Leveraging Computationally Derived Measures of Individual Differences in Learning and Decision-making to Predict Psychiatric Diagnosis, Symptoms and Changes in Symptom Severity Across Time
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,100
- 试验地点
- 4
- 主要终点
- Changes in DASS depression scale scores
研究概览
简要总结
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
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Basic Science
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •(healthy control participants):
- •Age range of 18 to
- •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.
排除标准
- •(healthy control participants):
- •Prior history and or current diagnosis of neurological disease.
- •Inclusion criteria (patients):
- •Age range of 18 to
- •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.
结局指标
主要结局
Changes in DASS depression scale scores
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 12 months
Computational parameter estimates related to novelty driven exploration and reward/predation risk tradeoffs will be correlated with DASS anxiety scale scores
次要结局
未报告次要终点
研究者
John P. O'Doherty, PhD
Fletcher Jones Professor of Decision Neuroscience
California Institute of Technology