A neuronal network model of interictal and recurrent ictal activity

TL;DR

This paper introduces a neuronal network model that explains the transition from interictal to ictal states in seizures, capturing key dynamical features and predicting early warning signals.

Contribution

It presents a novel neuronal network model based on saddle-node bifurcation to explain seizure transitions and interactions between networks causing recurrent seizures.

Findings

Model captures interictal and ictal dynamical features

Predicts early warning signals of seizure onset

Shows recurrent seizures emerge from network interactions

Abstract

We propose a neuronal network model which undergoes a saddle-node bifurcation on an invariant circle as the mechanism of the transition from the interictal to the ictal (seizure) state. In the vicinity of this transition, the model captures important dynamical features of both interictal and ictal states. We study the nature of interictal spikes and early warnings of the transition predicted by this model. We further demonstrate that recurrent seizures emerge due to the interaction between two networks.