Modeling seizures: From single neurons to networks

Depannemaecker, Damien; Destexhe, Alain; Jirsa, Viktor K.; Bernard, Christophe

doi:10.1016/j.seizure.2021.06.015

Cited by 23 publications

(17 citation statements)

References 66 publications

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“…Such microelectrode recordings showed that neuronal activity during seizures does not necessarily correspond to synchronized spikes over the whole neuron population, as previously modeled ( Soltesz and Staley, 2008 ), including models at different scales from cellular to whole-brain levels ( Depannemaecker et al, 2021 , 2022 ). In fact, it turns out that the dynamics of neural networks during seizures are more complex ( Jiruska et al, 2013 ) and still poorly understood.…”

Section: Introductionsupporting

confidence: 60%

A Model for the Propagation of Seizure Activity in Normal Brain Tissue

Depannemaecker

Carlu

Bouté

et al. 2022

eNeuro

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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 consider 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 lead 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.Significance: Our 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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Section: Introductionsupporting

confidence: 60%

A Model for the Propagation of Seizure Activity in Normal Brain Tissue

Depannemaecker

Carlu

Bouté

et al. 2022

eNeuro

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“…Since seizures can be triggered in most brain regions from most species, it has been proposed that simple mathematical rules should be sufficient to describe such a basic form of physiological activity, particularly their dynamics. Several conceptual frameworks have been proposed to explain seizure dynamics (Depannemaecker et al, 2021;Naze, 2015;Naze et al, 2015;Soltesz & Staley, 2008;Staley, 2015;Stefanescu et al, 2012;Y. Wang et al, 2017;Wendling et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A unified physiological framework of transitions between seizures, sustained ictal activity and depolarization block at the single neuron level

et al. 2022

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The majority of seizures recorded in humans and experimental animal models can be described by a generic phenomenological mathematical model, the Epileptor. In this model, seizure-like events (SLEs) are driven by a slow variable and occur via saddle node (SN) and homoclinic bifurcations at seizure onset and offset, respectively. Here we investigated SLEs at the single cell level using a biophysically relevant neuron model including a slow/fast system of four equations. The two equations for the slow subsystem describe ion concentration variations and the two equations of the fast subsystem delineate the electrophysiological activities of the neuron. Using extracellular K+ as a slow variable, we report that SLEs with SN/homoclinic bifurcations can readily occur at the single cell level when extracellular K+ reaches a critical value. In patients and experimental models, seizures can also evolve into sustained ictal activity (SIA) and depolarization block (DB), activities which are also parts of the dynamic repertoire of the Epileptor. Increasing extracellular concentration of K+ in the model to values found during experimental status epilepticus and DB, we show that SIA and DB can also occur at the single cell level. Thus, seizures, SIA, and DB, which have been first identified as network events, can exist in a unified framework of a biophysical model at the single neuron level and exhibit similar dynamics as observed in the Epileptor.Author Summary: Epilepsy is a neurological disorder characterized by the occurrence of seizures. Seizures have been characterized in patients in experimental models at both macroscopic and microscopic scales using electrophysiological recordings. Experimental works allowed the establishment of a detailed taxonomy of seizures, which can be described by mathematical models. We can distinguish two main types of models. Phenomenological (generic) models have few parameters and variables and permit detailed dynamical studies often capturing a majority of activities observed in experimental conditions. But they also have abstract parameters, making biological interpretation difficult. Biophysical models, on the other hand, use a large number of variables and parameters due to the complexity of the biological systems they represent. Because of the multiplicity of solutions, it is difficult to extract general dynamical rules. In the present work, we integrate both approaches and reduce a detailed biophysical model to sufficiently low-dimensional equations, and thus maintaining the advantages of a generic model. We propose, at the single cell level, a unified framework of different pathological activities that are seizures, depolarization block, and sustained ictal activity.

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“…This multi-omic study allows authors to revisit the data accumulated in recent years and points to the involvement of a wide variety of mechanisms and signaling pathways that underlie the pathophysiology of MTLE-HS. Depannemaecker and collaborators [24], present various modeling approaches spanning different scales, from single neurons to large-scale networks providing a basis for theorizing certain properties of seizures and for their classification according to their dynamical properties at onset and offset.…”

Section: Celebrating the Lasse 15th Editionmentioning

confidence: 99%