Decoding an Individual's Sensitivity to Pain from the Multivariate Analysis of EEG Data
Decoding an Individual's Sensitivity to Pain from the Multivariate Analysis of EEG Data
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DOI：
10.1093/cercor/bhr186
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发表时间：
2012-05-01
期刊：
影响因子：
3.7
通讯作者：
Ploner, Markus
中科院分区：
文献类型：
--
作者：
Schulz, Enrico;Zherdin, Andrew;Ploner, Markus
疼痛的感知特征在于其巨大的个体内和个体间的差异。不同的人对同样的痛苦事件的感知大不相同。在这里，我们的目标是从大脑活动预测个体的疼痛敏感性。我们对健康受试者反复施加相同的疼痛刺激，并使用脑电图（EEG）记录大脑活动。我们应用了多变量模式分析的时间-频率转换的单次试验EEG响应。我们的研究结果表明，在一组健康个体上训练的分类器可以预测另一个人的疼痛敏感性，准确率为83%。分类精度取决于疼痛诱发的反应在约8赫兹和疼痛引起的伽玛振荡在约80赫兹。这些结果表明，疼痛相关的神经元反应的时间谱模式提供了有价值的信息感知的疼痛。除此之外，我们的方法可能有助于建立一个客观的神经元标记的疼痛敏感性，这可能是记录从一个单一的脑电图电极。
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.