Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism

Oregon Convention Center 

Independent component analysis (ICA) is widely used to estimate spatial resting-state networks and their time courses in neuroimaging studies. Independent components correspond to sparse patterns of co-activating brain locations. Previous approaches for introducing sparsity to ICA replace the non-smooth objective function with smooth approximations, resulting in components that do not achieve exact zeros. We propose a novel Sparse ICA method that enables sparse estimation of independent source components by solving a non-smooth non-convex optimization problem via the relax-and-split framework. The proposed Sparse ICA method balances statistical independence and sparsity simultaneously and is computationally fast. In simulations, we demonstrate improved estimation accuracy of both source signals and signal time courses compared to existing approaches. We apply Sparse ICA to cortical surface resting-state fMRI in school-aged autistic children, and reveal differences in brain activity between certain regions in autistic children compared to normal children. Sparse ICA selects co-activating locations, which we argue is more interpretable than dense components from popular approaches.