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  Predicting primary progressive aphasias with support vector machine approaches in structural MRI data

Bisenius, S., Mueller, K., Diehl-Schmid, J., Fassbender, K., Grimmer, T., Jessen, F., et al. (2017). Predicting primary progressive aphasias with support vector machine approaches in structural MRI data. NeuroImage: Clinical, 14, 334-343. doi:10.1016/j.nicl.2017.02.003.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-6633-F Version Permalink: http://hdl.handle.net/21.11116/0000-0004-F2C3-E
Genre: Journal Article

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 Creators:
Bisenius, Sandrine1, Author              
Mueller, Karsten2, Author              
Diehl-Schmid, Janine3, Author
Fassbender, Klaus4, Author
Grimmer, Timo3, Author
Jessen, Frank5, Author
Kassubek, Jan6, Author
Kornhuber, Johannes7, Author
Landwehrmeyer, Bernhard6, Author
Ludolph, Albert6, Author
Schneider, Anja8, Author
Anderl-Straub, Sarah6, Author
Stuke, Katharina1, Author              
Danek, Adrian9, Author
Otto, Markus6, Author
Schroeter, Matthias L.1, Author              
The FTLDc Study Group, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
2Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
3Departments of Psychiatry and Psychotherapy, TU Munich, Germany, ou_persistent22              
4Department of Neurology, Saarland University Homburg, Germany, ou_persistent22              
5Department of Psychiatry and Psychotherapy, University Bonn, Germany, ou_persistent22              
6Department of Neurology, Ulm University, Germany, ou_persistent22              
7Department of Psychology and Psychotherapy, Friedrich Alexander University Erlangen, Germany, ou_persistent22              
8Department of Psychiatry, Georg August University, Göttingen, Germany, ou_persistent22              
9Department of Neurology, Ludwig Maximilians University Munich, Germany, ou_persistent22              

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Free keywords: Grey matter; Multi-center; Primary progressive aphasia; Support vector machine classification; Whole brain approach
 Abstract: Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early in- dividual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degen- eration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest ap- proach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Inter- estingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Al- though the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

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Language(s): eng - English
 Dates: 2017-01-272016-09-142017-02-032017-02-06
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.nicl.2017.02.003
PMID: 28229040
PMC: PMC5310935
Other: eCollection 2017
 Degree: -

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Project name : -
Grant ID : -
Funding program : -
Funding organization : Max-Planck International Research Network on Aging (MaxNetAging)
Project name : German Consortium for Frontotemporal Lobar Degeneration
Grant ID : O1GI1007A
Funding program : -
Funding organization : German Federal Ministry of Education and Research (BMBF)
Project name : -
Grant ID : PDF-IRG-1307
Funding program : -
Funding organization : Parkinson’s Disease Foundation
Project name : -
Grant ID : MJFF-11362
Funding program : -
Funding organization : Michael J. Fox Foundation
Project name : -
Grant ID : -
Funding program : -
Funding organization : LIFE–Leipzig Research Center for Civilization Diseases, University of Leipzig
Project name : -
Grant ID : -
Funding program : -
Funding organization : European Union (EU)
Project name : -
Grant ID : -
Funding program : European Regional Development Fund
Funding organization : European Commission (EC)
Project name : -
Grant ID : -
Funding program : -
Funding organization : Free State of Saxony

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Title: NeuroImage: Clinical
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Elsevier
Pages: - Volume / Issue: 14 Sequence Number: - Start / End Page: 334 - 343 Identifier: ISSN: 2213-1582
CoNE: /journals/resource/2213-1582