Department of Neurology, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, St. Louis, MO 63110, USA.
Neuron. 2013 Aug 21;79(4):798-813. doi: 10.1016/j.neuron.2013.07.035.
Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting-state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: (1) finding network nodes that participate in multiple subnetworks of the brain, and (2) finding spatial locations in which several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity.
集线器通过其在网络中的接触点的数量和位置，以强大的方式整合和分发信息。一些静息态功能磁共振连接性研究报告表明，默认模式系统的某些区域是大脑集线器；我们证明，以前基于度的集线器识别方法可能识别的是大脑系统的某些部分，而不是大脑网络的关键节点。我们利用两种方法来识别类似集线器的大脑区域：（1）找到参与大脑多个子网的网络节点，（2）找到在小体积内有多个系统表达的空间位置。这些方法集中在一组与以前关于集线器的报告不同的分布式区域上。这项工作确定了支持多个系统的区域，从而对大脑功能进行了空间受限的预测，这些预测可以根据损伤、诱发反应和活动的动态模式进行测试。
