Computational and neural mechanisms of statistical pain learning.
Computational and neural mechanisms of statistical pain learning.
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DOI：
10.1038/s41467-022-34283-9
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发表时间：
2022-11-03
影响因子：
16.6
通讯作者：
Seymour, Ben
中科院分区：
文献类型：
--
作者：
Mancini, Flavia;Zhang, Suyi;Seymour, Ben
疼痛总是随着时间的推移而变化。这些波动包含统计学意义上的信息，理论上，大脑可以通过学习这些信息来产生预期和控制反应。我们证明，人类学会提取这些神经元，并明确预测即将到来的疼痛强度的可能性，与最佳贝叶斯推理的方式一致，动态更新的信念。健康的参与者在脑功能磁共振成像期间接受了随机的，不稳定的低强度和高强度电刺激序列。推断的疼痛频率与感觉运动皮层区域和背侧纹状体的活动相关，而这些推断的不确定性则编码在右侧上级顶叶皮层。刺激频率的意外变化通过参与运动前区、前额叶和后顶叶区域来驱动内部模型的更新。这项研究扩展了我们对疼痛感觉处理的理解，包括疼痛时间统计的贝叶斯内部模型的生成。疼痛随着时间的推移而波动，这种波动是非随机的。在这里，作者表明，人类大脑可以通过参与感觉运动，顶叶和运动前区，以与最佳贝叶斯推理一致的方式学习预测这些变化。
Pain invariably changes over time. These fluctuations contain statistical regularities which, in theory, could be learned by the brain to generate expectations and control responses. We demonstrate that humans learn to extract these regularities and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian inference with dynamic update of beliefs. Healthy participants received probabilistic, volatile sequences of low and high-intensity electrical stimuli to the hand during brain fMRI. The inferred frequency of pain correlated with activity in sensorimotor cortical regions and dorsal striatum, whereas the uncertainty of these inferences was encoded in the right superior parietal cortex. Unexpected changes in stimulus frequencies drove the update of internal models by engaging premotor, prefrontal and posterior parietal regions. This study extends our understanding of sensory processing of pain to include the generation of Bayesian internal models of the temporal statistics of pain. Pain fluctuates over time in ways that are non-random. Here, the authors show that the human brain can learn to predict these changes in a manner consistent with optimal Bayesian inference by engaging sensorimotor, parietal, and premotor regions.
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