[R01] Cognitive Neurocomputational Task Reliability and Clinical Applications Consortium
Ente: National Institute of Mental Health
Scadenza: 2031-06-30
Importo max: 3.252.372 EUR
Paese: US
Descrizione
The Cognitive Neurocomputational Task Reliability and Clinical Applications in Serious Mental Illness
(CNTRaCS) consortium has made significant progress in unraveling the computational and neural mechanisms
of cognitive, sensory, and reward processing disruptions that contribute to functional disability across psychotic
disorders. CNTRaCS has used state-of-the-art computational, psychometric, and electrophysiological measures
to develop, optimize, and validate a suite of paradigms publicly available for use in a wide variety of settings,
ranging from the clinic to large-scale studies. We have shown that applying computational psychiatry tools to
parse cognitive mechanisms produces remarkably reliable estimates of precise parameters that allow us to
dissociate and identify specific processes that are disrupted either across psychotic illnesses or specifically in
schizophrenia. We will apply our previously validated approach to understanding the mechanisms underlying
hallucinations and delusions in schizophrenia—positive symptoms highly associated with distress, suicidality,
and violence that contribute to the economic burden associated with schizophrenia (estimated at $343.2 billion
dollars in 2019). We will use a formal modeling framework to test a predictive coding model of positive symptoms
that suggests that an imbalance between prior beliefs and incoming sensory evidence results in the experience
of perception in the absence of sensory input and the formation of beliefs that are resistant to updating. We will
tackle 7 paradigms designed to dissociate 4 constructs highly relevant to understanding the generation of
hallucinations and delusions – perceptual (i.e., external sensory information), conceptual (relations, causes,
events), contextual (change as a function of person or place), and social (intents and characteristics of people)
beliefs. We need to optimize the psychometrics of these computational metrics to ensure that these tools are
reliable, repeatable, tolerable, and not limited by ceiling and floor effects. We will utilize an open, flexible, and
scalable framework to deliver these tools online, facilitating “big data” studies that examine whether the
processes driving belief formation and updating are similar across the clinical to non-clinical spectrum. Aim 1 is
to test the hypothesis that model-based parameters for the measurement of belief are more precise and sensitive
than standard behavioral methods in assessing deficits in psychotic disorders and have an enhanced capability
to predict hallucinations and delusions in 200 individuals with psychosis (schizophrenia, schizoaffective) and 125
community controls, Aim 2 is to measure and optimize the psychometric properties of computational parameters
described in Aim 1 in a new sample of 200 individuals with psychosis and 125 healthy controls. Aim 3 is to
establish the feasibility and replicability of measurements and model-based analytic approaches outside the
laboratory for asses
Istituzione: WASHINGTON UNIVERSITY
PI: Deanna Barch, Cameron S. Carter, Molly Erickson, James M. Gold, ANGUS W MACDONALD
Progetto: 2R01MH084840-13A1
Settori: National Institute of Mental Health
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