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

Leveraging Computationally Derived Measures of Individual Differences in Learning and Decision-making to Predict Psychiatric Diagnosis, Symptoms and Changes in Symptom Severity Across Time

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

Study Overview

• Status: Not yet recruiting
• Conditions: Depressive Disorder; Anxiety Disorders; Behavior; Obsessive Compulsive Disorder (OCD)
• Intervention / Treatment: Behavioral: Behavioral task performance

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 Type: Interventional
• Enrollment (Estimated): 1100
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: John P O'Doherty, D.Phil; Phone Number: 626-395-5981; Email: jdoherty@hss.caltech.edu

Study Locations

• United States
  • California
    • Los Angeles, California, United States, 90095
      • UCLA Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
      • Contact: Reza Tadayon-nejad, MD PhD; Phone Number: 310-206-6389; Email: RTadayonNejad@mednet.ucla.edu
    • Pasadena, California, United States, 91125
      • California Insitute of Technology
      • Contact: John O'Doherty, D Phil; Phone Number: 626-395-5981; Email: jdoherty@hss.caltech.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion criteria (healthy control participants):

• Age range of 18 to 65.
• Not currently having a psychiatric diagnosis determined after psychiatric evaluation by Drs. Tadayon-Nejad and Wei (both are board certified psychiatrists).
• Ability to understand and perform experimental tasks, i.e. basic ability to communicate and comprehend tasks.
• Ability to give informed consent.

Exclusion criteria (healthy control participants):

• Prior history and or current diagnosis of neurological disease.

Inclusion criteria (patients):

• Age range of 18 to 65.
• Psychiatric diagnosis of any type of depressive disorders, any type of anxiety disorders or obsessive-compulsive disorder.
• Primary or comorbid bipolar disorders are allowed but only if not in the acute manic phase.
• Comorbidity with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are allowed.
• Ability to understand and perform experimental tasks, i.e. basic ability to communicate and comprehend tasks.
• Ability to give informed consent.

Exclusion criteria (patients):

• Prior history and or current diagnosis of neurological disease.
• History or current diagnosis of psychotic disorders.
• Currently active substance use disorder.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Basic Science
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: Behavioral task battery	Behavioral: Behavioral task performance; Measures of performance on behavioral tasks

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

• Sponsor: California Institute of Technology
• Investigators: Principal Investigator: John P O'Doherty, D.Phil, California Institute of Technology

Study record dates

Study Major Dates

• Study Start (Estimated): January 1, 2025
• Primary Completion (Estimated): December 31, 2029
• Study Completion (Estimated): December 31, 2029

Study Registration Dates

• First Submitted: November 18, 2024
• First Submitted That Met QC Criteria: November 21, 2024
• First Posted (Estimated): November 26, 2024

Study Record Updates

• Last Update Posted (Estimated): November 26, 2024
• Last Update Submitted That Met QC Criteria: November 21, 2024
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Keywords: Computational Psychiatry; Behavioral task battery
• Additional Relevant MeSH Terms: Mental Disorders; Mood Disorders; Anxiety Disorders; Depressive Disorder; Obsessive-Compulsive Disorder
• Other Study ID Numbers: 1R01MH138895

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Individual task performance data will be provided (trial data) and individual questionnaire scores will be available.
• IPD Sharing Access Criteria: Data will be provided to the NIH NDA database as requested per the grant conditions,
• IPD Sharing Supporting Information Type: ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No