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: 10454889
• 负责人: RAGHAVAN GOPALAKRISHNAN
• 金额: $ 15.93万
• 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/10454889
• 关键词: Acute Pain; Address; Affect; Age; American; Anterior; Area; 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 (Psychology); Fostering; Frequencies; Fright; Future; Goals; Impairment; Intervention; Investigation; Knowledge; Leadership; Learning; Life; Magnetoencephalography; Measures; Mediation; Mediator of activation protein; 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; Sensory; Signal Transduction; Societies; Temperament; Therapeutic Intervention; Training; Update; Walking; addiction; avoidance behavior; base; chronic back pain; chronic pain; chronic pain patient; classical conditioning; cooking; disorder risk; expectation; experience; frontal lobe; healing; improved; interest; maladaptive behavior; multidisciplinary; multimodality; 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; relating to nervous system; 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，是一个全国性的危机，影响着200多万人 据估计，美国每年的经济负担为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，而这些 由于缺乏可靠的替代疼痛管理策略，这一数字有大幅上升的风险。这个 慢性疼痛患者OUD的主要动机是避免疼痛。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，它夸大了疼痛，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，并提供非阿片类药物的替代 干预，我们需要对疼痛回避行为有一个机械性的理解。此外，识别特征 而影响不良适应疼痛回避行为的相关风险因素不仅有助于检测 慢性疼痛患者易患慢性疼痛，也能预防急性疼痛患者易患慢性疼痛。这 Proposal将疼痛回避行为概念化为线索-疼痛联想学习问题，基于良好的学习策略 建立了预测编码框架。根据预测编码，当预期和观察到感觉时 信息发散时，大脑中会产生预测误差(PE)信息。学习是一个过程，通过这个过程 体育作为一种教学信号，更新期望，激励行动以避免痛苦(例如，热炉子=痛苦)。 慢性疼痛患者线索-疼痛联想学习障碍导致感觉过度泛化 暗示和避免传播到技术上安全的暗示(例如烹饪=疼痛)。在AIM-1中，我们调查了 工具性疼痛损害的线索-疼痛联想学习的基本机制 结合计算强化学习模型的回避任务。在AIM-2中，我们研究 人格特征及相关危险因素对疼痛回避行为的影响 采用多层次的调解分析方法。在AIM-3中，我们使用以下方法确定疼痛回避的神经生理结构 从计算模型派生的回归变量。拟议的任务将在#年执行 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， Gopalakrishnan博士是一名生物医学工程师，在神经电生理学和信号方面拥有专业知识 加工，对慢性疼痛特别感兴趣。这个K01将为候选人提供所需的资源 提高他在疼痛、心理和成瘾方面的知识，并培训他的计算心理生理学 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，以便制定有效的预防和治疗策略，以减少 乌德及其对社会的负担。