Dyadic Recording

Context-Adaptive Multimodal Informatics for Psychiatric Discharge Planning

FNIBI / Current Projects / Actigraphy, Audio Diary Recording, Bipolar Disorder, Current Projects, Depression, Discharge Planning, Dyadic Recording, Ecological Momentary Assessment, Electronic Health Record, Facial Recording, Inpatient, Longitudinal, Road to Recovery, Schizophrenia, Smartphone, Social Signal Processing Algorithms /

January 1, 2022

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.

Circuit dynamics underlying longitudinal fluctuations in mood and cognition in bipolar patients

FNIBI / Current Projects / A Year in the Life, Actigraphy, Arterial Spin Labeling, Audio Diary Recording, Bipolar Disorder, Completed Projects, Depression, Dyadic Recording, Ecological Momentary Assessment, Eye-tracking, Facial Recording, Functional MRI, Neurocognitive Tasks, Neuroimaging, Schizophrenia, Smartphone, Social Signal Processing Algorithms /

January 1, 2018

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.

Intensive Real-Time Computational Phenotyping in Individuals with Borderline Personality Disorder

FNIBI / Past Projects / Actigraphy, Borderline Personality Disorder, Completed Projects, Dyadic Recording, Facial Recording, Smartphone, Social Signal Processing Algorithms /

January 1, 2010

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.