Biology subjects

Depannemaecker, D.

Publications and source records attributed to Depannemaecker, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Slow Ionic Dynamics Reshape Bistability in a Biophysical Mean-Field Model of Brain Activity

Brain states such as sleep, seizures, and resting wakefulness are often described in terms of transitions between distinct population states. Biophysically inspired mean-field models that couple neuronal firing statistics to slow ionic homeostasis provide a mechanistic bridge between detailed single-neuron physiology and macroscopic brain activity. Here we examine this issue in a biophysical mean-field model of cortical populations driven by potassium exchange and compare its dynamical repertoire with a matched spiking network across six physiological and pathological regimes. The fast subsystem contains a substantial region in which low- and high-activity fixed points coexist, but this conditional bistability is not inherited by the physiological resting branch of the complete slow-fast system. A generic fast-slow calculation identifies how feedback through a dynamical ionic variable changes the stability conditions of the fast subsystem, and an exact equilibrium reduction of the KIonEx-MF equations yields a single equilibrium on the physiological resting branch over the range examined. This resting state is excitable: perturbations generate intermittent synchronized events in both the mean-field and spiking-network descriptions. The results show how slow ionic dynamics can reshape conclusions drawn from a frozen fast subsystem and provide noise-driven excitability as an alternative route to spontaneous population transitions.

neuroscience↗

bioRxiv2026

Structural topology shapes regional roles in neuronal avalanche initiation and termination

Whole-brain dynamics unfold as fast, discrete, transient events known as, neuronal avalanches, whose propagation is constrained by the connectome, that is, the large-scale scaffolding of structural connections wiring the brain. However, the role of the connectome in determining the topographies of the start and end of these transient cascades remains unclear. Leveraging resting-state Magnetoencephalography (MEG) recordings and diffusion magnetic resonance imaging (dMRI) tractography across 42 healthy individuals, we examined whether the structural architecture of white matter tracts dictates the spatial origins and the end of cortical avalanches. We found that the probability of triggering and finishing an avalanche is strongly related to the structural nodal strength, showing a positive correlation that is highly robust to non-parametric spatial permutation testing and bootstrapping. This structure-function coupling is highly stable and robust across different activation thresholds, demonstrating that the choice of avalanche extraction threshold does not drive or alter our main findings. Collectively, our results suggest that structural properties determine a region's propensity to initiate and terminate neuronal avalanches.

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bioRxiv2026

The Internal Structure of Seizures Reflects Animal-Specific Trajectories of Disease Progression and Circadian Modulation in Epileptic Mice

Epileptic seizures arise from the abnormal, sustained recruitment of neuronal populations, yet the temporal organisation of activity within individual seizures remains poorly understood. We hypothesised that seizures follow structured trajectories through a restricted state space rather than random patterns of activity. To test this idea, we analysed continuous long-term EEG recordings from six mice with pilocarpine-induced temporal lobe epilepsy and developed a symbolic framework that transforms seizure activity into sequences of discrete burst states. Across more than one thousand spontaneous seizures, we found that seizures are organised by sparse, animal-specific transition rules linking a small repertoire of recurrent burst motifs. These rules evolve over the course of epilepsy: early seizures display substantial variability in burst composition, and, to a smaller but statistically significant degree, in transition structure, whereas later seizures become more stereotyped in which burst types they use, a pattern suggestive of consolidation of the epileptic network into a stable dynamical regime. Seizure microstructure was further modulated by circadian phase, indicating that biological rhythms influence not only when seizures occur but also how they unfold. Moreover, burst sequences exhibited higher-order temporal dependencies that could not be explained by first-order Markov statistics alone. Together, these results reveal an evolving seizure grammar that links ictal dynamics to disease progression and circadian regulation, establishing symbolic dynamics as a powerful framework for quantifying the internal organisation of seizures.

neuroscience↗

bioRxiv2026

Temporal persistence and structural organization of neuronal avalanche dynamics

Brain activity can be understood as a sequence of neuronal avalanches, i.e., transient episodes of coordinated activation that emerge across scales, from individual neurons and local networks to whole-brain dynamics. Avalanches are typically characterized by features such as size, duration, number of active components, and the silent time separating consecutive events. Although these features have been extensively characterized through their marginal distributions, their temporal organization and dependence on the underlying brain architecture remain poorly understood, leaving us without a framework for embedding neuronal avalanches within slower brain dynamics. Here, we analyzed eyes-closed resting-state magnetoencephalography recordings and the corresponding structural connectomes from 30 healthy participants to investigate the dynamics of avalanche sizes and silent times. We found that large avalanches preferentially followed short silent times, whereas small avalanches were more likely to occur after long silent periods. Based on the empirical joint distributions of avalanche size and silent time, we could define four types of events occurring above chance levels (avalanche large or small, preceding pause long or short). Mixed categories, combining a small value of one feature with a large value of the other, occurred more frequently than expected, while same-category events happened less often than chance. Furthermore, consecutive events tended to remain in the same category, a phenomenon referred to as persistence. We next investigated whether a brain region's connectivity profile shapes its propensity to participate in avalanches of different sizes. More strongly connected regions participated most often in small avalanches, whereas weakly connected regions were preferentially recruited during large avalanches. This pattern may reflect the greater sensitivity of highly connected hubs to fluctuations propagating through the network, resulting in frequent but spatially contained events. By contrast, the recruitment of more peripheral regions may require broader and stronger collective activity, occurring only during rarer, large-scale avalanches. In contrast, regional participation showed no clear association with the silent time preceding an avalanche. Together, these findings show that neuronal avalanches are neither temporally independent nor anatomically unconstrained: their sequence retains a memory of preceding events, while structural topology shapes which regions are recruited as avalanches grow. By connecting avalanche dynamics with slower temporal organization and the structural connectome, our results provide a multiscale framework for understanding how transient events are embedded within ongoing brain activity.

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bioRxiv2026

BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models

Mean-field models are extensively used in large-scale brain simulations because they provide a wieldy description of population dynamics while preserving key features of neural activity. Despite their widespread adoption, no common and reproducible methodology currently exists to systematically derive and validate mean-field models starting from biologically grounded single neuron dynamics. As a result, implementations are often ad hoc, difficult to reproduce and rarely reusable. Here we introduce BRIDGE, a modular, open-source Python pipeline that enables the bottom-up reconstruction, analysis, validation, and simulation of mean-field models from single neurons. The framework integrates single neurons modelling, network simulations, extraction of population statistics, parameters analysis, quantitative comparisons between spiking neural networks and corresponding mean-field representations, and simulation of network of mean-fields. Its flexible architecture allows users to incorporate different neuron models and to generate region-specific or state-dependent mean-field formulations. BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/742067v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1124404org.highwire.dtl.DTLVardef@2f8b2aorg.highwire.dtl.DTLVardef@1598f37org.highwire.dtl.DTLVardef@c9814b_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2026

Departure from OFF-State Microstate Dynamics Tracks Levodopa Response in Parkinson's Disease

Although dopaminergic therapies in Parkinsons disease primarily restore dopamine within nigrostriatal circuits, symptoms are better indexed by whole-brain dynamics than by local activity. In this manuscript, we hypothesize that L-Dopa therapy affects whole-brain dynamics alike, which in turn relate to clinical improvement. To test this hypothesis, we conducted a repeated-measures, source-reconstructed MEG study in 13 bradykinetic-dominant PD patients, recording resting-state cortical activity OFF medication and [~]1 hour after levodopa administration (ON). We characterize brain dynamics using a microstate framework, in which transition probabilities between microstates are used to contrast pathological OFF-state dynamics with those in the ON-state. Microstate dynamics were stable within medication states but reconfigured by L-Dopa, with greater departures from the OFF-state pattern associated, at the individual level, with larger clinical improvements. Our results suggest that individualized changes in microstate dynamics may serve as a neurophysiological marker of dopaminergic responsiveness.

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bioRxiv2026

A microcircuit model of astrocytic potassium buffering and neural synchronization

Neural synchronization is fundamental to brain function and, when it becomes excessive, underlies pathological conditions such as epilepsy. Among brain regions, the temporal lobes, and the hippocampus in particular, exhibit the highest epileptogenic potential, with mesial temporal lobe epilepsy representing the most prevalent form of the condition in humans. Within the hippocampus, extracellular potassium dynamics are central to non-synaptic epileptiform activity, and astrocytic potassium buffering mechanisms have emerged as key regulators of network excitability. Yet the specific contributions of astrocytic gap-junction coupling and potassium spatial buffering to neuronal synchronization across different spatial scales remain poorly understood. To address this gap, we developed a microcircuit biophysical model consisting of two astrocyte-neuron modules, each comprising one astrocyte coupled to five neurons. Astrocyte-neuron interactions are mediated exclusively through shared extracellular potassium dynamics. Using a reduced astrocyte model that captures both local membrane and syncytial potassium buffering, we systematically investigated how astrocytic potassium handling shapes neuronal activity patterns and inter-module synchronization. Our results demonstrate that astrocytes prevent the emergence of pathological states -- such as sustained ictal activity and depolarization block, by stabilizing extracellular potassium levels. Furthermore, we show that astrocytic gap-junction coupling strength critically regulates phase synchronization between neuronal modules: stronger coupling promotes inter-module synchrony under physiological conditions, whereas impaired astrocytic function drives networks toward pathological hypersynchronization when extracellular potassium is elevated. These findings support the hypothesis that astrocytic networks impose modularity on hippocampal neuronal assemblies, and suggest that astrocytic connexins may represent a relevant therapeutic target in epilepsy and other disorders characterized by aberrant neural synchronization. Author summary

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bioRxiv2026

Circadian Modulation of Excitation-Inhibition Balance Drives Ictal Transitions in a Mechanistic Model of Epileptic Networks

Epileptic seizures emerge from pathological synchronization in neuronal networks and are strongly influenced by circadian rhythms. Here, we developed a computational framework to investigate how circadian modulation of excitation/inhibition (E/I) balance shapes transitions from physiological to pathological activity. The model consists of interacting excitatory and inhibitory populations containing varying proportions of impaired neurons with altered intrinsic excitability. Circadian effects were incorporated through modulation of synaptic time constants, mimicking daily fluctuations in E/I dynamics. Network activity was characterized using firing rates and the Spike Time Tiling Coefficient (STTC), enabling simultaneous assessment of excitability and synchrony. Our results show that seizure-like dynamics arise from nonlinear interactions between network composition, neuronal impairment, and synaptic kinetics. Distinct dynamic regimes emerged, separated by sharp transitions in synchrony and activity patterns. These findings provide a mechanistic link between circadian regulation and seizure susceptibility, supporting the development of chronotherapy approaches for epilepsy.

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bioRxiv2025

Intra- and Interhemispheric Signatures of Criticality at the Onset of Synchronization

The cerebral cortex must flexibly alternate between locally segregated activity that supports specialization and long-range interactions that enable integration. How cortical networks balance these competing demands remains unclear. We propose that fluctuations around a critical point between ordered and disordered phases provide a natural framework to understand coordinated neuronal activity across scales. Using simultaneous spiking recordings from the left and right prefrontal cortex (PFC) of freely behaving rats, we found that signatures of criticality emerged at the onset of neuronal synchronization, both locally within individual hemispheres and globally across the combined population. Over time, cortical activity explored a continuum of intra-and inter-hemispheric synchrony levels, including states in which neurons were locally desynchronized yet maintained interhemispheric coupling. A computational model operating near its critical regime reproduced these empirical patterns, capturing the characteristic relationship between local and long-range coordination. These results suggest that cortical networks achieve flexible transitions between local and global computation by fluctuating around a critical regime.

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bioRxiv2025

The Connectome Modulates Critical Brain Dynamics Across Local and Global Scales

Neuronal activity in the brain has been hypothesized to operate near criticality-a dynamical regime poised between order and disorder that maximizes information processing, adaptability, and dynamic range. While criticality has been extensively studied at local scales (within neuronal populations) and at global scales (across interacting brain regions), the interplay between these levels remains poorly understood. Here, we propose a multiscale computational framework that bridges local and global criticality within a single, mechanistic model. At the mesoscopic level, individual brain regions are represented by neural mass models tuned near the transition between asynchronous and synchronous regimes. These regions are then coupled via an empirically derived mouse connectome to investigate how structural connectivity shapes the emergence of large-scale coordination. We show that (i) local near critical dynamics for an isolated brain region can be faithfully reproduced within a mean-field model framework, (ii) local distance to criticality is modulated by long-range coupling, (iii) whole-brain simulations reveal non-linear gradients of timescales and heterogeneous shifts towards/away from local criticality, and (iv) global criticality, manifested in scale-free avalanche distributions and optimal functional connectivity, emerges when local populations are locally tuned near criticality and coupled within an optimal range. These results demonstrate that local and global criticality are dynamically intertwined but not directly aligned, and that their relationship depends on the underlying structural connectivity. Our multiscale modeling framework provides a tractable tool for generating testable hypotheses on how brain criticality co-arises across scales and how it may be modulated in health and disease.

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bioRxiv2025

Next generation neural mass model with dopamine modulation mediated by D1-type receptors

Neuromodulation is a complex process in which chemical substances modulate brain activity, allowing its rich repertoire of behaviors. Among these substances, dopamine has a preponderant role, being involved in several mechanisms. Moreover, dysfunctions in the dopamine connections has been observed in pathology, such as Parkinsons disease and schizophrenia. To investigate the mechanism of neuromodulation, we expand a previously proposed mean-field formalism, that describes the average activity of a neural population, by adding the effect of dopamine modulation. This mean-field reduction allows for a direct comparison with the underlying neural network to test its ability to qualitatively reproduce population behavior. The resulting mathematical framework is able to capture network activity in distinct dynamical regimes and transitions between them. Thus, this approach provides a reliable foundation for the development of personalized medicine tools to study how the effect of dopamine modulation on single brain region affects whole brain behavior.

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bioRxiv2025

Degeneracy and bifurcation diversity at the single neuron level

Degeneracy--the capacity of structurally distinct systems to achieve similar functions--is a fundamental property of living organisms, enabling adaptability and resilience. Neurons can maintain stable activity patterns despite wide variability in ion channel expression, highlighting how distinct internal configurations can yield equivalent electrophysiological behaviors. However, it remains unclear how degeneracy manifests in the context of bifurcations, the critical transitions between activity regimes that underlie phenomena such as different firing patterns, seizures and depolarization block. Here, we investigate how different biophysical parameter sets can lead to equivalent bifurcation sequences. Using a minimal neuron model with three variable conductances, we systematically explore how changes in extracellular potassium--mimicking physiological and pathological conditions--affect neuronal dynamics. Our results reveal that neurons with distinct intrinsic properties can traverse the same bifurcation pathways, entering regimes of bursting, seizure-like activity, and depolarization block. Yet, the specific parameter set determines the sensitivity and thresholds for these transitions. This work clarifies how degeneracy extends to the dynamical landscape of neurons, with implications for understanding resilience and vulnerability in neural circuits. 1 Author SummaryBiophysical models expressed through differential equations can reproduce the dynamics of biological systems with varying levels of detail. By changing model parameters, simulations can capture the natural variability that allows biological systems to achieve the same behavior through different mechanisms. This property, called degeneracy, underlies the robustness of living systems to internal and external perturbations. In this work, we identify dis-tinct behaviors in single-neuron models and link them to electrophysiological patterns observed under elevated extracellular potassium during epileptic events. These patterns correspond to specific classes of spontaneous bursting activity within the framework of nonlinear dynamics. We show that different combinations of conductance parameters, at a fixed extracellular potassium level, can generate the same class of patterns, defining degeneracy groups. Finally, we assess the robustness of these groups by analyzing how they respond to changes in extracellular potassium. Our findings provide a basis for studying variability and resilience in the dynamics of more complex neuronal systems.

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bioRxiv2024

Principles and Operation of Virtual Brain Twins

Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based trials remain underutilized in neuroscience due to the brains complexity. A Virtual Brain Twin (VBT) is a personalized digital replica of an individuals brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This manuscript outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, epilepsy, and multiple sclerosis. Finally, we discuss potential extensions to other neurological disorders, such as Parkinsons disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brain-machine integration. "The brain is conceived as a self-organizing system operating close to instabilities where its activities are governed by collective variables, the order parameters, that enslave the individual parts, i.e., the neurons." Professor Hermann Haken (1927-2024).

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bioRxiv2024

Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models

To model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.

neuroscience↗

bioRxiv2024

A neural mass model with neuromodulation

The study of brain activity and its function requires the development of computational models alongside experimental investigations to explore different effects of multiple mechanisms at play in the central nervous system. Chemical neuromodulators, such as dopamine, play central roles in regulating the dynamics of neuronal populations. In this work, we propose a modular framework to capture the effects of neuromodulators at the neural mass level. Using this framework, we formulate a specific model for dopamine dynamics affecting D1-type receptors. We detail the dynamical repertoire associated with dopamine concentration evolution and characterize the transitions across qualitatively different oscillatory states as a function of relevant parameters, along with the variations in frequency of the emergent multi-scale oscillations.

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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 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↗

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.

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