Biology subjects

Brochier, T.

Publications and source records attributed to Brochier, T..

13 recordsLinked to original sources

bioRxiv2026

Reciprocal Fronto-Parietal Interactions Support Motor Anticipation during Sequential Reaching

Motor planning is a highly flexible process that adapts to changing behavioral demands. Frontal and parietal circuits are considered as critical nodes that support planning processes. However, it remains unclear whether the coordination of motor and parietal areas is preserved or adjusted in response to varying behavioral contexts. To address this open question, we trained two rhesus macaques to perform a visually-guided sequential reaching task, in which they could adapt their behavior to varying degrees of target predictability within the sequence. Analysis of eye and hand movements revealed distinct visuomotor strategies that were reflected in corresponding neural activity patterns. During motor anticipation, the direction of the upcoming reach toward predictable targets could be decoded from preparatory neural activity prior to target onset both in motor regions (PMd/M1) and parietal area 7A, whereas during reactive movements directional information emerged only after target onset. Using feature-specific information transfer analysis, we found that information about the upcoming movement direction was transmitted between 7A and PMd/M1 through bidirectional interactions. This pattern of interactions was preserved during motor anticipation and shifted earlier in time with respect to target onset. Our findings thus support a reciprocal fronto-parietal network that flexibly adjusts the timing of preparatory activity as different strategies are employed under varying behavioral constraints. Contrary to classical hierarchical models predicting serial activation across parietal and motor areas, parietal-to-motor interactions did not precede motor-to-parietal interactions, consistent with a heterarchical organization of cortical processing during flexible motor planning.

neuroscience↗

bioRxiv2025

Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex

The cerebral cortex is hierarchically organised from sensory to higher cognitive areas1-4. Several dynamical5-8 and anatomical1-4,8 measures, such as timescales and neurotransmitter receptor expression, have independently been linked to the cortical hierarchy. However, a systematic and quantitative characterisation of the relationship between spontaneous spiking activity and the cortical hierarchy remains elusive. Here, we test the hypothesis that single-neuron spontaneous spiking statistics uniquely characterise each cortical area, and that they quantitatively correlate with the cortical hierarchy. We study the spontaneous activity of neurons in seven macaque cortical areas (V1, V4, DP, 7A, M1, PMd, PFC)9-12 in the eyes-open and eyes-closed conditions recorded in a dim-lit room. First, we uncover that the firing rate, inter-spike interval variation, and cross-correlation form a unique fingerprint of the cortical areas, but only when considering them in combination. Second, we show that the differences between the spiking statistics correlate with multiple anatomical markers1,2,4,13-17 of the cortical hierarchy. This effect is much stronger in the eyes-closed condition, suggesting that visual input or the expectation thereof modulates the hierarchical organisation of spontaneous activity. We also observe an increase in timescales up the hierarchy, in agreement with previous findings5,18,19. In conclusion, we demonstrate that spontaneous single-neuron spiking activity reflects the hierarchical organisation of the cerebral cortex: distinct spiking statistics for hierarchically distant areas; similar statistics for nearby areas. Our results thus add a new dynamical dimension to the concept of the cortical hierarchy.

neuroscience↗

bioRxiv2025

Dynamic Norm-Based Coding of Vocal Identity in the Primate Auditory Cortex

Recognition of vocal identity is essential for primate social behavior, yet the neural mechanisms supporting this ability remain poorly understood. Cognitive models suggest that the brain may represent identity based on an average voice prototype. Here, we tested this hypothesis by recording spiking activity from single neurons in an fMRI-localized voice-selective region of the anterior temporal lobe in two rhesus macaques. Using synthetic "coo" calls systematically morphed along identity trajectories from "anti-voices" to caricatures, we found that early neuronal responses ([~]100 ms post-stimulus) exhibited robust V-shaped tuning to distance from the average voice. This pattern reflects an energy-efficient representational strategy in which neural activity increases with identity distinctiveness, also applied to face stimuli. Unexpectedly, a distinct subpopulation of neurons showed a delayed ([~]200 ms) rebound response selectively enhanced for the average voice. These findings reveal two temporally dissociable mechanisms for vocal identity encoding in primate auditory cortex, suggesting that the brain emphasizes both distinctiveness and prototypicality to support efficient and flexible voice recognition. Significance statementRecognizing who is speaking is fundamental to primate social life, yet how the brain encodes vocal identity remains poorly understood. Cognitive theories suggest that the brain may represent identity relative to an internal "average" voice, but direct neural evidence has been lacking. Here, we recorded single-neuron activity in a voice-selective brain region of rhesus macaques and found two distinct coding strategies: one that increases neural activity with identity distinctiveness, and another that selectively enhances the average voice itself. These results reveal dual mechanisms for vocal identity encoding in the primate brain, highlighting how the brain balances flexibility and efficiency in social communication.

neuroscience↗

bioRxiv2024

Voice identity invariance by anterior temporal lobe neurons

The ability to recognize speakers by their voice despite acoustical variation plays a significant role in primate social interactions. While neurons in the macaque anterior temporal lobe (ATL) show invariance to face viewpoint, whether they also encode abstract representations of caller identity is not known. Here we show that neurons in the voice-selective ATL of two macaques show invariance to voice identity via dynamic representations that minimize within-caller neuronal distances while maintaining distinct neural trajectories for different individuals. A small proportion of highly identity-selective neurons plays a central role although less selective neurons are also informative. Our findings provide a neural basis for voice identity recognition in primates and highlight the ATL as a key hub for integrating perceptual voice features into higher-level identity representations.

neuroscience↗

bioRxiv2024

Argon neuroprotection in a non-human primate model of transient endovascular ischemic stroke.

BackgroundPrevious studies have demonstrated the efficacy of argon neuroprotection in rodent models of cerebral ischemia. The objective of the present study was to confirm a potential neuroprotective effect of argon in a non-human primate model of endovascular ischemic stroke as an essential step before considering the use of argon as a neuroprotective agent in humans. MethodsThirteen adult monkeys (Macaca mulatta) were allocated to two groups: a control group (n=8) without neuroprotection and an argon group (n=5) in which argon inhalation (90 min) was initiated 30 minutes after onset of ischemia. Animals in both groups underwent brain MRI (pre-ischemic) at least 7 days before the intervention. The monkeys were subjected to focal cerebral ischemia induced by a transient (90 min) middle cerebral artery occlusion (tMCAO). After tMCAO, MRI was performed 1 hour after cerebral reperfusion. The ischemic core volume was defined by the apparent diffusion coefficient (aDC) and edema in fluid attenuated inversion recovery (FLAIR) acquisitions. MRI masks were applied to distinguish between cortical and subcortical abnormalities. In addition, a modified version of the Rankin scale was used to neurologically assess post-tMCAO. ResultsDespite variability in the ischemic core and edema volumes in the control group, argon significantly reduced ischemic core volume after ischemia compared to the control group (1.1{+/-}1.6 cm3 vs. 8.5{+/-}8.1 cm3; p=0.03). This effect was limited to cortical structures (0.6{+/-}1.1 cm3 vs. 7.4{+/-}7.2 cm3; p=0.03). No significant differences were observed in the edema volumes. Measures of neurological clinical outcome suggested a better prognosis in argon-treated animals. ConclusionsIn the tMCAO macaque model, argon induced effective neuroprotective effects, leading to a reduced ischemic core in cortical areas. These results support the potential use of this therapeutic approach for future clinical studies in stroke patients.

neuroscience↗

bioRxiv2023

Non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models

In the field of motor learning, few studies have addressed the case of non-instructed movement sequences learning, as they require long periods of training and data acquisition, and are complex to interpret. In contrast, such problems are readily addressed in machine learning, using artificial agents in simulated environments. To understand the mechanisms that drive the learning behavior of two macaque monkeys in a free-moving multi-target reaching task, we created two Reinforcement Learning (RL) models with different penalty criteria: "Time" reflecting the time spent to perfom a trial, and "Power" integrating the energy cost. The initial phase of the learning process is characterized by a rapid improvement in motor performance for both the 2 monkeys and the 2 models, with hand trajectories becoming shorter and smoother while the velocity gradually increases along trials and sessions. This improvement in motor performance with training is associated with a simplification in the trajectory of the movements performed to achieve the task goal. The monkeys and models show a convergent evolution towards an optimal circular motor path, almost exclusively in counter-clockwise direction, and a persistent inter-trial variability. All these elements contribute to interpreting monkeys learning in the terms of a progressive updating of action-selection patterns, following a classic value iteration scheme as in reinforcement learning. However, in contrast with our models, the monkeys also show a specific variability in the choice of the motor sequences to carry out across trials. This variability reflects a form of "path selection", that is absent in the models. Furthermore, comparing models and behavioral data also reveal sub-optimality in the way monkeys manage the trade-off between optimizing movement duration ("Time") and minimizing its metabolic cost ("Power"), with a tendency to overemphasize one criterion at the detriment of the other one. Overall, this study reveals the subtle interplay between cognitive factors, biomechanical constraints, task achievement and motor efficacy management in motor learning, and highlights the relevance of modeling approaches in revealing the respective contribution of the different elements at play. Author summaryThe way in which animals and humans learn new motor skills through free exploratory movements sequences solely governed by success or failure outcomes is not yet fully understood. Recent advances in machine learning techniques for continuous action spaces led us to construct a motor learning model investigate how animals progressively enhance the efficiency of their behaviors through numerous trials and errors. This study conducts a comprehensive comparison between deep learning models and experimental data from monkey behavior. Notably, we show that the progressive refinement of motor sequences, as they are observed in the animals, do not require the implementation of a complete model of their environment. Rather, it merely requires the capacity to anticipate both movement costs and final reward a few steps ahead in the future following a value iteration principle. Furthermore, the systematic deviations exhibited by the monkeys with respect to the computational model inform us on the presence of individual preferences in either minimizing the duration or the energy consumption, and also on the involvement of alternative "cognitive" strategies.

animal behavior and cognition↗

bioRxiv2023

Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region

High-dimensional brain activity is often organized into lower-dimensional neural manifolds. However, the neural manifolds of the visual cortex remain understudied. Here, we study large-scale multielectrode electrophysiological recordings of macaque (Macaca mulatta) areas V1, V4 and DP with a high spatio-temporal resolution. We find, for the first time, that the population activity of V1 contains two separate neural manifolds, which correlate strongly with eye closure (eyes open/closed) and have distinct dimensionalities. Moreover, we find strong top-down signals from V4 to V1, particularly to the foveal region of V1, which are significantly stronger during the eyes-open periods, a previously unknown effect. Finally, in silico simulations of a balanced spiking neuron network qualitatively reproduce the experimental findings. Taken together, our analyses and simulations suggest that top-down signals modulate the population activity of V1, causing two distinct neural manifolds. We postulate that the top-down modulation during the eyes-open periods prepares V1 for fast and efficient visual responses, resulting in a type of visual stand-by state.

neuroscience↗

bioRxiv2022

Parallel planning through an optimal neural subspace in motor cortex

How do patterns of neural activity in motor cortex contribute to the planning of a movement? A recent theory developed for single movements proposes that motor cortex acts as a dynamical system whose initial state is optimized during the preparatory phase of the movement. This theory makes important yet untested predictions about preparatory dynamics in more complex behavioral settings. Here, we analyzed preparatory activity in non-human primates planning not one, but two movements simultaneously. As predicted by the theory, we found that parallel planning was achieved by adjusting preparatory activity within an optimal subspace to an intermediate state reflecting a tradeoff between the two movements. The theory quantitatively accounted for the relationship between this intermediate state and fluctuations in the animals behavior down at the trial level. These results uncover a simple mechanism for planning multiple movements in parallel, and further point to motor planning as a controlled dynamical process.

neuroscience↗

bioRxiv2020

Long-range coordination patterns in cortex change withbehavioral context

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet this massively parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

neuroscience↗

bioRxiv2020

On the complexity of resting state spiking activity in monkey motor cortex

Resting state has been established as a classical paradigm of brain activity studies, mostly based on large scale measurements such as fMRI or M/EEG. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results to task- or stimulus-driven experiments, which might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons spiking activity in monkey motor cortex and show the differences from spontaneous and task-induced movement conditions. The resting state shows a higher dimensionality, reduced firing rates and less balance between population level excitation and inhibition than behavior-related states. Additionally, our results stress the importance of distinguishing between rest with eyes open and closed.

neuroscience↗

bioRxiv2020

Regulation of bacterial cell cycle progression by redundant phosphatases

In the model organism Caulobacter crescentus, a network of two-component systems involving the response regulators CtrA, DivK and PleD coordinate cell cycle progression with differentiation. Active phosphorylated CtrA prevents chromosome replication in G1 cells while simultaneously regulating expression of genes required for morphogenesis and development. At the G1-S transition, phosphorylated DivK (DivK~P) and PleD (PleD~P) accumulate to indirectly inactivate CtrA, which triggers DNA replication initiation and concomitant cellular differentiation. The phosphatase PleC plays a pivotal role in this developmental program by keeping DivK and PleD phosphorylation levels low during G1, thereby preventing premature CtrA inactivation. Here, we describe CckN as a second phosphatase akin to PleC that dephosphorylates DivK~P and PleD~P in G1 cells. However, in contrast to PleC, we do not detect kinase activity with CckN. The effects of CckN inactivation are largely masked when PleC is present, but become evident when PleC and DivJ, the major kinase for DivK and PleD, are absent. Accordingly, mild overexpression of cckN restores most phenotypic defects of a pleC null mutant. We also show that CckN and PleC are proteolytically degraded in a ClpXP-dependent way well before the onset of the S phase. Surprisingly, known ClpX adaptors are dispensable for PleC and CckN proteolysis, suggesting the existence of adaptors specifically involved in proteolytic removal of cell cycle regulators. Since cckN expression is induced in stationary phase, depending on the stress alarmone (p)ppGpp, we propose that CckN acts as an auxiliary factor responding to environmental stimuli to modulate CtrA activity under suboptimal conditions. ImportanceTwo-component signal transduction systems are widely used by bacteria to sense environmental signals and respond accordingly by modulating various cellular processes, such as cell cycle progression. In Caulobacter crescentus, PleC acts as a phosphatase that indirectly protects the response regulator CtrA from premature inactivation during the G1 phase of the cell cycle. Here, we provide genetic and biochemical evidence that PleC is seconded by another phosphatase, CckN. The activity of PleC and CckN phosphatases is restricted to G1 phase since both proteins are timely degraded by proteolysis just before the G1-S transition. This degradation requires new proteolytic adaptors as well as an unsuspected N-terminal motif for CckN. Our work illustrates a typical example of redundant functions between two-component proteins.

microbiology↗

bioRxiv2019

On the relationship between LFP beta oscillation amplitude and firing rate of individual neurons in monkey motor cortex

It is a long-standing controversial issue whether an intrinsic relationship between the local field potential (LFP) beta oscillation amplitude and the spike rate of individual neurons in the motor cortex exists. Beta oscillations are prominent in motor cortical LFPs, and their relationship to the local neuronal spiking activity has been extensively studied. Many studies demonstrated that the spikes of individual neurons lock to the phase of LFP beta oscillations. However, the results concerning whether there is also an intrinsic relationship between the amplitude of LFP beta oscillations and the firing rate of individual neurons are contradictory. Some studies suggest a systematic mapping of spike rates onto LFP beta amplitude, and others find no systematic relationship. To resolve this controversy, we correlated the amplitude of LFP beta oscillations recorded in motor cortex of two male macaque monkeys with spike counts of individual neurons during visuomotor behavior, in two different manners. First, in an analysis termed task-related correlation, data obtained across all behavioral task epochs was included. These task-related correlations were frequently significant, and in majority of negative sign. Second, in an analysis termed trial-by-trial correlation, only data from a fixed pre-cue task epoch was included, and correlations were calculated across trials. Such trial-by-trial correlations were weak and rarely significant. We conclude that there is no intrinsic relationship between the firing rate of individual neurons and LFP beta oscillation amplitude in macaque motor cortex, beyond each of these signals being modulated by external factors such as the behavioral task.\n\nSIGNIFICANCE STATEMENTWe addressed the long-standing controversial issue of whether there is an intrinsic relationship between the local field potential (LFP) beta oscillation amplitude and the spike rate of individual neurons in the motor cortex. In two complementary analyses of data from macaque monkeys, we first demonstrate that the unfolding behavioral task strongly affects both the LFP beta amplitude and the neuronal spike rate, creating task-related correlations between the two signals. However, when limiting the influence of the task, by restricting our analysis to a fixed task epoch, correlations between the two signals were largely eliminated. We conclude that there is no intrinsic relationship between the firing rate of individual neurons and LFP beta oscillation amplitude in motor cortex.

neuroscience↗

bioRxiv2017

Behavioral context determines network state and variability dynamics in monkey motor cortex

Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependence. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the local coefficient of variation (CV2), reflect variability on long and short time scales, respectively. We co-analyzed FF and CV2 in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that FF significantly decreases from wait to movement, while CV2 increases. The more regular firing (low CV2) during wait is related to an increased power of local field potential beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF{approx}CV2. This expectation was met during movement, but not during wait where FF>>CV22. We conclude that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. We discuss our results in the light of recent computational models that generate non-stationary network conditions.

neuroscience↗