Motif-based analysis of effective connectivity in brain networks

J. Meier, M. Märtens, A. Hillebrand, P. Tewarie, P. Van Mieghem

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)685-696
Number of pages12
JournalStudies in Computational Intelligence
Volume693
DOIs
Publication statusPublished - 2017

Cite this

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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.",
author = "J. Meier and M. M{\"a}rtens and A. Hillebrand and P. Tewarie and {Van Mieghem}, P.",
year = "2017",
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Motif-based analysis of effective connectivity in brain networks. / Meier, J.; Märtens, M.; Hillebrand, A.; Tewarie, P.; Van Mieghem, P.

In: Studies in Computational Intelligence, Vol. 693, 2017, p. 685-696.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Meier, J.

AU - Märtens, M.

AU - Hillebrand, A.

AU - Tewarie, P.

AU - Van Mieghem, P.

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