Meyer-Bläse, Anke and Theis, Fabian J. and Lange, Oliver and Puntonet, Carlos G. (2004) Tree-dependent and topographic independent component analysis for fMRI analysis. In: Puntonet, Carlos G., (ed.) Independent component analysis and blind signal separation: fifth international conference, ICA 2004, Granada, Spain, September 22 - 24, 2004; proceedings. Lecture Notes in Computer Science, 3195. Springer, Berlin, pp. 782-789. ISBN 3-540-23056-4, 978-3-540-23056-4.
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Recently, a new paradigm in ICA emerged, that of finding ldquoclustersrdquo of dependent components. This striking philosophy found its implementation in two new ICA algorithms: tree–dependent and topographic ICA. Applied to fMRI, this leads to 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 based on (1) correlation and associated time–courses and (2) 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 as an approximation of the mutual information the kernel generalized variance.
|Item Type:||Book Section|
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||01 Oct 2010 09:52|
|Last Modified:||01 Oct 2010 09:52|
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