How Pain Shapes Depression and Anxiety: A Hybrid Self-regulatory/Predictive Mind Perspective

Area of Science:

• Psychology
• Neuroscience
• Chronic Pain Research

Background:

• Many individuals with chronic pain maintain functional lifestyles, yet a subset experiences significant disability alongside comorbid mental health conditions like depression and anxiety.
• The reasons for differential resilience versus susceptibility to these comorbidities remain incompletely understood, necessitating further theoretical and empirical investigation.

Purpose of the Study:

• To deconstruct the pain adaptation process and address the question of differential resilience versus susceptibility in individuals with chronic pain.
• To integrate two explanatory frameworks: the Goal-Centered, Self-Regulatory, Automated Social Systems Psychology (GRASSP) model and the Bayesian Brain/Predictive Mind perspective.

Main Methods:

• Review and synthesis of theoretical and empirical literature on pain adaptation, self-regulation, and predictive processing.
• Application of the GRASSP model to explain how compromised regulatory functions may lead to depression and anxiety in chronic pain.
• Utilizing the Bayesian Brain/Predictive Mind framework to understand how aberrant predictive processing and interoceptive inference contribute to maladjustment.

Main Results:

• The GRASSP model suggests that impaired self-regulatory processes undermine pain processing, goal pursuit, and daily functioning, potentially causing depression and anxiety.
• The Bayesian model posits that maladjustment arises from failures in active inference, where biased or overly certain prior evaluations of internal states and external stimuli lead to inaccurate perceptions.
• Comorbid depression and anxiety in chronic pain may stem from error-prone predictive processing of aversive bodily sensations and their relationship to the environment.

Conclusions:

• A hybrid conceptual framework integrating motivational/cybernetic systems (GRASSP) and predictive processing (Bayesian Brain) offers a comprehensive understanding of chronic pain comorbidities.
• This integrated approach highlights novel and existing intervention targets for addressing depression and anxiety in individuals with chronic pain.
• Understanding the interplay between self-regulation, predictive processing, and interoceptive inference is crucial for developing effective treatments for chronic pain-related mental health conditions.