Clustering of dependent components: A new paradigm for fMRI signal detection

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). 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. © 2005 Hindawi Publishing Corporation.