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Publikation: Zeitschriftenartikel

Predicting prodromal Alzheimer's Disease in subjects with mild cognitive impairment using machine learning classification of multimodal multicenter diffusion-tensor and magnetic resonance imaging data


Grunddaten

Titel Predicting prodromal Alzheimer's Disease in subjects with mild cognitive impairment using machine learning classification of multimodal multicenter diffusion-tensor and magnetic resonance imaging data
Veröffentlicht in Journal of neuroimaging : official journal of the American Society of Neuroimaging. - Berlin [u.a.] : Wiley-Blackwell
Erscheinungsjahr 2015
Seiten (von – bis) 738 – 747
Band 25
Heft-Nr. 5
Jahr 2015
Publikationsform Elektronische Ressource
Publikationsart Zeitschriftenartikel
Sprache Englisch
DOI 10.1111/jon.12214
Letzte Änderung 14.01.2016 15:49:23
Bearbeitungsstatus durch UB Rostock abschließend validiert
Dauerhafte URL http://purl.uni-rostock.de/fodb/pub/49091
Links zu Katalogen Diese Publikation in der Universitätsbibliographie Diese Publikation im GBV-Katalog

Abstract

BACKGROUND Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI). METHODS We applied a Support Vector Machine (SVM) classifier to DTI and volumetric magnetic resonance imaging data from 35 amyloid-42 negative MCI subjects (MCI-A42), 35 positive MCI subjects (MCI-A42+), and 25 healthy controls (HC) retrieved from the European DTI Study on Dementia. The SVM was applied to DTI-derived fractional anisotropy, mean diffusivity (MD), and mode of anisotropy (MO) maps. For comparison, we studied classification based on gray matter (GM) and WM volume. RESULTS We obtained accuracies of up to 68% for MO and 63% for GM volume when it came to distinguishing between MCI-A42 and MCI-A42+. When it came to separating MCI-A42+ from HC we achieved an accuracy of up to 77% for MD and a significantly lower accuracy of 68% for GM volume. The accuracy of multimodal classification was not higher than the accuracy of the best single modality. CONCLUSIONS Our results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD.

Autoren

Dyrba, Martin
Barkhof, F.
Fellgiebel, A.
Hauenstein, Karlheinz Hans
Kirste, Thomas Link zur UB Rostock Link zum GBV-Katalog
Teipel, Stefan Link zur UB Rostock Link zum GBV-Katalog

Einrichtungen

IEF/Bereich Informatik
UMR/Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie
UMR/Klinik und Poliklinik für Psychiatrie und Psychotherapie (KPP)