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To spatially cluster resting state‐functional magnetic resonance imaging (rs‐fMRI) data into potential networks, there are only a few general approaches that determine the number of networks/clusters, despite a wide variety of techniques proposed for clustering. For individual subjects, extraction of a large number of spatially disjoint clusters results in multiple small networks that are spatio‐temporally...
Although resting state fMRI seems an ideal tool for investigating clinical populations, especially in case of reduced cooperation or tolerance, unbiased methods with high sensitivity for disease relevant pathologies remain to be identified. In this paper, we perform spectral clustering on the mean time series of automated anatomical labeling regions of interest for comparing the resting state networks...
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