Role of Network Science in the Study of Anesthetic State Transitions

Lee, UnCheol; Mashour, George A.

doi:10.1097/aln.0000000000002228

Cited by 70 publications

(62 citation statements)

References 146 publications

(152 reference statements)

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“…In fact, accumulating evidence suggests that network topology and reconfiguration, rather than single-dimensional measures of connectivity, may be required to discriminate anesthetic states. [37][38][39][40][41][42] Indeed, connectivity values varied widely among bandwidths and neuroanatomical regions during anesthetic maintenance and surgical phases. As such, the previously described dynamic connectivity analysis was performed to further explore these connectivity dynamics with higher temporal resolution across the entire anesthetic maintenance phase.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Cortical Connectivity during General Anesthesia in Surgical Patients

Vlisides

Zierau

et al. 2019

Anesthesiology

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Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Functional connectivity across the cortex has been posited to be important for consciousness and anesthesia, but functional connectivity patterns during the course of surgery and general anesthesia are unknown. The authors tested the hypothesis that disrupted cortical connectivity patterns would correlate with surgical anesthesia. Methods Surgical patients (n = 53) were recruited for study participation. Whole-scalp (16-channel) wireless electroencephalographic data were prospectively collected throughout the perioperative period. Functional connectivity was assessed using weighted phase lag index. During anesthetic maintenance, the temporal dynamics of connectivity states were characterized via Markov chain analysis, and state transition probabilities were quantified. Results Compared to baseline (weighted phase lag index, 0.163, ± 0.091), alpha frontal–parietal connectivity was not significantly different across the remaining anesthetic and perioperative epochs, ranging from 0.100 (± 0.041) to 0.218 (± 0.136) (P > 0.05 for all time periods). In contrast, there were significant increases in alpha prefrontal–frontal connectivity (peak = 0.201 [0.154, 0.248]; P < 0.001), theta prefrontal–frontal connectivity (peak = 0.137 [0.091, 0.182]; P < 0.001), and theta frontal–parietal connectivity (peak = 0.128 [0.084, 0.173]; P < 0.001) during anesthetic maintenance. Additionally, shifts occurred between states of high prefrontal–frontal connectivity (alpha, beta) with suppressed frontal–parietal connectivity, and high frontal–parietal connectivity (alpha, theta) with reduced prefrontal–frontal connectivity. These shifts occurred in a nonrandom manner (P < 0.05 compared to random transitions), suggesting structured transitions of connectivity during general anesthesia. Conclusions Functional connectivity patterns dynamically shift during surgery and general anesthesia but do so in a structured way. Thus, a single measure of functional connectivity will likely not be a reliable correlate of surgical anesthesia.

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Section: Discussionmentioning

confidence: 99%

Dynamic Cortical Connectivity during General Anesthesia in Surgical Patients

Vlisides

Zierau

et al. 2019

Anesthesiology

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“…Indeed, general anesthesia is posited to induce changes in network configuration as reflected by increased path length, increased clustering coefficient, reduced network efficiency, and disrupted hub structure. 10,29,36,37 Altered connectivity patterns, as demonstrated in figure 4D, may provide a window into the cortical connectivity changes that occur with the functional network reconfiguration during general anesthesia. Further investigation is required to understand how the alterations between multiple connectivity states relate to such network perturbations.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Cortical Connectivity during General Anesthesia in Healthy Volunteers

Vlisides

Kelz

et al. 2019

Anesthesiology

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Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Recent studies of anesthetic-induced unconsciousness in healthy volunteers have focused on functional brain connectivity patterns, but the protocols rarely parallel the depth and duration of surgical anesthesia. Furthermore, it is unknown whether there is a single functional connectivity pattern that correlates with general anesthesia for the duration of prolonged anesthetic exposure. Methods The authors analyzed electroencephalographic data in 30 healthy participants who underwent induction of anesthesia with propofol followed by 3 h of isoflurane anesthesia at age-adjusted 1.3 minimum alveolar concentration. Functional connectivity was assessed by frequency-resolved weighted phase lag index between frontal and parietal channels and between prefrontal and frontal channels, which were classified into a discrete set of states through k-means cluster analysis. Temporal dynamics were evaluated by the occurrence rate and dwell time distribution for each state as well as the transition probabilities between states. Results Burst suppression was present, with mean suppression ratio reducing from 44.8 ± 32.3% to 14.0 ± 20.2% (mean ± SD) during isoflurane anesthesia (P < 0.001). Aside from burst suppression, eight connectivity states were classified by optimizing the reproducibility of clustering solutions, with each characterized by distinct properties. The temporal progression of dominant states revealed a successive shifting trajectory from the state associated with alpha frontal-parietal connectivity to those associated with delta and alpha prefrontal-frontal connectivity during induction, which was reversed during emergence. Cortical connectivity was dynamic during maintenance period, and it was more probable to remain in the same state (82.0 ± 8.3%) than to switch to a different state (P < 0.001). However, transitions to other states were structured, i.e., occurred more frequently than expected by chance. Conclusions Anesthesia-induced alterations of functional connectivity are dynamic despite the stable and prolonged administration of isoflurane, in the absence of any noxious stimuli. Changes in connectivity over time will likely yield more information as a marker or mechanism of surgical anesthesia than any single pattern.

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“…All epochs and channels with noise or non-physiological artifacts were visually identified and removed. We then band-pass filtered the cleaned EEG data into delta (1-4 Hz), theta (4-8Hz), alpha , and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands. Analysis of the alpha band were presented in the main body of the paper, while analyses of other frequency bands are included in the supplementary material.…”

Section: Electroencephalographic Acquisition and Preprocessingmentioning

confidence: 99%

“…Graph theoretical analysis has demonstrated that global and topographic properties of brain networks are altered during anesthetic-induced unconsciousness [12][13][14][15][16]. While these advances have led to a clearer understanding of how anesthetic-induced unconsciousness creates inhospitable conditions for information processing and transfer [17], these measures describe whole-brain network processes rather than region-specific changes in brain functioning. Moreover, they have shown little ability to reveal subtle changes in the level of consciousness during state transitions, such as induction and emergence from anesthetic-induced unconsciousness.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, Kafashan et al showed that sevofluraneinduced unconsciousness disrupts motifs of weaker correlation strength within and between resting-state networks, but preserves motifs with higher correlation strength [24]. These findings suggest that motifs may reflect the underlying process by which anesthesia suppresses long-range functional connectivity and the complexity of sparsely connected brain regions [16,17,25,26]. This analysis technique shows great promise in revealing granular changes in the brain network (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Brain network motifs are markers of loss and recovery of consciousness

Duclos

Mahdid

Nadin

et al. 2020

Preprint

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Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-hour anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8)(9)(10)(11)(12)(13) band, five-minute epochs of EEG were extracted for: baseline; induction; unconsciousness; 30-, 10-and 5-minutes pre-recovery of consciousness; 30-and 180-minutes post-recovery of consciousness. We constructed a functional brain network using both the weighted and the directed phase lag index, on which we calculated graph theoretical network properties, and the frequency and topography of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p<0.05). While graph theoretical properties varied inconsistently across unconscious epochs (p-values<0.05), the topography of motifs 1 and 5 changed significantly between conscious and unconscious states (p<0.01). Our results suggest that anesthetic-induced unconsciousness is associated with a topographic re-organization of network motifs. As motif topographic re-organization may precede (motif 1) or accompany (motif 5) the behavioral return of consciousness, motifs could contribute to the understanding of the neural building blocks of consciousness.representing the spurious connectivity. The wPLI and dPLI values of the original, non-shuffled EEG data were compared to this distribution of surrogate data using a Wilcoxon signed rank test and were set to 0 (wPLI) or 0.5 (dPLI) if they did not achieve statistical significance. Statistical significance was set to p < 0.05. Construction and analysis of brain networksThe functional brain network was constructed using the wPLI of all pairwise combinations of electrode channels. We constructed a binary adjacency matrix Aij using a threshold of 35%: if the wPLIij value of nodes i and j was within the top 35% of all wPLI values, Aij = 1; otherwise, Aij = 0. This threshold was selected because it was previously shown to be the optimum threshold to avoid an isolated node in the EEG network during baseline [47]. From the binary adjacency matrix, we calculated basic graph theoretical network properties, including global efficiency, clustering coefficient, and modularity (Fig. 6). Global efficiency is the inverse of the average shortest path length ( ! ! ! ), where Lw is the average of the shortest path lengths (Lij) between all pairs of nodes in the network [48]. The clustering coefficient, calculated by averaging the clustering coefficients of all individual nodes (Ci), represents the degree to which nodes of a graph tend to cluster together, such that higher values imply networks with highly clustered or regular structures [49]. The modularity of the network represents the strength of division of a network into modules, such that high modularity implies a network w...

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Role of Network Science in the Study of Anesthetic State Transitions

Cited by 70 publications

References 146 publications

Dynamic Cortical Connectivity during General Anesthesia in Surgical Patients

Dynamic Cortical Connectivity during General Anesthesia in Surgical Patients

Dynamic Cortical Connectivity during General Anesthesia in Healthy Volunteers

Brain network motifs are markers of loss and recovery of consciousness

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