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  Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Meyer, S., Mueller, K., Stuke, K., Bisenius, S., Diehl-Schmid, J., Jessen, F., et al. (2017). Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data. NeuroImage: Clinical, 14, 656-662. doi:10.1016/j.nicl.2017.02.001.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-8CA9-C Version Permalink: http://hdl.handle.net/21.11116/0000-0004-F415-1
Genre: Journal Article

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 Creators:
Meyer, Sebastian1, Author
Mueller, Karsten1, Author              
Stuke, Katharina2, Author              
Bisenius, Sandrine2, Author              
Diehl-Schmid, Janine3, Author
Jessen, Frank4, Author
Kassubek, Jan5, Author
Kornhuber, Johannes6, Author
Ludolph, Albert C.5, Author
Prudlo, Johannes7, 8, Author
Schneider, Anja9, Author
Schümberg, Katharina2, Author              
Yakushev, Igor10, Author
Otto, Markus5, Author
Schroeter, Matthias L.2, 11, Author              
The FTLDc Study Group, Author              
Affiliations:
1Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3Departments of Psychiatry and Psychotherapy, TU Munich, Germany, ou_persistent22              
4Department of Psychiatry and Psychotherapy, University Bonn, Germany, ou_persistent22              
5Department of Neurology, Ulm University, Germany, ou_persistent22              
6Department of Psychology and Psychotherapy, Friedrich Alexander University Erlangen, Germany, ou_persistent22              
7Department of Neurology, University Medicine Rostock, Germany, ou_persistent22              
8German Center for Neurodegenerative Diseases, Rostock, Germany, ou_persistent22              
9Department of Psychiatry and Psychotherapy, Georg August University, Goettingen, Germany, ou_persistent22              
10Department of Nuclear Medicine, TU Munich, Germany, ou_persistent22              
11Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              

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Free keywords: Atrophy; Behavioral variant frontotemporal dementia; Diagnostic criteria; Frontotemporal lobar degeneration; MRI; Pattern classification
 Abstract: Purpose Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. Materials & methods Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, “leave one center out” conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis. Results Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 86.5%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach. Conclusion Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

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Language(s): eng - English
 Dates: 2017-01-052016-10-142017-02-032017-02-06
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.nicl.2017.02.001
PMID: 28348957
PMC: PMC5357695
Other: eCollection 2017
 Degree: -

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

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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: 656 - 662 Identifier: ISSN: 2213-1582
CoNE: https://pure.mpg.de/cone/journals/resource/2213-1582