Chimera states in brain networks: empirical neural vs. modular fractal connectivity

TL;DR

This paper investigates chimera states in brain networks using FitzHugh-Nagumo neuron models, comparing empirical neural connectivity with modular fractal networks to understand synchronization patterns relevant to epilepsy.

Contribution

It introduces a comparative analysis of chimera states in empirical and fractal brain network models, highlighting their stability and implications for epileptic seizure dynamics and surgical interventions.

Findings

Chimera states are present in both network types.

Node removal affects network synchronizability.

Simulations provide insights into seizure dynamics.

Abstract

Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states, and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.