title: Identifying important nodes in weighted functional brain networks: a comparison of different centrality approaches.
abstract: We compare different centrality metrics which aim at an identification of important nodes in complex networks. We investigate weighted functional brain networks derived from multichannel electroencephalograms recorded from 23 healthy subject under resting-state eyes-open or eyes-closed conditions. Although we observe the metrics strength, closeness, and betweenness centrality to be related to each other, they capture different spatial and temporal aspects of important nodes in these networks associated with behavioral changes. Identifying and characterizing of these nodes thus benefits from the application of several centrality metrics.
authors: Department of Epileptology, University of Bonn, Sigmund-Freud-Strasse 25, 53105 Bonn, Germany. mkuhnert@uni-bonn.de
authors: Marie-Therese
authors: MT
authors: Kuhnert
authors: Christian
authors: C
authors: Geier
authors: Christian E
authors: CE
authors: Elger
authors: Klaus
authors: K
authors: Lehnertz
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D005123:Eye; D005260:Female; D006801:Humans; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D013997:Time Factors