A Bayesian model for chronic pain

Eckert, Anna-Lena; Pabst, Kathrin; Endres, Dominik

doi:10.3389/fpain.2022.966034

Cited by 15 publications

(15 citation statements)

References 77 publications

(110 reference statements)

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“…Since the contributions of sensory input and expectations are weighted by their respective certainty, this framework allows for interesting predictions for perception under different levels of certainty in sensory input. In the context of pain, Bayesian models have successfully described several phenomena, including placebo hypoalgesia, nocebo hyperalgesia and chronic pain [13,18,24,25]. However, these models rely on parameters to be tuned by hand or fit to experimental data.…”

Section: Discussionmentioning

confidence: 99%

“…Bayesian inference has successfully been applied to describe a range of perceptual and sensorimotor tasks, both qualitatively and quantitatively [21][22][23]. Just as for other forms of perception, Bayesian models provide a framework for how expectations can modulate pain perception [13,18,24,25]. Pain is dynamic, meaning that the intensity, quality, and characteristics of the experience change over time.…”

Section: Single-layer Kalman Filtermentioning

confidence: 99%

“…Such models have been used to describe placebo hypoalgesia and nocebo hyperalgesia [13][14][15][16], and statistical pain learning [17] under experimental pain paradigms. As for chronic pain, Eckert et al, recently applied a Bayesian framework to illustrate how overly precise priors for being in a state of pain, combined with an ambiguous likelihood of perceiving pain from a sensory stimulation, increases the probability of transitioning to a chronic pain state [18]. Bayesian inference seems like a promising framework for modelling neuropathic pain, as damage to the nervous system often results in disrupted sensory input (corresponding to increased ambiguity of the likelihood), yet it remains an open question how overly precise priors in favor of pain might arise.…”

Section: Introductionmentioning

confidence: 99%

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

Ramne,

Sensinger

2024

Preprint

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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 generative model parameters, may be a key contributor to these phenomena. Next, we apply a hierarchical Bayesian approach to update the parameters of the generative model based on the difference between the predicted and the perceived pain, to reflect that people integrate prior experiences in their future expectations. In contrast with simpler approaches, this hierarchical model structure is able to show for placebo hypoalgesia and nocebo hyperalgesia how these phenomena can arise from prior experiences in the form of a classical conditioning procedure. We also demonstrate the phenomenon of offset analgesia, in which a disproportionally large pain decrease is obtained following a minor reduction in noxious stimulus intensity. Finally, we turn to simulations of neuropathic pain, where our hierarchical model corroborates that persistent non-neuropathic pain is a risk factor for developing neuropathic pain following denervation, and additionally offers an interesting prediction that complete absence of informative painful experiences could be a similar risk factor. Taken together, these results provide insight to how prior experiences may contribute to pain perception, in both experimental and neuropathic pain, which in turn might be informative for improving strategies of pain prevention and relief.

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Section: Discussionmentioning

confidence: 99%

Section: Single-layer Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ramne,

Sensinger

2024

Preprint

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“… 49 However, in our study, in which expectations were relatively stable throughout the experiment, temporal variations of expectations would be rather small. Since the temporal evolution of pain-related expectancies are an important factor in, e.g., chronic pain conditions, 21 , 50 future studies with amended protocols could consider these dynamics.…”

Section: Discussionmentioning

confidence: 99%

Agency affects pain inference through prior shift as opposed to likelihood precision modulation in a Bayesian pain model

Strube¹,

Horing²,

Rose³

et al. 2023

Neuron

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“… 13 , 19 Predictive coding and Bayesian models for perception have been widely presented in the literature, 20 , 21 , 22 , 23 but only recently been adopted in pain research. 12 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 Gradually, a more general computational framework is also beginning to emerge to accommodate the exponential growth of data in pain research.…”

Section: Introductionmentioning

confidence: 99%