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Proceedings Paper

Exploratory data analysis methods applied to fMRI
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Paper Abstract

Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by a very detailed ROC analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, SOM, "neural gas" network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.

Paper Details

Date Published: 28 March 2005
PDF: 8 pages
Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, (28 March 2005); doi: 10.1117/12.601004
Show Author Affiliations
Oliver Lange, Florida State Univ. (United States)
Anke Meyer-Base, Florida State Univ. (United States)
Uwe H. Meyer-Base, Florida State Univ. (United States)
Axel Wismuller, Ludwig-Maximilians-Univ. Munchen (Germany)
Monica Hurdal, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 5803:
Intelligent Computing: Theory and Applications III
Kevin L. Priddy, Editor(s)

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