Biology subjects

Tasserie, J.

Publications and source records attributed to Tasserie, J..

10 recordsLinked to original sources

bioRxiv2026

High-order brain interactions distinguish wakefulness, anaesthesia, and recovery induced by deep brain stimulation

Understanding how consciousness depends on large-scale brain interactions is key for both the neuroscience of consciousness and clinical translation. However, it requires moving beyond classical pairwise descriptions of functional connectivity, which cannot capture the collective dependencies emerging across multiple brain regions. Here, we use multivariate information theory measures to characterize how higher-order interactions re-organize across states of consciousness. Specifically, we apply O-information to resting-state fMRI data from non-human primates to quantify whether multiregional brain dynamics are dominated by synergistic or redundant information sharing. We analyse two complementary datasets: (i) wakefulness and anaesthesia-induced loss of consciousness using different molecular agents (propofol, sevoflurane, ketamine), and (ii) the recovery of consciousness driven by central thalamic deep brain stimulation during propofol anaesthesia, indexed by behavioural responsiveness. We identify optimal regional subsets whose O-information robustly discriminates conscious from non-responsive states under two complementary optimization polarities. The first captures elevated redundancy in conscious scans that decreases under anaesthesia, providing robust discrimination and placing high-voltage central-thalamus stimulation closer to wakefulness. The second captures a synergy-to-redundancy transition, prominent in multi-anaesthesia conditions but context-dependent across datasets. Discrimination performance depends on interaction order: redundancy-based signatures improve with increasing subset size, whilst synergy-based signatures peak at low orders. Higher-order informational features significantly outperform pairwise functional connectivity, particularly for synergistic signatures which remain invisible to correlations. These findings demonstrate that consciousness is reflected in the reconfiguration of higher-order interaction structures, with distinct informational substrates requiring multivariate characterization beyond pair-wise connectivity.

neuroscience↗

bioRxiv2025

Multi-modal, multi-species, and multi-task latent-space model for decoding level of consciousness

Assessing the degree and characteristics of consciousness is central to caring for patients with Disorders of Consciousness (DoC), yet current standard of care is a bedside questionnaire. Using a laminar neural probe we introduce a self-supervised, multi-modal representation learning approach that constructs an interpretable 2-D latent space of brain state from continuous neural recordings. We jointly encode local field potentials (LFPs), single-unit firing rates, and a movement proxy into a time-aware autoregressive VAE (TN-VAE). In rats, pigs and humans undergoing controlled depth anesthesia experiments, the learned latent trajectories nonlinearly, but smoothly, separate four expert-defined states (awake, light, moderate, deep) at 2s resolution, exceeding the temporal granularity of behavioral scoring (15m). The latent space supports linear readout of both coarse state and individual behavioral components, and its axes align with known physiology: delta/alpha power differentiates unconscious sub-states, while gamma and unit firing distinguish wakefulness. To enable cross-species use and incomplete modality sets, we add lightweight, species-specific stitching layers. This model pretrained on tri-modal rat data and fine-tuned with unimodal (LFP) pig data and unimodal human intraoperative data, successfully separates awake versus anesthetized states in new unseen human subjects, demonstrating zero-shot multi-subject transfer without per-patient calibration. The model further generalizes in a multi-task manner to predict the results of additional stimuli. These results highlight a path toward a foundation model for DoC that generalizes across sessions, subjects, species, and stimuli to enable scalable, continuous brain-state monitoring and clinical decision support.

neuroscience↗

bioRxiv2025

Restoring signatures of consciousness by thalamic stimulation in a whole-brain model of an anesthetized nonhuman primate

Treatment options for Disorders of Consciousness (DoC) are limited due to insufficient understanding of the underlying neurobiological mechanisms. Two primary strategies for characterizing DoC and assessing treatment efficacy are in vivo experiments with animal models, and in silico computational models. We combined both approaches by creating a whole-brain model tailored to the experimental functional magnetic resonance imaging (fMRI) data of a single anesthetized macaque. It was previously reported in an in vivo experiment that anesthesia-induced loss of consciousness was partially reversed by specific electrical stimulation of the thalamic central nuclei. The in silico model reproduced the brain dynamics underlying the restoration of consciousness, providing a potential explanation for the transition between these brain states as continuous trajectories unfolding in a low-dimensional space. Our results demonstrate that whole-brain computational models reproduce the spatiotemporal properties of fMRI recordings during loss of consciousness and during its recovery induced by electrical stimulation, enabling computational exploration of perturbation-based interventions to potentially personalize treatment and aid recovery of consciousness in DoC patients.

neuroscience↗

bioRxiv2025

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

Intrinsic dynamics of neuronal circuits shape information processing. Combining neuroimaging with causal perturbation offers the opportunity to understand how local dynamics mediate the link between neurobiology and functional repertoire. We compile a unique dataset of multi-scale neural activity during wakefulness and anaesthetic-induced suppression of information processing encompassing human, macaque, marmoset, mouse, zebrafish and nematode. Applying massive feature extraction, we comprehensively characterise local neural dynamics across >6,000 time-series features. Using dynamics as a common space for cross-species comparison reveals a conserved dynamical profile of anaesthesia across species, characterised by shorter intrinsic timescales of neural activity and dampened interregional synchrony. This dynamical regime is experimentally reversed in vivo by deep-brain stimulation of the macaque centromedian thalamus, restoring behavioural responsiveness. Spatially, this conserved dynamical phenotype covaries with conserved transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque, marmoset and mouse cortex. Biophysical modelling provides a mechanistic link between the macroscale dynamical phenotype of anaesthesia, and microscale effects of key molecular targets on the timescales of synaptic excitation and inhibition. Altogether, comprehensive dynamical phenotyping reveals a shared neural endpoint of anaesthesia: across species and scales, anaesthetics induce spatio-temporal isolation of local neural activity.

neuroscience↗

bioRxiv2024

Transcranial direct current stimulation modulates primate brain dynamics across states of consciousness.

The resting primate brain is traversed by spontaneous functional connectivity patterns that show striking differences between conscious and unconscious states. Transcranial direct current stimulation, a non-invasive neuromodulatory technique, can improve signs of consciousness in disorders of consciousness; however, can it influence both conscious and unconscious dynamic functional connectivity? We investigated the modulatory effect of prefrontal cortex (PFC) transcranial direct current stimulation (tDCS) on brain dynamics in awake and anesthetized non-human primates using functional MRI. In awake macaques receiving either anodal or cathodal tDCS, we found that cathodal stimulation robustly disrupted the repertoire of functional connectivity patterns, increased structure-function correlation, decreased Shannon entropy, and favored transitions towards anatomically-based patterns. Under deep sedation, anodal tDCS significantly altered brain pattern distribution and reduced structure-function correlation. The prefrontal stimulation also modified dynamic connectivity arrangements typically associated with consciousness and unconsciousness. Our findings offer compelling evidence that PFC tDCS induces striking modifications in the fMRI-based dynamic organization of the brain across different states of consciousness. This study contributes to an enhanced understanding of tDCS neuromodulation mechanisms and has important clinical implications for disorders of consciousness.

neuroscience↗

bioRxiv2023

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesized that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyzed temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different states of consciousness. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting- state fMRI structure. We then tracked how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks was preserved, their temporal dynamics were highly affected by anesthesia. Networks expressed for longer durations and co-activated in an anesthetic-specific configuration. Additionally, hierarchical brain organization was disrupted with a consciousness-level- signature role of the default mode network. In conclusion, network temporal dynamics is a reliable and robust cortical signature of consciousness, paving the way to its clinical translation.

neuroscience↗

bioRxiv2023

Local Orchestration of Global Functional Patterns Supporting Loss and Restoration of Consciousness in the Primate Brain

AbstractA central challenge of neuroscience is to elucidate how the orchestration of brain function is modulated by different states of consciousness. Here, we investigate the link between distributed structural and functional brain organisation in functional MRI signals of non-human primates, through bi-directional causal manipulations of consciousness. During varying levels of propofol, sevoflurane, or ketamine anaesthesia, and subsequent restoration of responsiveness by deep brain stimulation of the central thalamus, we investigate how loss of consciousness impacts distributed patterns of structure-function organisation across scales. Combining the specificity of electrical stimulation with global fMRI coverage of the entire cortex, we report that distributed brain activity under anaesthesia is increasingly constrained by brain structure across scales, coinciding with anaesthetic-induced collapse of multiple dimensions of hierarchical cortical organisation. Crucially, we show that these distributed signatures of anaesthetic-induced loss of consciousness are observed across different anaesthetics, and they are reversed by electrical stimulation of the central thalamus, coinciding with recovery of behavioural markers of consciousness during propofol anaesthesia. No such effects were observed upon stimulation of a control anatomical site, ventral lateral thalamus, demonstrating specificity. Through causal manipulations of consciousness that integrate pharmacology and electrical intracranial stimulation of the thalamus, our results identify global signatures of consciousness that are under local causal control by specific nuclei of the thalamus. Overall, the present work broadens our understanding of the link between brain network organisation and distributed function in supporting consciousness, and the interplay between local and global functional architecture.

neuroscience↗

bioRxiv2023

Brain mechanisms of reversible symbolic reference:a potential singularity of the human brain

The emergence of symbolic thinking has been proposed as a dominant cognitive criterion to distinguish humans from other primates during hominization. Although the proper definition of a symbol has been the subject of much debate, one of its simplest features is bidirectional attachment: the content is accessible from the symbol, and vice versa. Behavioural observations scattered over the past four decades suggest that this criterion might not be met in non-human primates, as they fail to generalise an association learned in one temporal order (A to B) to the reverse order (B to A). Here, we designed an implicit fMRI test to investigate the neural mechanisms of arbitrary audio-visual and visual-visual pairing in monkeys and humans and probe their spontaneous reversibility. After learning a unidirectional association, humans showed surprise signals when this learned association was violated. Crucially, this effect occurred spontaneously in both learned and reversed directions, within an extended network of high-level brain areas, including, but also going beyond, the language network. In monkeys, by contrast, violations of association effects occurred solely in the learned direction and were largely confined to sensory areas. We propose that a human-specific brain network may have evolved the capacity for reversible symbolic reference.

neuroscience↗

bioRxiv2020

A collaborative resource platform for non-human primate neuroimaging

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field.

neuroscience↗

bioRxiv2020

Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

Decoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called "brain states" corresponding to "functional configurations" of the brain. Here, we propose to use a selfsupervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. The Functional Connectivity (FC) matrices reflect the brain-state dynamic at a given time. Because it is key to consider the FC topologies, a specific graph-Convolutional Neural Network (gCNN), namely BrainNetCNN, is considered to predict the brain states in awake and anesthetized nonhuman primates. To avoid the circularity that remains in the training stage, where the target is composed of pseudo-labels, recent self-supervised techniques are implemented. Using a linear probe for the prediction, the network achieves a prediction accuracy consistent with state-of-the-art methods lying in [0.655, 0.759] depending on the experimental settings. To put forward the interest of such a representation, the transition probabilities and the set of connections found to be important for predicting a brain state are computed. This latter is directly linked with the level of consciousness. The results demonstrate that deep learning methods are not only able to predict brain states but also provide additional insight into cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

neuroscience↗