We used several metrics of variability to extract unsupervised features from video recordings of patients before and after deep brain stimulation (DBS) treatment for major depressive disorder (MDD). Our goal was to quantify the treatment effects on facial expressivity. Multiscale entropy (MSE) was used to capture the temporal variability in pixel intensity level at multiple time-scales. A dynamic latent variable model (DLVM) was used to learn a low dimensional (D = 20) set of dynamic factors that explain the observed covariance across the high-dimensional pixels (M = 30 × 30) within each video frame and across time. Our preliminary results indicate that unsupervised features learned from these video recordings can distinguish different phases of depression and recovery. The overarching goal of this research is to develop more refined markers of clinical response to treatment for depression.