Abstract
Alzheimer's disease is the most common form of neurodegenerative disorder and early detection is of great importance if new therapies are to be effectively administered. We have investigated whether the discrimination between early Alzheimer's disease (AD) and elderly healthy control subjects can be improved by adding magnetic resonance spectroscopy (MRS) measures to magnetic resonance imaging (MRI) measures. In this study 30 AD patients and 36 control subjects were included. High resolution T1-weighted axial magnetic resonance images were obtained from each subject. Automated regional volume segmentation and cortical thickness measures were determined for the images. 1H MRS was acquired from the hippocampus and LCModel was used for metabolic quantification. Altogether, this yielded 58 different volumetric, cortical thickness and metabolite ratio variables which were used for multivariate analysis to distinguish between subjects with AD and Healthy controls. Combining MRI and MRS measures resulted in a sensitivity of 97% and a specificity of 94% compared to using MRI or MRS measures alone (sensitivity: 87%, 76%, specificity: 86%, 83% respectively). Adding the MRS measures to the MRI measures more than doubled the positive likelihood ratio from 6 to 17. Adding MRS measures to a multivariate analysis of MRI measures resulted in significantly better classification than using MRI measures alone. The method shows strong potential for discriminating between Alzheimer's disease and controls.