Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data.

Abstract

To evaluate the relative effectiveness of three previously proposed methods of performing group independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Data were generated via computer simulation. Components were added to a varying number of subjects between 1 and 20, and intersubject variability was simulated for both the added sources and their associated time courses. Three methods of group ICA analyses were performed: across-subject averaging, subject-wise concatenation, and row-wise concatenation (e.g., across time courses). Concatenating across subjects provided the best overall performance in terms of accurate estimation of the sources and associated time courses. Averaging across subjects provided accurate estimation (R > 0.9) of the time courses when the sources were present in a sufficient fraction (about 15%) of 100 subjects. Concatenating across time courses was shown not to be a feasible method when unique sources were added to the data from each subject, simulating the effects of motion and susceptibility artifacts. Subject-wise concatenation should be used when computationally feasible. For studies involving a large number of subjects, across-subject averaging provides an acceptable alternative and reduces the computational load.