Human brain networks in health and disease.
Human brain networks in health and disease.
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DOI：
10.1097/wco.0b013e32832d93dd
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发表时间：
2009-08
影响因子：
4.8
通讯作者：
Bullmore ET
中科院分区：
文献类型：
--
作者：
Bassett DS;Bullmore ET
复杂网络统计物理学的最新发展已经被转化为神经成像数据，以增强我们对人脑结构和功能网络的理解。本文综述了图论方法在结构MRI、弥散MRI、功能MRI、脑电图和脑磁图数据分析中的应用。复杂的网络属性已被确定在所有形式的神经影像数据，并在一定范围内的空间和时间尺度的一些一致性。保守的属性包括小世界性，低布线成本的高效率信息传输，模块化和网络集线器的存在。已经发现结构和功能网络指标是可遗传的，并且随着正常老化而改变。临床研究，主要是在阿尔茨海默病和精神分裂症，已确定异常的网络配置的患者。未来的工作可能会涉及综合模型的结构和功能网络，并探讨网络配置和认知性能的相互依赖性。神经影像学数据的图论分析正在迅速发展，并可能提供一个相对简单但强大的定量框架来描述和比较不同实验和临床条件下的整个人脑结构和功能网络。
Recent developments in the statistical physics of complex networks have been translated to neuroimaging data in an effort to enhance our understanding of human brain structural and functional networks. This review focuses on studies using graph theoretical measures applied to structural MRI, diffusion MRI, functional MRI, electroencephalography and magnetoencephalography data. Complex network properties have been identified with some consistency in all modalities of neuroimaging data and over a range of spatial and time scales. Conserved properties include small-worldness, high efficiency of information transfer for low wiring cost, modularity, and the existence of network hubs. Structural and functional network metrics have been found to be heritable and to change with normal aging. Clinical studies, principally in Alzheimer’s disease and schizophrenia, have identified abnormalities of network configuration in patients. Future work will likely involve efforts to synthesize structural and functional networks in integrated models and to explore the inter-dependence of network configuration and cognitive performance. Graph theoretical analysis of neuroimaging data is growing rapidly and could potentially provide a relatively simple but powerful quantitative framework to describe and compare whole human brain structural and functional networks under diverse experimental and clinical conditions.