Toward a theory of coactivation patterns in excitable neural networks.

Arnaud Messé,Marc-Thorsten Hütt,Claus C Hilgetag,Daniele Marinazzo

doi:10.1371/journal.pcbi.1006084

Abstract

The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is of central importance in brain network science. It is an open question, however, how the SC-FC relationship depends on specific topological features of brain networks or the models used for describing neural dynamics. Using a basic but general model of discrete excitable units that follow a susceptible—excited—refractory activity cycle (SER model), we here analyze how the network activity patterns underlying functional connectivity are shaped by the characteristic topological features of the network. We develop an analytical framework for describing the contribution of essential topological elements, such as common inputs and pacemakers, to the coactivation of nodes, and demonstrate the validity of the approach by comparison of the analytical predictions with numerical simulations of various exemplar networks. The present analytic framework may serve as an initial step for the mechanistic understanding of the contributions of brain network topology to brain dynamics.

Highlights

The network perspective has become a powerful and central approach for representing and analyzing complex biological systems, spanning from the study of interacting genes to neuronal assemblies [1]
Functional connectivity, as reflected in the statistical dependencies of distributed activity, is widely used to probe the organization of complex systems such as the brain. While this measure has been helpful for characterizing brain states and highlighting alterations of brain dynamics in various diseases, the mechanisms underlying the generation of functional connectivity (FC) patterns remain poorly understood
Using a minimalist model of excitation, we investigate how the topology of excitable neural networks contributes to FC

Summary

Introduction

The network perspective has become a powerful and central approach for representing and analyzing complex biological systems, spanning from the study of interacting genes to neuronal assemblies [1]. The choice of the specific computational model does not seem to crucially affect the resulting global patterns of functional connectivity, as highly diverse models applied on the same network may result in very similar fits of the empirical FC [13, 14]. This finding suggests that the crucial aspect in producing functional connectivity may not be the specific local models, but the characteristic topology of the underlying SC. Ubiquitous topological features of brain networks are, for instance, modules (formed by nodes that are more frequently connected among each other than to the rest of the network) and hubs (central nodes that have more connections than average network nodes) [15, 16]

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