished) in transgenic Tg(vsx2:Gal4) [START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF] larvae to drive the expression of a fluorescent protein attached synaptophysin in V2a neurons and reveal the location of their presynaptic sites [START_REF] Menelaou | Differences in the morphology of spinal V2a neurons reflect their recruitment order during swimming in larval zebrafish[END_REF][START_REF] Tawk | Focal electroporation in zebrafish embryos and larvae[END_REF][START_REF] Zou | Targeted Electroporation in Embryonic, Larval, and Adult Zebrafish[END_REF].

Optogenetic stimulations to test how V2a RSNs control locomotor kinematics

Our results reveal how the activity of distinct subpopulations of V2a RSNs encodes the steering kinematic and the forward component of locomotion. To test how the neurons we identified modulate such kinematics, and how they are necessary and sufficient to drive these kinematics, it is possible to use optogenetic stimulation to activate or inhibit V2a neurons using the optogenetic toolbox developed in larval zebrafish [START_REF] Antinucci | A calibrated optogenetic toolbox of stable zebrafish opsin lines[END_REF] and 3D optical patterning [START_REF] Hernandez | Three-dimensional spatiotemporal focusing of holographic patterns[END_REF]. This project is led by the graduate student Xinyu Jia in our lab who can successfully elicit locomotion by activating V2a RSNs.

From the results of the two studies of my PhD thesis, we built two non-mutually exclusive hypotheses that we could test on the role of "forward component" neurons:

Working hypothesis 1: "forward component" neurons set the locomotor rhythm. To test this hypothesis, we could stimulate these neurons at different light intensities or pulse frequencies within a train and monitor if and how the tail beat frequency is modulated.

Working hypothesis 2: "forward component" neurons sustain locomotor activity. To test this hypothesis, we could 1) Activate these neurons with optogenetic stimulation for different durations and investigate whether the locomotor episode duration scales with the duration of the stimulation.

2) Optogenetically inhibit these neurons upon locomotion onset to test whether swimming is silenced.

Similarly, optogenetic manipulation could be used to test the necessity and sufficiency of the V2a "steering" neurons in the pontine and retropontine areas.

Altogether, our work highlighted distinct subsets of ipsilateral descending V2a neurons encoding the forward and the steering kinematic of slow forms of spontaneous locomotion. This work paves the way for a precise description of how brainstem descending circuits are functionally and molecularly organized to control the kinematics of locomotion at the cellular and population levels.

Material and Methods

This section relates to chapter 2. The experimental procedures for chapter 1 are provided within the manuscript.

Animal care and transgenic line experiments were performed on Danio rerio larvae of AB background with mitfa (-/-) mutation (Lister et al., 2001). Adult zebrafish were reared at a maximal density of 8 animals per liter in a 14/10 hr light/dark cycle environment at 28.5°C. Larval zebrafish were typically raised in petri dishes filled with system water following the same conditions in terms of temperature and lighting as for adults.

Experiments were performed at 20°C on animals aged of 6 dpf.

SCAPE microscope

The SCAPE microscope was built following instructions sent by Hillman's lab almost identically to the second version of the SCAPE microscope issued in 2019 [START_REF] Voleti | Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0[END_REF]. This document was shared with a confidentiality clause, we thus present only the material published in the two papers (Figure 1). A 488 nm collimated laser is used to illuminate the sample (Coherent, OBIS 1220123), whose intensity is controlled by a filter wheel built with a range of neutral densities (ThorLabs, FW212CNEB). The laser beam is shaped from a gaussian beam to a homogeneous extended line using a Powell lens (Laser Line LOCP-8.9R30-1.0). The illumination light hits a 1D Galvo system (ThorLabs, GVS011/M 1D Large Beam (10 mm) Diameter Galvo System) before going through an illumination Analysis of behavior kinematics. We extracted different kinematic parameters to characterize the swim bouts: absolute maximal bend amplitude, median bend amplitude, median tail beat frequency, bout duration and the number of oscillations (see also Part II -Manuscript). One bend corresponds to the max position of the tail tip on one side during a swim cycle. To compute the median tail beat frequency, we calculated, for each cycle, the inverse of the time it took to be achieved from the previous cycle (which we called instantaneous tail beat frequency). Median bend amplitude was computed as the median value of the bend amplitude (absolute) which happened during the swim bouts.

Manual classification of swim bouts as forward, left or right turns. We classified forward swims and turns according to their maximal tail angle as previously done in freely swimming larvae [START_REF] Quan | Somatostatin 1.1 contributes to the innate exploration of zebrafish larva[END_REF]. Swim bouts whose maximal tail bending amplitude was below 25° were classified as forward swims. Swim bouts whose maximal tail bending amplitude was between 25 and 60° were classified as turns, and the sign was assigned according to the sign of their maximal tail angle. Swim bouts whose maximal tail bending amplitude exceeded 60° were discarded from the analysis, as most of them corresponded to struggle-like behavior.

Calcium imaging data analysis and statistical tests

Detection of active neurons during swim bouts. To detect if a given neuron was active during a type of swim bouts (forward, left turns, right turns), we compared the distribution of spikes during all swim bouts of a given type to the distribution of spikes when the fish was not swimming (Figure 8, Figure 9, Figure 10). To build this distribution for a given neuron and a given type of swim bouts, we processed as follows: 1) for a given swim bout, we extracted the spiking rates between 200ms before the start of the bout and the end of the bout. 2) We compiled these spiking rates for all swim bouts of the same type. 3) we sampled the spiking rate when the fish was not moving as periods of time where the tail angle was zero. 4) These distributions did not follow a gaussian distribution and did not have the same sample size. Therefore, we used a non-parametric Wilcoxon rank-sum test to test if the distribution of spiking rate during the given type of bouts was greater than the distribution of spiking rate when the fish was not swimming. We used a threshold p-value of 0.01 to reject the null-hypothesis. Thus, the neuron was labeled as active during the type of swim bout if the resulting p-value from the test was lower than 0.01.

Linear regression to model spiking rate using kinematic parameters. We hypothesized that some V2a neurons drive distinct kinematics of locomotion. Within this frame, the neuronal activity of V2a neurons driving a locomotor kinematic should reflect the given kinematic of movement. To test this, we examined whether neurons' spike rates could be explained by the kinematic signals built as described above. We used generalized linear regression to model each neuron's spike rate at each time step t, as a Poisson model of the kinematic signal at time t (from package statsmodels, model=GML, family='Poisson" [START_REF] Seabold | Statsmodels: Econometric and Statistical Modeling with Python[END_REF]). We normalized the signals using zscore function. We tested for significance of the model and defined a neuron to be significantly explained by the kinematic signal if the p-value was below p0=0.001, and if the coefficient of the model was positive.

Data analysis and figures editing software

All the scripts used to analyze the data were written in Python 3.7, except for the skew correction step that was written in MATLAB. All the figures were generated using matplotlib (v3.

IV -General conclusion and perspectives

The work presented in this thesis revealed the MLR locus in larval zebrafish and how the MLR recruits V2a RSNs from distinct anatomical groups to produce and modulate kinematics of forward locomotion. We highlighted the role of V2a RSNs in the medulla, an overlooked region of the brainstem, by showing that a conserved population of command neurons encodes kinematic parameters setting the locomotor speed. We further investigated how these V2a RSNs encode the kinematics of locomotion at the population scale by building and optimizing a tilted light-sheet microscope. We identified ipsilateral descending V2a neurons (RSNs and spinal INs) recruited during the forward oscillatory component of low-speed locomotion located in the caudal medulla and rostral spinal cord, and an additional set of V2a RSNs in the pontine and retropontine regions that are specifically recruited for ipsilateral turns. Our results pave the way for building a precise description of the circuits driving spontaneous locomotion by manipulating neural activity and mapping the projections of the neurons we uncovered. Moreover, the modeling approach we took to analyze the recruitment of V2a RSNs as a function of the precise kinematics of movement opens perspectives to solve how other command neurons control the kinematics of locomotion. Overall, by combining genetic and optical tools, our approach can bring a new level of understanding of how brainstem descending circuits are functionally and spatially organized to control distinct components of locomotion in a "simple" swimming vertebrate species.

Altogether, our description of the MLR and the tools developed during my PhD put the larval zebrafish in a unique position to decipher how the MLR-RSN axis controls locomotor speed, direction, start, maintenance, and arrest. SCAPE microscopy together with the processing pipeline we developed and regression analysis not only enables simultaneous recording of all V2a neurons in the brainstem but also facilitates the collection of population calcium imaging at a large scale during hundreds of locomotor events. This approach has the potential to reveal how the MLR recruits V2a RSNs, as well as other genetically-defined hindbrain INs, to start, maintain, stop, and steer spontaneous locomotion. [START_REF] Shik | Control of walking and running by means of electrical stimulation of the midbrain[END_REF], le rat (Skinner and Garcia -Rill 1984), la lamproie (Sirota 2000), la salamandre (Cabelguen 2003); mais n'a jamais été décrite chez le poisson-zèbre, ni avec précision chez les autres espèces de poissons cartilagineux. 2) De nombreuses techniques d'imagerie fonctionnelles sont utilisées en routine dans les laboratoires travaillant sur les poissons zèbres. Cependant, ces techniques ne permettent par d'enregistrer l'activité de tous les V2a RSNs lors de la nage active avec une résolution temporelle et spatiale suffisante pour dissséquer l'encodage, à l'échelle de la population, des paramètres de nage par ces neurones impliqués dans le contrôle de la locomotion. Mon projet de thèse s'est articulé autour de 3 objectifs pour répondre à ces limites et initier l'étude de cette hypothèse.

Objectif 1: Identification de la MLR chez le poisson zèbre Objectif 2: Decrire comment la MLR module la locomotion vers l'avant chez le poisson zèbre, et quel V2a RSNs cette région recrute pour effectuer cette fonction.

Objectif 3: Reproduire un microscope à feuillet de lumière oblique, afin d'imager à haute vitesse toute la population de neurones V2a du tronc cérébral durant la nage spontanée et ainsi étudier le recrutement des neurones V2a à l'échelle de la population lors des différentes cinétiques de nage.

Resultats

Les objectifs 1 et 2 sont l'objet d'une article scientifique publié, à la date d'écriture du manuscrit, sur le plateforme libre de droit BioRxiv, intitulé "Functional coupling of the mesencephalic locomotor region and V2a reticulospinal neurons driving forward locomotion", don't je partage le titre de premier auteur avec le Dr Martin Carbo-Tano.

Objectif 1: Comme la MLR restait à définir chez le poisson zèbre, nous avons d'abord combiné plusieurs approches pour définir fonctionnellement et anatomiquement la MLR chez le poisson zèbre. Nous avons stimulé electriquement différentes régions dans la partie rostrale du tronc cérébrale, dans des larves dont la tête était fixée dans l'agarose et la queue laissée libre de bouger, pour enregistrer l'activité motrice générée par les stimulations. Grâce à une quantification précise des paramètres de mouvement de la queue, nous avons pu identifier une petite région, située médialement et dorsalement au locus coeruleus, dont la stimulation produisait efficacement de la nage vers l'avant. La stimulation de sites autour de cette région produisait des mouvements de la queue désynchronisés et non symmétriques. Nous avons défini cette région comme le MLR. Grâce à des techniques de traçage rétrograde dans des lignées transgéniques, combinées à des immuno-marquages, avons ensuite montré que les neurones de cette région projettent sur la formation réticulée, et que cette région contient des neurons glutamatergiques et cholinergiques.

Objectif 2: En modulant les paramètres de stimulation de la MLR, nous avons découvert que la MLR dirige et module spécifiquement la durée ainsi que la fréquence de la locomotion vers l'avant. En combinant les mêmes méthodes dans des larves transgéniques exprimant un indicateur calcique dans les neurons V2a, Tg(vsx2:GAL4)(UAS:GCaMP6s), nous avons pu montrer que la MLR recrute un petit nombre de V2a RSNs dans la médulla lors de la nage vers l'avant. En utilisant des méthodes régression, nous avons pu montrer que l'activité de ces V2a RSNs spécifiquement activés durant la nage vers l'avant corrèle avec la durée de l'épisode de nage. Nous avons ensuite modélisé les paramètres de nage telle que la fréquence de battements de la queue, l'amplitude du mouvement et leur augmentation dans le temps. En comparant ces paramètes à l'activité des neurones grâce à des méthodes régression linéaires multiples, nous avons montré que la majorité des neurons V2a spécifiques de la nage vers l'avant encodent la vitesse de battement de la queue. Notamment, un petit groupe de V2a RSNs dans la partie la plus caudale de la medulla encodent à la fois la vitesse de battement de la queue et la durée du mouvement.

Schema recapitulant les résultats des deux premiers objectifs. Vue sagitale d'un modèle du tronc cérébral de poisson zèbre, les rhombomères sont annotés de 1 à 8 (r1 à r8).

Nous avons apporté première description précise de la MLR chez le poisson zèbre, ce qui nous a permis d'identifiés des groupes spatialement distincts de V2a RSNs recrutés pour la nage vers l'avant. De plus, la possibilité d'initier des mouvements anormalement long nous a permis d'identifier que ces neurones V2a agissent comme des "maintain" neurones, puisque leur activité encodent la durée du mouvement. De plus, avec notre approche de modélisation de l'activité des neurones avec les paramètres de nage, nous avons découverts que ces mêmes neurones encodent les paramètres de mouvement qui définissent la vitesse à laquelle la locomotion est executée.

Objectif 3: Nous avons ensuite cherché à comprendre comment la cinétique de nage est encodé à l'échelle de la population. Pour enregistrer la population entière de V2a RSNs simultanément, nous avons implémenté un microscope à feuille de lumière oblique, développé par le laboratorie d'Elizabeth Hillman [START_REF] Bouchard | Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms[END_REF], Voletti 2019). Avec l'aide du Dr. Olivier Thouvenin, j'ai aligné le système et optimisé son utilisation pour imager le tronc cérébral des larves de poisson zèbre. Ensuite, en collaboration avec Marco Pascucci, nous avons développé un pipeline d'analyse en accès libre pour traiter ces données. Notamment, nous avons développé une étape de déconvolution en utilisant la fonction d'étalement du point mesurée experimentalement. Cette étape a permis d'augmenter le contraste des images, ce qui permet une meilleure détection des cellules, et a réduit le niveau de correlation entre les signaux de neurones.

J'ai ensuite collecté, à l'aide de ce système, des données d'imagerie calcique de tous les neurones V2a dans le tronc cérébral et dans la partie rostrale de la moelle épinière lors de la nage spontannée, jusqu'à une fréquence de 6 volumes par seconde. Pour ce faire, j'ai utilisé des larves trangéniques Tg(vsx2:GAL4);(UAS:GCaMP6s), après une ablation préalable des yeux afin d'examiner la nage spontanée des larves et non induite par un stimuli visuel fort que produit le laser de scan. Avec ces données, j'ai pu mettre en évidence qu'une sous-populations de V2a RSNs dans la moelle est recrutée pendant tous les types de mouvements typiquement produits par la larve durant l'exploration. La localisation de ce groupe est comparable aux V2a RSNs spécifiquement recrutés par le MLR décrits dans les résultats précédents. Par ailleurs, deux groupes distincts sont recrutés lorsque le poisson effectue du virage du côté où les somas de ces neurones sont situés. Ces résultats indiquent que pour déclencher et maintenir la locomotion vers l'avant, un groupe continu de neurones V2a descendants ipsilatéraux dans la moelle épinière rostrale et la médulla caudale entraînent des oscillations symétriques gauche-droite menant à des contractions musculaires dans la queue caudale. Dans nos conditions d'enregistrement, lorsque la nage se produit spontanément sous la forme de courtes épisodes composés de quelques oscillations seulement, l'activité de ces neurones V2a encode principalement la fréquence des battements de la queue. Il est intéressant de noter que pour provoquer des virages, qui sont composés d'une courbure initiale asymétrique de la queue et d'une locomotion vers l'avant, le même groupe continu de neurones V2a est recruté pour diriger la composante avant du virage. En outre, les neurones V2a spécifiques du virage situés principalement du même côté que le virage dans les régions rétropontine et pontine ventrale, codent l'amplitude du battement de queue, ce qui suggère que ces cellules sont recrutées pour diriger le mouvement vers le côté ipsilatéral.

Discussion

Les travaux présentés dans cette thèse ont révélé le locus MLR chez le poisson zèbre larvaire et comment la MLR recrute les V2a RSNs de groupes anatomiques distincts pour produire et moduler la cinétique de la locomotion vers l'avant. Nous avons mis en évidence le rôle des V2a RSNs dans la médulla en montrant qu'une population conservée de neurones de commande encode les paramètres cinétiques fixant la vitesse de locomotion. Nous avons ensuite étudié la façon dont ces V2a RSNs codent la cinétique de la locomotion à l'échelle de la population en construisant et en optimisant un microscope à feuillet de lumière inclinée. Nous avons identifié des neurones V2a descendants ipsilatéraux V2a (RSNs et INs spinaux) recrutés pendant la composante oscillatoire vers l'avant de la locomotion à basse vitesse, situés dans la médulla caudale et la moelle épinière rostrale Nous avons également identifié un ensemble supplémentaire de V2a RSNs dans les régions pontine et rétropontine qui sont spécifiquement recrutés pour les virages ipsilatéraux.

Nos résultats ouvrent la voie à l'élaboration d'une description précise des circuits qui initient, modulent, maintienent et arrêtent la locomotion spontanée en manipulant l'activité neuronale et en cartographiant les projections des neurones que nous avons découverts. De plus, l'approche de modélisation que nous avons adoptée pour analyser le recrutement des V2a RSNs en fonction de la cinétique instantannée du mouvement ouvre des perspectives pour résoudre comment d'autres population de neurones de commande contrôlent la cinétique de la locomotion. Dans l'ensemble, en combinant des outils génétiques et optiques, notre approche apporte un nouveau niveau de compréhension de la manière dont les circuits descendants du tronc cérébral sont organisés fonctionnellement et spatialement pour contrôler des composantes distinctes de la locomotion chez les vertébrés.

1 1 .

 11 V2a ipsilateral descending neurons in the caudal medulla and the rostral spinal cord likely generate the forward component of slow locomotion. 3.2 V2a RSNs encode directionality of locomotor output 3.3 Limits and further improvements of SCAPE microscope and analysis pipeline 3.4 Perspectives 3.4.1 Population calcium imaging of distinct genetically-defined hindbrain neurons using SCAPE microscopy 3.4.2 Projections to spinal CPGs controlling rhythm and amplitude of the locomotor output Circuits of locomotion in vertebrates In all vertebrates, the neural circuits underlying locomotion are viewed as hierarchically organized sets of motor centers distributed all along the central nervous system (Figure I.1,

Figure I. 1 .

 1 Figure I.1. Overall organization of neural circuits controlling locomotion in vertebrates.In the forebrain, the basal ganglia and the cortex, directly and indirectly interplay to select the locomotor action to initiate. In the brainstem, the initiation of locomotion is mediated by the mesencephalic locomotor region (MLR). The MLR projects to neurons located in the reticular formation of the brainstem, called reticulospinal neurons (RSNs), which also integrate various sensory inputs. The RSNs send descending projections to locomotor networks in the spinal cord that execute locomotion. These locomotor networks are composed of local circuits of interneurons that generate the rhythm and pattern of excitation necessary to activate in coordination the MNs, which directly contact muscle fibers. The spatiotemporal coordination of the MNs activity produces the coordinated contraction of muscles underlying locomotion. To ease visualization, not all neural circuits controlling locomotion are depicted here. Adapted from[START_REF] Arber | Connecting neuronal circuits for movement[END_REF].

  composed of local interneurons (INs) and motoneurons (MNs), which are under the control of command centers in the brainstem. The spatiotemporal contraction of distinct muscle sets underlying locomotion is directly controlled by the spinal MNs that are rhythmically active during locomotion (Figure I.2, Figure I.3)

Figure I. 2 .

 2 Figure I.2. Generation of locomotion in limbed and non-limbed animals. A, Cat locomotion, where propulsion of the body is generated through alternating stance and swing phases of the 4 limbs A1, Schematic representing a cat walking with the 4 limbs annotated: left front limb (LF), left hindlimb (LH), right front limb (RF), and right hindlimb (RH). A2, Comparison of stepping movement during walk and trot showing the coordination of the flexors and extensors muscles in each limb during the 2 gaits. The periods of activity of the flexor muscles in each limb (flexion of the limb, swing) are depicted in red. The

Figure I. 3 .

 3 Figure I.3. Rhythmic activity of muscles and motoneurons underlying locomotion, in intact animals or isolated spinal cord. A, EMG recordings showing the electrical activity in given sets of muscles during active locomotion in a swimming lamprey (A1) and a walking cat (A2). A1, EMG recordings showing the activity of different myotomes located at distinct axial levels: in the left part of body segment 5 (L5), and right body segment 5 (R5), and right body segments25 and 46 (R-25, R-46). During a swimming cycle, the muscles are segmentally recruited from the rostral to the caudal segments. The left and right hemi-segments myotomes are recruited alternatively at each mid-cycle. The locomotor frequency is given by the period of time necessary to execute a full swimming cycle, i.e., between two bursts of activity in the same hemi-segment. A2, EMG recordings showing the electrical activity of hindlimb muscles. During a walking cycle, there is a wave activation from the R-Tr to the L-VI which produces alternating the limb movement underlying the walking gait. The locomotor frequency is given by the period between the beginning of a burst activity in a muscle and the beginning of the next burst. B, Ventral nerve roots recording showing the motoneurons activity at distinct body segments during fictive locomotion in the lamprey spinal cord (B1), and in the mouse spinal cord (B2). B1, Ventral nerve root recording at 3 Hemi-segments of the lamprey isolated spinal cord, showing the wave of MNs activity along the rostrocaudal axis. B2, Transmitter induced (NMDA/5-HT) locomotor activity in mouse isolated spinal cord shows the alternating activity of left-right (R-L2 and L-L2) and flexor-extensor (R-L2 and R-L5). In these lumbar segments, the excitation is propagated from rostral to caudal, and from left to right, as it is in swimming species. Note that in B1 and B2, the period of the locomotor cycle is increased by a factor 10, which means that the locomotor frequency is decreased by a factor 10. Rebuilt from[START_REF] Goulding | Circuits controlling vertebrate locomotion: moving in a new direction[END_REF]. Recordings in lampreys from[START_REF] Wallén | Fictive locomotion in the lamprey spinal cord in vitro compared with swimming in the intact and spinal animal[END_REF].

  After the initial controversy, scientists in the motor field finally agreed on a model describing that the command generating oscillation during locomotion is centrally generated by spinal circuits, thereby named Central Pattern Generators (CPGs). The locomotor CPGs are built up of excitatory and inhibitory interneurons receiving descending commands, connected among themselves and projecting to motoneurons (Figure I.4). In limbed animals, the spinal CPGs set the alternation of limbs in the right order (pattern) and at a given locomotor frequency (rhythm) (Figure I.4A). In animals who locomote through body undulations, such as aquatic species, the spinal CPGs generate body undulations via a rostro-caudal wave of left-right muscle contractions that propel the body forward (Figure I.4B). Today, our understanding of the mechanism is the following. In all vertebrates, upon ignition, the excitatory INs in the spinal CPGs (Figure I.4A, in green, Figure I.4B, in red) bring excitation to MNs through monosynaptic inputs and propagate excitation to the spinal segments by projecting to other excitatory INs on the same side of the spinal cord (Buchanan and Grillner, 1987, for review see Grillner, 2003). Excitatory INs also excite inhibitory INs of 2 sorts: 1) commissural INs (Figure I.4A, in brown, Figure I.4B in blue) which directly inhibit the premotor INs and MNs on the other side, therefore ensuring that when one side is active, the other one is not, which fundamentally drives the left/right alternation (for review, see Maxwell and Soteropoulos, 2020). 2) The excitatory INs also excite ipsilateral inhibitory INs (Figure I.4A, example of the reciprocal Ia inhibitory INs), one subset of which inhibit the opposite group of MNs within the same side, therefore ensuring the asynchronous activation of flexor and extensor muscle groups within a locomotor cycle

Figure I. 4 .

 4 Figure I.4. A simplified description of swimming and limb CPGs generating the rhythmic and patterned activation of motoneurons during locomotion. In both models, the excitatory INs function as a burst-generating unit and provide excitatory drive to MNs and other excitatory and inhibitory INs. The generation of the rhythm does not require inhibitory INs. A-B, Schematic representation of halfcenters CPGs models in limb vertebrates. To ease visualization, only 2 working models are presented. Circles represent spinal INs and squares represent MNs. Excitatory and inhibitory connections are shown by lines ending with arrowheads and small filled circles, respectively. A, Single-level half-center model in which rhythm generation is produced by two excitatory interneuron populations (green stippled populations) interconnected by reciprocal inhibition (purple). The same interneurons excite the corresponding motoneuron populations as well as inhibitory interneurons responsible for rhythmic inhibition of motoneurons during locomotion. B, Two and a half center CPG architectures with separate

  The coordination of muscle groups across segments and sides of the body is controlled by multiple IN classes. The V0 neurons are commissural INs enabling the alternation of left and right sides (Figure I.5, V0d and V0v)

Figure I. 5 .

 5 Figure I.5. Classes of spinal interneurons emerging from distinct progenitor domains have complementary roles in the generation of locomotion and distinct molecular expressions. A, The transcription factor code defines the progenitor domains that generate distinct neuronal classes in the spinal cord during development. Pasted from[START_REF] Goulding | Circuits controlling vertebrate locomotion: moving in a new direction[END_REF]. B, Non-exhaustive connectivity map between IN classes building up the vertebrates' spinal CPGs. A circle represents a population of neurons. The colors represent the progenitor domain the neurons emerge from. Lines ending with arrows represent excitatory input, and lines ending with blocks represent inhibitory inputs. Dotted lines represent putative connectivity. V2a interneurons provide rhythmic excitatory drive to MNs and other CPG neurons(Kimura et al., 2006;[START_REF] Mclean | Continuous shifts in the active set of spinal interneurons during changes in locomotor speed[END_REF]. V0 neurons project to neurons on the other side of the spinal cord: excitatory V0e project to inhibitory Ia INs; while inhibitory V0i directly inhibit MNs (for review, see(Kiehn, 2016)). Ia inhibitory INs provide reciprocal inhibition between flexors and extensors and represent a subset of V1 and V2b classes[START_REF] Britz | A genetically defined asymmetry underlies the inhibitory control of flexor-extensor locomotor movements[END_REF][START_REF] Zhang | V1 and v2b interneurons secure the alternating flexor-extensor motor activity mice require for limbed locomotion[END_REF]. A subset of the V1 class differentiates into Renshaw cells (R), known to mediate the recurrent inhibition of MNs[START_REF] Sapir | Pax6 and engrailed 1 regulate two distinct aspects of renshaw cell development[END_REF]. Other V1 inhibitory INs provide inhibition to ventrally-located V2a INs, while other V2b inhibitory INs provide inhibition to dorsally-located V2a INs (see1.1.5). V3 neurons directly project onto MNs on both sides to control the number of motoneurons pools recruited (see 1.1.5)[START_REF] Böhm | Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish[END_REF][START_REF] Wiggin | V3 Interneurons are Active and Recruit Spinal Motor Neurons During In Vivo Fictive Swimming in Larval Zebrafish[END_REF][START_REF] Zhang | V3 spinal neurons establish a robust and balanced locomotor rhythm during walking[END_REF]. To ease visualization, the position of the neurons in the dorso-caudal and medial-lateral axis is not always respected (for more faithful representation, see[START_REF] Bikoff | Interneuron diversity and function in the spinal motor system[END_REF].

  . In 2007, McLean et al first described a topographical organization of V2a interneurons in larval zebrafish, showing a positive correlation between an excitatory neuron's dorsoventral location in the spinal cord and the locomotor frequency upon which the V2a IN was recruited (McLean et al., 2007). McLean and colleagues later specified that, upon increasing locomotor speed, there is a continuous shift in the set of active neurons, as sets of slow INs are inhibited during fast locomotion (McLean et al., 2008; Menelaou and McLean, 2019). For instance, V2a INs located ventrally are recruited for slow movements are born the latest, and connect to slow secondary MNs and other slow V2a INs (Figure I.6)

Figure I. 6 .

 6 Figure I.6. V2a spinal interneurons are organized in speed modules controlling the locomotor speed. Investigations in zebrafish revealed that spinal V2a INs are organized in speed modules topographically organized in the dorsoventral axis of the spinal cord. (for slow depicted herein red, intermediate in green, and fast in red). Slow V2a INs, localized in the ventral spinal cord, recruit slow sets of MNs that project slow-twitch muscles. Upon increasing locomotor speed, there is a shift in the distinct V2a speed modules recruited but additional recruitment of MNs pools. Pasted from (Grillner and Kozlov, 2021).

  Figure I.7A), and in a mouse brain (Figure I.7B).

Figure I. 7 .

 7 Figure I.7. Nomenclature of the brainstem used in this thesis. As proposed by(Watson et al., 2019), is based on developmental gene expression, progeny analysis, and fate mapping. A, Proposed nomenclature of the hindbrain in a generic brain. The hindbrain is divided into 4 regions, from caudal to rostral, which are segmented given the rhombomeres limits: medulla oblongata (Rh7-Rh11), retropontine (Rh5-Rh6), pontine region (Rh2-Rh4), prepontine (isthmus and Rh1). Anatomical landmarks are denoted as well, such as motor nuclei (3N, 4N, 5N, 7N), and the inferior olive (IO). B, Para-sagittal scheme of a mouse brain, where regions of the hindbrain are color-coded according to the proposed nomenclature.

Figure I. 8 .

 8 Figure I.8. RSNs control distinct aspects of locomotion. A, RSNs can control the episodic nature of locomotion. Example experiment in lampreys showing that distinct subsets of RSNs are active through the locomotor episode (maintain), only at the beginning (start) and at the start and end of the episode (stop)[START_REF] Juvin | A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion[END_REF]. In this study, the authors show that stop neurons are critical for prompt

Figure I. 9 .

 9 Figure I.9. V2a reticulospinal neurons are involved in key features of locomotion and are conserved in zebrafish and mice. A. V2a RSNs can start locomotion upon optogenetic stimulation (Pasted from[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF]. A1, V2a neurons in the brainstem of 3dpf larval zebrafish and the different regions where the light was shined in the transgenic Tg(vsx2:Gal4)(UAS:ChR2) larval zebrafish. A2. Example experiments in which blue light was shined over the entire hindbrain of a transgenic larval zebrafish, showing that the larva starts to swim upon light application. A3, Proportion of experimental trials during which illumination elicited swim-like activity, as a function of the region where the light was patterned and the power of light. Note that the most effective site to elicit swim-like activity is the caudal hindbrain (medulla). B. V2a RSNs located at the boundary between the retropontine and the medulla (Gi nuclei) steer locomotion by braking hindlimbs activity and steering forelimbs and head in mice. B1. In ex-vivo preparation of mouse spinal cord and brainstem, optogenetic

1. 2 . 3

 23 Multiple polysynaptic sensory pathways drive and modulate the activity of the RSNs Animals need to integrate information about their external environment to generate an appropriate locomotor response. The external sensory inputs are indirectly integrated by the RSNs which then convey the appropriate command to the spinal CPGs to initiate or modulate locomotion. Information from the surrounding environment is sensed by the visual, auditory, trigeminal and vestibular systems (for review, see Purves et al., 2019 Unit II -Sensation and sensory processing, Figure I.10). They are then conveyed to RSNs through disynaptic inputs via sensory interneurons amplifying the signal

Figure I. 10 .

 10 Figure I.10. Reticulospinal neurons act as a hub integrating sensory inputs and MLR command to start, steer, stop, and modulate the kinematics of locomotion. RSNs receive inputs from the MLR as well as other higher brain center and sensory pathways in order to appropriately recruit the spinal CPGs and MNs driving posture and locomotion. Schematic built from references cited in section I.2.

Figure I. 11 .

 11 Figure I.11. The MLR elicits forward locomotion whose intensity scales with the intensity of MLR stimulation in lampreys. In ex vivo preparations where the lamprey tail is kept intact, the MLR is electrically stimulated at 1.5 uA (A), 2uA (B), and 2.5 uA (C) at a fixed frequency (1-ms pulses at 10Hz). MLR stimulation elicits a wave of lateral displacement of the tail in the rostrocaudal direction. The vigor of the tail displacement (visible by tail snapshot superimposed A2-B2-C2) increases upon increasing the intensity of MLR stimulation. Both the muscle activity (represented in the EMG recording in A1-B1-C1) and the locomotor frequency increase upon the increasing intensity of the MLR stimulation. Pasted from(Sirota et al., 2000).

Figure I. 12 .

 12 Figure I.12. The mesencephalic locomotor region cellular-level control of locomotion. A, Sagittal view of the mouse brain highlighting the location of the MLR in mouse, build of the CuN (here named CnF) and the PPN, and the heterogeneity of neurotransmitters in the nuclei. B, Schematic summarizing the effects of stimulating different types of MLR neuron on locomotion. Stimulation of glutamatergic neurons in the CuN elicit high-speed locomotion, while stimulation of glutamatergic neurons in the PPN elicit low-speed locomotion, typically expressed during exploratory behavior. Stimulation of cholinergic neurons does not initiate locomotion but increases speed of ongoing locomotion. Results summarized from[START_REF] Caggiano | Midbrain circuits that set locomotor speed and gait selection[END_REF][START_REF] Josset | Distinct Contributions of Mesencephalic Locomotor Region Nuclei to Locomotor Control in the Freely Behaving Mouse[END_REF][START_REF] Roseberry | Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia[END_REF]. Pasted from[START_REF] Lau | Cellular-level understanding of supraspinal control: what can be learned from zebrafish?[END_REF].

Figure 13 -

 13 Figure 13 -Organization of the reticulospinal system in larval zebrafish. A, approximately 200 spinal projecting neurons are identifiable in the reticular formation from spinal backfills: these cells are organized in 27 clusters distributed across different rhombomeres. Copied from[START_REF] Metcalfe | Segmental homologies among reticulospinal neurons in the hindbrain of the zebrafish larva[END_REF]. B, Using optical backfills in Tg(vsx2:Kaede) larvae, Kimura and colleagues revealed that numerous V2a neurons in the brainstem project to the spinal cord, including most of V2a Neurons in the medulla: these later neurons were not revealed by classical techniques of spinal backfill relying on a chemical dye injected in segment 14. Right panel, all V2a photoconverted by spinal UV illumination, correspond to all V2a neurons that project an axon in the spinal cord. Copied from[START_REF] Kimura | Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming[END_REF].

  the developmental stage at which they were born (Figure I.14C1). While early-born V2a neurons differentiated in the embryo (before 24 hpf) were recruited for powerful movements, escape responses, or struggles, late-born neurons that differentiated at later developmental stages (between 36 and 72 hpf) were recruited for small amplitude and slow movements characteristic of exploration (Figure I.14C2). Using photoconvertible proteins, Koyama and

Figure I. 14 -

 14 Figure I.14 -V2aRSNs in larval zebrafish are essential to elicit different types of locomotion. A, Expression pattern of vsx2 in the hindbrain of a transgenic Tg(vsx2:GFP) larval zebrafish at 3dpf. B, A subset of V2a RSNs in the pontine and retropontine regions are necessary for the initial tail bending generating a turn. B1, Locations of the 3 sets of V2a RSNs, RoV3 (Rh3), MiV1 (Rh4), and MiV2 (Rh5). B2, Laser ablation of these RoV3, MiV1 and MiV2. B3, Example behavior response to light illumination on left or right side of the larval zebrafish, before and after ablating these V2a RSNs. Note that, in response to illumination on the non-ablated side, the tail and heading angles are unchanged after ablation. On the ablation side, the fish still swims in response to the illumination but the initial large bend towards the side of the illumination is not present after ablation. Pasted from(Huang et al., 2013)C1, V2a neurons born before 24 hpf are located on the lateral side of the V2a neurons. Example experiment in which transgenic Tg(vsx2:Kaede) larvae, in which V2a neurons express the photoconvertible protein Kaede, were illuminated with UV light at 24 hpf. The Kaede protein expressed in the neurons born at that time was photoconverted and turned from green to red. In the image, the neurons that appear purple were thus born before 24hpf, the neurons born after appearing green. Pasted(Pujala and Koyama, 2019) C2, V2a RSNs born the earliest are recruited during crude maneuvers that appear at early stages of the larval zebrafish development. V2a neurons that appear later on during development are positioned more medially in the stripe and sustain exploratory forms of locomotion that appear concomitantly. Pasted from[START_REF] Wyart | Building behaviors, one layer at a time[END_REF], commented from(Pujala and Koyama, 2019)

  objective through which the orthogonal emitted light goes and is further descanned to form the image on a stationary camera plane captured by a 2D CMOS sensor (Figure I.15A). The sample is scanned by sweeping the titled light-sheet, which relies on an angle scanning mirror (Figure I.15A). This angle scanning technique that does not require moving the sample nor the objective offers major advantages compared to regular light-sheet microscopy using translation scanning, which makes it more appropriate to record from the population of V2a neurons in the tail-free larval zebrafish as it, 1) Allows faster volumetric-recording, as the scanning speed becomes mainly dependent on the speed of acquisition of the fast 2D CMOS sensor, and signal to noise ratio considerations. 2) Can scan through the entire larval zebrafish hindbrain without creating mechanical vibration that would perturb the larvae's spontaneous behavior. 3) It is specially optimized for cylindrical geometry with one dimension larger than others, such as the larval zebrafish hindbrain. Thus, the SCAPE microscope offers a great opportunity to perform high-speed volumetric imaging of the spontaneously locomoting larval zebrafish hindbrain.

Figure I. 15 -

 15 Figure I.15 -SCAPE microscope. A, Illustration of SCAPE's scanning-descanning geometry, which sweeps an oblique light-sheet back and forth across the sample while the descanned detection plane remains stationary. ('scan', 'lateral' and 'depth' (x′-y′-z′) directions define the non-Cartesian SCAPE coordinate system). The only moving component is the slowly oscillating polygonal scanning mirror seen in B. B, Illustration of the SCAPE microscope system. The oblique light sheet illuminates the sample, and emitted light is collected by the same objective lens. Pasted from[START_REF] Voleti | Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0[END_REF]

  ManuscriptTitleFunctional coupling of the mesencephalic locomotor region and V2a reticulospinal neurons driving forward locomotionStatusUnder reviewAuthorsMartin Carbo-Tano 1* , Mathilde Lapoix 1,2* , Xinyu Jia 1 , François Auclair 3 , RéjeanDubuc 3,4# , Claire Wyart1#

Fig. 2 |

 2 Fig. 2 | Electrical stimulation of the MLR induces symmetric forward swimming. a, Schematic illustration of the behavioral experiments with MLR stimulation. b, Typical behavioral responses shown as superimposed images of the larval zebrafish tail (left panel), and corresponding tail angle trace (right panel), highlighting differences in symmetry and amplitude between a typical spontaneous swim episode (black, symmetrical and low amplitude tail bends), and electrically-evoked episodes that typically corresponded to a forward swim (blue, symmetrical and moderate amplitude) or to a struggle (magenta, asymmetrical and large amplitude). The gray bar indicates the stimulation duration. c, Left, representative example of tail angle traces. All locomotor episodes elicited by electrical stimulation were manually segmented and classified as either forward swims (blue) or struggles (magenta) (13 fish, 164 stimulations, 786 episodes). Right, values of maximum absolute tail angle versus median tail beat frequency (TBF) for all segmented episodes. Spontaneous episodes are shown as black dots. d, Distribution of forward index for all simulations applied: forward index = (number of forward episodes -number of struggles) / (total number of elicited episodes during stimulation). e, Brainstem location of all stimulation sites investigated: color code represents the median forward index calculated from all the stimulation applied for each stimulation site (pink, struggle, and blue forward swim, as depicted in panel d). The dashed line represents the MLR location covering all stimulation sites with a median forward index above 0. A, anterior; P, posterior; D, dorsal; V, ventral; L, left; R, right. Scale bar is 20 μm.

  spontaneous: median TBF = 21.6 ± 3.1 Hz; MLR-induced 21.3 ± 2.9 Hz). Upon closer inspection, we noticed that the TBF probability density distribution upon MLR stimulation was bimodal (Fig. 3e, one peak at 17.5 Hz and the other 23.2 Hz, red lines), possibly reflecting different responses to the frequency used for MLR stimulation. We found indeed that MLR-induced forward swims exhibited a median TBF around 17 Hz when the stimulation occurred at frequencies below 15 Hz (Fig. 3f, 5 Hz: 17.54 ± 1.31 Hz; 10 Hz: 18.7 ± 2.34 Hz; 15 Hz: 17.66 ± 1.07 Hz) while 20 Hz MLR stimulation elicited forward swims at 23 Hz (Fig. 3f, 23.1 ± 1.62 Hz). Altogether, our observations indicate that the properties of the MLR stimulation impacts the duration, the time onset, the amplitude, and the locomotor frequency of forward swims.

Fig. 3 |

 3 Fig.3| MLR stimulation parameters set the duration, the time onset, the amplitude and the locomotor frequency of forward swims. a, Typical example of tail angle traces for spontaneous forward swims (black, top trace) and forward swims induced by stimulating the MLR at 10 Hz, 1 µA for either 2 s (blue, middle trace) or 4 s (blue, bottom trace). The gray box indicates the duration of the train. b, Quantification of the duration of forward swims according to the duration of the MLR stimulation (10 Hz, 1 µA), displayed as a violin plot with black line indicating the median value (8 fish; spontaneous: 27 episodes, 0.41 ± 0.15 s, 2 s long stimulation: 19 episodes, 1.91 ± 0.44 s, 4s-long stimulation: 4 episodes, 3.65 ± 0.315 s; Kruskal-Wallis test, χ 2 = 38.6, df = 2, p < 0.001, Wilcoxon's test pairwise comparisons: spont. vs 2 s: p < 0.001; spont. vs 4 s: p < 0.001; 2 s. vs 4 s: p < 0.001). c, The delay to swim onset decreased with higher MLR stimulation intensities. Stimulation frequency was set to 10 Hz and duration to 2 s (9 fish; 0.1 µA: 17 episodes, 1.22 ± 1.97 s; 1 µA: 14 episodes, 0.15 ± 0.11 s; Mann-Whitney U Test, W = 199, p < 0.001). d, MLRevoked forward swims reliably exhibited larger tail bend amplitude compared to spontaneous forward swims (12 fish. spont: 67 episodes, 5.9 ± 3.16°; MLR stim: 172 episodes, 8.5 ± 3.8°; Mann-Whitney U Test, W = 8102, p < 0.001). Note the absence of the lowest bend amplitude values when stimulating the MLR. In all panels, the black line indicates the median value. e, The distribution of median tail beat frequency (TBF) upon MLR stimulation was bimodal with a first peak at 17.5 Hz and a second at 23.2 Hz (red line represents the peak of the distributions, normalmixEM function from R mixtools package 52 ): median TBF was indistinguishable for forward swims occurring spontaneously or upon MLR stimulation at 5 and 20 Hz (11 fish; spont: 57 episodes, 21.6 ± 3.1 Hz; MLR stim: 242 episodes, 21.3 ± 2.9 Hz; Mann-Whitney U Test, W = 6721, p = 0.7; the boxes at the bottom show the median and 25-75 % quantiles). f, 20 Hz MLR stimulation elicited swimming at higher frequency(23 Hz) than during spontaneous or lower frequency of MLR stimulation (~17Hz)(11 fish. 5 Hz: 10 episodes, 17.54 ± 1.31 Hz; 10 Hz: 60 episodes, 18.7 ± 2.34 Hz; 15 Hz: 18 episodes, 17.66 ± 1.07 Hz; 20 Hz: 154 episodes, 23.1 ± 1.618 Hz. Kruskal-Wallis test, χ 2 = 6237, df = 31, p <0.001, Wilcoxon's test pairwise comparisons: 5 Hz vs 10 Hz: p = 0.3; 5 Hz vs 15 Hz: p = 0.9; 5Hz vs 20 Hz: p <0.001; 10 Hz vs 15 Hz: p = 0.2; 10 Hz vs 20 Hz: p <0.001; 15 Hz vs 20 Hz: p <0.001). The red lines represent the peak values for each of the distributions from panel (e).

Fig. 4 |

 4 Fig.4| The MLR sends bilateral projections onto the reticular formation. a, Schematic representation of the retrograde tracing experimental design in which biocytin was unilaterally injected in the pontine and retropontine reticular formation between rhombomere 2 and 6 (r2-r6) to reveal neurons in the MLR locus projecting in the reticular formation (r, rhombomere). b, A typical Z projection stack acquired 2 hours after the backfill injection (asterisk) shows neurons with a soma located in the MLR area (dorsal view, white dash line circle, radius of ~30µm centered on the MLR). Scale bar is 100 µm. c, Location of all retrogradely-labeled neurons found in the MLR region denoted in panel b mapped onto a common brain space (11 fish, 156 cells, neurons backfilled contralateral to injection site: 8.00 ± 4.6, ipsilateral: 6.18 ± 4.6) in the dorsal (c1), sagittal (c2) and transverse (c3) planes. The number of cells found ipsi-and contra-laterally to the injection site are not statistically different (one sample t-test; t(19.8) = 0.9 p = 0.35) Scale bar is 100 µm. d, 3D rendering of neurons with soma located in the MLR from the singleneuron atlas50 (15 neurons). e, Some of the retrogradely-labeled neurons were glutamatergic as shown by overlap with the Tg(vglut2:DsRed) transgenic line: a maximal Z-stack projection over 10 µm revealed retrogradely-labeled neurons (red) shown in the dorsal plane together with DsRed-positive cells (cyan). Biocytin injection is denoted with an asterisk. Scale bar is 25 µm. f, g, Detailed view of regions denoted in panel e and corresponding to single planes acquired at different depths. Arrowheads indicate putative glutamatergic retrogradely-labeled neurons. Scale bar is 5 µm. h, Photomicrograph illustrating an example of the distribution of labeled neurons in the MLR region as seen in epifluorescence microscopy showing somata that are immunoreactive to Dbh (white) (h1) and ChAT (yellow) (h2) in the vicinity of retrogradely-labeled MLR neurons (red) (h3, h4). i, Based on the mapzebrain zebrafish space50 , schematic representation of the location of the retrogradely-labeled glutamatergic cells (cyan, 6 fish, 15 cells), chata + neurons (orange), chatb + cells whose location was estimated from56,57 (yellow), locus coeruleus (white), and the MLR locus defined by the delimitation of stimulation sites that effectively triggered forward swimming (dashed line) with all neighboring retrogradely-labeled neurons (red). For panels b, e, h: maximum Z projection stacks (schematized in the bottom right corner with squares including multiple lines); f:

Fig. 5 |

 5 Fig. 5 | Single pulse MLR stimulations gradually recruit a subset of V2a RSNs. a, Distribution and density of RSNs within the V2a population estimated from optical backfills experiments. a1, Example image of the hindbrain of 5 dpf Tg(vsx2:Kaede) larval zebrafish after photoconverting Kaede in the rostral spinal cord at 4.5 dpf. While green cells do not project to the spinal cord, all magenta or white cells contain photoconverted Kaede and are therefore counted as RSNs. Scale bar is 50 µm a2, The proportion of V2a RSNs identified as projecting an axon to the spinal cord reveals that a vast majority of V2a neurons are RSNs in pontine and retropontine ventral areas as well as in the medulla. b1, Schematic illustration of the experiments investigating vsx2+ (V2a) neurons recruitment in response to MLR stimulation. In paralyzed Tg(vsx2:GAL4;UAS:GCaMP6s) transgenic 6dpf larval zebrafish, local single 2ms-long 1-5 µA electrical current pulse MLR stimulations were applied using glass-coated tungsten microelectrode while the calcium activity of V2a RSNs was recorded in pontine, retropontine and medullary region (P, RP and Med respectively). b2, Typical calcium traces of reliably recruited V2a neurons in the different anatomical regions investigated, during a train of single shot MR stimulation of different intensities (top, colored traces: vP, ventral pontine; vRP, ventral retropontine; Med, medulla). Gray traces on the bottom represent neurons which exhibited a calcium response after at least one stimulation but were not labeled as reliably recruited. c, Identifiable V2a RSNs in the ventral pontine (vP)-retropontine (vRP) areas were reliably and gradually recruited by the MLR stimulation, c1, Example plane (top panel) showing the location of selected V2a RSNs (bottom panel, scale bar is 20um). c2, The amplitude of calcium responses of V2a RSNs in vP and vRP scaled with MLR stimulation intensity (3 fish, 1-2 planes per fish, 23 neurons, 2-9 neurons per plane). c3, Typical traces from identifiable V2a RSNs in the vP and vRP areas show fast rising slope and gradual response to increasing MLR stimulation intensity (1 typical fish, 2 planes represented). d, The recruitment of V2a neurons studied could be clustered into 3 groups: "reliably-recruited" (colored -color corresponds to reliably recruited neurons in a given anatomical area), "unreliably-recruited" (gray) and "non recruited" (black) (5 fish, total 1210 neurons; vP: 42 neurons, vRP: 88 neurons, Med: 1080 neurons) (see Methods). e, Proportions of reliably recruited V2a neurons responding at each MLR stimulation intensity (proportions are relative to numbers of V2a neurons in each anatomical group (defined in b). f, Kinematics of the V2a neurons' calcium response to MLR stimulation. f1, Distribution of rising slopes of calcium

Fig. 6 |

 6 Fig. 6 | Medullary V2a RSNs are specifically recruited during MLR induced-forward swimming and encode the number of oscillations, tail beat frequency and amplitude.

Fig. 6 |

 6 a, Schematic representation of behavioral traces during all experiments investigated in Fig 6. Episodes are represented as filled boxes whose color depict the behavior type (blue: forward episode, pink: struggle). b, Example bouts elicited during 40s long MLR stimulation corresponding to either pure forward swimming (top trace), bout of mixed episodes (bottom left) or pure struggle. Episodes are color-coded by manually curated categories. c, Tail angle trace and calcium activity clustered by similarity during the same example experiment than Fig. 6b. Here all functional clusters are represented. Each behavioral episode is colorcoded by manually curated categories (blue: forward swimming, magenta: struggle behavior). Mean neural activity for each cluster is represented in color-trace and individual traces in the clusters in black. Neurons in blue are the forward cluster shown in Fig. 6b. Neurons in the green clusters are active during struggle episodes. d, Dorsal view showing the spatial distribution of neurons color-coded by the functional cluster defined in c. Neurons in the green clusters are laterally located.

9 %

 9 photographed using an E600 epifluorescence microscope equipped with a DXM1200 digital camera (Nikon, Mississauga, ON). Grayscale images were converted to pseudocolors using Photoshop software (version 23.1.1, Adobe, San Jose, CA).

1 . 4

 14 V2a excitatory interneurons and locomotor speed, 1.1.5 Other INs classes contributing to the control of locomotor speed and strength).

Figure 1 .

 1 Figure 1. SCAPE microscope built in the Wyart lab and example image in the titled referential. A, Overview of the SCAPE microscope with the added components (see Material and Methods).In short, the SCAPE microscope is composed of 3 main parts: 1) A 2D illumination system composed of a Powell lens to shape the laser beam from a circle to an extended line, an illumination telescope, and a 1D galvo system to scan the imaging plane by rotating the beam in the back focal plane of the objective. 2) A high NA 20x microscope objective that forms a thin light-sheet and collects fluorescence photons in the orthogonal direction.3) A detection system that descans the beam, magnifies it using 2 telescopes, and corrects the light sheet angle using a system of 2 objectives mounted in a titled geometry to form a 2D image on a CMOS sensor orthogonal to the light beam propagation. T: (xyz) translation, M1 and M2: mirrors to collect IR image of the fish tail, C1: camera used to record the fish tail movement. C2: camera used to record a transmitted image of the fish brain. A close-up of the objective and the sample holder in A2. B, Example image of a 6dpf Tg(vsx2:GAL4)(UAS:GCaMP6s) larval zebrafish. B1, Schematic representing how the larval hindbrain is scanned by the illumination light-sheet (in blue) along the x' axis. The angle of the illumination sheet is fixed, and the sheet is shifted along the scan-axis to scan through the volume. B2, Uncorrected image of a single plane (in the x' axis) as recorded by the camera. As the image is not corrected for the tilt of the light sheet, it is represented in the (Lz',Ly) dimension.

Figure 2 .

 2 Figure 2. Pipeline workflow.The output images from the recording are given as T volumes of dimension (Lz',Lx',Ly) where T is the number of time steps recorded. These images are deconvolved using a Point Spread Function (PSF) measured experimentally from the 3D images of fluorescent beads. Then, the volumes are registered to the mean volume to correct for the eventual motion of the sample during the recording. The volumes are then transformed to a regular (zxy) space by applying skew correction[START_REF] Bouchard | Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms[END_REF]. We reshape the Nt volumes of (Lz,Lx,Ly) dimension into Nz volumes of (Lx,Ly,Nt) dimension, where Nz is the number of planes, each spaced by 7um, and apply on these stacks a segmentation and extraction algorithm to detect neurons as ROIs and extract their fluorescent traces over time.

2 . 4

 24 Plane wise segmentation, signal extraction, and spike rate deconvolution using suite2p).

Figure 3 .

 3 Figure 3. Deconvolution of a high-resolution volume of V2a RSNs in the hindbrain with the PSF improves image contrast. A, Example high-resolution volume, full volume color-coded by plane number (A1)and single plane (A2, zoomed-in A3) acquired with the SCAPE microscope by imaging a 6dpf Tg(vsx2:GAL4)(UAS:GCaMP6s) larval zebrafish. B, The same example images after a deconvolution step using the experimental PSF computed with the same acquisition parameters. Note how the contrast is improved in B3, and neurons that were previously hard to segment from are now more clearly separated in space. In the center is a 2D representation of the 3D PSF.

Figure 4 .

 4 Figure 4. Effect of deconvolution on the correlation between V2a neurons' signals recorded in the brainstem and spinal cord of one example fish. A Pearson correlation matrix between the fluorescence signals of all pairs of neurons in an example recording, before (A1) and after (A2) deconvolution (n=918 neurons, 1 fish). A single-pixel value in the image represents the Pearson correlation coefficient between

Figure 5 .Figure 6 .

 56 Figure 5. Skew correction to transform the volumes and compensate for the titled light-sheet geometry.A, Example volume image of a 6 dpf Tg(vsx2:GAL4)(UAS:GCaMP6s) larval zebrafish before skew correction. A1, The 3D volume is represented in 2D using depth color-coding, where depth is the scan-axis (x'). A2, 5 example planes of volume B, The same example volume after skew correction. Note that the y dimension doesn't change. B1, The 3D volume is represented in 2D using depth color-coding, where depth is the dorsoventral axis (z axis). B2, 5 example planes of the volume, from dorsal (top) to ventral (bottom).

Figure 7 .

 7 Figure 7. Manual categorization of swim bouts as forward, left turns and right turns and investigation of the kinematics of their tail curvature using ZebraZoom reveals a common forward component in forward, left turns, and right turns(Mirat et al., 2013). A, Tail angle traces of swimming episodes classified as forward (orange), left turns (magenta) and right turns (green) in one example experiment. A1, Mean tail angle trace of all episodes classified, the shade represents the 95% confidence interval (n=1 fish, n=1 recording, n=311 bouts classified as 117 forward, 127 left turns and 29 right turns). A2, Individual tail angle traces. To ease visualization, only the first 260ms of the trace are represented here. B, Tail curvature plots of example episodes for each category. B1, Schematic representing a larval zebrafish and the segmentation of the tail into 17 points performed by ZebraZoom. The angle between these points is used to build the curvature plot depicted in B2 (example forward episode), B3 (example left turn), B4 (example right turn) using ZebraZoom. The curvature value of each segment (from rostral to caudal, in y) is represented in the color scale, as a function of time (in x). Note the rostrocaudal propagation of a wave in all three types of swim bouts, mainly in the caudal-most segments of the tail. The left and right turns are distinguishable from the forward by the large bend at the tip of the tail, visible in the bottom left corner of panels B3 and B4.

Figure 8 .

 8 Figure 8. Identification of neurons active during forward bouts. A, Position and activity of example V2aneurons active or not active during forward episodes in one example fish. A1, Position of the example neurons (colored dots) amongst the population of all recorded V2a neurons (unfilled dots), in dorsal view (top) and sagittal view (bottom). A2, DF/F (colored trace), and spike rate traces (gray) of the example neurons during subsequent forward swims. The spike rate traces of each neuron estimated from the fluorescent trace are represented in gray. The tail angle trace is represented at the bottom in black. The periods during which the larva swam forward are pictured as the orange rectangles, the periods during which the larva was not swimming are pictured as gray rectangles. To ease visualization, the DF/F and spike rate traces are represented as their z score (in arbitrary units). B, Distribution of spike rates for all example neurons during forward episodes ("during forward", orange), or when the larva was not swimming ("quiet time", gray). To ease visualization, the density is represented in a logarithmic scale. To identify if a neuron was active during forward swims, we performed a Wilcoxon rank-sum statistical test to assess whether the distribution of spike rates when the fish was swimming forward was significantly greater than the distribution of spike rates when the larva was not swimming. Neurons 1 to 6 had a greater distribution of spike rate during forward swims (p < 0.01). Neurons 7, 8, and 9 did not have a greater distribution of spike rate during forward periods (p > 0.01).

Figure 9 .

 9 Figure 9. Distribution in the brainstem and rostral spinal cord of V2a neurons recruited during forward, left and right turns, or all swim bouts combined from one larval zebrafish. A, Distribution of V2a neurons active during forward swims in the horizontal plane (A1) and the sagittal plane (A2) (n=223/844 neurons), (B-D) Similar distribution for neurons active during left turns (n=327/844) (B1,B2) neurons active during right turns (n=223/844) (C1,C2), neurons active during both forward, left and right turns (n=75/844 neurons) (D1,D2). In D1 and D2, we named the neurons recruited during all swim bouts and symmetrical in relation to the midline (orange dashed line in D1) as the "forward component" cluster (n=56 neurons). Filled dots are active neurons. Unfilled dots are not active. The boundaries of the anatomical regions are depicted on the top.

Figure 10 .

 10 Figure 10. Distribution of V2a neurons active for turns only (not forward swim bouts) from one larval zebrafish. In pink, distribution of V2a neurons active during left turns but not forward swim bouts (n = 225 / 844 neurons), and in green, distribution of V2a neurons active during right turns but not forward swim bouts (n

Figure 11 .

 11 Figure 11. Distribution in the brainstem and rostral spinal cord of V2a neurons encoding tail beat frequency and tail amplitude from one single larval zebrafish. A, Neurons whose spiking rate encodes tail beat frequency are represented as filled red dots (n=269/844 neurons) (A1, horizontal plane, A2, sagittal plane). The orange rectangle highlights the "forward component" neurons. The brown rectangles highlight the "steering RP" and "steering vP" neurons. Almost all "forward component" neurons encode iTBF (53/56 neurons). B, Neurons whose spiking rate encodes positive amplitude of the tail angle for movement to the left side are represented as filled pink dots (n=143/844 neurons) (B1, horizontal plane, B2, sagittal plane). The brown rectangles highlight the "steering RP" and "steering vP" neurons. C, Neurons whose spiking rate encodes positive amplitude of the tail angle for movement to the left (n=143/844 neurons) (C1, horizontal plane, C2, sagittal plane). The brown rectangles highlight the "steering RP" and "steering vP" neurons. A high proportion of neurons in the "steering RP" and "steering vP" cluster encode ipsilateral tail bending amplitude ("steering RP": 91/120 neurons -75.8% -encode ipsilateral bending amplitude, "steering vP": 38/68 neurons -55.9%-encode ipsilateral tail bending amplitude). D, Neurons whose spiking rate encodes the increase in tail angle are represented in brown (n=25/844 neurons) (D1, horizontal plane, D2, sagittal plane).

  Animal handling and procedures were validated by the Paris Brain Institute (ICM) and the French National Ethics Committee (Comité National de Réflexion Éthique sur l'Expérimentation Animale; APAFIS # 2018071217081175) in agreement with EU legislation. To avoid pigmentation, all

  telescope and at the back focal plane of the objective (Olympus, 1-U2B965 XLUMPlanFL ×20/0.95 W objective lense). The galvo system scans the imaging plane by rotating the beam in the back focal plane of the objective. The laser beam is decentered from the center of the back focal plane of the objective to create the tilted light-sheet. The objective illuminates the sample with a thin titled light-sheet and collects fluorescence photons in the orthogonal direction. The emitted light is descanned and magnified by a detection system built of 2 telescopes, and corrects the light sheet angle using a system of 2 objectives (Morrel, MRD00205, CFI Plan Apo Lambda 20x/0.75NA/1.0mm attached to the detection telescope, and Morrel, MRD00105, CFI Plan Apo Lambda 10x/0.45NA/4.0mm attached to the camera system) mounted in a titled geometry to form a 2D image on a CMOS sensor (Andor, Zyla sCMOS Zyla-5.5-CL10) orthogonal to the light beam propagation. A DAQ board controls communication between electronic devices and the computer (National Instruments, NI USB-6361.

  Behavior kinematics under SCAPE in intact and enucleated larval zebrafish 2.4. V2a RSNs encode the direction, frequency, and amplitude of tail bends during slow spontaneous locomotion 2.4.1 Recruitment of V2a neurons during forward swims and turns 2.4.2 V2a RSNs in the caudal medial medulla and V2a INs in the rostral spinal cord are recruited during forward swimming component 2.4.3 V2a RSNs encoding directionality 2.4.4 V2a RSNs activity encodes the tail beat frequency and ipsilateral tail amplitude

	Table of content	i
	Acknowledgments	iv
	List of abbreviations	v
	Motivation of the thesis	vi
	I. Introduction	1
	1. Circuits of locomotion in vertebrates	1
	1.1. Spinal circuits generating locomotor rhythms and patterns	2
	1.1.1 Modular organization of motoneurons	4
	1.1.2 Initial description of the minimal neural circuits necessary for generating the motor
	pattern underlying locomotion.	5
	1.1.3 Insight into the diversity of spinal interneurons as genetically defined cell classes	8
	1.1.4 V2a excitatory interneurons and locomotor speed	9
	forward
	locomotion upon rising stimulation strength
	1.3.2 The MLR controls locomotion via projections onto RSNs
	I.3.4 The MLR is a diffuse region composed of diverse cell types whose functions are not trivial
	to investigate
	1.3.5 Recent use of modern optical techniques to manipulate the MLR allowed insights into the
	role of subpopulations of neurons.
	2. Linking neuronal populations to motor function by leveraging the optical and genetic accessibility
	of larval zebrafish
	2.1 Investigation of motor circuits in larval zebrafish

1.1.5 Other INs classes contributing to the control of locomotor speed and strength 1.2. Reticulospinal neurons 1.2.1 Reticulospinal neurons in the brainstem command locomotion by projecting onto spinal circuits 1.2.2 V2a RSNs form a genetically-defined population of neurons controlling multiple aspects of locomotion in mice and zebrafish 1.2.3 Multiple polysynaptic sensory pathways drive and modulate the activity of the RSNs 1.2.4 Upstream recruitment by locomotor regions such as the MLR conveys the command for internally-generated locomotion 1.3 The mesencephalic locomotor region 1.3.1 The MLR is a mesopontine region functionally defined by its ability to elicit graded 2.1.1 A powerful model organism for neurophysiological investigations underlying locomotion 2.1.2 Organization of RSNs in larval zebrafish 2.1.3 Recording neural activity during active locomotion 2.3.1

  INs in the spinal cord and V2a RSNs in the caudal medulla were significantly explained by the TBF(34/61 neurons, 55.7% of V2a INs in the spinal cord and 62/111 neurons, 55.9% of the V2a RSNs in the caudal medulla were explained by the iTBF), in contrast to retropontine(68/202 neurons, 33.7% of V2a neurons) and pontine regions(37/184 neurons, 20.1% of V2a neurons). Notably, the

"forward component" cluster of neurons was the most significantly explained by the instantaneous tail beat frequency (53/56 "forward component", mean p-value of all forward-component neurons: 0.0004, mean p-value of all other neurons encoding the iTBF: 0.001) (see Material and Methods).

  4.2, Caswell et al., 2021) orseaborn (v0.11.1, Waskom, 2021) libraries from Python. All statistical tests were performed using the stats package from scipy library (v1.7.3, (Virtanen et al., 2020)) or statsmodels (v.0.13.2, Seabold and Perktold, 2010) developed in Python. All the figures presented in the manuscript were edited in Inkscape (version 0.9, https://inkscape.org/).Data and code availabilityWe made available (as a python package) the pre-procesing pipeline for the volumetric images /github.com/mpascucci/pycroscopy3D. The scripts written to analyze the extracted signals will be made available upon publication of the work. The data generated are available upon request.

	recorded	with	the	SCAPE	microscope.	It	can	be	found	here:
	https:/

  IntroductionEn l'absence de stimuli sensoriels directs, les animaux doivent continuer de se déplacer dans leur environnement afin de satisfaire leurs besoins vitaux. Les différentes espèces de vertébrés utilisent des modalités de locomotion très différentes, mais le substrat neuronal qui génère le rythme et la coordination de ces comportements moteurs est très conservé. Le mouvement des différents groupes musculaires est produit coordonné dans le temps et l'espace par des circuits spinaux appelés Central Pattern Generators (CPGs). Ces CPGs locomoteurs sont sous le contrôle obligatoire de circuits dans le tronc cérébral des vertébrés, composés des neurones descendant appelés neurones réticulospinaux (RSNs), qui transmette les commandes de la région mésencéphalique locomotrice (MLR) pour initier et moduler le mouvement spontané. Tandis que les circuits spinaux générant le rythme et le schéma moteur produisant la locomotion sont largement identifiés, la nature des RSNs recrutés par le MLR pour initier et moduler la locomotion spontanée reste imprécise, en partie dû à la difficulté d'accès et l'hétérogénéité de ces systèmes. Dans cette étude, nous prenons avantage du modèle de la larve du poisson zèbre, optiquement et génétiquement accessible, pour examiner le recrutement d'une population de RSNs exprimant le facteur de transcription vsx2, aussi appelés V2a RSNs, durant la nage spontanée et induite par la stimulation du MLR. Les V2a RSNs sont des neurones glutamatergiques, projettant un axone ipsilatéral dans la moelle épinière. Ces neurones sont conservés à travers les espèces de vertébrés, et leur importance pour le contrôle de la direction, de l'initiation et de l'arrêt de la locomotion a été demontré chez la souris[START_REF] Bouvier | Descending Command Neurons in the Brainstem that Halt Locomotion[END_REF][START_REF] Cregg | Brainstem neurons that command mammalian locomotor asymmetries[END_REF], Usseglio 2020) et le poissonzèbre(Kimura 2013, Koyama and Pujala 2019). Cependant, le recrutement des V2a RSNs par le MLR est peu documenté (Bretnzer and Browstone, 2013), et le rôle précis des V2a dans la génération de la locomotion vers l'avant et la modulation des paramètres de nage telle que la vitesse, est inconnu.A partir de ces études, nous avons formulé l'hypothèse que le MLR recrute des modules anatomiques et fonctionnels de V2a RSNs afin d'initier et de moduler la locomotion spontanée vers l'avant. Le poisson zèbre est particulièrement adapté pour tester cette hypothèse, puisque ce modèle est génétiquement et opticalement accessible, se développe rapidement, et que donc à seulement 5 jours post fertilisation, chacun des neurones reticulospinaux est virtuellement accessible pour manipulation et enregistrement. Cependant, deux limites sont à adresser pour pouvoir utiliser ce modèle à son potentiel dans l'étude des circuits moteurs. 1) La MLR a été décrite fonctionellement et anatomiquement chez de nombreuses espèces de vertébrés, telles que le chat

Acknowledgments

Acknowledgements

We thank Sophie Nunes Figueiredo, Monica Dicu and Antoine Arneau from Animalliance for fish care. We thank Tod Thiele and Minoru Koyama for their comments on the results.

We are grateful to Harold Burgess and Ashwin Bhandiwad for providing critical inputs to the manuscript. We thank Faustine Ginoux for the morphological registration pipeline and Gautam Sridhar for his inputs on the modeling approach. We thank Danielle Veilleux for making the monopolar micro-electrodes and her help with the histology.

Funding

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement #813457, New

Data availability

The raw data supporting this study are available upon request.

Code availability

All python scripts used to process the data will be available upon publication of the manuscript at https://github.com/mathildelpx/Carbo-Tano_Lapoix_2022.

Data visualization and statistical analysis

Analysis was done either in R 120 (v4.0.4) using the tidyverse package 121 (v1.3.0) or in Python 3.7. Initial figures were done either in R (v4.0.4) using the tidyverse package (v1.3.0) or using the matplotlib 122 (v3.3.2) and seaborn 123 (v0.11.0) Python libraries. Figures were finally assembled in Inkscape (version 1.1, https://inkscape.org/). All statistical tests applied are specified in the legend of the corresponding figure. Statistical tests were performed in R (v4.04), except for the multiple linear model used in Fig. 5, for which we used the OLS function from the library stats.models developed for Python (https://github.com/statsmodels/statsmodels).

Extended Data Fig. 3 | Individual 3D morphology of neurons shown in Fig. 4d whose soma is localized in the MLR.

All images were taken from the web-interface of mapzebrain (https://fishatlas.neuro.mpg.de/, download: 12 Apr. 2020).

MLR stimulation recruits hindbrain V2a RSNs in the pontine, retropontine, and medullary regions

We next sought to investigate whether the zebrafish MLR recruits V2a RSNs. Because V2a brainstem neurons are not all reticulospinal neurons 34,35,37,41,59 , we first quantified the proportions of V2a RSNs by optically-backfilling the V2a brainstem neurons projecting to the spinal cord by photoconverting their axons at the rostral spinal cord in 4.5 dpf Tg(vsx2:Kaede) transgenic larvae (Fig. 5a1, see Methods). We took confocal stacks of the hindbrain and observed that only 57% of the V2a neurons were RSNs in the dorsal pontine (dP: 57.5% ± 18.1%) and 35% of cells in the dorsal retropontine (dRP: 35.5% ± 6.1%) (Fig. 5a2).

Methods

Animal care and transgenic lines utilized

Animal handling and procedures were validated by the Paris Brain Institute (ICM) and the French National Ethics Committee (Comité National de Réflexion Éthique sur l'Expérimentation Animale; APAFIS # 2018071217081175) in agreement with EU legislation.

To avoid pigmentation, all experiments were performed on Danio rerio larvae of AB background with mitfa (-/-) mutation 104,105 . Adult zebrafish were reared at a maximal density of 8 animals per liter in a 14/10 hr light/dark cycle environment at 28.5°C. Larval zebrafish were typically raised in petri dishes filled with system water following the same conditions in terms of temperature and lighting as for adults. Transgenic larvae expressing Kaede and used for photoconversion experiments were exceptionally raised in complete darkness to minimize global photoconversion at early stages. Experiments were performed at 20°C on animals aged between 4 and 7 days post fertilization (dpf) as described in each experimental protocol below.

The following transgenic lines were used in this study: Tg(vmat2:GFP) 106 , , TgBAC(vsx2:Gal4) 37 , TgBAC(vsx2:Kaede) 107 , Tg(UAS:GCaMP6s) 66 ; Tg(vglut2a:lRl-GFP) 108 .

In order to segment the image to detect the position of neurons and extract their fluorescence signal over time, we used the open-source, python-based algorithm suite2p [START_REF] Pachitariu | Suite2p: beyond 10,000 neurons with standard two-photon microscopy[END_REF].

This algorithm is developed for functional 2D images of calcium imaging recording. Other opensource algorithms are available to perform such processes on 3D volumetric data as CaImAn [START_REF] Giovannucci | CaImAn an open source tool for scalable calcium imaging data analysis[END_REF]. We tried to use embedded functions from CaImAn to both register and segment the volumetric data but could not export the functions and did not find the appropriate documentation to do so. In contrast, suite2p has extensive documentation and has a large community of active developers and users, who built a package of functions easy to export and implement directly into an analysis pipeline, as well as get support to develop new functionalities.

We decided to use this algorithm by decomposing our volumes into single planes. To decrease the probability of repeating the detection of the same cell in multiple planes, we extracted planes separated by 7µm (computed average diameter of V2a neurons' soma in our recordings). We performed segmentation and signal extraction and successfully detected around 95% of the visuallyidentifiable neurons.

Behavior description and categorization in head-embedded zebrafish larvae

We ask how the V2a RSNs in the hindbrain and V2a INs in the rostral spinal cord are active at the population level during spontaneous locomotion. To answer this question, we can now combine the SCAPE microscope we built and optimized and the pipeline we developed, to perform population calcium imaging experiments in larval zebrafish expressing a genetically-encoded calcium indicator (GECI) in V2a neurons. There is still one major concern on the experimental conditions as we image the larval zebrafish using a powerful laser at 488 nm, which is in the zebrafish's visible range [START_REF] Baden | Circuit mechanisms for colour vision in zebrafish[END_REF] and might be stimulating visually the larvae during the recordings. This might trigger locomotion in response to the visual stimuli and will not answer our question on how the locomotion is generated "spontaneously", i.e. in the absence of direct external stimuli. To overcome this issue, we decided to prevent any visual inputs by surgically removing their eyes at early larval stages, a process referred to as "enucleation" (see Material and Methods).

Behavior kinematics under SCAPE in intact and enucleated larval zebrafish

To understand the consequence of enucleation on larval zebrafish, we compared the swimming kinematics of larvae with and without eyes during SCAPE recordings (Figure 6A and6B).

We found that enucleated larvae produce longer swim bouts (Figure 6C1), with more oscillations (Figure 6C2). Moreover, enucleated larvae produce more symmetrical swims (Figure 6C3) and with lower tail bend amplitudes (Figure 6C4). Interestingly, intact larvae swam faster than enucleated larvae (Figure 4D). Upon closer inspection, it appears that enucleated larvae exhibited a similar unimodal, non-gaussian distribution of mean tail beat frequencies centered around lower values than Alternatively, this limited depth of field can be partly compensated by applying a more complex deconvolution algorithm. Up to now, we assumed that the PSF of the system is invariant/homogeneous within the entire range of observation. The PSF is in fact inhomogeneous across depth, as it is larger outside the depth of field, and applying the same PSF to all depth leads to artifacts and misestimation of neurons' geometry in these regions outside the depth of field. We could thus imagine implementing a different deconvolution algorithm which would take into account the spatially dependent PSF [START_REF] Lauer | Deconvolution With a Spatially-Variant PSF[END_REF]. The most precise algorithm would be to characterize the full distribution of 3D PSFs at each depth (or to propose a numerical estimation) but this is computationally intense and difficult to obtain experimentally. A more reasonable option would be to separate the imaged volumes into two (or a few) parts: a central region with an optimal PSF, and a border region where the PSF is significantly different.

Although it is simple to implement, it might create a few artifacts at the boundary between these two regions.

Given the fact that V2a RSNs could control locomotor kinematics or the start and halt of locomotion at the population level without a reliable code at the cell level [START_REF] Schwenkgrub | Deep imaging in the brainstem reveals functional heterogeneity in V2a neurons controlling locomotion[END_REF], it is important to allow for non-reliable recruitment when trying to detect V2a neurons driving a distinct locomotor component. The approach I developed to compare the distribution of the spiking rate allows for such non-reliable activity (Figure 8). This method relies on a deconvolution step to interpolate the spiking rate from the fluorescence signal using the decay time of the calcium signal. It has been widelyused in recent studies under the assumption of a single decay time that was only measured in a specific type of neuron [START_REF] Chen | Ultrasensitive fluorescent proteins for imaging neuronal activity[END_REF]. However, we know that the decay time of the calcium signal reflects both the cell intrinsic capacity and the genetically-encoded calcium indicator (GECI) capacity to buffer calcium. Thus, it can vary depending on the neuron type and its calcium buffering capability in particular between excitatory and inhibitory neurons [START_REF] Wyart | Dynamique de l'activité spontanée dans des réseaux de neurones hippocampiques d'architecture contrôlée en culture[END_REF], and is particularly hard to estimate with precision when the GECI concentration is unknown. For more precision of this type of analysis, it would be interesting to record using in vivo electrophysiology the firing activity of V2a RSNs while imaging their calcium activity to get a better estimate of the decay time among these neurons. glass-bottomed Petri dish where the zebrafish larvae were placed. We also added components below the sample holder to record the tail movement that are further described in the Behavior recording and analysis section.

Perspectives

The camera is controlled by the software ANDOR Solis that is interfaced with a MATLAB program written by the Hillman lab to control the acquisition. System calibration. To compute the pixel size in each axis, we followed a process indicated by the Hillman lab. For each given axis (x,y,z), we proceeded as follow: we imaged a volume of a sample of fluorescent beads (Invitrogen, F8888 Fluospheres 505/515, 1um). Then, we moved the sample of 0.1um along the given axis. We imaged again the sample after translation. We repeated this process to get the same sample of beads at 10 given positions along the desired axis. Then, we measured the average displacement of each beads across these iterations. The pixel size was given as the mean, for all beads imaged, of the mean displacement of the bead across images, in pixels / mean displacement of the sample across images, in um.

SCAPE imaging preprocessing

Image Deconvolution We deconvolved the volumetric images acquired on the SCAPE before correcting for the titled geometry of the microscope in order to optimize this process. In fact, In the acquisition space the images are the smallest (memory-wise) and are free of correction artifacts. We used an experimentally measured point spread function (PSF). This represents the impulse response of the optical microscope, which can be approximated by the image of a fluorescent bead smaller than the resolution of the microscope. We measured the experimental PSF by imaging multiple fluorescent beads (Invitrogen, F8888 Fluospheres 505/515, 1um) in a volume of similar size as the larval zebrafish hindbrain, computing the PSF of all beads, and then building a mean PSF from all measured PSFs.

Each uncorrected functional volume of dimension (Lz',Lx',Ly) was deconvolved with the Richardson-Lucy algorithm (RL) as in [START_REF] Laasmaa | Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images[END_REF] implemented in the IOCBIO/deconvolve library (https://gitlab.com/iocbio/deconvolve).

The RL deconvolution algorithm is well known and largely applied in biological imaging [START_REF] Lucy | An iterative technique for the rectification of observed distributions[END_REF][START_REF] Richardson | Bayesian-Based Iterative Method of Image Restoration*[END_REF]. It follows an iterative maximum-likelihood approach and assumes that the noise is Poisson distributed. Each pixel in an image deconvolved with RL is granted to be non-negative. The RL algorithm needs the specification of only one parameter: the number of iterations, which needs to be set heuristically to enhance image quality yet avoiding noise amplification artifacts [START_REF] Sage | DeconvolutionLab2: An open-source software for deconvolution microscopy[END_REF].

The images presented in the manuscript were normalized to obtain a Poisson noise fully compliant image and deconvolved with 15 iterations of the RL algorithm. On one CPU (Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz), the deconvolution of a file of dimension (Lx':130, Ly:510, Lz':279 pixels, or ~ 34MB) took approximately 91 seconds to complete.

Motion correction using ANTsPy registration module

We treated the acquired images for motion correction. Although the larval head is fixed in agarose, the larva can still move a bit, and, in spite of all possible precautions, the sample was still subjected to small yet disturbing vibrations. We decided to correct for motion correction in 3D using the registration module of ANTs (Advanced Normalization Tools) [START_REF] Tustison | The ANTsX ecosystem for quantitative biological and medical imaging[END_REF], https://github.com/ANTsX/ANTsPy).

We registered the images in the imaging referential (before skew correction) for the same optimization reasons explained in the deconvolution section.

We took the mean of the images in a whole video as a template and we registered each image against it. The mean image was chosen for its cost-effective calculation, low SNR and robustness to the fluorescence fluctuation. Then, we applied affine + deformable registration (referred to as SyN) with default parameters. The typical processing time on one CPU (Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz) for a file of dimension (Lx':130, Ly:510, Lz':279 pixels) was around 2min.

Skew correction

We then transformed the dimensions of our volumes from the (Lx',Ly,Lz') to (Lx,Ly,Lz) to correct for the titled-light-sheet geometry. To do so, we used a MATLAB script adapted from the from cellpose [START_REF] Stringer | Cellpose: a generalist algorithm for cellular segmentation[END_REF], with the parameter diameter of ROIs as 6 or 7 pixels. After segmentation and mask creation, the ROIs fluorescence was extracted as the sum of the fluorescence of all pixels within the mask. For each ROI, we then corrected the fluorescence trace by subtracting the neuropil signal (with a factor n=0.7). We calculated the DF/F of each cell as (F-F0)/F0, where F0 is the baseline signal calculated as the median fluorescence value on the lowest 10th percentile values. We removed remaining motion artifacts manually identified by masking them and, when needed, interpolating the missing values using linear interpolation. The spike rate was then inferred from the fluorescence trace corrected from motion artifacts using OASIS deconvolution algorithm [START_REF] Friedrich | Fast online deconvolution of calcium imaging data[END_REF] embedded in the package registration from suite2p, using a calcium decay of 1.8s [START_REF] Chen | Ultrasensitive fluorescent proteins for imaging neuronal activity[END_REF].

In vivo recording of calcium imaging activity Enucleation 3-4 dpf larvae were mounted in 3% agarose (Invitrogen, Thermo Fisher Scientific, USA) diluted in 1x sterile aCSF. The tool used for enucleation was a sharp tungsten electrode bent in hook shape that was cleaned before each procedure using ethanol 70%. The tool was gently inserted between the larva eye and the larva orbit to cut the optic tract without damaging the surrounding tissue.

The detached eye was then removed and the larvae were freed from agarose and kept in aCSF for at least 45min. Larvae were then placed back in Danieau's solution 0.3x.

Head-embedded preparations 5 dpf larvae were placed in 3.5cm glass-bottomed Petri dish and embedded in 3% agarose (Invitrogen, Thermo Fisher Scientific, USA). The agarose surrounding the tail, caudal to the swim bladder, was gently removed to leave the tail free to move. The dish was then filled with Danieau's solution 0.3X. The larvae were imaged the day following the mounting procedure.

Calcium imaging recordings using SCAPE microscope 6 dpf, Tg(vsx2:GAL4)(UAS:GCaMP6s) larvae were placed under the 20x objective and placed at optimal x,y,z positions. The field of view was set to capture the fluorescence from the entire hindbrain and, when possible, the first segments of the spinal cord. For whole hindbrain recordings, the volume rate of acquisition was always set between 4 and 6.5 volumes per second, and the scanning step (x' step) was set to 1.5um. The recordings lasted between 5 and 10min. The acquisition was synchronized to behavior monitoring by sending, using the MATLAB GUI, a short TTL pulse to a wave-form generator feeding frame signals to the behavior camera.

After the functional recording, we imaged the larval brain using a high-resolution stack with smaller steps of sampling to get an anatomical image for future registration to a zebrafish brain atlas.

Behavior recording and analysis

Behavior recording. Head-embedded larval zebrafish were illuminated from the side with a 45° angle using an 890 nm LED (ILH-IW01-85SL-SC211-WIR200, Intelligent LED Solutions, Berkshire, UK) and the reflected image was recorded at 300 Hz from below through a 5X20X/0.25 microscope objective 12.5mm (Zeiss, no:440125-0000-000) and imaged on a high-speed camera (UI-3060CP-M-GL R2, iDS, Germany) with a 50 mm lens (MVL50M23, Navitar, New York, USA). The 488 nm imaging laser was blocked with a 600 nm high pass filter (ThorLabs) located in front of the camera. The high-speed camera