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A comparison between neural and fuzzy cluster analysis techniques for functional MRI

BIOMEDICAL SIGnAL PROCESSInG AnD COnTROL 2006年第2期1卷 243-252页

作者： Lange, O. Meyer-Baese, A. Hurdal, M. Foo, S. Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32310 USA Florida State Univ Dept Math Tallahassee FL 32306 USA

Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between neural and fuzzy clustering techniques in a systematic fMRI study. For the fMRI data, a comparative quantitative evaluation based on ROC analysis between the Gath-Geva algorithm, 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 was performed. The most important findings in this paper are: (1) SOM is outperformed by all other neural and fuzzy techniques regardless of the chosen number of codebook vectors in terms of detecting small activation areas, (2) the variations among the other techniques are minimal, and (3) a small number of codebook vectors is in general required to obtain consistent task-related activation maps, as determined by the performance evaluation based on cluster validity indices. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： Gath-Geva algorithm fuzzy n-means algorithm Minimal free energy vector quantization "neural-gas" network Self-organizing map fMRI

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

Model-free functional MRI analysis using improved fuzzy clus...

引用

Conference on Intelligent Computing - Theory and Applications II

作者： Lange, O Meyer-Bäse, A Wismüller, A Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32310 USA

ISBN: (纸本)0819453447

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 this 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.

关键词： Gath-Geva algorithm fuzzy n-means algorithm minimal free energy vector quantization "neural gas" network self-organizing map fMRI

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