Comparison of Three Methods for Generating Group Statistical Inferences from Independent Component Analysis of Functional Magnetic Resonance Imaging Data

Abstract

Purpose: To evaluate the relative effectiveness of three previously proposed methods of performing group independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Materials and Methods: 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). Results: 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. Conclusion: 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.