title: A method for comparing group fMRI data using independent component analysis: application to visual, motor and visuomotor tasks.
abstract: Independent component analysis (ICA) is an approach for decomposing fMRI data into spatially independent maps and time courses. We have recently proposed a method for ICA of multisubject data; in the current paper, an extension is proposed for allowing ICA group comparisons. This method is applied to data from experiments designed to stimulate visual cortex, motor cortex or both visual and motor cortices. Several intergroup and intragroup metrics are proposed for assessing the utility of the components for comparisons of group ICA data. The proposed method may prove to be useful in answering questions requiring multigroup comparisons when a flexible modeling approach is desired.
authors.affiliation: Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA. vince.calhoun@yale.edu
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.forename: Tulay
authors.initials: T
authors.lastname: Adali
authors.forename: James J
authors.initials: JJ
authors.lastname: Pekar
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009044:Motor Cortex / Q000502:physiology; D009483:Neuropsychological Tests; D011597:Psychomotor Performance / Q000502:physiology*; D011930:Reaction Time; D013647:Task Performance and Analysis; D013997:Time Factors; D014793:Visual Cortex / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*