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Engineering >> 2023, Volume 20, Issue 1 doi: 10.1016/j.eng.2021.11.013

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

a Key Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou 310007, China
b Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China
c School of Electrical and Computer Engineering, National Technical University of Athens, Athens 15780, Greece
d Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China
e Department of Anesthesiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
f The N1 Institute for Health, Center for Life Sciences, National University of Singapore, Singapore 117456, Singapore

Received: 2021-07-20 Revised: 2021-10-21 Accepted: 2021-11-09 Available online: 2021-12-13

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Abstract

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

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