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

> **NIH NIH K01** · CLEVELAND CLINIC LERNER COM-CWRU · 2020 · $160,521

## Abstract

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.

## Key facts

- **NIH application ID:** 9953192
- **Project number:** 1K01DA050804-01
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** RAGHAVAN GOPALAKRISHNAN
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $160,521
- **Award type:** 1
- **Project period:** 2020-08-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9953192

## Citation

> US National Institutes of Health, RePORTER application 9953192, Pain avoidance behavior and its relation to risk for opioid use in chronic pain patients (1K01DA050804-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9953192. Licensed CC0.

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