title: Multivariate synchrony modules identified through multiple subject community detection in functional brain networks.
abstract: The functional connectivity of the human brain may be described by modeling interactions among its neural assemblies as a graph composed of vertices and edges. It has recently been shown that functional brain networks belong to a class of scale-free complex networks for which graphs have helped define an association between function and topology. These networks have been shown to possess a heterogenous structure composed of clusters, dense regions of strongly associated nodes, which represent multivariate relationships among nodes. Network clustering algorithms classify the nodes based on a similarity measure representing the bivariate relationships and similar to unsupervised learning is performed without a priori information. In this paper, we propose a method for partitioning a set of networks representing different subjects and reveal a community structure common to multiple subjects. We apply this community identifying algorithm to functional brain networks during a cognitive control task, in particular the error-related negativity (ERN), to evaluate how the brain organizes itself during error-monitoring.
authors.affiliation: Department of Electrical Engineering, Michigan State University, 2120 Engineering Building, East Lansing, MI 48824, USA. bolanosm@msu.edu
authors.forename: Marcos E
authors.initials: ME
authors.lastname: Bolaños
authors.forename: Edward M
authors.initials: EM
authors.lastname: Bernat
authors.forename: Selin
authors.initials: S
authors.lastname: Aviyente
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D006801:Humans; D015999:Multivariate Analysis