Chimera States and Seizures in a Mouse Neuronal Model

TL;DR

This study models mouse brain activity using chimera states in a neuronal network, revealing how mixed synchrony patterns can mimic epileptic seizures, with implications for understanding seizure dynamics.

Contribution

It introduces a novel neuronal network model based on the mouse connectome that demonstrates chimera states as a potential mechanism for seizures.

Findings

Chimera states emerge in the model under specific connection parameters.

The model exhibits epileptiform activity resembling seizures.

Persistent chimera states are observed across a range of parameters.

Abstract

Chimera states---the coexistence of synchrony and asynchrony in a nonlocally-coupled network of identical oscillators---are often used as a model framework for epileptic seizures. Here, we explore the dynamics of chimera states in a network of modified Hindmarsh-Rose neurons, configured to reflect the graph of the mesoscale mouse connectome. Our model produces superficially epileptiform activity converging on persistent chimera states in a large region of a two-parameter space governing connections (a) between subcortices within a cortex and (b) between cortices. Our findings contribute to a growing body of literature suggesting mathematical models can qualitatively reproduce epileptic seizure dynamics.