Weight-conserving characterization of complex functional brain networks

• Published in: NeuroImage (Orlando, Fla.) Vol. 56; no. 4; pp. 2068 - 2079
• Main Authors: Rubinov, Mikail, Sporns, Olaf
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.06.2011; Elsevier Limited
• Subjects: Algorithms; Alzheimer's disease; Brain - physiology; Brain Mapping - methods; Complex networks; Functional connectivity; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging; Models, Neurological; Nerve Net - physiology; Open access; Functional connectivity; Complex networks
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.03.069

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. ► Modularity and centrality in functional brain networks with negative weights. ► Degenerate high-modularity partitions of functional brain networks. ► Weighted null model of functional brain networks.