Spatially Local and Temporally Smooth PCA for fMRI

PCA has found use as an exploratory technique for fMRI analysis. However underlying it is an implicit model that while allowing temporal non-stationary covariance assumes the same covariance structure for all voxels. Here we relax this assumption for the first time by developing a version of PCA that allows the covariance structure to vary spatially. The new method is applied to real data and provides interesting new insight.