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  Meta-analyses based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI

Dukart, J., Müller, K., Barthel, H., Villringer, A., Sabri, O., & Schroeter, M. L. (2013). Meta-analyses based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI. Psychiatry Research: Neuroimaging, 212(3), 230-236. doi:10.1016/j.pscychresns.2012.04.007.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000E-F0D9-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-AD08-2
Genre: Journal Article

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 Creators:
Dukart, Jürgen1, 2, 3, Author              
Müller, Karsten4, Author              
Barthel, Henryk2, 5, Author
Villringer, Arno1, 2, 6, Author              
Sabri, Osama2, 5, Author
Schroeter, Matthias L.1, 2, 6, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany, ou_persistent22              
3Laboratoire de Recherche en Neuroimagerie (LREN), Centre hospitalier universitaire vaudois, Lausanne, Switzerland, ou_persistent22              
4Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
5Department of Nuclear Medicine, University of Leipzig, Germany, ou_persistent22              
6Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_634558              

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Free keywords: Multimodal imaging; Support vector machine classification; Multicenter validation; ADNI
 Abstract: The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification. For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained. Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%. In conclusion, our results suggest SVM classification based on quantitative meta-analyses of multicenter data as a valid method for individual AD diagnosis. Furthermore, combining imaging information from MRI and FDG-PET might substantially improve the accuracy of AD diagnosis.

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Language(s): eng - English
 Dates: 2012-04-042011-07-282012-11-102013-06-30
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.pscychresns.2012.04.007
PMID: 23149027
Other: Epub 2012
 Degree: -

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Title: Psychiatry Research: Neuroimaging
  Other : Psychiatry Res. Neuroimaging
Source Genre: Journal
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Publ. Info: -
Pages: - Volume / Issue: 212 (3) Sequence Number: - Start / End Page: 230 - 236 Identifier: ISSN: 0925-4927
CoNE: https://pure.mpg.de/cone/journals/resource/954925566740