Clinical trial NCT06705179: Can Computational Measures of Task Performance Predict Psychiatric Symptoms and Changes in Symptom Severity Across Time
Official title: Leveraging Computationally Derived Measures of Individual Differences in Learning and Decision-making to Predict Psychiatric Diagnosis, Symptoms and Changes in Symptom Severity Across Time
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Behavior
Depressive Disorder
Anxiety Disorders
Obsessive Compulsive Disorder (OCD)
Keywords: Computational Psychiatry
Behavioral task battery
Lead sponsor: California Institute of Technology
Sponsor class: OTHER
First posted: 2024-11-26
Enrollment: 1100
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: BASIC_SCIENCE

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Changes in DASS depression scale scores
timeFrame: 12 months
measure: Changes in DASS anxiety scale scores
timeFrame: 12 months
measure: Changes in OCI-R scores
timeFrame: 12 months
measure: OCI-R scores
timeFrame: 12 months
measure: DASS depression scale scores
timeFrame: 12 months
measure: DASS anxiety scale scores
timeFrame: 12 months
Secondary outcomes:
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: UCLA Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
state: California
city: Pasadena
country: United States
facility: California Insitute of Technology
state: California
Study officials:
affiliation: California Institute of Technology
name: John P O'Doherty, D.Phil
role: PRINCIPAL_INVESTIGATOR
References: