Analgesia for the Bayesian Brain: How Predictive Coding Offers Insights Into the Subjectivity of Pain
Friedrich Lersch, Fabienne Frickmann, Richard D. Urman, Gabriel Burgermeister, Kaya Siercks, Markus M. Luedi, Sven Straumann
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. 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.