A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain.

ABSTRACT: Pain perception is influenced not only by sensory input from afferent neurons but also by cognitive factors such as prior expectations. It has been suggested that overly precise priors may be a key contributing factor to chronic pain states such as neuropathic pain. However, it remains an open question how overly precise priors in favor of pain might arise. Here, we first verify that a Bayesian approach can describe how statistical integration of prior expectations and sensory input results in pain phenomena such as placebo hypoalgesia, nocebo hyperalgesia, chronic pain, and spontaneous neuropathic pain. Our results indicate that the value of the prior, which is determined by the internal model parameters, may be a key contributor to these phenomena. Next, we apply a hierarchical Bayesian

SUBMITTER: Ramne M 

PROVIDER: S-EPMC11556707 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature