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

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.

摘要