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; J D Cohen; J Haxby

doi:10.1073/pnas.0903525106

Independent component analysis for brain fMRI does not select for independence

Proc Natl Acad Sci U S A. 2009 Jun 30;106(26):10415-22. doi: 10.1073/pnas.0903525106. Epub 2009 Jun 25.

Authors

I Daubechies¹, E Roussos, S Takerkart, M Benharrosh, C Golden, K D'Ardenne, W Richter, J D Cohen, J Haxby

Affiliation

¹ Center for the Study of Brain, Mind and Behavior, Princeton University, Princeton, NJ 08544, USA. ingrid@math.princeton.edu

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Brain / diagnostic imaging
Brain / physiology
Brain Mapping / methods*
Computer Simulation
Humans
Image Interpretation, Computer-Assisted
Image Processing, Computer-Assisted
Magnetic Resonance Imaging / methods*
Radiography
Reproducibility of Results
Signal Processing, Computer-Assisted

Abstract

Publication types

MeSH terms

Grants and funding