Model-free functional MRI analysis using cluster-based methods

Thomas Dan Otto; Anke Meyer-Baese; Monica Hurdal; DeWitt Sumners; Dorothee Auer; Axel Wismuller

doi:10.1117/12.487254

4 August 2003 Model-free functional MRI analysis using cluster-based methods

Thomas Dan Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sumners, Dorothee Auer, Axel Wismuller

Proceedings Volume 5103, Intelligent Computing: Theory and Applications; (2003) https://doi.org/10.1117/12.487254
Event: AeroSense 2003, 2003, 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 "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.

Citation Download Citation

Thomas Dan Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sumners, Dorothee Auer, and Axel Wismuller "Model-free functional MRI analysis using cluster-based methods", Proc. SPIE 5103, Intelligent Computing: Theory and Applications, (4 August 2003); https://doi.org/10.1117/12.487254

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