Independent component analysis of functional magnetic resonance imaging

Independent component analysis (ICA) has recently demonstrated considerable promise in characterizing fMRI data, primarily due to its intuitive nature and ability for flexible characterization of brain function. As typically applied, spatial brain networks are assumed to be systematically non-overlapping. Often temporal coherence of brain networks is also assumed, although convolutive and other models can be utilized to relax this assumption. 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 chapter, we 1) introduce ICA and review current algorithms, 2) relate ICA to several well-known pattern recognition techniques, 3) introduce fMRI data and its properties, 4) review the basic motivation for using ICA on fMRI data, and 5) review the current work on ICA of fMRI and the incorporation of prior information.