Liu Haining, Shi Ruijuan, Liao Runchao, Liu Yanli, Che Jiajun, Bai Ziyu, Cheng Nan, Ma Hailin
Psychology Department, Chengde Medical University, Chengde 067000, China.
Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde 067000, China.
Brain Sci. 2022 Dec 7;12(12):1677. doi: 10.3390/brainsci12121677.
(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.
(1) 目的：本研究旨在探讨高海拔对脑电图（EEG）数据神经关联中持续注意力基础的抑制控制过程的影响，并探究反映抑制控制的EEG数据是否包含对高海拔慢性缺氧和平原对照组进行分类的有价值信息。(2) 方法：招募了35名慢性高海拔缺氧成年人和32名匹配的对照组。要求他们使用事件相关电位执行去/不去持续注意力任务（GSAT）。基于相关的ERP成分和神经振荡，训练了三种机器学习算法，即支持向量机（SVM）、逻辑回归（LR）和决策树（DT），以建立二元分类模型。(3) 结果：在行为上，我们发现高海拔（HA）组在所有观察期的遗漏错误率均低于低海拔（LA）组。同时，ERP结果显示，HA参与者的持续电位（SP）潜伏期明显短于LA参与者，表明对反应相关冲突保持警惕。同时，事件相关频谱微扰（ERSP）分析表明，暴露于高海拔的低地移民可能有前额叶皮质（PFC）的代偿性激活，这由慢α、β和θ频段神经振荡反映。最后，机器学习结果显示，SVM在持续注意力后期实现了最佳分类F1分数，F1分数为0.93，准确率为92.54%，灵敏度为91.43%，特异性为93.75%，ROC曲线下面积（AUC）为0.97。结果证明SVM分类算法可用于识别慢性高海拔缺氧。(4) 结论：与其他方法相比，SVM具有良好的整体性能，且随着任务时间的增加而提高，说明ERP和神经振荡可能为识别慢性高原缺氧提供神经电生理标志物。
