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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

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Dukart,  Jürgen
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;
Laboratoire de Recherche en Neuroimagerie (LREN), Centre hospitalier universitaire vaudois, Lausanne, Switzerland;

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Müller,  Karsten
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Schroeter,  Matthias L.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-F0D9-3
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.