A method for making group inferences from functional MRI data using independent component analysis

Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this app roach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has be en successfully utilized to analyze single-subject fMRI data sets, and an e xtension of this work would be to provide for group inferences. However, un like univariate methods (e.g., regression analysis, Kolmogorov-Smirnov stat istics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for dra wing group inferences using ICA of fMRI data, and present its application t o a simple visual paradigm that alternately stimulates the left or right vi sual field. Our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital /parietal cortex, and a non-task-related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculat ed? (2) How are these components to be combined across subjects? (3) How sh ould the final results be thresholded and/or presented? We show that the me thodology we present provides answers to these questions and lay out a proc ess for making group inferences from fMRI data using independent component analysis. (C) 2001 Wiley-Liss, Inc.