Journal
NEUROIMAGE
Volume 46, Issue 4, Pages 1041-1054Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.02.048
Keywords
Independent component analysis; fMRI; Maximum mean correlation
Funding
- NIH [NCRR P41 RR015241]
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Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method. (C) 2009 Elsevier Inc. All rights reserved.
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