Cohesive parcellation aims to produce parcels whose member voxels highly correlate to the parcel’s mean (exemplar) time series. The previously presented single subject version of cohesive parcellation is improved and extended to the group level using a novel hybrid parallel hierarchical framework. The resulting group parcellation compares favorably to traditional anatomical and connectivity-based parcellations over several measures of cluster validity at both the group level and when projected to individual subjects.
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