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      Weight-conserving characterization of complex functional brain networks.

      1 ,
      NeuroImage
      Elsevier BV

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          Abstract

          Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.

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          Author and article information

          Journal
          Neuroimage
          NeuroImage
          Elsevier BV
          1095-9572
          1053-8119
          Jun 15 2011
          : 56
          : 4
          Affiliations
          [1 ] Black Dog Institute and School of Psychiatry, University of New South Wales, Sydney, Australia. m.rubinov@student.unsw.edu.au
          Article
          S1053-8119(11)00348-X
          10.1016/j.neuroimage.2011.03.069
          21459148
          a12734a2-74b1-475b-80b4-6ebe269c14ea
          Copyright © 2011 Elsevier Inc. All rights reserved.
          History

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