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Multivariate synchrony modules identified through multiple subject community detection in functional brain networks.

Authors
Type
Published Article
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
1557-170X
Publication Date
Volume
2011
Pages
2534–2537
Identifiers
DOI: 10.1109/IEMBS.2011.6090701
PMID: 22254857
Source
Medline
License
Unknown

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.

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