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：
10357944
负责人：
Joshua Beckmann
金额：
$ 62.51万
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 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10357944
关键词：
Abate Abstinence Address Animals Area Back Behavioral Behavioral Mechanisms 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

Abate Abstinence Address Animals Area Back Behavioral Behavioral Mechanisms 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

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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.

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，基于实验室解决此问题的努力包括阿片类药物选择自我管理程序这结合了建模此定义特征的非药水替代方案。使用这些程序的研究通常是安排的竞争增强剂，以确定可能性。但是，这样的确定性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。重要的是，在动态，不确定的环境中的决策显着改变了选择选项的价值，并且需要连续更新选项值，以吸引学习过程和相关的皮质纹状体网络，在OUD中起作用。动态环境中的决策已通过概率增强学习选择（PRLC）任务成功建模。整合在使用加强学习（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。这种方法发现了基于生物学的决策多种脑部疾病异常，但尚未系统地应用于 oud，考虑到的RL和神经科学方法的翻译是合乎逻辑的适应不良的选择行为，代表该疾病，阿片类药物使用者的强化可能性不同自然环境以及OUD个人记录的学习障碍。那，我们对动态阿片类药物使用决策的机制的理解存在关键的差距，并且强烈的科学信念应用RL框架来填补这些空白。这个项目提议严格的PRLC 任务，RL建模，神经记录/FMRI神经影像学技术和完整的翻译研究在老鼠和人类中设计。第一组跨物种实验将证明Opoid的影响在动态决策中暴露和退出，并揭示神经行为和神经生物学任务绩效异常的过程。第二组实验将使用PRLC任务静脉注射雷曲霉，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大小替代增强器。该项目将对该领域产生重大影响建立强化学习理论在不良适应动态的研究中的实验应用 OUD中的药物使用决策，以揭示可以针对未来的行为和神经机制预防和治疗发展。