Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features

The aim of this study is to look for differential effects in white matter (WM) of bipolar disorder (BD) and Alzheimer's disease (AD) patients. We proceed by investigating the feasibility of discriminating between BD and AD patients, and from healthy controls (HC), using multivariate data analysis based on diffusion tensor imaging (DTI) data features. Specifically, support vector machine (SVM) classifiers were trained and tested on fractional anisotropy (FA). Voxel sites are selected as features for classification if their Pearson's correlation between FA values at voxel site across subjects and the indicative variable specifying the subject class is above the threshold set by a percentile of its empirical distribution. To avoid double dipping, selection was performed only on training data in a leave one out cross-validation study. Classification results show that FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in AD vs. HC, BD vs. HC, and AD vs. BD leave-one-out cross-validation studies. The localization of the discriminant voxel sites on a probabilistic tractography atlas shows effects on seven major WM tracts in each hemisphere and two commissural tracts.

► Feature selection based on the correlation of fractional anisotropy (FA) with the subject class.
► Accurate image co-registration pipeline for anatomical reference of FA volumes defined.
► Voxel sites found by feature selection on AD, BD and HC agree with findings in the literature.
► Two way classification among AD, BD and HC compares or improves over reported results.
► Tract atlas localization in seven major tracts on each hemisphere and two commissural tracts.