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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
Author(s) -
Dyrba Martin,
Barkhof Frederik,
Fellgiebel Andreas,
Filippi Massimo,
Hausner Lucrezia,
Hauenstein Karlheinz,
Kirste Thomas,
Teipel Stefan J.
Publication year - 2015
Publication title -
journal of neuroimaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.822
H-Index - 64
eISSN - 1552-6569
pISSN - 1051-2284
DOI - 10.1111/jon.12214
Subject(s) - fractional anisotropy , diffusion mri , medicine , dementia , cognitive impairment , white matter , magnetic resonance imaging , neuroimaging , alzheimer's disease , nuclear medicine , nuclear magnetic resonance , artificial intelligence , disease , radiology , psychiatry , computer science , physics
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.

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