Circuit Dynamics Underlying Longitudinal Fluctuations in Mood and Cognition in Bipolar Patients
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
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. However, because patient clinical state in borderline personality disorder (BPD) is inherently unstable, findings from such traditional case-control studies are fundamentally limited in generalizability by the clinical heterogeneity of the patient sample and a host of state factors that can confound group-level comparisons. Indeed, these confounding factors likely contribute to the lack of convergence in the BPD literature with respect to biological understanding, since few studies even attempt to measure and account for these critical factors. To be sure, longitudinal studies in patients with severe personality psychopathology pose significant logistical and technical challenges, since keeping track of patients in different clinical states is logistically challenging, and especially when intensive characterization is required, have until recently been cost-prohibitive. And yet longitudinal studies are essential for gaining an understanding of the biological and experience-dependent factors that exacerbate and ameliorate mood and cognition in individuals with BPD.
Capturing Longitudinal Fluctuations in Behavior, Mood, and Cognition in Patients with Obsessive-Compulsive Disorder
While substantial prior research has provided valuable insights into the underlying neurobiology of OCD, most studies of neuropsychiatric signs and symptoms have focused on either clinician-report or self-report measures, or have employed neuropsychological tests or cognitive neuroscience tasks, as the behavioral readout with which to correlate biological findings. We aim to use technology to target individual patients by revealing behavioral abnormalities using actigraphy signals.
Detecting Activity During Mental Health Hospitalization Using Wearable Devices
Inpatient psychiatric treatment settings provide care for individuals with a range of behavioral disturbances and psychopathology, which often manifests as profound alterations in the amount and nature of physical behavior. We aim to establish an efficient and consistent process to identify clinically significant levels of physical activity (e.g., sleep, restlessness, agitation) that could both prove useful for quantifying the overall level treatment success or failure in an individual patient, while also eventually providing realtime support for clinical staff on the unit.
Feasibility and Clinical Utility of MultiSense for Nonverbal Feature Extraction in Psychosis
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 inpatients hospitalized for psychosis at McLean. The Multisense project is a collaboration with Dr. Louis-Philippe 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.