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Summary: A Method for Making Group Inferences from
Functional MRI Data Using Independent
Component Analysis
V.D. Calhoun,1,2* T. Adali,2
G.D. Pearlson,1
and J.J. Pekar3,4
1
Division of Psychiatric Neuro-Imaging, Johns Hopkins University, Baltimore, Maryland
2
University of Maryland, Department of CSEE, Baltimore, Maryland
3
Department of Radiology, Johns Hopkins University, Baltimore, Maryland
4
FM Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute,
Baltimore, Maryland
Abstract: Independent component analysis (ICA) is a promising analysis method that is being increas-
ingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive
paradigms for which detailed models of brain activity are not available. Independent component analysis
has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work
would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis,
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