DynAMos: “The Dynamic Affective Movie Clip Database for Subjectivity Analysis” is a new video database that includes holistic and dynamic emotion ratings from over 100 participants watching 22 affective movie clips. Recognizing the subjectivity inherent to emotional experiences, we provide in the database the full distribution of all participants’ ratings.More
Social Signal Processing Algorithms
Determining when a patient is ready for discharge is an open problem, as currently 20% of those hospitalized for psychiatric illness will be hospitalized again within 30 days. To address this issue, we are working to identify behavioral biomarkers for severe mental illness in 400 inpatients hospitalized for psychosis at McLean. The Multisense project is a collaboration with Dr. with LP Morency‘s team at Carnegie Mellon University, where information is extracted from both audio and video of both clinician and patient during a series of semi-structured inpatient interviews. Multimodal analysis techniques are used both to predict clinical scale and discharge-readiness scores, as well as to visualize a summary of each interview and extract relevant interpretable features.More
The scientific motivation for longitudinal study of individuals with mental illness has increasingly become an imperative, if we are to translate biological insights into discoveries that benefit individual patients. For studies intended to reveal differences between patients and healthy individuals at a group level, characterizing patients during periods of profound changes in mental status is a sensible approach, since it maximizes biological signal while minimizing the need to keep track of patients over time.More
Research on affective disorders, such as bipolar disorder, and on psychotic disorders is hampered by a lack of basic understanding of the course of dynamic circuit properties that might underlie fluctuations in mood and cognition.Bipolar and psychotic disorders at its core are unstable clinical conditions: at its extremes, it can result in periods of profound changes in mood and cognition (i.e., mania, major depression, and psychosis). And yet, remarkably little has been done to understand the basic mechanisms underlying the fluctuating course common to these individuals. We hope to better understand and characterize the natural course of changes in mood and cognition and associated environmental variables in individuals with severe affective and psychotic disorders using mobile behavioral technology. We believe that this will further enable advances in our understanding of how these disorders develop, paving the way for the development and evaluation of new treatment strategies.