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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., Trampel, R., & Turner, R. (2014). Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping. Frontiers in Neuroscience, 8: 66, pp. 1-8. doi:10.3389/fnins.2014.00066.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-803B-C Version Permalink: http://hdl.handle.net/21.11116/0000-0006-DBD1-7
Genre: Journal Article

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Stelzer, J, Author
Buschmann, T, Author
Lohmann, G1, 2, Author              
Margulies, DS, Author
Trampel, R, Author
Turner, R, Author
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497796              

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 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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 Dates: 2014-04
 Publication Status: Published online
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 Identifiers: DOI: 10.3389/fnins.2014.00066
BibTex Citekey: StelzerBLMTT2014
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Title: Frontiers in Neuroscience
  Other : Front Neurosci
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 8 Sequence Number: 66 Start / End Page: 1 - 8 Identifier: ISSN: 1662-4548
ISSN: 1662-453X
CoNE: https://pure.mpg.de/cone/journals/resource/1662-4548