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用于理解既往经历对疼痛感知和神经性疼痛影响的计算框架。

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

机构信息

Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.

Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada.

出版信息

PLoS Comput Biol. 2024 Oct 31;20(10):e1012097. doi: 10.1371/journal.pcbi.1012097. eCollection 2024 Oct.

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 approach to update the parameters of the internal 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.

摘要

疼痛感知不仅受到传入神经元的感觉输入的影响,还受到认知因素的影响,例如先前的期望。有人认为,过于精确的先验可能是导致神经性疼痛等慢性疼痛状态的一个关键因素。然而,过于精确的支持疼痛的先验是如何产生的,这仍然是一个悬而未决的问题。在这里,我们首先验证了贝叶斯方法可以描述先验期望和感觉输入的统计整合如何导致安慰剂镇痛、反安慰剂痛觉过敏、慢性疼痛和自发性神经性疼痛等疼痛现象。我们的结果表明,先验的价值(由内部模型参数决定)可能是这些现象的一个关键因素。接下来,我们应用分层贝叶斯方法根据预测疼痛和感知疼痛之间的差异来更新内部模型的参数,以反映人们在未来的期望中整合先前的经验。与更简单的方法相比,这种分层模型结构能够展示安慰剂镇痛和反安慰剂痛觉过敏等现象如何通过经典条件作用的形式从先前的经验中产生。我们还演示了抵消镇痛现象,即在减少有害刺激强度后,会获得不成比例的疼痛大幅度减轻。最后,我们转向神经性疼痛的模拟,其中我们的分层模型证实持续的非神经性疼痛是去神经后发展为神经性疼痛的一个风险因素,此外还提供了一个有趣的预测,即完全缺乏有意义的疼痛体验也可能是一个类似的风险因素。总之,这些结果提供了关于先前经验如何有助于实验和神经性疼痛中的疼痛感知的见解,这反过来又可能为改善疼痛预防和缓解策略提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7278/11556707/0b918d844741/pcbi.1012097.g001.jpg

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