Brain Graphs: Graphical Models of the Human Brain Connectome
Brain Graphs: Graphical Models of the Human Brain Connectome
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DOI：
10.1146/annurev-clinpsy-040510-143934
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发表时间：
2011-01-01
影响因子：
18.4
通讯作者：
Bassett, Danielle S.
中科院分区：
文献类型：
--
作者：
Bullmore, Edward T.;Bassett, Danielle S.
脑图提供了一种相对简单且日益流行的建模人脑连接体的方法，使用图论将神经系统抽象地定义为一组节点（表示解剖区域或记录电极）和互连边缘（表示结构或功能连接）。这些图的拓扑和几何性质可以测量，并与随机图和来自其他神经科学数据或其他（非神经）复杂系统的图进行比较。结构和功能的人脑图一直表现出关键的拓扑特性，如小世界性，模块性和异构度分布。脑图也是物理嵌入的，以便几乎最大限度地减少布线成本，这是一个关键的几何属性。在这里，我们提供了一个概念性的审查和方法指南，以图形分析的人类神经影像学数据，重点是一些关键的假设，问题和权衡面临的调查。
Brain graphs provide a relatively simple and increasingly popular way of modeling the human brain connectome, using graph theory to abstractly define a nervous system as a set of nodes (denoting anatomical regions or recording electrodes) and interconnecting edges (denoting structural or functional connections). Topological and geometrical properties of these graphs can be measured and compared to random graphs and to graphs derived from other neuroscience data or other (nonneural) complex systems. Both structural and functional human brain graphs have consistently demonstrated key topological properties such as small-worldness, modularity, and heterogeneous degree distributions. Brain graphs are also physically embedded so as to nearly minimize wiring cost, a key geometric property. Here we offer a conceptual review and methodological guide to graphical analysis of human neuroimaging data, with an emphasis on some of the key assumptions, issues, and trade-offs facing the investigator.