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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

基本信息

批准号：
10565857
负责人：
Joshua Beckmann
金额：
$ 61.68万
依托单位：
UNIVERSITY OF KENTUCKY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-15 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10565857
关键词：
Abate Abstinence Address Animals Area Back Behavioral Behavioral Mechanisms Brain Diseases Brain region Choice Behavior Chronic Clinical Cognition Computer Models Corpus striatum structure Cues Decision Making Disease Drug usage Environment FDA approved 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 Prevention Prevention strategy Probability Probability Learning Procedures Process Psychological reinforcement Rattus Research Research Design Safety Schedule Self Administration Signal Transduction Task Performances Techniques Time Translating Translations Update Withdrawal experience experimental study human subject innovation learning engagement neural neuroadaptation neurobehavioral neuroimaging neuromechanism non-drug novel opioid exposure opioid use opioid use disorder opioid user opioid withdrawal participant safety reinforcer remifentanil theories therapy development translational study treatment strategy

项目摘要

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.

摘要阿片类药物使用障碍（OUD）的特征是决定使用阿片类药物而牺牲其他活动。因此，以实验室为基础的解决这一问题的努力包括阿片类药物选择自我管理程序结合非药物替代品来模拟这一定义特征。使用这些程序的研究通常调度竞争的调度器以使得概率是确定的。然而，这种确定性结果不代表现实世界的经验，其中与药物有关的选择的后果往往是不可预测的。重要的是，在动态的、不确定的背景下做出决策，价值的选择选项，并要求不断更新的选项价值，这涉及学习过程和相关的皮质纹状体网络在OUD中功能异常。动态环境中的决策已经成功地使用概率学习选择（PRLC）任务建模。整合这些任务与重复学习（RL）计算建模已被用来捕捉时刻，时刻变化的机制，动态选择，和应用神经科学技术，开始识别潜在的神经生物学。这种方法揭示了基于生物学的决策在多种脑部疾病的异常，但尚未被系统地应用于实验研究， OUD，将RL和神经科学方法结合起来翻译成OUD是合乎逻辑的，适应不良的选择行为，典型的障碍，在阿片类药物使用者的不同强化概率，自然环境，以及在OUD患者中记录的学习障碍。因此，在本发明中，我们对动态阿片类药物使用决策的机制的理解存在重大差距，强有力的科学前提应用RL框架来填补这些空白。该项目提出了严格的PRLC 任务，RL建模，神经记录/fMRI神经成像技术和补充，翻译研究在老鼠和人类身上的设计。第一组跨物种实验将证明阿片类药物的影响暴露和退缩对动态决策的影响，并揭示了神经行为和神经生物学异常任务表现的潜在过程。第二组实验将使用PRLC任务，其中静脉注射瑞芬太尼是一种典型的阿片类激动剂，具有良好的安全性，可作为替代方案一个非药物治疗师，以确定与药物选择相关的行为和神经“概况”，以及停药期间和存在大量药物时药物选择的增加和减少幅值替代选择器。该项目将对实地产生重大影响，建立了适应性学习理论在适应不良动态研究中的实验应用 OUD中的药物使用决策，以揭示未来可以针对的行为和神经机制预防和治疗的发展。