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International audience; Expectancy is a major function of the nervous system that prepares the organism to react in a meaningful way to the sensory flow depending on the recent internal and external contexts. While this anticipation ability has been mainly studied under passive conditions, here we aim at unravelling the neuronal mechanisms underlying expectancy coding in the context of an active tactile sensorimotor task in mice.In this purpose, we designed a new behavioral task in which mice are trained to contact several times a fixed object with a spared single whisker to obtain a reward. During sessions performed with expert animals, we randomly interleaved omission trials where the object is removed between two whisks. The sudden withdrawal of the object while mice are engaged in a process of touch-related perception therefore creates a deviance between expected and received tactile inputs.We developed a purpose-built system for the real time monitoring of single whisker contacts, at a rate that is compatible with the frequency range of the whisking process and with the extremely small forces produced by mono-whisker touches. Using this setup, we trained mice to generate sequences of four successive whisker contacts, at a sustained pace and in a reproducible manner. The random omission of the object in expert mice has revealed a rapid reorganization of their whisking motor strategy: while the first whisking cycles have an amplitude adapted to the usual object position, subsequent whisking cycles show a gradual increase in the protraction angle as mice sweep their whiskers beyond the field of the object.With the aim of revealing neuronal signatures of tactile expectations in the primary somatosensory cortex (S1), we have performed pilot acute electrophysiological multisite recordings in this area during sessions including omission trials in expert mice. The analysis will include a comparison of action potential discharges in layers 2-3 and 4-5 of S1 during standard trials, when the object is immobile, with the ones elicited during omission trials. We will specifically focus our attention on the neuronal activity at the expected moment of touch during omission trials, when error signals are likely to emerge from the deviance between expected and actual tactile sensory inputs.

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of simple stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. Recent studies have argued that OFF responses reflect strongly transient sensory coding at the population level, suggesting they may be generated by a collective, network mechanism. We examine here the hypothesis that OFF responses are single-cell signatures of network dynamics and propose a simple model that generates transient OFF responses through recurrent interactions. To test this hypothesis, we analyse the responses of large populations of neurons in auditory cortex recorded using two-photon calcium imaging in awake mice passively listening to auditory stimuli. Adopting a population approach, we find that the OFF responses evoked by individual stimuli define trajectories of activity that evolve in low-dimensional neural subspaces. A geometric analysis of the population OFF responses reveals that, across different stimuli, these neural subspaces lie on largely orthogonal dimensions that define low-dimensional transient coding channels. We show that a linear network model with recurrent interactions provides a simple mechanism for the generation of the strong OFF responses observed in auditory cortex, and accounts for the low-dimensionality of the transient channels and their global structure across stimuli. We finally compare the network model with a single-cell mechanism and show that the single-cell model, while explaining some prominent features of the data, does not account for the structure across stimuli captured by the network model.

cote interne IRCAM: Bathellier14a; None / None; International audience; Natural sounds display strong intensity ?uctuations over time. However, we currently understand better auditory processing in the frequency domain than in the temporal domain. Recently, auditory psychophysics showed that humans perceive a sound whose intensity increases as louder than a sound whose intensity decreases over time, although the overall intensity of the sounds are the same. The underlying neuronal mechanisms of this striking perceptual asymmetry are still elusive. To test if the direction of intensity variation is asymmetrically processed by the auditory system, we have recorded the activity of large populations of neurons in the auditory cortex of awake mice while playing sounds of ramping-up and ramping-down intensities with various durations. We observed that long ramps (> 250ms) produce complex cortical population dynamics with different sets of neurons ?ring at the beginning compared to the end of the ramps. This indicates that the coding of intensity variations coding is strongly distributed in the auditory cortex. More interestingly, we observed that population ?ring rate is overall larger for increasing ramps than for decreasing ramps, suggesting that, also for mice, sounds ramping up could be perceptually more salient than sounds ramping down. To test this hypothesis, we performed behavioral experiments in which the saliency of a sound is measured through associative learning speed. We observed that increasing ramps are more rapidly associated to a correct behavior than decreasing ramps, showing that increasing sound intensities are more salient than decreasing sound intensities for mice. Altogether, these novel observations indicate that strongly non-linear processes in the auditory system shape both the perception and the cortical representation of time-varying sounds, to eventually reinforce rising and thus potentially approaching sound sources.

An accurate analysis of functional MRI measurements requires a precise understanding of the physiological processes involved in this measure. This PhD work shows both investigations in hemodynamic models and algorithms to use such models in the analysis of brain imaging measurements. A particular concern with functional MRI is the temporal modeling of the responses to neural activity. Today, the most standard analysis methods use the General Linear Model framework, which supposes a linear relationship between brain activity and the BOLD response. We show how it is possible alternatively to use nonlinear models in data analysis. Our estimation of parameters by energy minimization is the equivalent to linear regression, and our adaptation of the Fisher statistical test enables activation detection, hypothesis testing, and eventually comparison between different models. We then have extended our methods to the analysis of multiple modalities data, and in particular, proposed a method to estimate the cortical activity underlying simultaneous fMRI and EEG measurements. We were able to achieve accurate estimation on synthetic data. Additionally to these methodological researches, we have investigated the model equations with an Optical Imaging experiment. We have focused on the dynamic of the blood flow, which is at a crossroad between electrical, metabolism and oxygenation processes. We have identified specific questioning facts about this dynamic, such as nonlinearity with respect to electrical synaptic activity, and delays with respect to the blood volume response. Furthermore, we have conceived a new method for estimating fast erythrocyte motions in the blood from intrinsic optical imaging signal, which might provide a new useful measurement of this blood flow. In the following synopsis, we summarize the main features of this PhD work, through highlighting the purposes, methods, conclusions and implications of each chapter. Also, we tried to present an objective criticism of this work, through mentioning both its original contributions and its weaknesses. We hope that this summary will help the reader to rapidly navigate through the thesis, while understanding the relations between its different components., L'enjeu de la présente thèse est de proposer de nouvelles méthodes d'analyse des données d'imagerie cérébrale acquises en Imagerie par Résonance Magnétique fonctionnelle (IRMf). Elle s'est concentrée en particulier sur la compréhension des signaux temporels mesurés en IRMf et leur lien avec l'activité cérébrale. En effet, les variations du signal que l'on observe en IRMf sont dues à des changements de l'afflux du sang dans le cerveau et de l'oxygénation de ce sang. Ces changements sont liés à l'activité des neurones, et l'on nomme ce phénomène la réponse hémodynamique. Cette réponse hémodynamique fait l'objet d'un important effort de modélisation, de manière à mieux pouvoir interpréter les données d'IRMf. Et cette thèse contient des travaux liés à la fois à la modélisation pour elle-même, avec l'étude de certains détails des modèles hémodynamiques, et à la fois à l'utilisation de ces modèles pour l'analyse des données, avec en particulier l'analyse des données IRMf et la fusion entre des données d'IRMf et d'Electroencéphalographie (EEG). Ainsi, la première partie de la thèse est consacrée à l'utilisation de modèles hémodynamiques en IRMf. En effet, aujourd'hui, les méthodes standard d'analyse de données d'IRM fonctionnelle utilisent le Modèle Général Linéaire (GLM), qui suppose une relation linéaire entre l'activité des neurones, la réponse hémodynamique et les mesures IRMf. Nous montrons qu'il est aussi possible d'utiliser des modèles plus plausible du point de vue biologique, et éventuellement non-linéaires pour analyser les données. A la place de la régression linéaire utilisée habituellement, nous proposons une identification de modèle basées sur une minimisation d'énergie, et nous proposons d'adapter les tests de Fisher utilisés habituellement dans le cadre du GLM pour pouvoir réaliser dans le nouveau cadre la détection d'activations, le test d'hypothèses cognitives, ainsi que des comparaisons entre différents modèles. La seconde partie quant à elle est expérimentale: nous avons étudié les équations de différents modèles hémodynamiques grâce à des expérience d'Imagerie Optique chez le singe éveillé, dans le cadre d'une collaboration avec Ivo Vanzetta dans l'équipe "Dynamique de la perception visuelle et de l'action'' au CNRS Marseille. Nous nous sommes intéressés en particulier à la dynamique du flux sanguin, qui est de première importance car elle fait le lien entre les activités électriques et métaboliques et les changements du volume et de l'oxygénation du sang. Nous avons mis en évidence des aspects de la réponse hémodynamique qui ne sont pas prévus par les modèles actuels, tels qu'une non-linéarité de cette réponse du flux par rapport à l'intensité de la réponse électrique. Par ailleurs, dans le cadre de la même collaboration, nous avons conçu une méthode pour estimer la vitesse des globules rouges dans les vaisseaux sanguins filmés en Imagerie Optique, qui constitue une nouvelle technique de mesure de ce flux sanguin. Enfin, dans la troisième partie, nous avons étendu les méthodes présentées dans la première partie à l'analyse de données de modalités multiple, et en particulier, proposons une méthode pour estimer l'activité cérébrale à partir d'enregistrement simultanés en IRMf et en EEG. Cette méthode est validée sur des données synthétiques. Le présent synopsis résume les points importants de ces travaux: les objectifs, les méthodes, les conclusions et conséquences pour chaque chapitre. Nous avons également tenté d'en présenter une critique objective, en mentionnant à la fois ce qui constitue des contributions originales et les faiblesses restantes. Nous espérons que ce résumé permettra au lecteur de se repérer rapidement dans cette thèse, et de bien comprendre les relations entre ses différentes composantes.

There is an increasing interest in using physiologically plausible models in fMRI analysis. These models do raise new mathematical problems in terms of parameters estimation and interpretation of the measured data. We present some theoretical contributions in this area, using different variations of the Balloon Model (Buxton98,Friston00,Buxton04) as example models. We propose 1) a method to analyze the models dynamics and their stability around equilibrium, 2) a new way to derive least square energy gradient for parameter estimation, 3) a quantitative measurement of parameter estimation efficiency, and 4) a statistical test for detecting voxel activations. We use these methods in a visual perception checker-board experiment. It appears that the different hemodynamic models considered better capture some features of the response than linear models. In particular, they account for small nonlinearities observed for stimulation durations between 1 and 8 seconds. Nonlinearities for stimulation shorter than one second can also be explained by a neural habituation model (Buxton04), but further investigations should assess whether they are rather not due to nonlinear effects of the flow response. Moreover, the tools we have developed prove that statistical methods that work well for the GLM can be nicely adapted to nonlinear models. The activation maps obtained in both frameworks are comparable.