Motif-based analysis of effective connectivity in brain networks

Conference paper (2017)

Authors

J.M. Meier Network Architectures and Services - EEMCS
M. Märtens Network Architectures and Services - EEMCS
A. Hillebrand Amsterdam UMC
P. Tewarie University of Nottingham, Amsterdam UMC
P.F.A. Van Mieghem Network Architectures and Services - EEMCS
Research Group
Network Architectures and Services (EEMCS) (TU Delft)
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Published Date

2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Network Architectures and Services

Abstract

Network science has widely studied the properties of brain networks. Recent work has observed a global back-to-front pattern of information flow for higher frequency bands in magnetoencephalography data. However, the effective connectivity at a local level remains yet to be analyzed. On a local level, the building blocks of all networks are motifs. In this study, we exploit the measure of dPTE to analyze motifs of the estimated effective connectivity networks. We find that some 3- and 4-motifs, the bidirectional two-hop path and its extended 4-node versions, are significantly overexpressed in the analyzed networks in comparison with random networks. With a recently developed motif-based clustering algorithm we separate the effective connectivity network in two main clusters which reveal its higher-order organization with a strong information flow between posterior hubs and anterior regions.

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