Pain avoidance behavior and its relation to risk for opioid use in chronic pain patients

项目总结/摘要 阿片类药物使用障碍（OUD），即阿片类药物滥用和成瘾是一个影响200多万人的国家危机 美国人每年的经济负担估计为785亿美元。目前，估计有1亿人 美国人患有慢性疼痛。近30%的慢性疼痛患者也患有OUD， 由于缺乏可靠的替代疼痛管理策略，数字有急剧上升的风险。的 慢性疼痛患者中OUD的主要动机是疼痛回避。恐惧和条件性回避 以前与疼痛相关的线索是典型的适应不良行为，其夸大疼痛导致阿片类药物的使用。 有效减少慢性疼痛患者对阿片类药物的依赖，并提供替代的非阿片类药物 因此，我们需要对疼痛回避行为有一个机械的理解。此外，识别特征 影响适应不良疼痛回避行为的OUD相关风险因素不仅有助于检测 慢性疼痛患者易患OUD，也可预防急性疼痛患者易患慢性疼痛。这 建议将疼痛回避行为概念化为线索疼痛联想学习问题，基于良好的 建立预测编码框架。根据预测编码，当预期和观察到感官 当信息发散时，在大脑中产生预测错误（PE）消息。学习是一个过程， 体育作为一个教学信号，以更新的期望，激励行动，以避免疼痛（例如，热炉=疼痛）。 慢性疼痛患者表现出线索-疼痛联想学习受损，导致感觉过度概括 暗示和回避扩散到技术上安全的暗示（例如烹饪=疼痛）。在aim-1中，我们研究了 使用工具性疼痛的受损线索-疼痛联想学习的基本机制 避免任务结合计算强化学习模型。在aim-2中，我们检查了 人格特质和OUD相关危险因素对疼痛回避行为的影响 使用多层次中介分析。在aim-3中，我们使用 从计算模型得出的回归量。拟议的任务将在 脑磁图（MEG），一种研究大脑节律和振荡的大脑绘图工具。候选人， 博士Gopalakrishnan是一名生物医学工程师，在神经元电生理学和信号方面具有专业知识 处理，对慢性疼痛特别感兴趣。此K 01将为候选人提供所需的资源， 提高他在疼痛，OUD和成瘾方面的知识，并在计算心理生理学方面培训候选人 和多模式临床试验研究。候选人的目标是提高我们对基础科学的理解 为了制定有效的预防和治疗策略， OUD及其对社会的负担。

PROJECT SUMMARY/ABSTRACT Opioid use disorder (OUD) i.e. opioid abuse and addiction is a national crisis that affects more than 2 million Americans with an estimated economic burden of $78.5 billion each year. Currently, an estimated 100 million Americans suffer from chronic pain. Nearly 30% of chronic pain patients also suffer from OUD, and these numbers are at risk to rise dramatically due to the lack of reliable alternate pain management strategies. The principle motive for OUD among chronic pain patients is pain avoidance. Fear and conditioned avoidance of cues formerly associated with pain are typical maladaptive behavior that exaggerates pain leading to opioid use. To effectively reduce opioid dependency among chronic pain patients and provide alternate non-opioid interventions, we need a mechanistic understanding of pain avoidance behavior. Additionally, identifying traits and OUD related risk factors that influence maladaptive pain avoidance behavior can help not only to detect chronic pain patients vulnerable to OUD, but also prevent acute pain patients vulnerable to chronic pain. This proposal conceptualizes pain avoidance behavior as a cue-pain associative learning problem, based on the well- established predictive coding framework. According to predictive coding, when expected and observed sensory information diverge, a prediction error (PE) message is generated in the brain. Learning is the process by which PE acts as a teaching signal to update expectations that motivate actions to avoid pain (e.g. hot stove = pain). Chronic pain patients' display impaired cue-pain associative learning resulting in overgeneralization of sensory cues and avoidance spreading to technically safe cues (e.g. cooking = pain). In aim-1, we investigate the fundamental mechanisms involved in impaired cue-pain associative learning using an instrumental pain avoidance task in conjunction with computational reinforcement learning models. In aim-2, we examine the influence of personality traits and OUD related risk factors as possible moderators of pain avoidance behavior using multi-level mediation analysis. In aim-3, we identify neurophysiological constructs of pain avoidance using regressors derived from computational models. The proposed task will be performed in Magnetoencephalography (MEG), a brain mapping tool to study brain rhythms and oscillations. The candidate, Dr. Gopalakrishnan, is a Biomedical Engineer with expertise in neuronal electrophysiology and signal processing, with special interest in chronic pain. This K01 will provide the candidate the resources needed to enhance his knowledge in pain, OUD and addiction, and train the candidate in computational psychophysiology and multimodal clinical trials research. The candidate's goal is to improve our understanding of basic science behind pain avoidance behavior in order to develop effective prevention and treatment strategies that will reduce OUD and its burden on society.