Department of Neurology, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany.
Cereb Cortex. 2012 May;22(5):1118-23. doi: 10.1093/cercor/bhr186. Epub 2011 Jul 17.
The perception of pain is characterized by its tremendous intra- and interindividual variability. Different individuals perceive the very same painful event largely differently. Here, we aimed to predict the individual pain sensitivity from brain activity. We repeatedly applied identical painful stimuli to healthy human subjects and recorded brain activity by using electroencephalography (EEG). We applied a multivariate pattern analysis to the time-frequency transformed single-trial EEG responses. Our results show that a classifier trained on a group of healthy individuals can predict another individual's pain sensitivity with an accuracy of 83%. Classification accuracy depended on pain-evoked responses at about 8 Hz and pain-induced gamma oscillations at about 80 Hz. These results reveal that the temporal-spectral pattern of pain-related neuronal responses provides valuable information about the perception of pain. Beyond, our approach may help to establish an objective neuronal marker of pain sensitivity which can potentially be recorded from a single EEG electrode.
疼痛的感知具有巨大的个体内和个体间可变性。不同的个体对完全相同的疼痛事件的感知差异很大。在这里，我们旨在从大脑活动中预测个体的疼痛敏感性。我们反复向健康的人类受试者施加相同的疼痛刺激，并通过脑电图 (EEG) 记录大脑活动。我们对时频转换的单次 EEG 反应进行了多元模式分析。我们的结果表明，在一组健康个体上训练的分类器可以以 83%的准确率预测另一个个体的疼痛敏感性。分类准确性取决于约 8 Hz 的疼痛诱发反应和约 80 Hz 的疼痛诱导伽马振荡。这些结果表明，与疼痛相关的神经元反应的时频模式提供了有关疼痛感知的有价值信息。此外，我们的方法可能有助于建立疼痛敏感性的客观神经标记，该标记可以潜在地从单个 EEG 电极记录。
