Learning the statistics of pain: computational and neural mechanisms
Learning the statistics of pain: computational and neural mechanisms
复制标题
学习疼痛的统计数据：计算和神经机制
DOI：
10.1101/2021.10.21.465270
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Mancini F
中科院分区：
文献类型：
--
作者：
Mancini F
疼痛总是随着时间的推移而变化，这些时间波动充满了对身体安全的不确定性。从理论上讲，随着时间的推移，疼痛的统计数据包含了可以学习的有用信息，使大脑能够产生预期并告知行为。为了研究这一点，我们将健康参与者暴露于左手的低强度和高强度电刺激的概率序列，其中包含刺激频率的突然变化。我们证明，人类可以学习提取这些神经元，并明确预测即将到来的疼痛强度的可能性与最佳贝叶斯模型与动态更新的信念一致的方式。我们使用功能性MRI研究了受试者执行任务时的大脑活动，这使我们能够从这些统计推断的不确定性和更新中剖析这些统计推断的潜在神经相关性。我们发现高强度疼痛的推断频率（后验概率）与双侧感觉运动皮层、次级躯体感觉皮层和右侧尾状核的活动相关。对疼痛的统计推断的不确定性被编码在右侧上级顶叶皮层中。这种分层贝叶斯模型的内在部分是频率的意外变化导致信念转变和更新内部模型的方式。这反映在连续后部分布之间的KL差异上，并且与运动前皮质、背外侧前额叶皮质和后顶叶皮质的大脑反应相关。总之，本研究扩展了传统上被认为是一个感觉疼痛通路，致力于过程疼痛强度，包括贝叶斯内部模型的产生的时间统计的疼痛强度水平在感觉运动区，这是动态更新，通过参与运动前区，前额叶和顶叶区域。
Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high-intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions.
登录
查看更多内容
DOI：
10.1093/oso/9780198866282.003.0013
发表时间：
2020-11
期刊：
Neurocognitive Mechanisms
影响因子：
--
作者：
G. Piccinini
通讯作者：
G. Piccinini
影响因子：
4.3
作者：
Meyniel F;Maheu M;Dehaene S
通讯作者：
Dehaene S
DOI：
10.1056/nejmoa1204471
发表时间：
2013-04-11
期刊：
The New England journal of medicine
影响因子：
--
作者：
Wager TD;Atlas LY;Lindquist MA;Roy M;Woo CW;Kross E
通讯作者：
Kross E
[记忆的神经关联]。
DOI：
10.5692/clinicalneurol.53.1234
发表时间：
2013
期刊：
Rinsho shinkeigaku = Clinical neurology
影响因子：
--
作者：
T. Fujii
通讯作者：
T. Fujii
DOI：
10.1073/pnas.1615773114
发表时间：
2017-05-09
影响因子：
11.1
作者：
Meyniel, Florent;Dehaene, Stanislas
通讯作者：
Dehaene, Stanislas