Complex synchronization patterns in the human connectome network

TL;DR

This paper numerically investigates synchronization in the human connectome, revealing a broad dynamic regime characterized by metastable states and the influence of noise, which may relate to brain function and dysfunction.

Contribution

It provides a detailed characterization of a broad dynamic regime in the human connectome, highlighting the role of hierarchical modularity and noise in synchronization dynamics.

Findings

Identification of a broad dynamic regime similar to a Griffiths phase

Synchronization trapped in metastable local coherence states

Noise enables escape from attractors, fostering complex activity patterns

Abstract

A major challenge in neuroscience is posed by the need for relating the emerging dynamical features of brain activity with the underlying modular structure of neural connections, hierarchically organized throughout several scales. The spontaneous emergence of coherence and synchronization across such scales is crucial to neural function, while its anomalies often relate to pathological conditions. Here we provide a numerical study of synchronization dynamics in the human connectome network. Our purpose is to provide a detailed characterization of the recently uncovered broad dynamic regime, interposed between order and disorder, which stems from the hierarchical modular organization of the human connectome. In this regime -similar in essence to a Griffiths phase- synchronization dynamics are trapped within metastable attractors of local coherence. Here we explore the role of noise, as an effective description of external perturbations, and discuss how its presence accounts for the ability of the system to escape intermittently from such attractors and explore complex dynamic repertoires of locally coherent states, in analogy with experimentally recorded patterns of cerebral activity.