Anxiety Disorders, Behavior, Depressive Disorder, Obsessive Compulsive Disorder (OCD)
Conditions
Keywords
Computational Psychiatry, Behavioral task battery
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.
Interventions
BEHAVIORALBehavioral task performance
Measures of performance on behavioral tasks
Sponsors
California Institute of Technology
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
BASIC_SCIENCE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
| 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. |
Countries
United States
Contacts
Primary ContactJohn P O'Doherty, D.Phil
Outcome results
None listed