A Translational Determination of the Mechanisms of Maladaptive Choice in Opioid Use Disorder

阿片类药物使用障碍适应不良选择机制的转化测定

• 批准号: 9913503
• 负责人: Joshua Beckmann
• 金额: $ 62.04万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9913503
• 关键词: Abate; Abstinence; Address; Animals; Area; Back; Behavioral; Behavioral Mechanisms; Biological; Brain Diseases; Brain region; Choice Behavior; Chronic; Clinical; Cognition; Computer Models; Cues; Decision Making; Disease; Drug usage; Environment; FDA approved; Fibrinogen; Food; Foundations; Functional Magnetic Resonance Imaging; Future; Half-Life; Human; Impairment; Individual; Intravenous; Knowledge; Learning; Mental disorders; Methods; Modeling; Neurobiology; Neurologic; Neurosciences; Opioid; Opioid agonist; Outcome; Oxycodone; Performance; Pharmaceutical Preparations; Postdoctoral Fellow; Prevention; Prevention strategy; Probability; Procedures; Process; Psychological reinforcement; Rattus; Research; Research Design; Safety; Schedule; Self Administration; Signal Transduction; Task Performances; Techniques; Time; Translating; Translations; Update; Withdrawal; base; experience; experimental study; human subject; innovation; neuroadaptation; neurobehavioral; neuroimaging; neuromechanism; non-drug; novel; opioid exposure; opioid use; opioid use disorder; opioid user; opioid withdrawal; participant safety; reinforcer; relating to nervous system; remifentanil; theories; therapy development; translational study; treatment strategy

ABSTRACT Opioid use disorder (OUD) is characterized by the decision to use opioids at the expense of other activities. Lab-based efforts to address this problem have therefore included opioid choice self-administration procedures that incorporate a non-drug alternative to model this defining feature. Studies using these procedures have typically scheduled competing reinforcers so that the probabilities are certain. However, such deterministic outcomes are not representative of real-world experiences in which the consequences from drug-related choices are often unpredictable. Importantly, decision-making in a dynamic, uncertain context significantly alters the value of choice options and requires continuous updating of option values, which engages learning processes and related corticostriatal networks that function abnormally in OUD. Decision-making in dynamic environments has been successfully modeled using probabilistic reinforcement-learning choice (PRLC) tasks. The integration of these tasks with reinforcement-learning (RL) computational modeling has been used to capture moment-to- moment changes in the mechanisms of dynamic choice, and the application of neuroscience techniques has begun to identify the underlying neurobiology. This approach has uncovered biologically-based decision-making abnormalities in multiple brain disorders, but has yet to be systematically applied to the experimental study of OUD, The translation of combined RL and neuroscience approaches to OUD is logical considering the maladaptive choice behavior that typifies the disorder, the varying reinforcement probabilities in opioid users’ natural environments, and the learning impairments that have been documented in individuals with OUD. Thus, there are critical gaps in our understanding of the mechanisms underlying dynamic opioid use decisions, and a strong scientific premise for applying an RL framework to fill these gaps. This project proposes rigorous PRLC tasks, RL modeling, neurorecording/fMRI neuroimaging techniques and complementary, translational study designs in rats and humans. The first set of cross-species experiments will demonstrate the impact of opioid exposure and withdrawal on dynamic decision-making and reveal the neurobehavioral and neurobiological processes underlying abnormal task performance. The second set of experiments will use a PRLC task in which intravenous remifentanil, a prototypical opioid agonist with a favorable safety profile, is available as an alternative to a non-drug reinforcer to determine the behavioral and neural “profiles” associated with drug choice, as well as the increases and decreases in drug choice that occur during withdrawal and in the presence of a large magnitude alternative reinforcer, respectively. This project will have a significant impact on the field by establishing the experimental application of reinforcement-learning theory to the study of maladaptive dynamic drug-use decision-making in OUD to reveal behavioral and neural mechanisms that can be targeted for future prevention and treatment development.

抽象的 阿片类药物使用障碍（OUD）的特征是决定使用阿片类药物以其他活动为代价。 因此，基于实验室的范围可以解决添加问题，因此包括阿片类药物选择自我管理程序 这结合了对此定义功能建模的非药水替代方案。 通常是计划的竞争增强剂，因此概率是确定的。 结果并不代表现实世界的经验，在这种经验中，与药物相关选择的后果 通常是不可预测的。 选择选项的价值，并且需要连续更新选项值，以吸引学习过程 以及在OUD中异常起作用的相关皮质纹状体网络。 已通过概率加强学习选择（PRLC）任务成功建模 在使用加强学习（RL）计算建模的这些任务中 动态选择机制的力矩变化，神经科学技术的应用具有 开始识别潜在的神经生物学。 多种脑部疾病异常，但尚未将其系统地应用于 oud，组合的RL和神经科学方法的翻译是逻辑上的 适应不良的选择行为，代表该疾病 因此，自然环境和学习障碍已记录在OUD中。 我们对动态阿片类药物使用决策机制的理解存在关键差距， 使用RL框架来填补这些差距的强大科学前提。 任务，RL建模，神经记录/FMRI神经影像学技术和压缩，转化研究 大鼠和人类的设计。 在动态决策中暴露和退出，并揭示神经虫和神经生物学 过程的基础异常任务绩效。 静脉注射雷曲霉，一种具有良好安全性的原型阿片类动力学家，可作为替代品可用 到非药物增强剂来确定与药物选择相关的行为和神经“特征”，以及 戒断期间发生的药物选择的增加和减少 该项目分别对该项目产生重大影响。 建立强化学习理论的实验应用应用于不良适应性动态的研究 毒品使用的决策在上面陶醉于行为和神经杂音，可以将来转向未来 预防和治疗发展。