Vincent J Schmithorst1, Scott K Holland. 1. Imaging Research Center, Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA. vince@athena.cchmc.org
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. Copyright 2004 Wiley-Liss, Inc.
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. Copyright 2004 Wiley-Liss, Inc.
Authors: Veronika Schöpf; Christian Windischberger; Christian H Kasess; Rupert Lanzenberger; Ewald Moser Journal: MAGMA Date: 2010-06-03 Impact factor: 2.310
Authors: Prasanna Karunanayaka; Vincent J Schmithorst; Jennifer Vannest; Jerzy P Szaflarski; Elena Plante; Scott K Holland Journal: Neuroimage Date: 2010-01-04 Impact factor: 6.556