Brain network clustering with information flow motifs

Marcus Märtens, Jil Meier, Arjan Hillebrand, Prejaas Tewarie, Piet Van Mieghem

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
28 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number25
Pages (from-to)1-18
Number of pages18
JournalApplied Network Science
Volume2
DOIs
Publication statusPublished - 2017

Keywords

  • Network motifs
  • Network clustering
  • Brain networks
  • Information flow
  • Effective connectivity
  • OA-Fund TU Delft

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