Comparison of two exploratory data analysis methods for fMRI: unsupervised clustering versus independent component analysis.
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 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, "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.
Meyer-Baese
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Department of Electrical and Computer Engineering, Florida State University, Tallahassee. FL 32310-6046, USA. amb@eng.fsu.edu
Wismueller
Axel
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Lange
Oliver
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D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D005074:Evoked Potentials, Visual / Q000502:physiology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D016571:Neural Networks, Computer; D025341:Principal Component Analysis / Q000379:methods*