Learning to avoid pain: Computational mechanisms and application to methamphetami

描述(由申请人提供)：尽管在生活的多个领域付出了代价，但坚持使用药物是大多数药物依赖定义的核心。一个关键的问题是，尽管成本不断上升，但为什么个人仍然选择使用药物，而不是避免使用药物。神经生物学研究已经开始通过调查滥用药物如何影响构成情感、动机和学习的大脑系统来回答这个问题。几个主要的理论集中在食欲大脑的“奖励”系统中的敏感化。越来越多的证据表明，快感缺失和厌恶学习也是至关重要的，但相对较少的研究探讨了人类厌恶学习系统的变化与药物滥用之间的联系。迫切需要这样的工作，以便在动物和人类研究之间提供更深层次和更具体的联系，并将基本的神经生物学机制与人类吸毒行为联系起来。这就是这项提案的目标。我们将计算强化学习方法与实验热疼痛模型相结合，计算强化学习方法成功地表征了食欲学习系统的特征，热疼痛模型成功地表征了参与产生和调制疼痛和厌恶预期的大脑系统。这两个成功的研究传统的新组合是一种强大的方法，可以为健康的个人和吸毒者提供避免学习的新措施，并与成瘾动物模型联系起来。在目标1中，我们开发了一个人类大脑系统的标准化模型，该模型涉及学习预测疼痛、它们随时间的动态变化以及它们与回避行为的关系。三个实验使用功能磁共振成像(FMRI)来描述涉及代表疼痛和回避学习的大脑系统。我们专注于疼痛处理和学习网络之间的相互作用，可能支持学习互补方面的多个记忆系统，以及期望对这些过程的影响。在目标2中，我们将这项工作扩展到甲基苯丙胺(MA)滥用者的特征，这是一个不断增长但研究不足的人群，构成了越来越多的公共安全和公共卫生挑战。我们将对MA滥用者和对照组进行比较研究，以a)表征疼痛表征和回避学习系统的差异；以及b)在两个月的随访中使用基于大脑系统的测量来前瞻性地预测药物使用模式。这些目标的成功完成将有助于研究人员利用成瘾动物模型的理论和发现，将它们与人类药物使用行为联系起来。它还将提供大脑和行为中厌恶体验和厌恶学习的新衡量标准，这将为持续药物滥用的神经生物学原因提供重要的见解。这些发现可以a)为行为、认知和药理学药物滥用治疗计划提供信息；b)提出防止药物使用达到临床依赖阶段的新方法；以及c)确定可以帮助预防和治疗计划更具体地针对个人的亚类型。 与公共健康相关：对大脑中奖惩引导学习的神经生物学研究为为什么药物依赖者尽管在生活的许多领域付出了巨大代价仍继续选择药物提供了强有力的解释。神经生物学提供了理论，导致了新的行为、认知和药物干预的发展，以帮助打破药物滥用的循环。这项工作的一个重要前沿是研究厌恶的大脑过程和经历(身体或心理痛苦)如何激励继续使用药物。我们提出了一些第一批工作，以研究疼痛回避学习如何在人脑中的机械计算水平上发挥作用，以及这些系统的变化如何与吸毒者的甲基苯丙胺使用模式联系在一起。这些研究的结果将有助于开发新的药物滥用模式和为吸毒者提供新的治疗方法，这将有助于减少吸毒者的个人健康成本和公共卫生负担。

DESCRIPTION (provided by applicant): The persistent use of drugs in spite of costs in multiple areas of life is central to most definitions of drug dependence. A critical question is why individuals continue to choose to use drugs, rather than avoid them, despite increasing costs. Neurobiological research has begun to answer this question by investigating how drugs of abuse impact brain systems that underlie affect, motivation, and learning. Several major theories focus on sensitization in appetitive brain 'reward' systems. A growing body of evidence suggests that anhedonia and aversive learning are also critical, but relatively little work addresses how changes in human aversive learning systems are linked to drug abuse. Such work is critically needed to provide deeper and more specific links between animal and human research, and to link basic neurobiological mechanisms with human drug-taking behavior. That is the goal of this proposal. We combine a computational reinforcement learning approach, which has successfully characterized appetitive learning systems, with an experimental thermal pain model that has successfully characterized brain systems involved in generating and modulating pain and aversive expectancies. This novel combination of two successful research traditions is a powerful approach that can provide new measures of avoidance learning in healthy individuals and drug abusers and links with animal models of addiction. In Aim 1, we develop a normative model of human brain systems involved in learning to predict pain, their dynamics over time, and their relationship to avoidance behavior. Three experiments use functional magnetic resonance imaging (fMRI) to characterize brain systems involved in representing pain and avoidance learning. We focus on interactions between pain-processing and learning networks, multiple memory systems that may support complementary aspects of learning, and the effects of expectation on these processes. In Aim 2, we extend this work to characterize methamphetamine (MA) abusers, a growing but under-studied population that presents an increasing public safety and public health challenge. We will conduct a comparative study of MA abusers and controls to a) characterize differences in pain representation and avoidance learning systems; and b) use brain systems-based measures to prospectively predict patterns of drug use during a two-month follow up. Successful completion of these aims will help researchers to leverage theories and findings from animal models of addiction by linking them to human drug-use behavior. It will also provide new measures of aversive experience and aversive learning in both brain and behavior, which will provide important insights into the neurobiological causes of persistent drug abuse. Such findings can a) inform behavioral, cognitive, and pharmacological drug-abuse treatment programs; b) suggest new ways of preventing drug use from reaching the stage of clinical dependence, and c) identify sub-types of individuals that can help prevention and treatment programs be tailored more specifically to individuals. PUBLIC HEALTH RELEVANCE: Neurobiological studies of reward- and punishment-guided learning in the brain offer powerful explanations for why drug-dependent individuals continue to choose drugs in spite of substantial costs in many areas of life. Neurobiological accounts have provided theories that lead to the development of new behavioral, cognitive, and pharmacological interventions to help break the cycle of drug abuse. An important frontier in this effort is the study of how aversive brain processes and experience (physical or psychological pain) motivate continued drug use. We propose some of the first work to look at how pain-avoidance learning works at a mechanistic, computational level in the human brain, and how changes in these systems are linked to methamphetamine use patterns in abusers. Findings from these studies will inform the development of new models of drug abuse and new treatments for users, which will help to reduce the personal health costs to abusers and the public health burden.