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12 April 2004 Tree-dependent and topographic independent component analysis for fMRI analysis
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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). Recently, a new paradigm in ICA emerged, that of finding “clusters” of dependent components. This striking philosophy found its implementation in two new ICA algorithms: tree-dependent and topographic ICA. For fMRI, this represents the unifying paradigm of combining two powerful exploratory data analysis methods, ICA and unsupervised clustering techniques. For the fMRI data, a comparative quantitative evaluation between the two methods, tree-dependent and topographic ICA was performed. The comparative results were evaluated by (1) task-related activation maps, (2) associated time-courses and (3) ROC study. It can be seen that topographic ICA outperforms all other ICA methods including tree-dependent ICA for 8 and 9 ICs. However, for 16 ICs topographic ICA is outperformed by both FastICA and tree-dependent ICA (KGV) using an approximation of the mutual information the kernel generalized variance.
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Oliver Lange, Anke Meyer-Base, Axel Wismueller M.D., Monica Hurdal, DeWitt Sumners, and Dorothee P. Auer "Tree-dependent and topographic independent component analysis for fMRI analysis", Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004);

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