Yang J, et al. - Using a multisite, multimodal imaging cohort and several modeling techniques—penalized logistic regression, random forest, and support vector machine (SVM), researchers identified biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. It was noted that binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. According to the findings obtained, the poor accuracy of classification and predictive outcomes found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification.