Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

The use of MRI as a diagnostic tool for mental disorders has been a consistent goal of neuroimaging research. Despite this, the vast majority of prior work is descriptive rather than predictive. The current study examines the utility of applying support vector machine (SVM) learning to MRI measures of brain white matter in order to classify individuals with major depressive disorder (MDD). In a precisely matched group of individuals with MDD (n 25) and healthy controls (n 25), SVM learning accurately (70%) classified patients and controls across an unselected brain map of white matter fractional anisotropy values (FA). Using a feature selection approach, where maximal discriminative voxels were selected, classification accuracy increased to over 90%.