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  Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping

Stelzer, J., Buschmann, T., Lohmann, G., Margulies, D. S., Trampel, R., & Turner, R. (2014). Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping. Frontiers in Neuroscience, 8: 66. doi:10.3389/fnins.2014.00066.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0018-F7F2-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7C7B-9
Genre: Journal Article

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 Creators:
Stelzer, Johannes1, 2, Author              
Buschmann, Tilo1, 3, Author              
Lohmann, Gabriele1, 4, 5, Author              
Margulies, Daniel S.6, Author              
Trampel, Robert1, Author              
Turner, Robert1, Author              
Affiliations:
1Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634550              
2Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Denmark, ou_persistent22              
3Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany, ou_persistent22              
4Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Germany, ou_persistent22              
5Department of High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, ou_persistent22              
6Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546              

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Free keywords: fMRI; MVPA; Searchlight; Nonparametric statistics; Decoding
 Abstract: Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a non-parametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM). The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

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Language(s): eng - English
 Dates: 2013-12-102014-03-212014-04-16
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fnins.2014.00066
PMID: 24795548
PMC: PMC3997040
Other: eCollection 2014
 Degree: -

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Title: Frontiers in Neuroscience
  Other : Front Neurosci
Source Genre: Journal
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Pages: - Volume / Issue: 8 Sequence Number: 66 Start / End Page: - Identifier: ISSN: 1662-4548
ISSN: 1662-453X
CoNE: https://pure.mpg.de/cone/journals/resource/1662-4548