Black Dog Institute and School of Psychiatry, University of New South Wales, Sydney, Australia.
Neuroimage. 2010 Sep;52(3):1059-69. doi: 10.1016/j.neuroimage.2009.10.003. Epub 2009 Oct 9.
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.
脑连接组学数据集由通过解剖束或功能关联连接的脑区网络组成。复杂网络分析——一种新的多学科方法,旨在研究复杂系统——旨在用少量具有神经生物学意义和易于计算的指标来描述这些脑网络。在本文中,我们讨论了从连接数据构建脑网络,并描述了结构和功能连接最常用的网络度量。我们描述了各种检测功能整合和分离、量化个体脑区或通路中心性、描述局部解剖电路模式以及测试网络对损伤的弹性的度量。我们讨论了结构和功能网络连接以及跨被试比较网络的问题。最后,我们描述了伴随本文的一个 Matlab 工具箱(http://www.brain-connectivity-toolbox.net),其中包含了一系列复杂的网络度量和大规模的神经解剖连接组学数据集。