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.
/PUBLIC HEALTH RELEVANCE Opioid use and addiction among chronic pain population is a serious public health concern with significant economic and social burden. The current proposal addresses a critical gap in our knowledge relating to pain avoidance behavior which when maladaptive in the chronic pain state is a major driver for opioid abuse. Knowledge about behavioral and neurophysiological constructs of pain avoidance behavior in chronic pain patients will help identify and treat those at risk for opioid use disorder, and develop long-term non-addictive therapeutic interventions.
