Diagnostic support for psychiatric disorders is a very interesting goal because of the lack of biological markers with sufficient sensitivity and specificity in psychiatry. The approach consists of a feature extraction process based on the results of Pearson correlation of known measures of white matter integrity obtained from diffusion weighted images: fractional anisotropy (FA) and mean diffusivity (MD), followed by a classification step performed by statistical support vector machines (SVM), different implementations of artificial neural networks (ANN) and learn vector quantization (LVQ) classifiers. The most discriminant voxels were found in frontal and temporal white matter. A total of 100% classification accuracy was achieved in almost every case, although the features extracted from the FA data yielded the best results. The study has been performed on publicly available diffusion weighted images of 20 male subjects.