Modelling on the very large-scale connectome

TL;DR

This paper reviews the critical dynamics of simple models on large human brain connectomes, highlighting universality, Griffiths Phases, and synchronization phenomena, with implications for understanding brain activity patterns.

Contribution

It introduces a comprehensive analysis of dynamical models on large-scale connectomes, emphasizing the role of universality and rare-region effects in brain dynamics.

Findings

Identification of Griffiths Phases in brain network models

Synchronization phenomena consistent with experimental data

Dynamical scaling behavior observed in models

Abstract

In this review, we discuss critical dynamics of simple nonequilibrium models on large connectomes, obtained by diffusion MRI, representing the white matter of the human brain. In the first chapter, we overview graph theoretical and topological analysis of these networks, pointing out that universality allows selecting a representative network, the KKI-18, which has been used for dynamical simulation. The critical and sub-critical behaviour of simple, two- or three-state threshold models is discussed with special emphasis on rare-region effects leading to robust Griffiths Phases (GP). Numerical results of synchronization phenomena, studied by the Kuramoto model, are also shown, leading to a continuous analog of the GP, termed frustrated synchronization. The models presented here exhibit dynamical scaling behaviour with exponents in agreement with brain experimental data if local homeostasis is provided.