Complex network measures of brain connectivity: Uses and interpretations

被引：8367

作者：

Rubinov, Mikail ^{[2
,3
,4
,5
]}

Sporns, Olaf ^{[1
]}

机构：

[1] Indiana Univ, Dept Psychol & Brain Sci, Bloomington, IN 47405 USA

[2] Univ New S Wales, Black Dog Inst, Sydney, NSW, Australia

[3] Univ New S Wales, Sch Psychiat, Sydney, NSW, Australia

[4] Queensland Inst Med Res, Mental Hlth Res Div, Brisbane, Qld 4006, Australia

[5] CSIRO, Informat & Commun Technol Ctr, Sydney, NSW, Australia

来源：

NEUROIMAGE | 2010年 / 52卷 / 03期

关键词：

GRAPH-THEORETICAL ANALYSIS; SMALL-WORLD; FUNCTIONAL CONNECTIVITY; COMMUNITY STRUCTURE; HIERARCHICAL ORGANIZATION; CORTICAL NETWORKS; HEALTH;

D O I：

10.1016/j.neuroimage.2009.10.003

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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. (C) 2009 Elsevier Inc. All rights reserved.

引用

页码：1059 / 1069

页数：11

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