TY - JOUR
T1 - Motif-based analysis of effective connectivity in brain networks
AU - Meier, J.
AU - Märtens, M.
AU - Hillebrand, A.
AU - Tewarie, P.
AU - Van Mieghem, P.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85007306408&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-50901-3_54
DO - https://doi.org/10.1007/978-3-319-50901-3_54
M3 - Article
SN - 1860-949X
VL - 693
SP - 685
EP - 696
JO - Studies in Computational Intelligence
JF - Studies in Computational Intelligence
ER -