Clustering of resting state networks.
Clustering of resting state networks.
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DOI：
10.1371/journal.pone.0040370
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Shimony JS
中科院分区：
文献类型：
--
作者：
Lee MH;Hacker CD;Snyder AZ;Corbetta M;Zhang D;Leuthardt EC;Shimony JS
该研究的目的是使用数据驱动的聚类算法来证明健康大脑中静息状态活动的层次结构。模糊-C-均值聚类算法被施加到休息状态的功能磁共振成像数据在皮质和皮质下灰质从两组分别收购，17个健康人和第二个21个健康人。使用不同数目的簇和不同的起始条件。一个集群的分散性措施确定的最佳数量的集群。内积度量提供了不同聚类之间相似性的度量。两个聚类结果分别发现了任务负性系统和任务正性系统。集群分散措施最小化与7和11集群。7和11个聚类结果中的每个聚类都与任务消极或任务积极系统相关联。应用该算法找到七个集群恢复先前描述的静息状态网络，包括默认模式网络，额顶叶控制网络，腹侧和背侧注意网络，躯体运动，视觉和语言网络。语言和腹侧注意网络有显着的皮层下参与。在不同条件下运行的大多数算法中，都发现了这种包裹，并且对不同的初始化方法具有鲁棒性。使用不同的最佳数量的集群确定静息状态网络的静息状态活动的聚类与以前获得的结果。这项工作加强了观察，休息状态网络是分层组织的。
The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm. The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization. The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.