Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane
Anesthetic-induced loss of consciousness (LOC) has been studied using functional connectivity (FC) and functional network analysis (FNA), manifested as fragmentation of the whole-brain functional network. However, how the fragmented brain networks reversibly recover during the recovery of consciousn...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/9950296/ |
| _version_ | 1827927038668832768 |
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| author | Kangli Dong Qishun Wei Delin Zhang Lu Zhang Guozheng Wang Xing Chen Jun Liu |
| author_facet | Kangli Dong Qishun Wei Delin Zhang Lu Zhang Guozheng Wang Xing Chen Jun Liu |
| author_sort | Kangli Dong |
| collection | DOAJ |
| description | Anesthetic-induced loss of consciousness (LOC) has been studied using functional connectivity (FC) and functional network analysis (FNA), manifested as fragmentation of the whole-brain functional network. However, how the fragmented brain networks reversibly recover during the recovery of consciousness (ROC) remains vague. This study aims to investigate the changes in brain network structure during ROC, to better understand the network fragmentation during anesthesia, thus providing insights into consciousness monitoring. We analyzed EEG data recorded from 15 individuals anesthetized by sevoflurane. By investigating the properties of functional networks generated using different brain atlases and performing community detection for functional networks, we explored the changes in brain network structure to understand how fragmented brain networks recover during the ROC. We observed an overall larger FC magnitude during LOC than in the conscious state. The ROC was accompanied by the increasing binary network efficiency, decreasing FC magnitude, and decreasing community similarity with the functional atlas. Furthermore, we observed a negative correlation between modularity and community number (<inline-formula> <tex-math notation="LaTeX">$\text{p} < 0.001$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\textit {BF}_{{10}} >4000$ </tex-math></inline-formula>, linear regression test), in which modularity increased and community number decreased during ROC. Our results show that a larger FC magnitude reveals excessive synchronization of neuronal activities during LOC. The increasing binary network efficiency, decreasing community number, and decreasing community similarity indicate the recovery of functional network integration. The increasing modularity implies the recovery of functional network segregation during ROC. The results suggest the limitation of FC magnitude and modularity in monitoring anesthetized states and the potential of integrated information theory to evaluate consciousness. |
| first_indexed | 2024-03-13T05:46:38Z |
| format | Article |
| id | doaj.art-f3ef6d5e2bff485b92da2c670dcb354d |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:38Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-f3ef6d5e2bff485b92da2c670dcb354d2023-06-13T20:09:29ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-013130431510.1109/TNSRE.2022.32219659950296Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by SevofluraneKangli Dong0https://orcid.org/0000-0002-1657-4899Qishun Wei1https://orcid.org/0000-0001-7091-8114Delin Zhang2Lu Zhang3Guozheng Wang4Xing Chen5Jun Liu6https://orcid.org/0000-0002-8411-4093Key Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, ChinaKey Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, ChinaDepartment of Anesthesiology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, ChinaDepartment of Rehabilitation, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, ChinaKey Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, ChinaKey Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, ChinaKey Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, ChinaAnesthetic-induced loss of consciousness (LOC) has been studied using functional connectivity (FC) and functional network analysis (FNA), manifested as fragmentation of the whole-brain functional network. However, how the fragmented brain networks reversibly recover during the recovery of consciousness (ROC) remains vague. This study aims to investigate the changes in brain network structure during ROC, to better understand the network fragmentation during anesthesia, thus providing insights into consciousness monitoring. We analyzed EEG data recorded from 15 individuals anesthetized by sevoflurane. By investigating the properties of functional networks generated using different brain atlases and performing community detection for functional networks, we explored the changes in brain network structure to understand how fragmented brain networks recover during the ROC. We observed an overall larger FC magnitude during LOC than in the conscious state. The ROC was accompanied by the increasing binary network efficiency, decreasing FC magnitude, and decreasing community similarity with the functional atlas. Furthermore, we observed a negative correlation between modularity and community number (<inline-formula> <tex-math notation="LaTeX">$\text{p} < 0.001$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\textit {BF}_{{10}} >4000$ </tex-math></inline-formula>, linear regression test), in which modularity increased and community number decreased during ROC. Our results show that a larger FC magnitude reveals excessive synchronization of neuronal activities during LOC. The increasing binary network efficiency, decreasing community number, and decreasing community similarity indicate the recovery of functional network integration. The increasing modularity implies the recovery of functional network segregation during ROC. The results suggest the limitation of FC magnitude and modularity in monitoring anesthetized states and the potential of integrated information theory to evaluate consciousness.https://ieeexplore.ieee.org/document/9950296/Anesthesiaconsciousnesselectroencephalogramfunctional networkscommunity detection |
| spellingShingle | Kangli Dong Qishun Wei Delin Zhang Lu Zhang Guozheng Wang Xing Chen Jun Liu Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane IEEE Transactions on Neural Systems and Rehabilitation Engineering Anesthesia consciousness electroencephalogram functional networks community detection |
| title | Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane |
| title_full | Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane |
| title_fullStr | Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane |
| title_full_unstemmed | Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane |
| title_short | Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane |
| title_sort | recovery of brain network integration and segregation during the loss and recovery of consciousness induced by sevoflurane |
| topic | Anesthesia consciousness electroencephalogram functional networks community detection |
| url | https://ieeexplore.ieee.org/document/9950296/ |
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