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

BACKGROUNDAlzheimer'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). METHODSWe 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. RESULTSWe 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. CONCLUSIONSOur results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD.