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Pattern recognition methods in classifying fMRI data

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84029

Ku,  S-P
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84066

Macke,  J
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84063

Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Ku, S.-P., Gretton, A., Macke, J., & Logothetis, N. (2008). Pattern recognition methods in classifying fMRI data. Talk presented at 9th Conference of the Junior Neuroscientists of Tübingen (NeNa 2008). Ellwangen, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C6F3-6
Abstract
Pattern recognition methods have shown that fMRI data can reveal signicant 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. Many follow up studies have employed dierent methods to analyze human fMRI data with varying degrees of success. In this presentation I would like to discuss and compare four popular pattern recognition methods: correlation analysis, support vector machines (SVM), linear discriminant analysis and Gaussian nave Bayes (GNB), using data collected at high eld (7T) 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 methods perform above chance level. An important factor in overall classication performance is careful preprocessing of the data, including dimensionality reduction, voxel selection, and outlier elimination.