Model-free functional MRI analysis based on unsupervised clustering

Authors:
Axel Wismüller;Anke Meyer-Bäse;Oliver Lange;Dorothee Auer;Maximilian F. Reiser;DeWitt Sumners
Affiliations:
Department of Clinical Radiology, Ludwig-Maximilians University, Munich 80336, Germany and Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL;Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL;Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL;Max Planck Institute of Psychiatry, Munich 80804, Germany;Department of Clinical Radiology, Ludwig-Maximilians University, Munich 80336, Germany;Department of Mathematics, Florida State University, Tallahassee, FL
Venue:
Journal of Biomedical Informatics
Year:
2004

Citing 1
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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 rigourosly 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 fuzzy clustering scheme based on deterministic annealing is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) both "neural gas" and the fuzzy clustering technique outperform Kohonen's map in terms of identifying signal components with high correlation to the fMRI stimulus. (2) the "neural gas" outperforms the two other methods with respect to the quantization error, and (3) Kohonen's map outperforms the two other methods in terms of computational expense. The applicability of the new algorithm is demonstrated on experimental data.