Fig. 1.

Schematic diagram of the proposed method. The rsfMRI data of each individual subject are arranged as a matrix with each row for one time point and each column for one voxel. The rsfMRI data of a group of subjects are simultaneously decomposed into non-negative subject specific FNs with their corresponding time courses in a collaborative setting with 3 regularization terms: 1) a voxel-wise group sparsity regularization term is adopted as an inter-subject consensus prior so that spatial correspondence and variations of FNs of different subjects are preserved simultaneously; 2) a data locality regularization term is adopted to enhance both functional coherence and spatial proximity of voxels so that spatially continuous and functionally coherent voxels are encouraged to reside in the same FN; and 3) an intra-subject parsimonious regularization term is adopted to eliminate redundant FNs with similar functional profiles using automatic relevance determination techniques.