BehaviorCABxtimeNCT06705179
Can Computational Measures of Task Performance Predict Psychiatric Symptoms and Changes in Symptom Severity Across Time
A study testing Behavioral task performance in people with Behavior and Depressive Disorder, run by California Institute of Technology at 2 sites in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Behavior, Depressive Disorder, Anxiety Disorders
- What is being tested?
- Behavioral task performance
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,100 participants
- Where
- 2 sitesUnited States
- Study dates
- 1 Jan 2025to 31 Dec 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
- Age range of 18 to 65.
- Psychiatric diagnosis of any type of depressive disorders, any type of anxiety disorders or obsessive-compulsive disorder.
Show all 10Show fewer
- 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.
Who cannot join
- Prior history and or current diagnosis of neurological disease.
- Prior history and or current diagnosis of neurological disease.
- History or current diagnosis of psychotic disorders.
- Currently active substance use disorder.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States2 sites
UCLA Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
Los Angeles, California, 90095
Reza Tadayon-nejad, MD PhD, 310-206-6389, RTadayonNejad@mednet.ucla.edu
California Insitute of Technology
Pasadena, California, 91125
John O'Doherty, D Phil, 626-395-5981, jdoherty@hss.caltech.edu
About this study
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
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
Read the 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.
- Being tested
- Behavioral task performance
- Keywords
- Computational Psychiatry, Behavioral task battery
- Medical subject headings
- Behavior, Depressive Disorder, Anxiety Disorders, Obsessive-Compulsive Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Behavioral task performanceBehavioural
Measures of performance on behavioral tasks
| Study group | Role | What they receive |
|---|---|---|
| Behavioral task battery | Other | Not described Receives: Behavioral: Behavioral task performance |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Nov 2024
- Study start1 Jan 2025 (estimated)
- Primary completion31 Dec 2029 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for BehaviorStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- California Institute of Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, John P. O'Doherty, PhD, California Institute of Technology
- Investigators
- John P O'Doherty, D.Phil, Principal investigator, California Institute of Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06705179
- Sponsor's protocol ID
- 1R01MH138895
- Acronym
- CABxtime
- Status last verified
- Nov 2024
- First submitted
- 18 Nov 2024
- Last updated
- 26 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual task performance data will be provided (trial data) and individual questionnaire scores will be available.
Study at a glance
From the registry record, last updated by the sponsor on 26 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06705179
- Acronym / sponsor ID
- CABxtime / 1R01MH138895
- Keywords
- Computational Psychiatry, Behavioral task battery
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,100 participants (estimated)
- Lead sponsor
- California Institute of TechnologyUniversity, hospital or other
- Study start
- 1 Jan 2025 (estimated)
- Primary completion
- 31 Dec 2029 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 26 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Behavioral task battery | Other | Behavioral: Behavioral task performance |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (10 listed)
- 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.
- Age range of 18 to 65.
Key exclusion criteria (4 listed)
- Prior history and or current diagnosis of neurological disease.
- Prior history and or current diagnosis of neurological disease.
- History or current diagnosis of psychotic disorders.
- Currently active substance use disorder.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Behavioral task performanceMeasures of performance on behavioral tasks | Behavioural | Behavioral task battery |
Primary outcome
Changes in DASS depression scale scores
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 12 months
Computational parameter estimates related to novelty driven exploration and reward/predation risk tradeoffs will be correlated with DASS anxiety scale scores
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Nov 2024.
- Individual participant dataSharing planned: Yes. Individual task performance data will be provided (trial data) and individual questionnaire scores will be available.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
John P O'Doherty, D.Phil principal investigator
California Institute of Technology
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Behavioral task performance in people with Behavior and Depressive Disorder, run by California Institute of Technology at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 26 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact John P O'Doherty, D.Phil (626-395-5981, jdoherty@hss.caltech.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in DASS depression scale scores (time frame: 12 months). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Nov 2024.