Multi-level bootstrap analysis of stable clusters in resting-state fMRI

作者：Bellec Pierre^*; Rosa Neto Pedro; Lyttelton Oliver C; Benali Habib; Evans Alan C

来源：NeuroImage, 2010, 51(3): 1126-1139.

DOI：10.1016/j.neuroimage.2010.02.082

• Bootstrap
• Clustering
• Functional MRI
• Hierarchical clustering
• k-Means
• Multi-level analysis
• Resting-state networks
• Stability analysis

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.

• 出版日期2010-7-1