Biology subjects

Debregeas, G.

Publications and source records attributed to Debregeas, G..

16 recordsLinked to original sources

bioRxiv2026

Fiber-Coupled Rotating Two-Photon Light-Sheet Microscopy for Physiological Vestibular Imaging in Larval Zebrafish

Physiological vestibular stimulation requires macroscopic rotation of the animal, making simultaneous high-resolution neural imaging technically demanding. Here we present a fiber-coupled rotating two-photon light-sheet microscope for physiological pitch-axis vestibular imaging in larval zebrafish. Two-photon excitation is attractive in this context because near-infrared excitation avoids visible-light contamination of the visual environment. Femtosecond pulses were delivered to the rotating microscope through a broadband hollow-core negative-curvature fiber previously characterized in static light-sheet configurations. The rotating instrument maintained the axial mechanical stability required for cellular-resolution imaging, with submicron mean axial displacement over the characterized angular range. Fiber performance under rotation was also stable: bend-induced transmission changes were small in the relevant curvature regime, and circular input polarization suppressed rotation-dependent polarization artefacts to below 1\% in the sample plane. The dominant rotation-sensitive optical limitation was not the fiber but perturbation of the thermal lens in the sample chamber. Reducing fluid motion along the excitation path suppressed this artefact and restored stable imaging during dynamic rotation. With this system, we recorded brain-wide vestibular responses during sinusoidal pitch stimulation and resolved anatomically organized cellular response patterns across the brain. These results establish rotating two-photon light-sheet microscopy as a practical solution for vestibular imaging in experiments that require physiological stimulation together with darkness or controlled visual conditions.

neuroscience↗

bioRxiv2026

Cross-individual translation of spontaneous zebrafish brain activity through a shared latent representation

Spontaneous activity is a hallmark of brain function, reflecting the underlying circuit organization. Identifying conserved structure across individuals in this self-sustained activity has remained a longstanding challenge, especially in vertebrates where one-to-one neuron correspondence is inaccessible. Here, we introduce latent-aligned Restricted Boltzmann Machines (LaRBMs), an unsupervised generative approach that uncovers a common representational space from cell-resolved whole-brain recordings in larval zebrafish. This latent space consists of spatially localized co-activation motifs, or cell assemblies, that generalize across animals and form interpretable building blocks of population-wide activity. LaRBMs enable bidirectional mapping of instantaneous whole-brain activity patterns between individuals: activity patterns from one fish can be encoded into the latent space and decoded into another. The translated patterns are assigned high probability by the recipient model and retain the original spatial organization. These results show that spontaneous activity in the vertebrate brain is highly stereotyped at the level of functional cell assemblies and can be reliably captured through a common latent code. Because it provides an interpretable and quantitative framework for functional cross-individual alignment, LaRBM paves the way for comparative phenotyping of brain activity across developmental, genetic, and pathological variation. Significance StatementSpontaneous brain activity, without external stimuli, shapes development, constrains coding, and reflects neural organization. Whether this activity reveals similar organization across individuals has been unclear. Using single-cell, whole-brain recordings in larval zebrafish and statistical learning, we find that spontaneous dynamics share a common structure across animals. Spatially organized co-activated neuron assemblies recur across individuals and act as building blocks of population activity. This shared representation enables fictive translation of activity from one fish into anothers neural space while preserving spatial and statistical plausibility. These results suggest that brains organize population activity by similar principles to represent internal states. Our findings reveal conserved organization in the vertebrate brain and establish a quantitative framework for comparing brain dynamics across individuals and conditions.

neuroscience↗

bioRxiv2025

A sensorimotor instability drives a locomotor transition during fish development

Animals rely on movement to survive -- to explore their environment, find food and mates and avoid danger. During development, changes in body shape, muscle strength and physiological needs drive the continuous adjustment of locomotor patterns. How these changes are orchestrated in a flexible and adaptive manner remains unknown. We explore this question in Danionella cerebrum, a miniature freshwater fish that is emerging as an important vertebrate model in systems neuroscience. We identify a clear transition in locomotion, from continuous to burst-and-coast swimming occurring around 3 weeks of age. We demonstrate that this transition is an energy saving strategy, and that it reflects an insta-bility in the sensorimotor process governing speed regulation. Rather than a preprogrammed developmental switch, it is therefore directly tied to the animal swimming strength. We confirmed this finding by manipulating sensory feedback in order to induce a similar transition at fixed developmental stages. Together, our results illustrate a dynamic interplay between body, brain, and environment during development, offering new insights into the principles governing adaptive locomotion.

biophysics↗

bioRxiv2025

Logarithmic coding leads to adaptive stabilization in the presence of sensorimotor delays

Animals respond to sensory stimuli with motor actions, which in turn generate new sensory inputs. This sensorimotor loop is constrained by time delays that impose a trade-off between responsiveness and stability. Additionally, as the relationship between a motor command and the corresponding sensory feedback is context-dependent, the response must be adapted in real time. It is generally believed that this adaptation process relies on an internal model that is continuously updated through prediction error minimization. Here, we experimentally reveal an alternative strategy based on a simpler feedback mechanism that does not require any internal model. We developed a virtual reality system for the miniature transparent fish Danionella cerebrum that enables in vivo brain-wide imaging during fictive navigation. By systematically manipulating the feedback parameters, we dissected the motor control process that allows the animal to stabilize its position using optic flow. The sensorimotor loop can be fully described by a single delay differential equation, whose solutions quantitatively capture the observed behavior across all experimental conditions. Both behavioral and neural data indicate that the observed adaptive response arises from the logarithmic nonlinearities at the sensory (Weber-Fechner law) and motor (Hennemans size principle) ends. These fundamental properties of the nervous system, conserved across species and sensory modalities, have traditionally been interpreted in terms of efficient coding. Our findings unveil a distinct functional role for such nonlinear transformations: ensuring stability in sensorimotor control despite inherent delays and sensory uncertainty.

neuroscience↗

bioRxiv2024

Linking Brain and Behavior States in Zebrafish Larvae Locomotion using Hidden Markov Models

Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analysis, we exploit the generative nature of the HMM, using the neural sequences to generate synthetic trajectories whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.

animal behavior and cognition↗

bioRxiv2024

Distinct and Asymmetric Neuronal Responses to Pitch- and Roll-Axis Vestibular Stimulation in larval zebrafish

SO_SCPLOWUMMARYC_SCPLOWThe vestibular apparatus plays a pivotal role in maintaining postural equilibrium and processing movement signals, underscoring its importance in studying certain neurological disorders. Here, we present a rotating light-sheet microscope, enabling brain-wide functional recordings during dynamic vestibular stimulation along the pitch axes in addition to the roll axis in head-restrained zebrafish larvae. The system incorporates a double galvanometer mirror configuration, is amenable to 3D printing, and enhances scanning efficiency. Employing this apparatus, we have successfully conducted the first comprehensive mapping of zebrafish brain responses to dynamic pitch-tilt vestibular stimulation. Through Fourier and regression analyses, we report an asymmetry in neuronal recruitment during nose-up versus nose-down pitch tilts within critical regions, including the cerebellum, oculomotor nucleus, caudal hindbrain, and vestibular nucleus, highlighting physiological adaptations to downward motion. We identified specific brain regions, notably the cerebellum and medial-rostral rhombencephalon, that respond to roll-but not pitch-tilt vestibular stimuli. Furthermore, we have identified a transgenic line that closely correlates with our functional mappings and demonstrates a significant response to vestibular stimulation. The elucidation of brain-wide neuronal circuits involved in vestibular processing establishes a foundational framework for subsequent detailed investigations into the molecular and genetic mechanisms underlying postural control and motion perception.

neuroscience↗

bioRxiv2023

A Versatile and Open Source One- and Two-Photon Light-Sheet Microscope Design

Two-photon light sheet microscopy offers great potential for a range of biological applications. Still, its practical implementation is impeded by the high cost of laser sources, the complexity of construction, and the challenges associated with adapting to existing microscope setups. Here, we release an open-source design that addresses these limitations by providing detailed building instructions for transforming a brightfield microscope into a versatile one- and two-photon light sheet system. Our design incorporates a specially designed broadband hollow core fiber, enabling the simultaneous utilization of visible laser alongside an expansive pulsed laser source from another setup. This integration allows for uncompromised image resolution and speed. Furthermore, the design reduces the complexity of construction, alignment, and overall cost, thereby significantly enhancing the accessibility of this technology (https://github.com/LJPZebra/OLU).

neuroscience↗

bioRxiv2023

Magnetic actuation of otoliths allows behavioral and brain-wide neuronal exploration of vestibulo-motor processing in larval zebrafish.

The vestibular system in the inner ear plays a central role in sensorimotor control by informing the brain about the orientation and acceleration of the head. However, most experiments in neurophysiology are performed using head-fixed configurations, depriving animals of vestibular inputs. To overcome this limitation, we decorated the utricular otolith of the vestibular system in larval zebrafish with paramagnetic nanoparticles. This procedure effectively endowed the animal with magneto-sensitive capacities: applied magnetic field gradients induced forces on the otoliths resulting in robust behavioral responses comparable to that evoked by rotating the animal by up to 25{degrees}. We recorded the whole-brain neuronal response to this fictive motion stimulation using light-sheet functional imaging. Experiments performed in unilaterally injected fish revealed the activation of a commissural inhibition between the brain hemispheres. This magnetic-based stimulation technique for larval zebrafish opens new perspectives to functionally dissect the neural circuits underlying vestibular processing and to develop multisensory virtual environments, including vestibular feedback. HighlightsO_LIAfter injecting a ferrofluid into the inner ear of a larval zebrafish, the ear-stones can be actuated via magnetic forces. C_LIO_LIThis method allows one to elicit vestibular-like behavioral responses without impairing physiological inner ear functions. C_LIO_LIIt is compatible with brain-scale functional imaging and thus offers a promising avenue to investigate the neural underpinnings of vestibular-driven behaviors. C_LI eTOCFerrofluid injection into zebrafish inner ear allows magnetic manipulation of ear-stones to evoke vestibular responses in static animals. This in vivo method is compatible with brain-scale imaging, offering a promising approach to investigate neural mechanisms underlying vestibular-driven behaviors.

neuroscience↗

bioRxiv2022

A scalable assay for chemical preference of small freshwater fish

Sensing the chemical world is of primary importance for aquatic organisms, and small freshwater fish are increasingly used in toxicology, ethology, and neuroscience by virtue of their ease of manipulation, tissue imaging amenability, and genetic tractability. However, precise behavioral analyses are generally challenging to perform due to the lack of knowledge of what chemical the fish are exposed to at any given moment. Here we developed a behavioral assay and a specific infrared dye to probe the preference of young zebrafish for virtually any compound. We found that the innate aversion of zebrafish to citric acid is not mediated by modulation of the swim but rather by immediate avoidance reactions when the product is sensed and that the preference of juvenile zebrafish for ATP changes from repulsion to attraction during successive exposures. We propose an information-based behavioral model for which an exploration index emerges as a relevant behavioral descriptor, complementary to the standard preference index. Our setup features a high versatility in protocols and is automatic and scalable, which paves the way for high-throughput preference compound screening at different ages.

animal behavior and cognition↗

bioRxiv2022

Emergence of time persistence in an interpretable data-driven neural network model

Establishing accurate as well as interpretable models of network activity is an open challenge in systems neuroscience. Here we infer an energy-based model of the ARTR, a circuit that controls zebrafish swimming statistics, using functional recordings of the spontaneous activity of hundreds of neurons. Although our model is trained to reproduce the low-order statistics of the network activity at short time-scales, its simulated dynamics quantitatively captures the slowly alternating activity of the ARTR. It further reproduces the modulation of this persistent dynamics by the water temperature and visual stimulation. Mathematical analysis of the model unveils a low-dimensional landscape-based representation of the ARTR activity, where the slow network dynamics reflects Arrhenius-like barriers crossings between metastable states. Our work thus shows how data-driven models built from large neural populations recordings can be reduced to low-dimensional functional models in order to reveal the fundamental mechanisms controlling the collective neuronal dynamics.

neuroscience↗

bioRxiv2021

Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here we recorded the activity from ~ 40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ~ 200 neural assemblies, which compose neurophysiological circuits and whose various com-binations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.

neuroscience↗

bioRxiv2021

Variability and thermal modulation of locomotor statistics in zebrafish

BackgroundVariability is a hallmark of animal behavior. It contributes to survival by endowing individuals and populations with the capacity to adapt to ever-changing environmental conditions. Intra-individual variability is thought to reflect both endogenous and exogenous modulations of the neural dynamics of the central nervous system. However, how variability is internally regulated and modulated by external cues remains elusive. Here we address this question by analyzing the statistics of spontaneous exploration of freely swimming zebrafish larvae, and by probing how these locomotor patterns are impacted when changing the water temperatures within an ethologically relevant range. ResultsWe show that, for this simple animal model, five short-term kinematic parameters - interbout interval, turn amplitude, travelled distance, turn probability and orientational flipping rate - together control the long-term exploratory dynamics. We establish that the bath temperature consistently impacts the means of these parameters, but leave their pairwise covariance unchanged. These results indicate that the temperature merely controls the sampling statistics within a well-defined kinematic space delineated by this robust statistical structure. At a given temperature, individual animals explore the behavioral space over a timescale of tens of minutes, suggestive of a slow internal state modulation that could be externally biased through the bath temperature. By combining these various observations into a minimal stochastic model of navigation, we show that this thermal modulation of locomotor kinematics results in a thermophobic behavior, complementing direct gradient-sensing mechanisms. ConclusionsThis study establishes the existence of a well-defined locomotor space accessible to zebrafish larvae during spontaneous exploration, and quantifies self-generated modulation of locomotor patterns. Intra-individual variability reflects a slow diffusive-like probing of this space by the animal. The bath temperature in turn restricts the sampling statistics to sub-regions, endowing the animal with basic thermophobicity. This study suggests that in Zebrafish, as well as in other ectothermic animals, ambient temperature could be used to efficiently manipulate internal states in a simple and ethological way.

animal behavior and cognition↗

bioRxiv2021

Evolutionary divergence of locomotion in two related vertebrate species

Locomotion exists in diverse forms in nature and is adapted to the environmental constraints of each species1. However, little is known about how closely related species with similar neuronal circuitry can evolve different navigational strategies to explore their environments. We established a powerful approach in comparative neuroethology to investigate evolution of neuronal circuits in vertebrates by comparing divergent swimming pattern of two closely related larval fish species, Danionella translucida (DT) and Danio rerio or zebrafish (ZF)2,3. During swimming, we demonstrate that DT utilizes lower half tail-beat frequency and amplitude to generate a slower and continuous swimming pattern when compared to the burst-and-glide swimming pattern in ZF. We found a high degree of conservation in the brain anatomy between the two species. However, we revealed that the activity of a higher motor region, referred here as the Mesencephalic Locomotion Maintenance Neurons (MLMN) correlates with the duration of swim events and differs strikingly between DT and ZF. Using holographic stimulation, we show that the activation of the MLMN is sufficient to increase the frequency and duration of swim events in ZF. Moreover, we propose two characteristics, availability of dissolved oxygen and timing of swim bladder inflation, which drive the observed differences in the swim pattern. Our findings uncover the neuronal circuit substrate underlying the evolutionary divergence of navigational strategies and how they are adapted to their respective environmental constraints.

neuroscience↗

bioRxiv2019

From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

Bridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here we focus on light-seeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predict the stationary distribution of the fishs body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can similarly capture the statistics of both spontaneous and contrast-driven navigation.

animal behavior and cognition↗

bioRxiv2018

Whole-brain calcium imaging during physiological vestibular stimulation in larval zebrafish

During in vivo functional imaging, animals are head-fixed and thus deprived from vestibular inputs, which severely hampers the design of naturalistic virtual environments. To overcome this limitation, we developed a miniaturized ultra-stable light-sheet microscope that can be dynamically rotated during imaging along with a head-restrained zebrafish larva. We demonstrate that this system enables whole-brain functional imaging at single-cell resolution under controlled vestibular stimulation. We recorded for the first time the dynamic whole-brain response of a vertebrate to physiological vestibular stimulation. This development largely expands the potential of virtual-reality systems to explore complex multisensory-motor integration in 3D.

neuroscience↗

bioRxiv2017

Blind sparse deconvolution for inferring spike trains from fluorescence recordings

The parallel developments of genetically-encoded calcium indicators and fast fluorescence imaging techniques makes it possible to simultaneously record neural activity of extended neuronal populations in vivo, opening a new arena for systems neuroscience. To fully harness the potential of functional imaging, one needs to infer the sequence of action potentials from fluorescence time traces. Here we build on recently proposed computational approaches to develop a blind sparse deconvolution algorithm (BSD), which we motivate by a theoretical analysis. We demonstrate that this method outperforms existing sparse deconvolution algorithms in terms of robustness, speed and/or accuracy on both synthetic and real fluorescence data. Furthermore, we provide solutions for the practical problems of thresholding and determination of the rise and decay time constants. We provide theoretical bounds on the performance of the algorithm in terms of precision-recall and temporal accuracy. Finally, we extend the computational framework to support temporal superresolution whose performance is established on real data.

neuroscience↗