Biology subjects

Destexhe, A.

Publications and source records attributed to Destexhe, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans

Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.

neuroscience↗

bioRxiv2023

Dynamics and bifurcation structure of a mean-field model of adaptive exponential integrate-and-fire networks

The study of brain activity spans diverse scales and levels of description, and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking networks presents challenges for understanding their dynamics. To tackle this, a mean-field formulation offers a potential approach for dimensionality reduction while retaining essential elements. Here, we focus on a previously developed mean-field model of Adaptive Exponential (AdEx) networks, utilized in various research works. We provide a systematic investigation of its properties and bifurcation structure, which was not available for this model. We show that this provides a comprehensive description and characterization of the model to assist future users in interpreting their results. The methodology includes model construction, stability analysis, and numerical simulations. Finally, we offer an overview of dynamical properties and methods to characterize the mean-field model, which should be useful for for other models.

neuroscience↗

bioRxiv2023

A Multi-Scale Study of Thalamic State-Dependent Responsiveness

The thalamus is the brains central relay station, orchestrating sensory processing and cognitive functions. However, how thalamic function depends on internal and external states, is not well understood. A comprehensive understanding would necessitate the integration of single cell dynamics with their collective behavior at population level. For this we propose a biologically realistic mean-field model of the thalamus, describing thalamocortical relay neurons (TC) and thalamic reticular neurons (RE). We perform a multi-scale study of thalamic responsiveness and its dependence on cell and brain states. Building upon existing single-cell experiments we show that: (1) Awake and sleep-like states can be defined via the absence/presence of the neuromodulator acetylcholine (ACh), which controls bursting in TC and RE. (2) Thalamic response to sensory stimuli is linear in awake state and becomes nonlinear in sleep state, while cortical input generates nonlinear response in both awake and sleep state. (3) Stimulus response is controlled by cortical input, which suppresses responsiveness in awake state while it wakes-up the thalamus in sleep state promoting a linear response. (4) Synaptic noise induces a global linear responsiveness, diminishing the difference in response between thalamic states. Finally, the model replicates spindle oscillations within a sleep-like state, exhibiting a qualitative change in activity and responsiveness. The development of this novel thalamic mean-field model provides a new tool for incorporating detailed thalamic dynamics in large scale brain simulations. Author summaryThe thalamus is a fascinating brain region that acts as the gate for information flow between the brain and the external world. While its role and importance in sensory and motor functions is well-established, recent studies suggest it also plays a key role in higher-order functions such as attention, sleep, memory, and cognition. However, understanding how the thalamus acts on all these functions is challenging due to its complex interactions at both the neuron level and within larger brain networks. In this study, we used a mathematical model grounded in experimental data that realistically captures the behavior of the thalamus, connecting the scales of individual neurons with larger populations. We found that the thalamus functions differently depending on whether the brain is in an awake or a sleep-like state: When awake, the thalamus processes sensory information in a straightforward way, resulting in a faithful information transmission to the cortex. But during sleep, only significant or important stimuli create a response. Importantly, this behavior can be controlled by cortical-like input and noise. With this study, we shed light on how the thalamus might modulate and interact with various brain functions across different scales and states. This research provides a deeper understanding of the thalamuss role and could inform future studies on sleep, attention, and related brain disorders.

neuroscience↗

bioRxiv2023

A mean-field model of gamma-frequency oscillations in networks of excitatory and inhibitory neurons

Gamma oscillations are widely seen in the cerebral cortex in different states of the wake-sleep cycle and are thought to play a role in sensory processing and cognition. Here, we study the emergence of gamma oscillations at two levels, in networks of spiking neurons, and a mean-field model. At the network level, we consider two different mechanisms to generate gamma oscillations and show that they are best seen if one takes into account the synaptic delay between neurons. At the mean-field level, we show that, by introducing delays, the mean-field can also produce gamma oscillations. The mean-field matches the mean activity of excitatory and inhibitory populations of the spiking network, as well as their oscillation frequencies, for both mechanisms. This mean-field model of gamma oscillations should be a useful tool to investigate large-scale interactions through gamma oscillations in the brain.

neuroscience↗

bioRxiv2023

Cortical mapping of the sensory response reveals strong brain-state dependence of the late component

Sensory information must be integrated across a distributed brain network for stimulus processing and perception. Recent studies have revealed specific spatiotemporal patterns of cortical activation for the early and late components of sensory-evoked responses, which are associated with stimulus features and perception, respectively. However, our understanding of how the brain state influences the sensory-evoked activation across the mouse cortex remains limited. In this study, we utilized isoflurane to modulate the brain state and conducted wide-field calcium imaging of Thy1-GCaMP6f mice to monitor the distributed activation evoked by multi-whisker stimulation. Our findings reveal that the level of anesthesia strongly shapes the spatiotemporal features and the functional connectivity of the sensory-activated network. As anesthesia levels decrease, we observe increasingly complex responses, accompanied by the emergence of the late component within the sensory-evoked response. The persistence of the late component under anesthesia raises new questions regarding the potential existence of perception during unconscious states.

neuroscience↗

bioRxiv2023

A whole-brain model of auditory discrimination

Whole-brain simulations have been proposed previously to simulate global properties such as brain states or functional connectivity. Here, our aim is to build a whole-brain model to simulate a simple cognitive paradigm involving multiple brain areas. We focus on auditory discrimination, using a paradigm designed for the macaque cortex. To model at the whole-brain scale, we use The Virtual Brain (TVB) [18] simulation environment. TVB is a computational framework which simulates the brain as a network of small brain regions, where each node models neuronal populations and the connectivity between nodes determines the pathway of information flow over the brain. We use Adaptive Exponential (AdEx) neuronal population models [4, 11] to describe each node. For the connectivity, we use the open-access CoCoMac connectivity dataset [2], which is a matrix containing the connection weights between the nodes. We focus on a cognitive task that mainly involves the prefrontal cortex (PFC). In the auditory discrimination task, our pipeline starts from the primary auditory cortex stimulated by the auditory signals, it is then modulated in the PFC so that the stimulus discrimination occurs, after competition. Finally, it ends in the primary motor cortex which outputs the neuronal activity determining the motor action. Because the AdEx mean-fields can provide access to neuronal activity or local field potentials, we think that the present model constitutes a useful tool to promote interactions between theory and experiments for simple cognitive tasks in macaque monkey.

neuroscience↗

bioRxiv2023

Asynchronous and slow-wave oscillatory states in connectome-based models of mouse, monkey and human cerebral cortex

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can lead to brain-wide models of mouse, macaque, and human. We illustrate this approach by showing the transition from wakefulness to sleep simulated with multi-scale models in the three species. We compare the level of synchrony between the three species and found that the mouse brain displays a higher overall synchrony of slow-waves compared to monkey and human brains. We show that these differences are due to the different delays of axonal signal propagation between regions associated to brain-size differences between the species. We also make the program code publicly available, which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mouse, monkey, and human.

neuroscience↗

bioRxiv2023

How Absence Seizures Impair Sensory Perception: Insights from Awake fMRI and Simulation Studies in Rats

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and- wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in GAERS, a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero-echo-time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.

neuroscience↗

bioRxiv2023

Dynamical properties of self-sustained and driven neural networks

In the awake brain, cerebral cortex displays asynchronous-irregular (AI) states, where neurons fire irregularly and with low correlation. Neural networks can display AI states that are self-sustained through recurrent connections, or in some cases, need an external input to sustain activity. In this paper, we aim at comparing these two dynamics and their consequences on responsiveness. We first show that the first Lyapunov exponent (FLE) can differ between self-sustained and driven networks, the former displaying a higher FLE than the late. Next, we show that this impact the dynamics of the system, leading to a tendency for self-sustained networks to be more responsive, both properties that can also be captured by mean-field models. We conclude that there is a dynamical and excitability difference between the two types of networks besides their apparent similar collective firing. The model predicts that calculating FLE from population activities in experimental data could provide a way to identify if real neural networks are self-sustained or driven.

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

A mean-field to capture asynchronous irregular dynamics of conductance-based networks of adaptive quadratic integrate-and-fire neuron models

Mean-field models are a class of models used in computational neuroscience to study the behaviour of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behaviour of "mean field" variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semi-analytical approach, has previously been applied to different types of single-neuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.

bioinformatics↗

bioRxiv2023

High-density exploration of activity states in a multi-area brain model

Biophysically-grounded whole-brain models were built recently using tractography data to interconnect multiple mesoscopic models, which can simulate the dynamics of neuronal populations with only a few equations. Mean-field models of neural populations, specifically the Adapting AdEx mean-field, was used for this purpose because it can integrate key biophysical mechanisms such as spike-frequency adaptation and its regulation at cellular scales, to the emergence of brain-scale dynamics. Using this approach, with the Virtual Brain (TVB) environment, it has been possible to model the macroscopic transitions between brain states, described by variation in brain-scale dynamics between asynchronous and rapid dynamics during conscious brain states, and synchronized slow-waves, with Up-and-Down state dynamics during unconscious brain states, emerging from mechanisms at the cellular level. Transitions between brain states are driven by changes in neuromodulation that can be due to intrinsic regulation during sleep-wake cycles or extrinsic factors such as anesthetics, which, in turn, affect spike-frequency adaptation. Here, we perform a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing, to thoroughly explore the properties of this model. We find that there is a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the parameter space of the TVB-AdEx model reveals features identified experimentally in sleep and anesthesia.

neuroscience↗

bioRxiv2023

A biologically plausible decision-making model based on interacting cortical columns

We present a novel decision-making model with two populations. Each population is composed of Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells in cortical layer 2/3. Each population votes for one of the two visual alternatives shown on a monitor in human and macaque experiments. The model is biophysically plausible since it is based on long-range cortico-cortical connections between the layer 2/3 populations. These connections are excitatory. They contact both Regularly Spiking and Fast Spiking cells. This long-range excitation is conflicted by an inhibition based on local connections within the populations. This configuration introduces a competition between the layer 2/3 populations, sufficient for making a decision to choose between two alternatives shown on the monitor. We integrate the model with a reward-driven learning mechanism. This allows the model to learn the optimal strategy maximizing the cumulative reward in the long term. We test the model on two decision-making tasks applied on human and macaque. This model elaborates certain biophysical details which were not considered by simpler phenomenological models proposed for similar decision-making tasks. Finally, the model can be embedded in a brain simulator such as The Virtual Brain to study decision-making in terms of large-scale brain dynamics.

neuroscience↗

bioRxiv2023

A Theoretical Formalization of Consequence-Based Decision-Making

Learning to make adaptive decisions depends on exploring options, experiencing their consequence, and reassessing ones strategy for the future. Although several studies have analyzed various aspects of value-based decision-making, most of them have focused on decisions in which gratification is cued and immediate. By contrast, how the brain gauges delayed consequence for decision-making remains poorly understood. To investigate this, we designed a decision-making task in which each decision altered future options. The task was organized in groups of consecutively dependent trials, and the participants were instructed to maximize the cumulative reward value within each group. In the absence of any explicit performance feedback, the participants had to test and internally assess specific criteria to make decisions. This task was designed to specifically study how the assessment of consequence forms and influences decisions as learning progresses. We analyzed behavior results to characterize individual differences in reaction times, decision strategies, and learning rates. We formalized this operation mathematically by means of a multi-layered decision-making model. By using a mean-field approximation, the first layer of the model described the dynamics of two populations of neurons which characterized the binary decision-making process. The other two layers modulated the decision-making policy by dynamically adapting an oversight learning mechanism. The model was validated by fitting each individual participants behavior and it faithfully predicted non-trivial patterns of decision-making, regardless of performance level. These findings provided an explanation to how delayed consequence may be computed and incorporated into the neural dynamics of decision-making, and to how learning occurs in the absence of explicit feedback.

neuroscience↗

bioRxiv2023

Experimental and computational analysis of REM sleep distributed cortical activity in mice

Although classically Rapid-Eye Movement (REM) sleep is thought to generate desynchronized activity similar to wakefulness, it was found that some brain regions can express Slow Wave activity (SWA), a pattern which is normally typical of slow-wave sleep. To investigate possible underlying mechanisms, we analyze experimental recordings and introduce a computational model of mice cerebral cortex in REM sleep. We characterized the patterns of slow-wave activity across somatosensory and motor areas, and find that the most prominent REM-related SWA is present in the primary (S1) and secondary (S2) somatosensory areas, more rarely seen in motor cortex, and absent from prefrontal cortex or hippocampus. The SWA also tends to be synchronized in S1 and S2. We next investigated possible mechanisms by using a computational model of the mouse brain consisting of adaptive Exponential (AdEx) mean-fields connected together according to the mouse connectome. To compare with experimental data, the local field potential is calculated in each mouse brain region. To reproduce the experiments, we had to assume a heterogeneous level of adaptation in different cortical regions during REM sleep. In these conditions, the model reproduces some of the experimental observations in the somato-motor areas and the other cortical areas. We then used the model to test how the presence of SWA affects cortical responsiveness. Indeed, we find that the areas expressing SWA have diminished evoked responses, which may participate to a diminished responsiveness during REM sleep.

neuroscience↗

bioRxiv2022

A multi-layer mean-field model for the cerebellar cortex: design, validation, and prediction

Mean-field (MF) models can be used to summarize in a few statistical parameters the salient properties of an inter-wired neuronal network incorporating different types of neurons and synapses along with their topological organization. MF are crucial to efficiently implement the modules of large-scale brain models maintaining the specificity of local microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar network (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF satisfactorily reproduced the average dynamics of the different neuronal populations in response to various input patterns and predicted the modulation of Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.

bioengineering↗

bioRxiv2022

Core neurons are structural crossroads of cortical dynamics

Cortical activity often consists of recurring population events of correlated neuronal firing and highly reproducible firing patterns. Because of their resemblance with attractor dynamics, the attractor model prevails today, although it has not been firmly demonstrated. Here, we used a unique dataset, with co-registered two-photon calcium imaging and electron microscopy of the same cortical tissue, to test the central assumption of attractor networks: recurrently active "core" neurons should be strongly interconnected. We report that, contrary to the attractor paradigm, core neurons have fewer weaker connections compared to other neurons. Instead, core neurons funnel the flow of multiple connectivity pathways. Computational models give a mechanistic account of these features showing that distance-dependent connectivity forms converging-diverging motifs and, at their funneling centers, core neurons are found. Thus, reproducible cortical activity and connectivity can be explained without postulating underlying attractor networks but rather by the existence of overlapping information flows.

neuroscience↗

bioRxiv2022

Response dynamics of spiking network models to incoming seizure-like perturbation.

Epilepsies are characterized by paroxysmal electrophysiological events and seizures, which can propagate across the brain. One of the main unsolved questions in epilepsy is how epileptic activity can invade normal tissue and thus propagate across the brain. To investigate this question, we propose three computational models at the neural network scale to study the underlying dynamics of seizure propagation, understand which specific features play a role, and relate them to clinical or experimental observations. We consider both the internal connectivity structure between neurons and the input properties in our characterization. We show that a paroxysmal input is sometimes controlled by the network while in other instances, it can entrain the network activity to itself produce paroxysmal activity, and thus will further propagate to efferent networks. We further show how the details of the network architecture are essential to determine this switch to a seizure-like regime. We investigated the nature of the instability involved and in particular found a central role for the inhibitory connectivity. We propose a probabilistic approach to the propagative/non-propagative scenarios, which may serve as a guide to control the seizure by using appropriate stimuli. SignificanceOur computational study shows the specific role that the inhibitory population can have and the possible dynamics regarding the propagation of seizure-like behavior in three different neuronal networks. We find that both structural and dynamical aspects are important to determine whether seizure activity invades the network. We show the existence of a specific time window favorable to the reversal of the seizure propagation by appropriate stimuli.

neuroscience↗