 We will use support vector machines SVMs, as these have been successfully applied to predicting treatment outcomes in MDD from fMRI data. After preprocessing, SVM as implemented in PROBID software package will be used to investigate the accuracy of whole brain resting and task-related Blood Oxygen Level Dependent BOLD data in predicting response to antidepressant treatment. Individual brain scans will be treated as points located in a high dimensional space defined by BOLD response values in the preprocessed images. During the training phase, a linear decision boundary in this high dimensional space will be defined by a hyperplane that separates the individual scans according to a class label. SVM classifier will be trained by providing examples of the form where X represents the fMRI data and C represents the class label C 1 for remitters, and C -1 for non-remitters. Once the hyperplane is determined from the training data, it will be used to predict the class label of a test sample. A linear kernel SVM will be used to extract the weight sector as an image SVM discriminating map. A leave-one-out cross validation method will be used to validate the classifier. This procedure involves excluding a single subject from each group and the classifier will be trained using the remaining subjects. The subject pair excluded will be used to test the ability of the classifier to distinguish between remitters and non-remitters. The procedure will be repeated for each subject pair in the sample in order to assess the overall accuracy of SVM. To establish whether the classification accuracy is statistically significant, we will perform permutation testing. This will involve repeating the classification procedure 1000 times with a random permutation of the training group labels and counting the number of permutations that achieve higher sensitivity and specificity than the one observe with the true labels. The p value will be calculated by dividing this number by 1000. Bonferroni correction or false discovery rate will be used to correct for multiple testing. This SVM analysis and permutation testing will be performed on the pretreatment scan and 2-week post-treatment scan separately. The task-related data and resting state data will be analyzed separately. 