Classifying Alzheimer's disease using probability distribution distance of fractional anisotropy and trace from diffusion tensor imaging in combination with whole-brain segmentations

Abstract

Using diffusion tensor imaging (DTI), we developed and validated an automated classification procedure for Alzheimer’s disease (AD); specifically, DTI-derived fractional anisotropy (FA) and trace images from 22 AD subjects and 15 healthy control (HC) subjects were used. A total of four types of region of interest (ROI)-based features were tested, including the probability distribution distances of FA and trace images, within each of 162 whole-brain segmented ROIs, under both discrete and continuous intensity distribution modeling. The continuous modeling was conducted through a mixture of Gaussians, the parameters of which were estimated using maximum likelihood estimation via the expectation-maximization algorithm. We used principal component analysis (PCA) to reduce the dimension of the feature space and then linear discriminant analysis and support vector machine (SVM) for automated classification. According to our 10-times 10-fold cross-validation experiments, using the combination of PCA and linear SVM, the continuous distance of the trace image yielded the best classification performance with the accuracy being 87.84%±3.43% and the area under the receiver operating characteristic curve being 0.9121±0.0176, indicating its great potential as an effective AD biomarker.