Current methods of clinical assessment of depression depend almost entirely on verbal reports (clinical interviews or questionnaires) of patients, their family, or caregivers. They lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of depressive symptoms, especially those related to the timing of dyadic interaction between clinician and patient, much of which may occur outside the awareness of either individual. Automated facial image analysis (AFA) analysis is capable of extracting both the type and timing of nonverbal indicators of depression. Our hypothesis is that quantitative measures of the configuration and timing of facial expression, head motion, and gaze, obtainable by AFA, will improve clinical assessment of symptom severity and evaluation of treatment outcomes when combined with information from interviews and self-report questionnaires. In a clinical trial for the treatment of depression, we will test the hypotheses that AFA can track changes in severity of depressive symptoms over the course of treatment in a manner similar to how standard measures (eg. Hamilton score) track these changes; and that positive treatment outcome is better predicted by combining AFA with standard measures than by use of standard measures alone.