Dynamic changes in network synchrony reveal resting-state functional networks

TL;DR

This study uses neural models to explore how large-scale brain networks dynamically organize through changes in synchrony, revealing mechanisms behind resting-state functional connectivity.

Contribution

It introduces a simulation framework combining neural models and realistic brain network topology to analyze dynamic synchrony in resting-state networks.

Findings

Patterns of correlated activity emerge from dynamical properties that maximize synchrony.

Fast changes in network synchrony demonstrate the flexibility of large-scale brain dynamics.

Simulated network dynamics reflect complex temporal patterns observed in resting-state fMRI.

Abstract

Experimental fMRI studies have shown that spontaneous brain activity i.e. in the absence of any external input, exhibit complex spatial and temporal patterns of co-activity between segregated brain regions. These so-called large-scale resting-state functional connectivity networks represent dynamically organized neural assemblies interacting with each other in a complex way. It has been suggested that looking at the dynamical properties of complex patterns of brain functional co-activity may reveal neural mechanisms underlying the dynamic changes in functional interactions. Here, we examine how global network dynamics is shaped by different network configurations, derived from realistic brain functional interactions. We focus on two main dynamics measures: synchrony and variations in synchrony. Neural activity and the inferred hemodynamic response of the network nodes are simulated using system of 90 FitzHugh-Nagumo neural models subject to system noise and time-delayed interactions. These models are embedded into the topology of the complex brain functional interactions, whose architecture is additionally reduced to its main structural pathways. In the simulated functional networks, patterns of correlated regional activity clearly arise from dynamical properties that maximize synchrony and variations in synchrony. Our results on the fast changes of the level of the network synchrony also show how flexible changes in the large-scale network dynamics could be.