Clustering of Dependent Components: A New Paradigm for fMRI Signal Detection

• Published in: EURASIP journal on advances in signal processing 2005-11, Vol.2005 (19), p.490821-490821
• Main Authors: Meyer-Bäse, Anke, Hurdal, Monica K, Lange, Oliver, Ritter, Helge
• Format: Article
• Language: English
• Subjects: dependent component analysis; fMRI; topographic ICA; tree-dependent ICA
• ISSN: 1687-6180; 1687-6172; 1687-6180
• DOI: 10.1186/1687-6180-2005-490821

: 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 intriguing idea found its implementation into 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. The most important findings in this paper are that (1) both tree-dependent and topographic ICA are able to identify signal components with high correlation to the fMRI stimulus, and that (2) 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 tree-dependent ICA (KGV) using as an approximation of the mutual information the kernel generalized variance. The applicability of the new algorithm is demonstrated on experimental data.