EURASIP Journal on Applied Signal Processing
Volume 2005 (2005), Issue 19, Pages 3089-3102
doi:10.1155/ASP.2005.3089
Clustering of Dependent Components: A New Paradigm for fMRI Signal Detection
1Department of Electrical and Computer Engineering, Florida State University, Tallahassee 32310-6046, FL, USA
2Department of Mathematics, Florida State University, Tallahassee 32306-4510, FL, USA
3Neuroinformatics Group, Faculty of Technology, University of Bielefeld, Bielefeld 33501, Germany
Received 1 February 2004
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.