Interesting fMRI studies were also published last year. Among them, one used multivariate analysis with a support vector machine (SVM) to distinguish TD patients from healthy controls \cite{core}. This classificatory algorithm reached a correct general accuracy of 67% to distinguish the two groups, especially on the basis of resting-state fMRI data within the striatum, the fronto-parietal cortex and the cerebellum. In addition, the authors distinguished medicated and unmedicated patients with an accuracy of 69% based on the activity of the striatum, the insular and cerebellar networks. Thus, SVM algorithm seems to be of interest to identify multiple abnormal brain activities in patients.