Model-free functional MRI analysis using improved fuzzy cluster analysis techniques

Oliver Lange; Anke Meyer-Baese; Axel Wismueller M.D.; Monica Hurdal; DeWitt Sumners; Dorothee Auer

doi:10.1117/12.541778

12 April 2004 Model-free functional MRI analysis using improved fuzzy cluster analysis techniques

Oliver Lange, Anke Meyer-Baese, Axel Wismueller M.D., Monica Hurdal, DeWitt Sumners, Dorothee Auer

Proceedings Volume 5421, Intelligent Computing: Theory and Applications II; (2004) https://doi.org/10.1117/12.541778
Event: Defense and Security, 2004, Orlando, Florida, United States

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 Gath-Geva algorithm 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 the fuzzy n-means algorithm, Kohonen's self-organizing map, fuzzy n-means algorithm with unsupervised initialization, minimal free energy vector quantizer and the "neural gas" network is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in the paper are: (1) the Gath-Geva algorithms outperforms for a large number of codebook vectors all other clustering methods in terms of detecting small activation areas, and (2) for a smaller number of codebook vectors the fuzzy n-means with unsupervised initialization outperforms all other techniques. The applicability of the new algorithm is demonstrated on experimental data.

Citation Download Citation

Oliver Lange, Anke Meyer-Baese, Axel Wismueller M.D., Monica Hurdal, DeWitt Sumners, and Dorothee Auer "Model-free functional MRI analysis using improved fuzzy cluster analysis techniques", Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); https://doi.org/10.1117/12.541778

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