Traditional strategies for diagnosing psychiatric disorders focus on definitions based on
constellations of co-occurring symptoms. Tracking symptom severity across therapy arcs
relies on administering scales that again ask questions about symptoms. These strategies
have allowed some degree of standardization but suffer from having a phenomenological
rather than mechanistic foundation (in terms of diagnosis) as well as subjectivity and
temporal sparseness (in terms of measurement).
The investigators apply a transdiagnostic framework based on the neurobiological concept
of approach behavior. Several psychiatric disorders (including OCD, uni- and bipolar
depression, PTSD and anxiety disorders, and addiction disorders) are characterized by
dysfunction in approach behavior. The investigators study the neurobehavioral basis of
approach dysfunction in a cohort of individuals with severe OCD and bipolar disorder
(BD). To study these behaviors, the investigators deploy a suite of wearables and
peripherals (Oura ring, Apple Watch, iPhone, audioband) that allow continuous and dense
measurement of behaviors relevant to the approach hypothesis: socialization, activity,
and sleep patterns. The investigators perform these measurements in two selected
environments. One is a novel apartment-style setting (neurobehavioral unit, NBU) that
combines the high-bandwidth data acquisition capability of a lab with the naturalistic
relevance and comfort of the home. The second is the truly natural and maximally
ethologically relevant setting of the ambulatory "home" environment in which people spend
the majority of their time.
The participants will be individuals planned for deep brain stimulation (DBS) implant for
their OCD or BD. The bi-directional (stimulate as well as record) nature of the DBS
systems will allow neural recordings that the investigators will synchronize with the
behavioral data streams. The investigators will apply predictive computational models in
conjunction with the causal manipulation provided by stimulation to test mechanistic
hypothesis relating neurophysiology, behavior, and clinical status. In Aim 1, The
investigators study reward-driven decision making by employing an augmented reality
approach-avoidance task in the NBU. In Aim 2, the investigators test the neurobehavioral
models' ability to predict clinical status from passively (and therefore low burden to
patient-participants) acquired data in the "home" environment. In Aim 3, the
investigators identify neural predictors of the patterns of sleep dysregulation
associated with these disorders using the unique environment of the NBU. In Aim 4, the
investigators examine critical concepts of ethics and ethology that arise with this new
field of naturalistic, chronic brain-behavior relationship investigation.
The investigators hope that methods validated and lessons learned in this project will
improve understanding of the mechanistic basis of a range of psychiatric disorders and
thereby allow greater rational design of therapeutic delivery.
