Behavioral and Neuronal Correlates of Human Mood States

Optimizing treatments in mental health requires an easy to obtain, continuous, and objective measure of internal mood. Unfortunately, current standard-of-care clinical scales are sparsely sampled, subject to recency bias, underutilized, and are not validated for acute mood monitoring. The recent shift to remote care also requires novel methods to measure internal mood. Recent advances in computer vision have allowed the accurate quantification of observable speech patterns and facial representations. The continuous and objective nature of these audio-facial behavioral outputs also enable the study of their neural correlates. Here, the investigators hypothesize that video-derived audio-facial behaviors have discrete neural representations in the limbic network and can provide a critical set of reliable longitudinal estimates of mood at low cost across home and clinic settings.