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中国临床药理学与治疗学 ›› 2022, Vol. 27 ›› Issue (12): 1400-1407.doi: 10.12092/j.issn.1009-2501.2022.12.010

• 麻醉药与脑功能障碍专刊 • 上一篇    下一篇

麻醉相关神经网络在麻醉深度监测中的研究进展

丁加慧1,2,周 瑜1,2,袁天杰1,2,夏俊明2,李文献2,韩园2   

  1. 1复旦大学,上海 200433;2复旦大学附属眼耳鼻喉科医院,上海 200031
  • 收稿日期:2022-10-24 修回日期:2023-01-03 出版日期:2022-12-26 发布日期:2023-01-13
  • 通讯作者: 韩园,女,博士,副主任医师,副教授,研究方向:角膜痛与镇痛中枢机制。 E-mail: yuan.han@fdeent.org
  • 作者简介:丁加慧,女,硕士,研究方向:角膜痛与镇痛中枢机制。 E-mail: 22211260024@m.fudan.edu.cn
  • 基金资助:
    上海市科委自然科学基金面上项目(21ZR1411300)

Research progress of anesthesia-related neural network in depth of anesthesia monitoring

DING Jiahui1,2, ZHOU Yu1,2, YUAN Tianjie1,2, XIA Junming2, LI Wenxian2, HAN Yuan2   

  1. 1Fudan University, Shanghai 200433, China; 2Fudan University Affiliated Eye, Ear, Nose and Throat Hospital, Shanghai 200031, China 
  • Received:2022-10-24 Revised:2023-01-03 Online:2022-12-26 Published:2023-01-13

摘要: 麻醉深度把握不当不仅不利于麻醉的快速平稳复苏,也可影响患者术后转归。因此,精准把控麻醉深度是麻醉学领域亟待解决的临床和科学问题。目前临床主要基于脑电图(EEG)信号监测衍生出的不同算法模型进行麻醉深度的监测,然而并不能满足麻醉医生对于麻醉深度精准把握的要求。近年来,有关麻醉相关神经网络机制及其调制的研究提示,其作为麻醉深度监测的方法具有潜在价值。麻醉相关神经网络主要包括睡眠觉醒环路、丘脑-皮质环路及皮质-皮质网络。深入理解参与麻醉引起意识消失的神经网络,将更为精准地指导麻醉深度监测,为提高临床麻醉复苏质量提供可能。

关键词: 全身麻醉, 麻醉深度监测, 神经网络, 意识

Abstract: Improper control of depth of anesthesia is not only detrimental to the rapid and stable recovery of anesthesia, but also affects the postoperative outcome of patients. Therefore, accurate control of anesthesia depth is an urgent clinical and scientific problem in the field of anesthesiology. At present, different algorithm models derived from electroencephalogram (EEG) signals are used to monitor the depth of anesthesia, but they cannot meet the requirements of anesthesiologists to accurately evaluate the depth of anesthesia. In recent years, the research on the mechanism and modulation of anesthesia-related neural network suggests that it has potential value as a method to monitor depth of anesthesia. Anesthesia-related neural networks mainly include sleep-wake circuit, thalamic-cortical circuit and corticocortical network. A thorough understanding of the neural network involved in the loss of consciousness caused by anesthesia will guide the depth of anesthesia monitoring more accurately and provide possibility for improving the quality of clinical anesthesia resuscitation.

Key words: general anesthesia, depth of anesthesia monitoring, neural network, consciousness

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