ArticleApplied network science2017
Brain network clustering with information flow motifs.
Article in Applied network science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Modelling a multiplex brain network by local transfer entropy.Scientific reports · 2021Article
- Motifs enable communication efficiency and fault-tolerance in transcriptional networks.Scientific reports · 2020Article
- Multiplex network motifs as building blocks of corporate networks.Applied network science · 2018Article
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Authors and funding
5 authors.
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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.
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