Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:1928-1931. doi: 10.1109/EMBC46164.2021.9629617.
Understanding neural correlates of consciousness and its alterations poses a grand challenge for modern neuroscience. Even though recent years of research have shown many conceptual and empirical advances, the evolution of a system that can track anesthesia-induced loss of consciousness is hindered by the lack of reliable markers. The work presented herein estimates the functional connectivity (FC) between 21 scalp electroencephalogram (EEG) recordings to evaluate its utility in characterizing changes in brain networks during propofol sedation. The sedation dataset in the University of Cambridge data repository was used for analyses. FC was estimated using the debiased estimator of the squared Weighted Phase Lag Index (dWPLI2). Spectral FC networks before, during, and after sedation was considered for 5 EEG sub-bands. Results demonstrated significantly higher alpha band FC during baseline, mild and moderate sedation, and recovery stages. A striking association between frontal brain activity and propofol-sedation was also noticed. Furthermore, inhibition of frontal to parietal and frontal to occipital connections were observed as characteristic features of propofol-induced alterations in consciousness. A random subspace ensemble framework using logistic model tree as the base classifier, and 18 functional connections as features, yielded a cross-validation accuracy of 98.75% in discriminating baseline, mild and moderate sedation, and recovery stages. These findings validate that EEG-based FC can reliably distinguish altered conscious states associated with anaesthesia.
理解意识的神经相关及其改变是现代神经科学面临的一项重大挑战。尽管近年来的研究已经取得了许多概念和实证上的进展,但由于缺乏可靠的标志物,一种能够跟踪麻醉诱导意识丧失的系统的发展受到了阻碍。本文旨在通过估计 21 个头皮脑电图 (EEG) 记录之间的功能连接 (FC),评估其在描述异丙酚镇静期间脑网络变化中的效用。剑桥大学数据存储库中的镇静数据集用于分析。FC 使用加权相位滞后指数 (WPLI) 的无偏估计量 (dWPLI2) 进行估计。考虑了 5 个 EEG 子频带在镇静前、镇静中和镇静后时的频谱 FC 网络。结果表明,在基线、轻度和中度镇静以及恢复阶段,alpha 频段 FC 显著升高。还注意到额叶脑活动与异丙酚镇静之间存在显著关联。此外,还观察到额叶到顶叶和额叶到枕叶连接的抑制是异丙酚诱导意识改变的特征。使用逻辑模型树作为基分类器的随机子空间集成框架,并使用 18 个功能连接作为特征,在区分基线、轻度和中度镇静以及恢复阶段方面的交叉验证准确率为 98.75%。这些发现验证了基于 EEG 的 FC 可以可靠地区分与麻醉相关的改变的意识状态。