Explorative Analysis of fMRI Data: Why Functional PCA should Usually be Preferred to ICA

Independent component analysis (ICA [1]) has been proposed as an exploratory technique for fMRI data. Unfortunately ICA generates many components with little information about how to select from them. Here we propose a new technique, functional PCA, to retrieve the signal associated to the experimental paradigm in an explorative setting. ICA posits a linear model in which the observed images Y (each column being an image) are linear combinations M of independent non-Gaussian signal sources P: Y=MP The distributional nature of ICA's assumptions makes it an excellent explorative technique in general. However, its…