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

• Published in: Psychiatry research. Neuroimaging Vol. 212; no. 3; pp. 230 - 236
• Main Authors: Dukart, Juergen, Mueller, Karsten, Barthel, Henryk, Villringer, Arno, Sabri, Osama, Schroeter, Matthias Leopold
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 30.06.2013; Elsevier
• Subjects: ADNI; Adult and adolescent clinical studies; Aged; Aged, 80 and over; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Biological and medical sciences; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Female; Humans; Magnetic Resonance Imaging - methods; Male; Medical sciences; Middle Aged; Multicenter validation; Multimodal Imaging; Neuroimaging - methods; Neurology; Organic mental disorders. Neuropsychology; Positron-Emission Tomography - methods; Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Radiology; Support Vector Machine; Support vector machine classification; ADNI; Multimodal imaging; Multicenter validation; Support vector machine classification; Radionuclide study; Human; Nervous system diseases; 2-deoxy-2-fluoroglucose; Alzheimer disease; Review; Nuclear magnetic resonance imaging; Cerebral disorder; Metaanalysis; Central nervous system disease; Classification; Medical imagery; Degenerative disease; Positron emission tomography
• ISSN: 0925-4927; 1872-7506
• DOI: 10.1016/j.pscychresns.2012.04.007

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.