Thomas D. Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sumners, Dorothee Auer, Axel Wismuller
Abstract
Conventional model-based or statistical analysis methods for
functional MRI (fMRI) are easy to implement, and are effective in
analyzing data with simple paradigms. However, they are not applicable in
situations in which patterns of neural response are complicated and when
fMRI response is unknown. In this paper the "neural gas" network is
adapted and rigorously studied for analyzing fMRI data. The algorithm
supports spatial connectivity aiding in the identification of activation
sites in functional brain imaging. A comparison of this new method with
Kohonen's self-organizing map and with a minimal free energy vector
quantizer is done in a systematic fMRI study showing comparative
quantitative evaluations. The most important findings in this paper are:
(1) the "neural gas" network outperforms the other two methods in terms of
detecting small activation areas, and (2) computed reference function
several that the "neural gas" network outperforms the other two methods.
The applicability of the new algorithm is demonstrated on experimental
data.
Reference
Thomas D. Otto, Anke Meyer-Baese,
Monica Hurdal, De Witt Sumners, Axel Wismuller and Dorothee Auer,
Model-Free Functional MRI Analysis Using Cluster-Based Methods,
in K. L. Priddy and P. J. Angeline (eds), Intelligent Computing: Theory and Applications, Vol. 5103 of Proceedings of SPIE, pp. 17-24, 2003.