Independent component analysis for brain fMRI does not select for independence

I. Daubechies, E. Roussos, S. Takerkart, M. Benharrosh, C. Golden, K. D'Ardenne, W. Richter, Jonathan D. Cohen, J. Haxby

Research output: Contribution to journalArticle

178 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)10415-10422
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Volume106
Issue number26
DOIs
StatePublished - Jun 30 2009

All Science Journal Classification (ASJC) codes

  • General

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    Daubechies, I., Roussos, E., Takerkart, S., Benharrosh, M., Golden, C., D'Ardenne, K., Richter, W., Cohen, J. D., & Haxby, J. (2009). Independent component analysis for brain fMRI does not select for independence. Proceedings of the National Academy of Sciences of the United States of America, 106(26), 10415-10422. https://doi.org/10.1073/pnas.0903525106