Brain network clustering with information flow motifs

被引：18

作者：

Märtens M. ^{[1
]}

Meier J. ^{[1
]}

Hillebrand A. ^{[2
]}

Tewarie P. ^{[3
]}

Van Mieghem P. ^{[1
]}

机构：

[1] Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft

[2] Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam

[3] Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham

来源：

Applied Network Science | / 2卷 / 1期

关键词：

Brain networks; Effective connectivity; Information flow; Network clustering; Network motifs;

D O I：

10.1007/s41109-017-0046-z

中图分类号：

学科分类号：

摘要：

Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands. Motifs are the building blocks of networks on this level and have previously been identified as important features for healthy and abnormal brain function. In this study, we present a network construction that enables us to search and analyze motifs in different frequency bands. We give evidence that the bi-directional two-hop path is the most important motif for the information flow in functional brain networks. A clustering based on this motif exposes a spatially coherent yet frequency-dependent sub-division between the posterior, occipital and frontal brain regions. © 2017, The Author(s).

引用

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