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

Martin Dyrba, Frederik Barkhof, Andreas Fellgiebel, Massimo Filippi, Lucrezia Hausner, Karlheinz Hauenstein, Thomas Kirste, Stefan J. Teipel, Federica Agosta, Janusch Blautzik, Arun L W Bokde, Michael Ewers, Florian Fischer, Giovanni B. Frisoni, Lutz Frolich, Harald Hampel, Frank Hentschel, Michael Hüll, Frank Jessen, Vanja KljajevicStefan Klöppel, Thomas Meindl, Laurence O'Dwyer, Michela Pievani, Petra J W Pouwels

Research output: Contribution to journalArticle

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-β42 negative MCI subjects (MCI-Aβ42-), 35 positive MCI subjects (MCI-Aβ42+), 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-Aβ42- and MCI-Aβ42+. When it came to separating MCI-Aβ42+ 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.

Original languageEnglish
Pages (from-to)738-747
Number of pages10
JournalJournal of Neuroimaging
Volume25
Issue number5
DOIs
Publication statusPublished - Sep 1 2015

Keywords

  • Alzheimer's disease (AD)
  • Diffusion tensor imaging (DTI)
  • Mild cognitive impairment (MCI)
  • Multicenter study
  • Multiple kernels Support Vector Machine (MK-SVM)

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

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    Dyrba, M., Barkhof, F., Fellgiebel, A., Filippi, M., Hausner, L., Hauenstein, K., Kirste, T., Teipel, S. J., Agosta, F., Blautzik, J., Bokde, A. L. W., Ewers, M., Fischer, F., Frisoni, G. B., Frolich, L., Hampel, H., Hentschel, F., Hüll, M., Jessen, F., ... Pouwels, P. J. W. (2015). 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. Journal of Neuroimaging, 25(5), 738-747. https://doi.org/10.1111/jon.12214