Functional Segmentation of fMRI Data Using Adaptive Non-negative Sparse PCA (ANSPCA)

We propose a novel method for functional segmentation of fMRI data that incorporates multiple functional attributes such as activation effects and functional connectivity, under a single framework. Similar to PCA, our method exploits the structure of the correlation matrix but with neighborhood information adaptively integrated to encourage detection of spatially contiguous clusters yet without falsely pooling non-active voxels near the functional boundaries. In addition, our method adaptively combines PCA and replicator dynamics, which we show to be equivalent to non-negative sparse PCA, based on the sparsity of the activation pattern. We validate our method quantitatively on synthetic data and demonstrate that it outperforms methods including replicator dynamics, PCA, Gaussian mixture models, and general linear models. Furthermore, when applied to real fMRI data, our method successfully segmented the Brodmann area 6 into its known functional sub-regions, whereas other conventional methods that we examined failed to attain such delineation.