 Machine Learning Classification Support Vector Machine: The support vector machine SVM is a computer based analytical technique designed for high dimensional biological data such as fMRI data and provides the best classification of individual observations into distinct groups 38. Diagnostic classification depression diagnosis and healthy control and classification of treatment responsiveness responders and non-responders have been examined in a clinical population with fMRI data using SVM 39-41. This technique consists of two phases: training phase and testing phase. During the training phase an SVM is trained to develop a decision function or hyperplane that separates the data into two groups according to a class label. In the testing phase, this decision function can be used to predict the class label of a new subject as being a responder or non-responder. The accuracy of prediction by SVM depends on its specificity identification of true negatives and sensitivity identification of true positives. In recent years a few neuroimaging studies have employed SVM to structural and functional MRI data in order to predict the MDD patients who improved with treatment and who did not. Fu et al 2008 showed that applying SVM on emotional task-related fMRI data, 62 of patients who achieved remission sensitivity and 75 of patients who did not achieve remission specificity following 8 weeks of fluoxetine treatment could be predicted. But these results were not statistically significant due to small sample sizes remitters 8, non-remitters10. Similarly, Costafreda et al 2009 applied SVM to pretreatment structural scans and showed prediction with a sensitivity of 88.9 and a specificity of 88.9 and accuracy of 88.9 in a small sample comprised of 18 patients 40. In a recent study involving 61 MDD patients, SVM analysis of pretreatment white matter data predicted clinical outcome of refractory and non- refractory depression with an accuracy of 65.22, sensitivity of 56.2 and specificity of 73.91 41. Although the results of the later study were statistically significant, the low sensitivity and accuracy may limit its clinical use. Moreover, the structural imaging may not be useful to examine predictive value of early treatment changes in the brain function. In summary, there are no studies, to date that have applied SVM to functional data generated from a large sample for use in evaluating predictive accuracy at the individual level. 