BACKGROUND: Growing evidence documents the potential of machine learning for 
developing brain based diagnostic methods for major depressive disorder (MDD). 
As symptom severity may influence brain activity, we investigated whether the 
severity of MDD affected the accuracies of machine learned MDD-vs-Control 
diagnostic classifiers.
METHODS: Forty-five medication-free patients with DSM-IV defined MDD and 19 
healthy controls participated in the study. Based on depression severity as 
determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were 
sorted into three groups: mild to moderate depression (HRSD 14-19), severe 
depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected 
functional magnetic resonance imaging (fMRI) data during both resting-state and 
an emotional-face matching task. Patients in each of the three severity groups 
were compared against controls in separate analyses, using either the 
resting-state or task-based fMRI data. We use each of these six datasets with 
linear support vector machine (SVM) binary classifiers for identifying 
individuals as patients or controls.
RESULTS: The resting-state fMRI data showed statistically significant 
classification accuracy only for the very severe depression group (accuracy 66%, 
p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) 
and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. 
With task-based fMRI data, the automated classifier performed at chance in all 
three severity groups.
CONCLUSIONS: Binary linear SVM classifiers achieved significant classification 
of very severe depression with resting-state fMRI, but the contribution of brain 
measurements may have limited potential in differentiating patients with less 
severe depression from healthy controls.