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  Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control

Stelzer, J., Chen, Y., & Turner, R. (2013). Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control. NeuroImage, 65, 69-82. doi:10.1016/j.neuroimage.2012.09.063.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-10AB-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-AFE0-B
Genre: Journal Article

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 Creators:
Stelzer, Johannes1, Author              
Chen, Yi2, 3, Author              
Turner, Robert4, Author              
Affiliations:
1Max Planck Research Group Music Cognition and Action, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634555              
2Max Planck Fellow Research Group Attention and Awareness, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634553              
3Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
4Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634550              

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Free keywords: MVPA; fMRI; Statistics; Multiple testing; Cluster size control; Second level analysis
 Abstract: An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate frameworks. However, the new brain-decoding methods have also posed new challenges for analysis and statistical inference on the group level. We discuss why the usual procedure of performing t-tests on accuracy maps across subjects in order to produce a group statistic is inappropriate. We propose a solution to this problem for local MVPA approaches, which achieves higher sensitivity than other procedures. Our method uses random permutation tests on the single-subject level, and then combines the results on the group level with a bootstrap method. To preserve the spatial dependency induced by local MVPA methods, we generate a random permutation set and keep it fixed across all locations. This enables us to later apply a cluster size control for the multiple testing problem. More specifically, we explicitly compute the distribution of cluster sizes and use this to determine the p-values for each cluster. Using a volumetric searchlight decoding procedure, we demonstrate the validity and sensitivity of our approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, our results showed a higher sensitivity. We discuss the theoretical applicability and the practical advantages of our approach, and outline its generalization to other local MVPA methods, such as surface decoding techniques.

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Language(s): eng - English
 Dates: 2012-09-252012-10-042013-01-15
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2012.09.063
PMID: 23041526
Other: Epub 2012
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 65 Sequence Number: - Start / End Page: 69 - 82 Identifier: ISSN: 1053-8119
CoNE: /journals/resource/954922650166