Analgesia for the Bayesian Brain: How Predictive Coding Offers Insights Into the Subjectivity of Pain

Abstract

Purpose of Review In order to better treat pain, we must understand its architecture and pathways. Many modulatory approaches of pain management strategies are only poorly understood. This review aims to provide a theoretical framework of pain perception and modulation in order to assist in clinical understanding and research of analgesia and anesthesia. Recent Findings Limitations of traditional models for pain have driven the application of new data analysis models. The Bayesian principle of predictive coding has found increasing application in neuroscientific research, providing a promising theoretical background for the principles of consciousness and perception. It can be applied to the subjective perception of pain. Summary Pain perception can be viewed as a continuous hierarchical process of bottom-up sensory inputs colliding with top-down modulations and prior experiences, involving multiple cortical and subcortical hubs of the pain matrix. Predictive coding provides a mathematical model for this interplay.

Unveiling the Nexus of Cervical Proprioception, Postural Stability, and Impeding Factors in Cervical Spondylosis: Insights from Reposition Errors, Limits of Stability, and Mediation Analysis

“…The current study reveals the mediating role of pain and kinesiophobia in shaping the relationship between cervical proprioceptive reposition errors and limits-of-stability variables. Pain, a hallmark of CS, emerges as a potential influencer of both proprioceptive accuracy and stability [38,39]. The integration of pain into the mediation analysis substantiates its role as a driving factor in this relationship.…”