4.7 Article

Functional connectivity of distant cortical regions: Role of remote synchronization and symmetry in interactions

Journal

NEUROIMAGE
Volume 97, Issue -, Pages 1-8

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2014.04.039

Keywords

Computational model; Remote synchronization; Functional connectivity; Resting-state networks

Funding

  1. BMBF [01Q1001B]

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\ Functional MRI (fMRI) of ongoing brain activity at rest i.e. without any overt-directed behavior has revealed patterns of coherent activity, so called resting-state functional networks. The dynamical organization of nodes into these functional networks is closely related to the underlying structural connections. However, functional correlations have also been observed between cortical regions without apparent neural links, and mechanisms generating functional connectivity between distant cortical regions are largely unknown. It has been suggested that indirect connections and collective effects governed by the network properties of the cortex play a significant role. We use numerical simulations to investigate these mechanisms with reference to remote synchronization and network symmetry. Neural activity and the inferred hemodynamic response of the network nodes are modeled as sets of self-sustained oscillators, which are embedded in topologies of complex functional brain interactions. The coupling topology is based on connectivity maps derived from fMRI and DTI experiments. Consequently, our network model includes important information on whether direct or indirect neural connections exist between functionally associated regions. In the simulated functional networks, remote synchrony between pairs of nodes clearly arises from symmetry in the interactions, which are quantified by the number of shared neighbors. A larger joint neighborhood positively correlates with a higher level of synchrony. Therefore, our results indicate that a large overlapping neighborhood in complex networks of brain interactions gives rise to functional similarity between distant cortical regions. (C) 2014 Elsevier Inc. All rights reserved.

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