Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Clustering of Dependent Components: A New Paradigm for fMRI Signal Detection | EURASIP Journal on Applied Signal Processing 2005 19 3089-3102 2005 Hindawi Publishing Corporation Clustering of Dependent Components A New Paradigm for fMRI Signal Detection Anke Meyer-Base Department of Electrical and Computer Engineering Florida State University Tallahassee FL 32310-6046 USA Email amb@ Monica K. Hurdal Department of Mathematics Florida State University Tallahassee FL 32306-4510 USA Email mhurdal@ Oliver Lange Department of Electrical and Computer Engineering Florida State University Tallahassee FL 32310-6046 USA Email oliver@ Helge Ritter Neuroinformatics Group Faculty of Technology University of Bielefeld 33501 Bielefeld Germany Email helge@ Received 1 February 2004 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 identity 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 treedependent ICA KGV using as an
