Can Computational Measures of Task Performance Predict Psychiatric Symptoms and Changes in Symptom Severity Across Time

Not Applicable

Not yet recruiting

• Conditions: Behavior; Depressive Disorder; Anxiety Disorders; Obsessive Compulsive Disorder (OCD)

• Registration Number: NCT06705179
• Lead Sponsor: California Institute of Technology

Brief Summary

This study investigates the computational mechanisms associated with psychiatric disease dimensions. The study will characterize the relationship between computational parameter estimates of task performance and psychiatric symptoms and diagnoses with a longitudinal approach over a 12 month interval. Participants will be healthy participants recruited through Prolific an on-line crowdsourcing service, and psychiatric patients and healthy participants recruited via UCLA Psychiatry Clinics and UCLA's STAND Program

Detailed Description

The goal of computational psychiatry is to gain knowledge about underlying neurocomputational processes that underpin psychiatric disorders and to leverage this knowledge for improving diagnosis and treatment. A key step toward achieving this goal is to develop measures of individual differences in computations obtained from a single individual that are reliable, robust and meaningfully relevant to psychiatric dysfunction. In order to attain these objectives, it is essential we substantiate relationships between candidate computational mechanisms and diagnostic categories, symptom dimensions and treatment outcomes. In the present study, a computational assessment task battery (CAB) will be utilized that is designed to measure individual differences across a multidimensional array of computational processes. The study aims to separate three different variance components contributing to variability in computational parameter estimation: occasion-related variance due to incidental day to day changes in task performance, state-dependent variance that is related to meaningful variation across time in the underlying computations within an individual, and trait-related differences pertaining to stable individual differences in computations across individuals. To accomplish this, repeated assessments will be implemented using this battery across a 1-year interval within an on-line sample, and use hierarchical Bayesian modeling to separate the effect of occasion, state and trait-related variance on these parameter estimates. These variance components will then be related to diagnostic categories, symptom dimensions and symptom severity measures in a diverse cohort of psychiatric patients (mostly with depression, anxiety and OCD) recruited in Southern California. Finally, the relationship will be tracked between the computational parameter estimates and changes in symptoms across time in a subset of these patients. This study promises to significantly advance understanding of how to reliably extract diagnostically relevant computationally-derived measures of cognitive phenotypes that could eventually be migrated to the clinic.

Study Timeline

• Status Verified: 2024-11
• Study First Submitted: 2024-11-18
• Study First Submitted QC: 2024-11-21
• Last Update Submitted: 2024-11-21
• Study First Posted: 2024-11-26
• Last Update Posted: 2024-11-26
• Study Start: 2025-01-01
• Primary Completion: 2029-12-31
• Study Completion: 2029-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 1100

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Primary Outcome Measures

Name	Time	Method
Changes in DASS depression scale scores	12 months	Changes in computational parameter estimates related to gain/loss learning, reward/effort tradeoff and reward/predation risk tradeoffs will correlate with changes in DASS depression scale scores across time.
Changes in DASS anxiety scale scores	12 months	Changes in computational parameter estimates related to novelty driven exploration and reward/predation risk tradeoffs will be correlated with changes in DASS anxiety scale scores
Changes in OCI-R scores	12 months	Changes in computational parameter estimates related to the balance between model-based vs model-free reinforcement-learning will be correlated with changes in OCI-R symptoms across time.
OCI-R scores	12 months	Computational parameter estimates related to the balance between model-based vs model-free reinforcement-learning will be correlated with OCI-R scores.
DASS depression scale scores	12 months	Computational parameter estimates related to gain/loss learning, reward/effort tradeoff and reward/predation risk tradeoffs will correlate with DASS depression scale scores.
DASS anxiety scale scores	12 months	Computational parameter estimates related to novelty driven exploration and reward/predation risk tradeoffs will be correlated with DASS anxiety scale scores

Trial Locations

Locations (2)

UCLA Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; 🇺🇸 Los Angeles, California, United States

California Insitute of Technology; 🇺🇸 Pasadena, California, United States