Explicitly Nonlinear Connectivity-Matrix Independent Component Analysis in Resting fMRI Data

Connectivity-matrix independent component analysis (cmICA) is a data-driven method to calculate brain voxel maps of functional connectivity. It is a powerful approach, but one limitation is that it can only capture linear relationships. In this work, we focus on measuring the explicitly nonlinear relationships between the voxel connectivity to identify brain spatial map in which demonstrate explicitly nonlinear dependencies. We expand cmICA using normalized mutual information (NMI) after removing the linear relationships and find highly structured resting networks which would be completely missed by existing functional connectivity approaches.