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  MRI pattern recognition in Multiple Sclerosis normal-appearing brain areas

Weygandt, M., Hackmack, K., Pfueller, C., Bellmann-Strobl, J., Paul, F., Zipp, F., et al. (2011). MRI pattern recognition in Multiple Sclerosis normal-appearing brain areas. PLoS One, 6(6): e21138. doi:10.1371/journal.pone.0021138.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-2A23-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-C395-4
Genre: Journal Article

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Weygandt_MRIPattern.pdf (Publisher version), 856KB
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2011
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© 2011 Weygandt et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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 Creators:
Weygandt, Martin1, Author
Hackmack, Kerstin1, Author
Pfueller, Caspar2, Author
Bellmann-Strobl, Judith2, 3, Author
Paul, Friedemann2, 3, Author
Zipp, Frank4, Author
Haynes, John-Dylan1, 5, Author              
Affiliations:
1Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
2NeuroCure Cluster of Excellence, Charité University Medicine Berlin, Germany, ou_persistent22              
3Experimental and Clinical Research Center, Charité University Medicine Berlin, Germany, ou_persistent22              
4Department of Neurology, Johannes Gutenberg University, Mainz, Germany, ou_persistent22              
5Max Planck Fellow Research Group Attention and Awareness, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634553              

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 Abstract: Objective Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing­remitting type) in lesioned areas, areas of normal­appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques. Methods A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information. Results Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10−13). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10−7). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10−10). Interpretation We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.

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Language(s): eng - English
 Dates: 2011-05-202011-06-17
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pone.0021138
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Sciene
Pages: - Volume / Issue: 6 (6) Sequence Number: e21138 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850