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

Abstract

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.

Microscale Neuronal Activity Collectively Drives Chaotic and Inflexible Dynamics at the Macroscale in Seizures

“…This study used whole-brain neuronal network modelling to infer the microscale alterations driving generalised seizure dynamics. Large scale models of epileptic networks have already demonstrated key microscale properties underlying seizures – such as inhibition exhaustion ( Liou et al, 2020 ), excessive inhibitory to inhibitory coupling ( Depannemaecker et al, 2022 ) and hub neurons ( Hadjiabadi et al, 2021 ; Morgan & Soltesz, 2008 ). Here, we studied 3 key parameters – network connectivity, synaptic weights and intrinsic excitability, drawing inspiration from theoretical models of criticality ( Zeraati et al, 2021 ), to relate predictions from criticality theory with observed seizure avalanche dynamics.…”

From phenomenological to biophysical models of seizures

“…To model a larger scale, it is necessary to use spiking network models, which represent large populations of neurons and their interactions [74,16,14,75,20,84,97]. Some of these models can be constrained by dynamical properties [62].…”

“…Other models make use of the different biophysical properties described for different brain regions, such as the thalomo-cortical loop or the hippocampus [21,22,89,14,80]. Such models are useful to understand the onset [97] and the propagation of seizures in populations of cells [100,74,16]. Spiking network models form the basis for building population models [99,7]; a useful tool to study seizure network properties [73].…”

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

“…These networks are made up of an excitatory population and an inhibitory population in similar proportions to what is observed in the cortex. These networks are useful for studying complex dynamics associated with the large dimensions of these systems [Carlu et al, 2020, Depannemaecker et al, 2022]. However, having a large number of dimensions can be a limiting factor for the understanding of the representation of its dynamics [Depannemaecker et al, 2023].…”