Independent component analysis (ICA) has found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed a priori models of brain activity are not available. ICA has been successfully utilized in a number of exciting fMRI applications including the identification of various signal-types (e.g. task and transiently task-related, and physiology-related signals) in the spatial or temporal domain, the analysis of multi-subject fMRI data, the incorporation of a priori information, and for the analysis of complex-valued fMRI data (which has proved challenging for standard approaches). In this paper, we 1) introduce fMRI data and its properties, 2) review the basic motivation for using ICA on fMRI data, and 3) review the current work on ICA of fMRI with some specific examples from our own work. The purpose of this paper is to motivate ICA research to focus upon this exciting application
