Theis, Fabian J. and Gruber, Peter and Keck, I. R. and Lang, Elmar (2005) Functional MRI analysis by a novel spatiotemporal ICA algorithm. In: Duch, Włodzisław, (ed.) Artificial neural networks: Biological inspirations - ICANN 2005: 15th international conference, Warsaw, Poland, September 11-15, 2005; proceedings. Lecture notes in computer science, 3696. Springer, Berlin, pp. 677-682. ISBN 978-3-540-28752-0.
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Data sets acquired from functional magnetic resonance imaging (fMRI) contain both spatial and temporal structures. In order to blindly extract underlying activities, the common approach however only uses either spatial or temporal independence. More convincing results can be achieved by requiring the transformed data to be as independent as possible in both domains. First introduced by Stone, spatiotemporal independent component analysis (ICA) is a promising algorithm for fMRI decomposition. We propose an algebraic spatiotemporal ICA algorithm with increased performance and robustness. The feasibility of the algorithm is demonstrated in an application to the analysis of an fMRI data sets of a human brain performing an auditory task.
|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 07:55|
|Last Modified:||01 Oct 2010 07:55|