title: Independent component analysis for brain FMRI does indeed select for maximal independence.
abstract: A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources.
authors.affiliation: Medical Image Analysis Lab, The Mind Research Network, Albuquerque, New Mexico, USA. vcalhoun@unm.edu
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.forename: Vamsi K
authors.initials: VK
authors.lastname: Potluru
authors.forename: Ronald
authors.initials: R
authors.lastname: Phlypo
authors.forename: Rogers F
authors.initials: RF
authors.lastname: Silva
authors.forename: Barak A
authors.initials: BA
authors.lastname: Pearlmutter
authors.forename: Arvind
authors.initials: A
authors.lastname: Caprihan
authors.forename: Sergey M
authors.initials: SM
authors.lastname: Plis
authors.forename: Tülay
authors.initials: T
authors.lastname: Adalı
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D006801:Humans; D008279:Magnetic Resonance Imaging; D025341:Principal Component Analysis