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  Comparison of Pattern Recognition Methods in Classifying High-resolution BOLD Signals Obtained at High Magnetic Field in Monkeys

Ku, S.-P., Gretton, A., Macke, J., & Logothetis, N. (2008). Comparison of Pattern Recognition Methods in Classifying High-resolution BOLD Signals Obtained at High Magnetic Field in Monkeys. Magnetic Resonance Imaging, 26(7), 1007-1014. doi:10.1016/j.mri.2008.02.016.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C701-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-426B-A
Genre: Journal Article

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Ku, S-P1, 2, Author              
Gretton, A2, 3, Author              
Macke, J2, 3, 4, Author              
Logothetis, NK1, 2, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
4Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              

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 Abstract: Pattern recognition methods have shown that functional magnetic resonance imaging (fMRI) data can reveal significant information about brain activity. For example, in the debate of how object categories are represented in the brain, multivariate analysis has been used to provide evidence of a distributed encoding scheme [Science 293:5539 (2001) 2425–2430]. Many follow-up studies have employed different methods to analyze human fMRI data with varying degrees of success [Nature reviews 7:7 (2006) 523–534]. In this study, we compare four popular pattern recognition methods: correlation analysis, support-vector machines (SVM), linear discriminant analysis (LDA) and Gaussian naïve Bayes (GNB), using data collected at high field (7 Tesla) with higher resolution than usual fMRI studies. We investigate prediction performance on single trials and for averages across varying numbers of stimulus presentations. The performance of the various algorithms depends on the nature of the brain activity being categorized: for several tasks, many of the methods work well, whereas for others, no method performs above chance level. An important factor in overall classification performance is careful preprocessing of the data, including dimensionality reduction, voxel selection and outlier elimination.

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 Dates: 2008-09
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.mri.2008.02.016
BibTex Citekey: 4877
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Title: Magnetic Resonance Imaging
Source Genre: Journal
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Publ. Info: New York : Elsevier
Pages: - Volume / Issue: 26 (7) Sequence Number: - Start / End Page: 1007 - 1014 Identifier: ISSN: 0730-725X
CoNE: https://pure.mpg.de/cone/journals/resource/954925533026