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

慢性疼痛患者的疼痛回避行为及其与阿片类药物使用风险的关系

• 批准号: 10676176
• 负责人: RAGHAVAN GOPALAKRISHNAN
• 金额: $ 15.55万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676176
• 关键词: Acute Pain; Address; Affect; Age; American; Anterior; Area; Association Learning; Avoidance Learning; Basic Science; Behavior; Behavioral; Biomedical Engineering; Brain; Brain Mapping; Clinical Research; Clinical Trials; Code; Complex Regional Pain Syndromes; Computer Models; Coupling; Cues; Data; Data Analyses; Data Collection; Development; Disease Marker; Economic Burden; Economics; Educational process of instructing; Electrophysiology (science); Equipment and supply inventories; Event; Extinction; Fostering; Frequencies; Fright; Future; Goals; Impairment; Intervention; Investigation; Knowledge; Leadership; Learning; Life; Magnetoencephalography; Measures; Mediation; Mediator; Mental disorders; Methods; Modeling; Neurons; Neurosciences; Opiate Addiction; Outcome; Pain; Pain management; Pathway interactions; Patients; Periodicity; Personality Traits; Persons; Placebo Effect; Population; Prefrontal Cortex; Prevention; Prevention strategy; Process; Psychological reinforcement; Psychology; Psychophysiology; Public Health; Recording of previous events; Recovery; Research; Resolution; Resources; Rewards; Risk; Risk Factors; Role; Science; Self Direction; Sensory; Signal Transduction; Societies; Temperament; Therapeutic Intervention; Training; Update; Walking; addiction; avoidance behavior; biomarker identification; chronic back pain; chronic pain; chronic pain patient; computational neuroscience; cooking; disorder risk; expectation; experience; healing; improved; interest; maladaptive behavior; multidisciplinary; multimodality; neural; neurophysiology; non-opioid analgesic; novel; opioid abuse; opioid use; opioid use disorder; pain model; pain outcome; pain patient; pain perception; post stroke pain; prevent; public health relevance; response; response to injury; signal processing; social; tool; training opportunity; trait; treatment strategy

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.

项目摘要/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场国家危机，影响超过200万 估计经济负担为每年785亿美元的美国人。目前，估计有1亿 美国人患有慢性疼痛。近30％的慢性疼痛患者也患有OUD，这些 由于缺乏可靠的替代疼痛管理策略，数字有大幅上升的风险。这 慢性疼痛患者中OUD的原理动机是避免疼痛。恐惧和有条件的回避 以前与疼痛相关的提示是典型的适应不良行为，夸大了导致阿片类药物使用的疼痛。 有效地减少慢性疼痛患者的阿片类药物依赖性并提供替代的非阿片类药物 干预措施，我们需要对避免疼痛行为的机械理解。另外，确定特征 以及影响不良适应不良疼痛行为的相关风险因素，不仅可以帮助检测 慢性疼痛患者容易受到OUD的影响，但也可以预防急性疼痛患者容易受到慢性疼痛的影响。这 提案将避免痛苦行为概念化为提示辅助的关联学习问题，基于良好 建立的预测编码框架。根据预测编码，当预期和观察到的感觉 信息差异，大脑中生成了预测错误（PE）消息。学习是一个过程 PE充当教学信号，以更新期望，以激发行动以避免疼痛（例如热炉=疼痛）。 慢性疼痛患者的表现受损的提示伴侣学习导致感觉过度笼统 提示和回避扩散到技术安全的提示（例如烹饪=疼痛）。在AIM-1中，我们调查了 使用工具疼痛的提示 - 培养协会学习涉及的基本机制 避免任务与计算加强学习模型结合使用。在AIM-2中，我们检查了 人格特征和与OUD相关的风险因素的影响，可能是避免疼痛行为的主持人 使用多级中介分析。在AIM-3中，我们确定了使用的神经生理结构 从计算模型得出的回归器。建议的任务将在 磁脑摄影（MEG），这是一种研究脑节律和振荡的脑作图工具。候选人， Gopalakrishnan博士是一位生物医学工程师，具有神经元电生理学和信号的专业知识 处理，对慢性疼痛特别感兴趣。该K01将为候选人提供所需的资源 增强他在疼痛，OUD和成瘾方面的知识，并在计算心理生理学上训练候选人 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 避免疼痛行为的背后，以制定有效的预防和治疗策略，以减少 Oud及其对社会的负担。