Independent component analysis applied to self-paced functional MR imaging paradigms

Self-paced functional MR imaging (fMRI) paradigms, in which the task timing is determined by the subject's performance, can offer several advantages over commonly applied paradigms with predetermined stimulus timing. Independent component analysis (ICA) does not require specification of a timed response function, and could be an advantageous method of deriving results from fMRI data sets with varying response timings and durations. In this study normal volunteers (N = 10) each performed two self-paced fMRI motor and arithmetic paradigms. Individual data sets were analyzed with the Infomax spatial ICA algorithm. Conventional regression analysis was performed for comparison purposes. Spatial ICA effectively produced task-related components from each of the self-paced data sets, even in a few cases where regression analysis yielded non-specific functional maps. For the motor paradigm, these components consistently mapped to primary motor areas. ICA of the arithmetic paradigm yielded multiple task-related components that variably mapped to regions of parietal and frontal lobes. Regression analysis generally yielded similar spatial maps. The multiple task-related ICA components that were sometimes produced from each self-paced data set can be challenging to identify and evaluate for significance. These preliminary results indicate that ICA is useful as an exploratory and complementary method to conventional regression analysis for fMRI of self-paced paradigms.