A method for comparing group fMRI data using independent component analysis: Application to visual, motor and visuomotor tasks

Vince D. Calhoun, Tulay Adali, James J. Pekar

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1181-1191
Number of pages11
JournalMagnetic Resonance Imaging
Volume22
Issue number9
DOIs
StatePublished - Nov 1 2004

Keywords

  • Brain
  • Functional
  • ICA
  • Independent component analysis
  • fMRI

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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