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- Title
Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence.
- Authors
Calhoun, Vince D.; Potluru, Vamsi K.; Phlypo, Ronald; Silva, Rogers F.; Pearlmutter, Barak A.; Caprihan, Arvind; Plis, Sergey M.; Adalı, Tülay
- 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.
- Subjects
INDEPENDENT component analysis; FUNCTIONAL magnetic resonance imaging; COMPUTATIONAL biology; BRAIN imaging; BIOENGINEERING; NEUROSCIENCES; ALGORITHMS
- Publication
PLoS ONE, 2013, Vol 8, Issue 8, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0073309