Spatial and temporal independent component analysis of functional MRI datacontaining a pair of task-related waveforms

Independent component analysis (ICA) is a technique that attempts to separa te data into maximally independent groups. Achieving maximal independence i n space or time yields two varieties of ICA meaningful for functional MRI ( fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these exper iments was to study ICA with two predictable components present and evaluat e the importance of the underlying independence assumption in the applicati on of ICA. Four novel visual activation paradigms were designed, each consi sting of two spatiotemporal components that were either spatially dependent , temporally dependent, both spatially and temporally dependent, or spatial ly and temporally uncorrelated, respectively. Simulated data were generated and fMRI data from six subjects were acquired using these paradigms. Data from each paradigm were analyzed with regression analysis in order to deter mine if the signal was occurring as expected. Spatial and temporal ICA were then applied to these data, with the general result that ICA found compone nts only where expected, e.g., S(T)ICA "failed" (i.e., yielded independent components unrelated to the "self-evident" components) for paradigms that w ere spatially (temporally) dependent, and "worked" otherwise. Regression an alysis proved a useful "check" for these data, however strong hypotheses wi ll not always be available, and a strength of ICA is that it can characteri ze data without making specific modeling assumptions. We report a careful e xamination of some of the assumptions behind ICA methodologies, provide exa mples of when applying ICA would provide difficult-to-interpret results, an d offer suggestions for applying ICA to fMRI data especially when more than one task-related component is present in the data. Hum. Brain Mapping 13:4 3-53, 2001. (C) 2001 Wiley-Liss, Inc.