title: Independent component analysis for brain fMRI does not select for independence.
abstract: InfoMax and FastICA are the independent component analysis algorithms most used and apparently most effective for brain fMRI. We show that this is linked to their ability to handle effectively sparse components rather than independent components as such. The mathematical design of better analysis tools for brain fMRI should thus emphasize other mathematical characteristics than independence.
authors: Center for the Study of Brain, Mind and Behavior, Princeton University, Princeton, NJ 08544, USA. ingrid@math.princeton.edu
authors: I
authors: I
authors: Daubechies
authors: E
authors: E
authors: Roussos
authors: S
authors: S
authors: Takerkart
authors: M
authors: M
authors: Benharrosh
authors: C
authors: C
authors: Golden
authors: K
authors: K
authors: D'Ardenne
authors: W
authors: W
authors: Richter
authors: J D
authors: JD
authors: Cohen
authors: J
authors: J
authors: Haxby
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011859:Radiography; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted