Functional MRI Analysis by a Novel Spatiotemporal ICA Algorithm
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- Theis F.J., Gruber P., Keck I.R., Lang E.W. (2005) Functional MRI Analysis by a Novel Spatiotemporal ICA Algorithm. In: Duch W., Kacprzyk J., Oja E., Zadrożny S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg
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.
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