 Despite significant advances in pharmacological treatment, the global burden of depression is increasing worldwide. The major challenge in antidepressant treatment is the clinicians inability to predict the variability in individual response to the treatment. The development of biomarkers to predict treatment outcomes would enable clinician to find the right medication for a particular patient at the early stage of the treatment and thus could reduce prolonged suffering and ineffective protracted treatment. Brain imaging studies that examined brain predictors of treatment response based on group comparisons have limited value in classifying individuals as responders or non-responders. Machine learning classification techniques such as the support vector machine SVM method have proven useful in the classification of individual brain image observations into distinct groups or classes. However, studies that have applied the SVM method to structural and functional magnetic resonance scans fMRI involved small sample sizes and were confounded by placebo responses. Furthermore, a recent meta-analysis of clinical trials and EEG studies have shown that early clinical responses and brain changes at the early phase of antidepressant treatment may predict later clinical outcomes suggesting that neural markers measured in the early phase of antidepressant treatment may improve predictive accuracy. However, there is no fMRI study to date that has examined the predictive accuracy of data obtained in early phase of the treatment. We have preliminary fMRI data relating to early treatment response that form the basis of this proposed study. 