Keck, I. R. and Theis, Fabian J. and Gruber, Peter and Lang, Elmar and Specht, K. and Fink, G. and Tomé, A. M. and Puntonet, Carlos G. (2005) Automated clustering of ICA results for fMRI data analysis. In: Fonseca, José, (ed.) Proc. 2nd Int. Conf. Comput. Intelligence Medicine Healthcare (CIMED), 29th June - 1st July 2005, Lisbon, Portugal. CD-rom. IEE Healthcare Technologies, Lisbon. ISBN 0863415202, 9780863415203.
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While independent component analysis can be a fruitful method to analyze fMRI data, the manual work that is usually necessary in viewing the results is complex and time consuming and thus limits its clinical application. In this article we try to solve this problem by presenting a new way to cluster the results of an ICA into few, easy to classify activation maps by using incomplete ICA. These maps are then the basis for a further in-deep analysis of the fMRI data. We demonstrate our approach on a real world WCST example.
|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:||15 Oct 2010 08:45|
|Last Modified:||15 Oct 2010 08:45|