Biology subjects

Jarraya, B.

Publications and source records attributed to Jarraya, B..

15 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↗

bioRxiv2026

From Coarse to Rich: Successive Waves of Visual Perception in Prefrontal Cortex

The ventrolateral prefrontal cortex (vlPFC) is well known for its involvement in high-level functions such as cognitive control and language. However, vlPFCs role in visual processing is less clear. Here, we investigated how neuronal ensembles in the vlPFC dynamically encode different types of visual information. Using chronic recording of spiking activity, we investigated vlPFCs representational geometry in a macaque monkey passively viewing a large set of naturalistic images, and compared this to representations in deep neural networks (DNNs). We found that the vlPFC processes visual information in two stages. First, an "early" response from 50 to 90 ms after stimulus onset encodes the low spatial frequency component of an image. It contains sufficient information to form a coarse estimate of the position and category of a salient object. Then, from 100 ms on, the representational geometry changes and contains much richer information. This late period contains non-categorical information typically present in conscious experiences such as the orientation of a face and natural scenes in the background. The late window also enables sub-category identification, which is boosted by the low spatial category prior. These results suggest that the vlPFC has a dual role in natural vision: first forming fast low-spatial-frequency-based priors shaping feed-forward visual processing, and subsequently maintaining a detailed and rich representation of a visual scene. SignificanceWhat information does the prefrontal cortex represent during natural vision, and how does this relate to conscious experience? Theories of consciousness differ sharply on whether prefrontal activity reflects detailed perceptual content or only high-level, task-related information. Using dense neural recordings during passive viewing of thousands of naturalistic images, we show that the ventrolateral prefrontal cortex processes visual information in two distinct stages: an early, coarse estimate of the visual scene is followed by a richer, high-dimensional representation that includes sub-category identity and other perceptual details. These findings reveal that prefrontal circuits encode more of the content of visual experience than previously assumed, even in the absence of a task.

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

Neural Activity dynamic in Primate Cortex Across Consciousness Levels: Insights from High-Density Neuropixel Recording

This study investigates the anesthesia mechanisms induced by sevoflurane and how it modulates neural activity in the posterior parietal cortex (PPC) and prefrontal cortex (PFC) in Non Human Primates (NHPs) using high density Neuropixel probes. Spiking and local field potentials (LFPs) were recorded in two macaque monkeys under going four sevoflurane concentrations (2%, 3%, 4%, and 6%). We aimed to (i) quantify the emergence of anesthesia-induced Up/Down state dynamics, (ii) track changes in oscillatory power and inter-regional synchrony, and (iii) determine whether frontal and parietal areas exhibit differential sensitivity to rising and falling anesthetic depth. Across different anesthetic levels, we observed characteristic slow oscillations in delta range in both PFC and PPC, with neurons transitioning between high-firing "Up" states and near-silent "Down" states. Deeper anesthesia extended Down states, suppressed mean firing rates, and reduced the frequency and duration of Up states. In M1, no single units were detected in PFC, and only a few were recorded in M2. We suspect misalignment of the probe with PFC pyramidal cells and extensive suppression in PFC as the main reasons. Recurrent Neural Networks (RNN) was used to extract Up/Down states from LFP activities, based one the pattern observed in the PPC. The PPC [->] PFC information flow observed in Transfer Entropy analysis suggests that even under anesthesia, some level of feedforward-like interactions may persist. Up states originate in deep cortical layers and propagate toward superficial layers, following a bottom-up progression, indicating that deep-layer pyramidal neurons, which receive strong thalamic input, may be the primary drivers of Up states. The short Up states under deep anesthesia might represent a failed ignition attempt, where the brain momentarily tries to reactivate but cannot sustain functional activity due to global inhibition. LFP analyses revealed that although the absolute delta power remains high at different anesthetic levels, the relative delta band power is anti correlated with anesthetic depth, due to sporadic short (20ms to 40ms) burst in gamma (30-100 Hz) that appeared transient in nature. Lower sensitivity to anesthesia dose changes were observed in PFC as compared to PPC. This could explain why anesthesia first impairs cognitive function before affecting basic sensory responses. These results indicates that the traditional Up/Down state models might oversimplify anesthetic brain dynamics. While anesthesia is often described as a state of simple global slow-wave oscillations, the observed Up/Down state durations are not uniform, they fluctuate, follow non-trivial transition patterns, and differ between PFC and PPC.

neuroscience↗

bioRxiv2025

Anaesthesia disrupts mesoscale signal propagation in spite of enhanced responsivity in frontal but not in parietal cortex

Loss of consciousness under anaesthesia is accompanied by widespread silencing of neurons, and disruption of cortical dynamics. Yet, how this affects mesoscale signal propagation within higher-order associative areas, crucially implicated in theories of consciousness, remains poorly understood. Here we combined intracortical microstimulation, and simultaneous multielectrode recordings in the ventrolateral prefrontal cortex (vlPFC) and the posterior parietal cortex (PPC) of macaques across wakefulness, and graded depths of anaesthesia. Spiking responses revealed distinct regimes: in the PFC, higher-amplitude stimulation elicited a delayed single rebound after sustained inhibition, whereas in the PPC, a faster and double-rebound profile emerged. Despite enhanced local spiking and LFP responsivity under anaesthesia, we found a strong and selective suppression of lateral signal propagation in the PFC - a breakdown strikingly absent in the PPC. This dissociation suggests that anaesthesia disrupts consciousness not merely by silencing cortical populations, but by impairing mesoscale integrative processes critical for neuronal dynamics at multiple scales. Our findings demonstrate in a causal and spatially-resolved manner, that lateral signal spread within higher-order cortex is a key mechanism underlying conscious awareness, and its loss under anaesthesia.

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.

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bioRxiv2024

Brain Charts for the Rhesus Macaque Lifespan

Recent efforts to chart human brain growth across the lifespan using large-scale MRI data have provided reference standards for human brain development. However, similar models for nonhuman primate (NHP) growth are lacking. The rhesus macaque, a widely used NHP in translational neuroscience due to its similarities in brain anatomy, phylogenetics, cognitive, and social behaviors to humans, serves as an ideal NHP model. This study aimed to create normative growth charts for brain structure across the macaque lifespan, enhancing our understanding of neurodevelopment and aging, and facilitating cross-species translational research. Leveraging data from the PRIMatE Data Exchange (PRIME-DE) and other sources, we aggregated 1,522 MRI scans from 1,024 rhesus macaques. We mapped non-linear developmental trajectories for global and regional brain structural changes in volume, cortical thickness, and surface area over the lifespan. Our findings provided normative charts with centile scores for macaque brain structures and revealed key developmental milestones from prenatal stages to aging, highlighting both species-specific and comparable brain maturation patterns between macaques and humans. The charts offer a valuable resource for future NHP studies, particularly those with small sample sizes. Furthermore, the interactive open resource (https://interspeciesmap.childmind.org) supports cross-species comparisons to advance translational neuroscience research.

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.

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bioRxiv2021

Prefrontal neural ensembles encode an internal model of visual sequences and their violations

When exposed to sensory sequences, do macaque monkeys spontaneously form abstract internal models that generalize to novel experiences? Here, we show that neuronal populations in macaque ventrolateral prefrontal cortex encode visual sequences by factorizing them into separate codes for the specific pictures presented and for their abstract sequential structure. Ventrolateral prefrontal neurons were recorded while macaque monkeys passively viewed visual sequences and sequence mismatches in the local-global paradigm. Even without any overt task or response requirements, prefrontal populations spontaneously built up representations of sequence structure, serial order, and image identity within distinct but superimposed neuronal subspaces. Representations of sequence structure rapidly updated following single exposure to a mismatch sequence, while orthogonal populations represent mismatches for sequences of different complexity. Finally, those representations generalized across sequences following the same structure but comprising different images. These results suggest that prefrontal populations spontaneously encode rich internal models of visual sequences that reflect both content-specific and abstract information.

neuroscience↗

bioRxiv2021

Macroscopic quantities of collective brain activity during wakefulness and anesthesia

The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the systems capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

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bioRxiv2020

Hierarchical disruption in the cortex of anesthetized monkeys as a new signature of consciousness loss

Anesthesia induces a reconfiguration of the repertoire of functional brain states leading to a high function-structure similarity. However, it is unclear how these functional changes lead to loss of consciousness. Here we suggest that the mechanism of conscious access is related to a general dynamical rearrangement of the intrinsic hierarchical organization of the cortex. To measure cortical hierarchy, we applied the Intrinsic Ignition analysis to resting-state fMRI data acquired in awake and anesthetized macaques. Our results reveal the existence of spatial and temporal hierarchical differences of neural activity within the macaque cortex, with a strong modulation by the depth of anesthesia and the employed anesthetic agent. Higher values of Intrinsic Ignition correspond to rich and flexible brain dynamics whereas lower values correspond to poor and rigid, structurally driven brain dynamics. Moreover, spatial and temporal hierarchical dimensions are disrupted in a different manner, involving different hierarchical brain networks. All together suggest that disruption of brain hierarchy is a new signature of consciousness loss.

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↗