Objective. Unconsciousness is a key feature related to general anesthesia (GA) but is difficult to be evaluated accurately by anesthesiologists clinically. Approach. To tracking the loss of consciousness (LOC) and recovery of consciousness (ROC) under GA, in this study, by investigating functional connectivity of the scalp electroencephalogram, we explore any potential difference in brain networks among anesthesia induction, anesthesia recovery, and the resting state. Main results. The results of this study demonstrated significant differences among the three periods, concerning the corresponding brain networks. In detail, the suppressed default mode network, as well as the prolonged characteristic path length and decreased clustering coefficient, during LOC was found in the alpha band, compared to the Resting and the ROC state. When to further identify the Resting and LOC states, the fused network topologies and properties achieved the highest accuracy of 95%, along with a sensitivity of 93.33% and a specificity of 96.67%. Significance. The findings of this study not only deepen our understanding of propofol-induced unconsciousness but also provide quantitative measurements subserving better anesthesia management.

客观的。无意识是与全身麻醉 (GA) 相关的一个关键特征，但临床麻醉师很难对其进行准确评估。方法。为了跟踪 GA 下的意识丧失 (LOC) 和意识恢复 (ROC)，在本研究中，通过研究头皮脑电图的功能连通性，我们探索了麻醉诱导、麻醉恢复和静息之间脑网络的任何潜在差异。状态。主要结果。这项研究的结果表明三个时期之间存在显着差异，涉及相应的大脑网络。具体而言，与静止和 ROC 状态相比，在 alpha 波段发现了 LOC 期间被抑制的默认模式网络以及延长的特征路径长度和降低的聚类系数。当进一步识别 Resting 和 LOC 状态时，融合网络拓扑和属性实现了 95% 的最高准确度，以及 93.33% 的灵敏度和 96.67% 的特异性。意义。这项研究的结果不仅加深了我们对丙泊酚引起的无意识的理解，而且还提供了有助于更好的麻醉管理的定量测量。