Multi-level bootstrap analysis of stable clusters in resting-state fMRI
Multi-level bootstrap analysis of stable clusters in resting-state fMRI
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DOI：
10.1016/j.neuroimage.2010.02.082
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发表时间：
2010-07-01
期刊：
影响因子：
5.7
通讯作者：
Evans, Alan C.
中科院分区：
文献类型：
--
作者：
Bellec, Pierre;Rosa-Neto, Pedro;Evans, Alan C.
在静息态功能磁共振成像（fMRI）中，已经开发了多种方法来识别具有自发、连贯活动的脑网络。我们在这里提出了一个通用的统计框架来量化这种静止状态网络（RSN）的稳定性，这是实现与k-均值聚类。该方法的核心在于引导可用的数据集来大量复制聚类过程，并量化所有复制中的稳定特征。这种稳定聚类的自举分析（BASC）有几个好处：（1）它可以以多级方式实现，以在个体受试者的水平和组的水平上研究稳定的RSN;（2）它提供了RSN稳定性的原则性测量;（3）稳定性测量的最大化可以用作选择RSN数量的自然标准。仿真研究验证了纯合成数据的多层次BASC的良好性能。稳定的网络也来自43名受试者的真实的静息状态研究。在组水平上，确定了7个RSN，这与文献中先前的研究结果一致。个体和组水平稳定性图之间的比较表明，BASC能够在这两个水平的分析之间建立成功的对应关系，同时保留一些有趣的受试者特定特征，例如，某些受试者的视觉和额顶叶网络中皮质下区域的特定参与。(C)2010年爱思唯尔公司All rights reserved.
A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group: (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects. (C) 2010 Elsevier Inc. All rights reserved.