Confidence of probabilistic predictions modulates the cortical response to pain.
Confidence of probabilistic predictions modulates the cortical response to pain.
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概率预测的置信度调节皮质对疼痛的反应。
DOI：
10.1073/pnas.2212252120
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发表时间：
2023-01-24
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
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作者：
脑电对疼痛反应的功能意义长期以来一直存在争议，因为它们具有戏剧性的变异性。这项研究表明，这种可变性可能部分与疼痛输入序列中出现的概率预测的置信度有关。对疼痛预测的信心与大脑皮质脑电对疼痛的反应呈负相关。这表明，当信心较高时，大脑对感觉输入的依赖程度较低，并向我们表明，信心加权的统计学习调节了大脑皮层对疼痛的反应。疼痛通常会随着时间的推移而演变，大脑需要学习这种时间演变，以预测疼痛在未来可能发生的变化，并指导行为。这个过程被称为时间统计学习(TSL)。最近，有研究表明，疼痛序列的TSL可以使用最优贝叶斯推理来实现，该推理是在体感加工区域编码的。在这里，我们调查这些概率预测的置信度是否调制脑电对有害刺激的反应，使用TSL任务。置信度衡量概率预测的不确定性，而不考虑其实际结果。贝叶斯模型规定，对概率预测的置信度应该与输入和权重学习相结合，以便它调节脑电对有害刺激反应的早期成分，这应该通过负相关性来捕捉：当置信度较高时，早期神经反应较小，因为大脑更多地依赖预期/预测，对感觉输入的依赖较少(反之亦然)。我们表明，参与者能够使用贝叶斯推理预测序列转移概率，但有一些遗忘。然后，我们发现这些概率预测的置信度与顶点势的N2和P2分量的幅度呈负相关：参与者对他们的预测越有信心，顶点势就越小。这些结果证实了贝叶斯学习模型的关键预测，并阐明了早期脑电对伤害性刺激的反应在信心加权统计学习中的功能意义。
The functional significance of EEG responses to pain has long been debated because of their dramatic variability. This study indicates that such variability can be partly related to the confidence of probabilistic predictions emerging from sequences of pain inputs. The confidence of pain predictions is negatively associated with the cortical EEG responses to pain. This indicates that the brain relies less on sensory inputs when confidence is higher and shows us that confidence-weighted statistical learning modulates the cortical response to pain. Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the vertex potential: the more confident were participants about their predictions, the smaller the vertex potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning.
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影响因子：
16.2
作者：
Baliki, Marwan N.;Geha, Paul Y.;Fields, Howard L.;Apkarian, A. Vania
通讯作者：
Apkarian, A. Vania
DOI：
10.1523/jneurosci.1984-11.2011
发表时间：
2011-09-28
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Baliki MN;Baria AT;Apkarian AV
通讯作者：
Apkarian AV
影响因子：
3.7
作者：
Churyukanov, Maxim;Plaghki, Leon;Mouraux, Andre
通讯作者：
Mouraux, Andre
影响因子：
1.7
作者：
GarciaLarrea, L;Peyron, R;Mauguiere, F
通讯作者：
Mauguiere, F
影响因子：
7.7
作者：
Grahl A;Onat S;Büchel C
通讯作者：
Büchel C