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Stochastic synchronization of dynamics on the human connectome.

Abstract：

:Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network's topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome's subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Pang JC,Gollo LL,Roberts JA

doi

10.1016/j.neuroimage.2021.117738

subject

Has Abstract

pub_date

2021-01-14 00:00:00

pages

117738

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(21)00015-X

journal_volume

229

pub_type

杂志文章

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