Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

Vince D. Calhoun, Vamsi K. Potluru, Ronald Phlypo, Rogers F. Silva, Barak A. Pearlmutter, Arvind Caprihan, Sergey M. Plis, Tülay Adali

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere73309
JournalPloS one
Volume8
Issue number8
DOIs
StatePublished - Aug 29 2013

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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