A new measure of centrality for brain networks.
A new measure of centrality for brain networks.
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DOI：
10.1371/journal.pone.0012200
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发表时间：
2010-08-16
期刊：
影响因子：
3.7
通讯作者：
Hayasaka S
中科院分区：
文献类型：
--
作者：
Joyce KE;Laurienti PJ;Burdette JH;Hayasaka S
网络理论的最新发展使得可以从相互关联的组件网络的角度研究人脑的结构和功能。在形成网络的许多节点中，有些节点起着至关重要的作用，被称为网络结构的中心。中心节点可以通过中心性度量来识别，其中度、介数和特征向量中心性是三种最流行的度量。度标识连接最多的节点，而介数中心性标识位于最多旅行路径上的节点。特征向量中心性将连接到其他高度节点的节点视为高度中心。在这里提出的工作中，我们提出了一个新的中心性度量称为杠杆中心性，认为一个节点的连接程度相对于其邻居的连接。网络中一个节点的杠杆中心性取决于它的近邻依赖该节点获取信息的程度。虽然在概念上相似，但本文讨论的特征向量中心性和杠杆中心性之间存在本质区别。度，介数，特征向量和杠杆中心性进行了比较，使用从健康志愿者产生的功能性大脑网络。函数制图也被用来识别邻域中心（网络邻域内度高的节点）。省级枢纽提供当地社区内的结构，连接枢纽调解多个社区之间的连接。事实证明，杠杆产生的信息不是由度，介数或特征向量中心捕获的，并且在识别社区枢纽时更准确。我们建议，该指标可能能够识别网络中具有高度影响力的关键节点。
Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.
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DOI：
10.1073/pnas.0504136102
发表时间：
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影响因子：
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影响因子：
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