title: Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data.
abstract: 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.
authors.affiliation: F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA. zwmcd@yahoo.com
authors.forename: Weiming
authors.initials: W
authors.lastname: Zeng
authors.forename: Anqi
authors.initials: A
authors.lastname: Qiu
authors.forename: BettyAnn
authors.initials: B
authors.lastname: Chodkowski
authors.forename: James J
authors.initials: JJ
authors.lastname: Pekar
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging; D008297:Male; D025341:Principal Component Analysis