Complex network measures of brain connectivity: Uses and interpretations

• Published in: NeuroImage (Orlando, Fla.) Vol. 52; no. 3; pp. 1059 - 1069
• Main Authors: Rubinov, Mikail, Sporns, Olaf
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2010; Elsevier Limited
• Subjects: Brain - physiology; Medical research; Models, Neurological; Nerve Net - physiology; Network topologies; Neural Networks (Computer); Standard deviation
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2009.10.003

Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis—a new multidisciplinary approach to the study of complex systems—aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets.