Alzheimer's disease: diagnosis by different methods of voxel-based morphometry.

Abstract

The purpose of this study was to determine the optimal computational options in voxel-based morphometry (VBM) for discrimination between Alzheimer's disease (AD) patients and healthy control (HC) subjects. Structural magnetic resonance images of 24 AD patients and 26 HC subjects were analyzed using VBM to determine brain regions with significant gray matter (GM) loss due to AD. The VBM analyses were performed with 4 different computational options: gray matter concentration (GMC) analysis with and without global normalization, and gray matter volume (GMV) analysis, with and without global normalization. Statistical maps calculated with the 4 computational options were obtained at 3 different P-value thresholds (P < 0.001, P < 0.0005, and P < 0.0001, uncorrected for multiple comparisons), yielding a total of 12 sets of maps, from which regions-of-interest (ROI) were generated for subsequent analyses of performance in terms of discrimination between AD patients and HC subjects as based on the mean value of either the GMC or GMV within the ROI for each of the 12 maps. Discrimination performance was evaluated by means of comparing the area-under-the-curve derived from the receiver-operating characteristic analysis as well as on the accuracy of the discrimination. Discrimination based on GMC analysis resulted in better performance than that based on GMV analysis. The best discrimination performance was achieved with GMC analysis either with or without proportional global normalization. The findings suggested that GMC-based VBM is better suited than GMV-based VBM for discrimination between AD patients and HC subjects.