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

doi:10.1073/pnas.0903525106

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

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203 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 language	English (US)
Pages (from-to)	10415-10422
Number of pages	8
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	106
Issue number	26
DOIs	https://doi.org/10.1073/pnas.0903525106
State	Published - Jun 30 2009

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AU - Roussos, E.

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AU - Golden, C.

AU - D'Ardenne, K.

AU - Richter, W.

AU - Cohen, Jonathan D.

AU - Haxby, J.

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Independent component analysis for brain fMRI does not select for independence

Abstract

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