Investigating dynamic functional network patterns after propofol-induced loss of consciousness

•Dynamic brain functional network during anesthesia is supported by 5 metastable network patterns.•The occurrence of each metastable network pattern is associated with the level of anesthesia.•An anatomical connectivity supported functional network pattern persists across conscious levels. We invest...

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Bibliographic Details
Published in:Clinical neurophysiology Vol. 130; no. 3; pp. 331 - 340
Main Authors: Zhang, Yun, Wang, Chunshu, Wang, Yubo, Yan, Fei, Wang, Qiang, Huang, Liyu
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.03.2019
Subjects:
Adult
Anesthesia
Anesthesia, General
Anesthetics, Intravenous - administration & dosage
Brain - drug effects
Brain - physiopathology
Clustering
Dynamic functional connectivity
EEG source imaging
Electroencephalography
Humans
Male
Nerve Net - drug effects
Nerve Net - physiopathology
Propofol - administration & dosage
Unconsciousness - physiopathology
Anesthesia
Dynamic functional connectivity
EEG source imaging
Clustering
ISSN:1388-2457, 1872-8952, 1872-8952
Online Access:Get full text
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Summary:•Dynamic brain functional network during anesthesia is supported by 5 metastable network patterns.•The occurrence of each metastable network pattern is associated with the level of anesthesia.•An anatomical connectivity supported functional network pattern persists across conscious levels. We investigated the changes of dynamic brain functional network from awaken state to the anesthesia level suitable for surgery. 60-channel EEG data of 22 subjects are acquired at wakefulness, light anesthesia and deep anesthesia. The activity of 68 cortical regions are obtained by using EEG source imaging. Sliding window analysis is employed to obtain a dynamic sequence of brain functional network. K-means clustering algorithm is then employed to identify the common brain functional network patterns. Five common brain functional network patterns were identified across all conscious levels. The occurrence of each meta-stable network pattern was associated with the level of anesthesia. A transition functional network pattern was found to transfer to the anesthesia dominating or wakefulness dominating network pattern depending on the conscious level. Furthermore, a functional network pattern persisted during both wakefulness and anesthesia is found to be supported by the anatomical connectivity. Dynamic changes of brain functional network exist in both awaken and anesthesia state. These findings suggest that dynamic brain functional network analysis plays a critical role in decoding the mechanism of general anesthesia. The obtained five metastable network patterns may be employed for monitoring the depth of anesthesia.
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ISSN:1388-2457
1872-8952
1872-8952
DOI:10.1016/j.clinph.2018.11.028