Utility of adaptive design optimization for developing rapid and reliable behavioral paradigms for substance use disorders

利用自适应设计优化来开发快速可靠的药物滥用行为范例

• 批准号: 10637895
• 负责人: Woo-Young Ahn
• 金额: $ 55.28万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10637895
• 关键词: Address; Alcohols; Assessment tool; Attention; Bayesian Modeling; Behavior; Behavioral Model; Behavioral Paradigm; Biological Assay; Brain; Bulgaria; Classification; Cocaine use disorder; Cognition; Computational Science; Computer software; Computing Methodologies; Country; Data; Data Collection; Decision Making; Diagnostic; Dimensions; Disease; Effectiveness; Emotional; Etiology; Feedback; Goals; Heterogeneity; Hour; Individual; Influentials; Infrastructure; Intervention; Knowledge; Measures; Methods; Modeling; Monitor; Neurobiology; Neurocognitive; Neurosciences; Opioid; Outcome; Participant; Pathway interactions; Patient Self-Report; Patients; Phenotype; Population Heterogeneity; Procedures; Prognosis; Research; Sampling; Smoking Cessation Intervention; South Korea; Stimulant; Substance Use Disorder; Testing; Time; Tobacco; Validation; Veterans; addiction; alcohol use disorder; biomarker discovery; biomarker performance; clinical practice; cognitive function; cognitive process; computerized tools; cost; design; discounting; disease classification; endophenotype; executive function; heuristics; improved; incentive salience; indexing; machine learning algorithm; mobile application; neurobehavioral; neuroimaging; neuropsychiatry; novel; open source; opioid use disorder; patient subsets; prevent; prognostic; rapid test; response; software development; tobacco smokers; trait; treatment response; undergraduate student; web platform; Address; Alcohols; Assessment tool; Attention; Bayesian Modeling; Behavior; Behavioral Model; Behavioral Paradigm; Biological Assay; Brain; Bulgaria; Classification; Cocaine use disorder; Cognition; Computational Science; Computer software; Computing Methodologies; Country; Data; Data Collection; Decision Making; Diagnostic; Dimensions; Disease; Effectiveness; Emotional; Etiology; Feedback; Goals; Heterogeneity; Hour; Individual; Influentials; Infrastructure; Intervention; Knowledge; Measures; Methods; Modeling; Monitor; Neurobiology; Neurocognitive; Neurosciences; Opioid; Outcome; Participant; Pathway interactions; Patient Self-Report; Patients; Phenotype; Population Heterogeneity; Procedures; Prognosis; Research; Sampling; Smoking Cessation Intervention; South Korea; Stimulant; Substance Use Disorder; Testing; Time; Tobacco; Validation; Veterans; addiction; alcohol use disorder; biomarker discovery; biomarker performance; clinical practice; cognitive function; cognitive process; computerized tools; cost; design; discounting; disease classification; endophenotype; executive function; heuristics; improved; incentive salience; indexing; machine learning algorithm; mobile application; neurobehavioral; neuroimaging; neuropsychiatry; novel; open source; opioid use disorder; patient subsets; prevent; prognostic; rapid test; response; software development; tobacco smokers; trait; treatment response; undergraduate student; web platform

ABSTRACT A key problem in substance use disorders (SUD) is their etiological and functional heterogeneity, which is not well captured by the current psychiatric nosology. An influential neuroscience-based heuristic framework, Addictions Neuroclinical Assessment (ANA), proposes that to address this heterogeneity, the assessment of addictions should be multi-dimensional and focus on three key domains: executive function (EF), incentive salience (IS), and negative emotionality (NE), assessed with comprehensive batteries of self-report and neurobehavioral tasks. While computational tools have increased the knowledge extracted from these tasks, there are surprisingly few high-quality assays for monitoring and characterizing these domains. The burden of administration of current assessment batteries may take up to 10 hours and most assessment instruments lack precision in identifying underlying etiological mechanisms. Critically, most neurobehavioral and neuroimaging tasks have low test-retest reliability, which limits their utility for biomarker discovery. To address these limitations, we propose to apply Bayesian adaptive design optimization (ADO; Myung & Pitt, 2009) to established tasks that index the three ANA domains, with the goal of developing rapid, robust, and reliable neurobehavioral probes of these domains. ADO is a general-purpose computational machine-learning algorithm that optimizes data collection and extracts the maximal information from participant responses in the fewest possible trials. Our preliminary data show that ADO led to 0.95 or higher test-retest reliability of the delay discounting rate in under 1-2 minutes of testing, captured approximately 10% more variance in test-retest reliability, and was 3-5 times more precise and 3-8 times more efficient than conventional assessment methods (Ahn et al., 2020). The current study proposes to develop and evaluate a battery of ADO-based tasks, software, and mobile apps using state- of-the-science computational approaches that will significantly reduce the time for neurocognitive task administration, while increasing task reliability, precision, and efficiency. To capture the heterogeneity of addiction, this battery will be tested with neurotypical individuals and several diverse populations with different types of SUD (opioid, stimulant, alcohol, and tobacco) in three countries (USA, South Korea, Bulgaria) where we have developed infrastructure for this type of research. This value-added perspective would be useful for out- of-sample validation of our models and allow us to address not only the generalizability of the ANA domains to different types of SUD, but also the cross-cultural generalizability of the domains, which has not been examined. The specific aims of the study are to: (1) Develop a battery of reliable and efficient ADO-based neurobehavioral tasks of the ANA domains and assess its test-retest reliability in neurotypical individuals; (2) Assess the predictive utility of the newly developed ADO tasks for SUD outcomes by testing patients with different types of SUD; and (3) Design web-based platforms and mobile apps for measuring cognition with the newly developed ADO tasks, and open-source software platforms with the ADO and other computational methods we develop.

抽象的 物质使用障碍的关键问题（SUD）是他们的病因和功能异质性，不是 当前的精神病学疗法捕获了很好的捕获。一个有影响力的基于神经科学的启发式框架， 成瘾神经临床评估（ANA）提出要解决这种异质性，评估 成瘾应该是多维的，并专注于三个关键领域：执行功能（EF），激励措施 显着性（IS）和负面情绪（NE），并通过自我报告的全面电池进行评估 神经行为任务。尽管计算工具增加了从这些任务中提取的知识，但 令人惊讶的是，很少有高质量的测定法用于监测和表征这些领域。负担 当前评估电池的管理可能需要长达10个小时，并且大多数评估工具都缺乏 识别潜在的病因机制的精度。至关重要的是，大多数神经行为和神经影像学 任务的测试可靠性较低，这限制了其实用性生物标志物发现。为了解决这些限制， 我们建议将贝叶斯自适应设计优化（ADO； Myung＆Pitt，2009）应用于确定的任务 索引三个ANA域，目的是开发快速，健壮和可靠的神经行为探针 这些域。 ADO是一种通用计算机学习算法，可优化数据 在最少的试验中，收集并从参与者响应中提取最大信息。我们的 初步数据显示，ADO导致延迟折现率在下 1-2分钟的测试，捕获的测试可靠性差异大约增加了10％，为3-5次 比常规评估方法更精确，效率高3-8倍（Ahn等，2020）。电流 研究建议使用状态 - 科学计算方法将大大减少神经认知任务的时间 管理，同时提高任务可靠性，精度和效率。捕获异质性 成瘾，该电池将与神经典型的个体和几个不同的人群进行测试 在三个国家（美国，韩国，保加利亚）的SUD类型（阿片类药物，兴奋剂，酒精和烟草） 我们已经开发了此类研究的基础设施。这种增值的观点将对外部有用 样本验证我们的模型，并使我们不仅可以解决ANA域的普遍性 不同类型的SUD，也是尚未检查的域的跨文化概括。 该研究的具体目的是：（1）开发一系列可靠，高效的基于ADO的神经行为 ANA领域的任务并评估其在神经型个体中的重测可靠性； （2）评估预测 通过测试具有不同类型SUD的患者，新开发的用于SUD结果的ADO任务的实用性；和 （3）设计基于Web的平台和移动应用程序，用于通过新开发的ADO任务来衡量认知， 以及我们开发的ADO和其他计算方法的开源软件平台。