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

Li, Jun; Dong, Kangli; Sun, Yi; Kakkos, Ioannis; Huang, Fan; Wang, Guozheng; Qi, Peng; Chen, Xing; Zhang, Delin; Bezerianos, Anastasios; Sun, Yu

doi:10.1016/j.eng.2021.11.013

Cited by 9 publications

(7 citation statements)

References 211 publications

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“…2015 , Liang et al. 2016 ), and a recent review found diverse results based on different methods ( Liu et al. 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG

Nilsen,

Arena,

Storm

2024

Neuroscience of Consciousness

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To investigate mechanisms underlying loss of consciousness, it is important to extend methods established in humans to rodents as well. Perturbational complexity index (PCI) is a promising metric of “capacity for consciousness” and is based on a perturbational approach that allows inferring a system’s capacity for causal integration and differentiation of information. These properties have been proposed as necessary for conscious systems. Measures based on spontaneous electroencephalography recordings, however, may be more practical for certain clinical purposes and may better reflect ongoing dynamics. Here, we compare PCI (using electrical stimulation for perturbing cortical activity) to several spontaneous electroencephalography-based measures of signal diversity and integrated information in rats undergoing propofol, sevoflurane, and ketamine anesthesia. We find that, along with PCI, the spontaneous electroencephalography-based measures, Lempel–Ziv complexity (LZ) and geometric integrated information (ΦG), were best able to distinguish between awake and propofol and sevoflurane anesthesia. However, PCI was anti-correlated with spontaneous measures of integrated information, which generally increased during propofol and sevoflurane anesthesia, contrary to expectations. Together with an observed divergence in network properties estimated from directed functional connectivity (current results) and effective connectivity (earlier results), the perturbation-based results seem to suggest that anesthesia disrupts global cortico-cortical information transfer, whereas spontaneous activity suggests the opposite. We speculate that these seemingly diverging results may be because of suppressed encoding specificity of information or driving subcortical projections from, e.g., the thalamus. We conclude that certain perturbation-based measures (PCI) and spontaneous measures (LZ and ΦG) may be complementary and mutually informative when studying altered states of consciousness.

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“…2015 , Liang et al. 2016 ), and a recent review found diverse results based on different methods ( Liu et al. 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG

Nilsen,

Arena,

Storm

2024

Neuroscience of Consciousness

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“…Furthermore, long-range integration during top-down processing (supports consciousness) appears to evolve with temporal dynamics of alpha band [42], [43]. We band-passed the source-level signals to obtain the alpha band (8)(9)(10)(11)(12). To evaluate the FC magnitude, we extracted the signal phase of alpha-band EEG using Hilbert transform, and then the Phase-locking values (PLV) [44] were employed as a connectivity measurement, which isolates the amplitudes and phases of the original signals for a certain frequency range and then calculates the phasic interrelationship between two signals:…”

Section: Fc Estimation and Construction Of Brain Functional Networkmentioning

confidence: 99%

“…For instance, the breakdown of FC represents the impaired connectivity between brain areas; modularity stands for the ability for specialized function to occur within densely interconnected groups of brain regions (segregation); characteristic path length and global efficiency in functional network analysis (FNA) represent the ability to rapidly combine segregation from distributed brain regions (integration) [5], [6]. FC and FNA provide insights into the neurological underpinnings of loss of consciousness (LOC) during anesthesia by evaluating the global and local structured behavior of brain networks [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane

Dong

Wei

Zhang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 (p <0.001 and BF 10 >4000, 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 increas-Manuscript

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“…Many studies have confirmed that complexity and brain networks are effective indicators for evaluating the level of consciousness during GA in normal subjects [17][18][19]. Complexity indices and network indices are related but represent different aspects of a system.…”

Section: Introductionmentioning

confidence: 99%

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Liang,

Lan,

Wang

et al. 2023

J. Neural Eng.

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Objective. General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed. Approach. We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel–Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces. Main results. Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p < 0.001), PFC increased (p < 0.001) and PCMI decreased (p < 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p < 0.001) and nodal efficiency (NE) (p < 0.001) decreased in these two spaces. Global network indicators normalized average path length ( L a v e / L r ) (p < 0.01) and modularity (Q) (p < 0.05) only decreased in sensor space, while the normalized average clustering coefficient ( C a v e / C r ) and small-world index ( σ ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicators L a v e / L r and Q were not significantly different from the preoperative resting state in sensor space. Significance. GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.

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

Cited by 9 publications

References 211 publications

Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG

Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG

Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

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