Bullmore Ed, Sporns Olaf
University of Cambridge, Behavioural & Clinical Neurosciences Institute, Department of Psychiatry, Addenbrooke's Hospital, Cambridge, CB2 2QQ, UK.
Nat Rev Neurosci. 2009 Mar;10(3):186-98. doi: 10.1038/nrn2575. Epub 2009 Feb 4.
Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
复杂网络定量分析领域的最新进展主要基于图论,这些进展已迅速应用于脑网络组织研究。在人类神经影像学的全脑尺度以及非人类动物的细胞尺度上,大脑的结构和功能系统都具有复杂网络的特征,如小世界拓扑结构、高度连接的枢纽和模块化。在本文中,我们回顾了采用多种实验方式(包括人类的结构和功能磁共振成像、扩散张量成像、脑磁图和脑电图)研究复杂脑网络的相关研究,并对图论的基本原理进行了通俗易懂的介绍。我们还强调了这一快速发展领域未来发展需要解决的一些技术挑战和关键问题。