Sun Guanghao, Wen Zhenfu, Ok Deborah, Doan Lisa, Wang Jing, Chen Zhe Sage
Department of Psychiatry, New York University School of Medicine, New York, NY, United States.
Department of Anesthesiology, Perioperative Care, and Pain Medicine, New York University School of Medicine, New York, NY, United States.
J Neurosci Methods. 2021 Jan 1;347:108964. doi: 10.1016/j.jneumeth.2020.108964. Epub 2020 Sep 30.
Advances in human neuroimaging has enabled us to study functional connections among various brain regions in pain states. Despite a wealth of studies at high anatomic resolution, the exact neural signals for the timing of pain remain little known. Identifying the onset of pain signals from distributed cortical circuits may reveal the temporal dynamics of pain responses and subsequently provide important feedback for closed-loop neuromodulation for pain.
Here we developed an unsupervised learning method for sequential detection of acute pain signals based on multichannel human EEG recordings. Following EEG source localization, we used a state-space model (SSM) to detect the onset of acute pain signals based on the localized regions of interest (ROIs).
We validated the SSM-based detection strategy using two human EEG datasets, including one public EEG recordings of 50 subjects. We found that the detection accuracy varied across tested subjects and detection methods. We also demonstrated the feasibility for cross-subject and cross-modality prediction of detecting the acute pain signals.
In contrast to the batch supervised learning analysis based on a support vector machine (SVM) classifier, the unsupervised learning method requires fewer number of training trials in the online experiment, and shows comparable or improved performance than the supervised method.
Our unsupervised SSM-based method combined with EEG source localization showed robust performance in detecting the onset of acute pain signals.
人类神经影像学的进展使我们能够研究疼痛状态下不同脑区之间的功能连接。尽管有大量高解剖分辨率的研究，但疼痛发生时间的确切神经信号仍鲜为人知。从分布式皮层回路中识别疼痛信号的起始可能揭示疼痛反应的时间动态，并随后为疼痛的闭环神经调节提供重要反馈。
在此，我们基于多通道人类脑电图记录开发了一种用于顺序检测急性疼痛信号的无监督学习方法。在脑电图源定位之后，我们使用状态空间模型（SSM）基于感兴趣的局部区域（ROI）检测急性疼痛信号的起始。
我们使用两个人类脑电图数据集验证了基于SSM的检测策略，其中包括一个50名受试者的公开脑电图记录。我们发现检测准确率因受试对象和检测方法而异。我们还展示了跨受试者和跨模态预测检测急性疼痛信号的可行性。
与基于支持向量机（SVM）分类器的批量监督学习分析相比，无监督学习方法在在线实验中所需的训练试验次数更少，并且表现出与监督方法相当或更好的性能。
我们基于SSM的无监督方法与脑电图源定位相结合，在检测急性疼痛信号的起始方面表现出强大的性能。
