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

基本信息

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
项目类别：
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

项目摘要

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 (SUD) 的一个关键问题是其病因学和功能异质性，但这一点并不存在。当前的精神病学疾病分类学很好地捕捉到了这一点。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）建议，为了解决这种异质性，评估成瘾应该是多维度的，并关注三个关键领域：执行功能（EF）、激励显着性（IS）和负面情绪（NE），通过自我报告和综合评估神经行为任务。虽然计算工具增加了从这些任务中提取的知识，令人惊讶的是，用于监测和表征这些领域的高质量分析方法很少。的负担当前评估电池的管理可能需要长达 10 小时，并且大多数评估工具缺乏准确识别潜在病因机制。至关重要的是，大多数神经行为和神经影像学任务的重测可靠性较低，这限制了它们在生物标志物发现方面的效用。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）应用于既定任务：索引三个 ANA 域，目标是开发快速、稳健且可靠的神经行为探针这些域。 ADO 是一种通用计算机器学习算法，可优化数据在尽可能少的试验中收集并从参与者的反应中提取最大的信息。我们的初步数据显示，ADO 导致延迟贴现率的重测可靠性达到 0.95 或更高 1-2 分钟的测试，捕获了大约 10% 的重测可靠性差异，是 3-5 倍比传统评估方法更精确，效率提高 3-8 倍（Ahn 等人，2020）。目前的研究建议使用状态来开发和评估一系列基于 ADO 的任务、软件和移动应用程序科学计算方法将显着减少神经认知任务的时间管理，同时提高任务的可靠性、精确度和效率。捕捉异质性成瘾性，这种电池将在神经典型个体和具有不同特征的几个不同人群中进行测试三个国家（美国、韩国、保加利亚）的 SUD（阿片类药物、兴奋剂、酒精和烟草）类型我们已经为此类研究开发了基础设施。这种增值的观点对于输出是有用的对我们的模型进行样本外验证，使我们不仅能够解决 ANA 域的通用性问题不同类型的 SUD，还有领域的跨文化普遍性，这一点尚未得到检验。该研究的具体目标是：（1）开发一系列可靠且高效的基于 ADO 的神经行为模型 ANA 领域的任务并评估其在神经典型个体中的重测可靠性； (2) 评估预测通过测试不同类型 SUD 的患者，利用新开发的 ADO 任务来评估 SUD 的结果；和 (3) 设计基于网络的平台和移动应用程序，用于使用新开发的 ADO 任务测量认知，以及使用 ADO 和我们开发的其他计算方法的开源软件平台。