Locality regularized sparse subspace clustering with application to cortex parcellation on resting fMRI

Parcellating the brain into functional homogeneous regions could be achieved by performing clustering on fMRI data. Mathematically, parcellation is about how to construct the affinity matrix between voxels and identify subspaces within. Most previous methods derive the affinity matrix by using Pearson correlation, which is sensitive to noises inherent in fMRI. One recent work uses Sparse Representation to reconstruct the signals of each voxel by a sparse linear combination of other voxels, and the reconstruction coefficients are used as the affinity matrix for parcellation. We extend a popular Sparse Representation method — Sparse Subspace Clustering (SSC), by incorporating a locality regularization that encourages a voxel to be more represented by nearby voxels, and establish the Local-SSC method. Local-SSC performs comparably to SSC on simulated rs-fMRI data, and outperforms SSC on real fMRI data. Our approach demonstrates the benefit of the locality regularization to parcellation, and suggests improvement for other applications involving functional con-nectome analysis.