Dynamic Cortical Connectivity during General Anesthesia in Surgical Patients. (June 2019)

• Record Type: Journal Article
• Authors: Vlisides, Phillip E.; Li, Duan; Zierau, Mackenzie; Lapointe, Andrew P.; Ip, Ka I.; McKinney, Amy M.; Mashour, George A.
• Is Part Of: Anesthesiology. Volume 130:Number 6(2019)
• Publication Date: 2019-06
• Subjects: Anesthesiology -- Periodicals; Anesthetics -- Periodicals; Anesthesia -- Periodicals; 617.9605
• DOI: 10.1097/ALN.0000000000002677
• Languages: English
• ISSNs: 0003-3022

Abstract

Editor's Perspective: What We Already Know about This Topic: Animal data, along with recent human observations (in this issue of ANESTHESIOLOGY*), suggest that cortical oscillations and connectivity shift dynamically during what appears to be stable general anesthesia Clinical evidence in the perioperative setting to support these observations is currently lacking What This Article Tells Us That Is New: During anesthesia and surgery, cortical networks display a dynamic interplay among brain states, rather than a static equilibrium These findings suggest that a single measure of connectivity may not be a reliable correlate of surgical anesthesia depth 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. Abstract : During anesthesia and surgery, cortical networks display a dynamic interplay among brain states rather than a static equilibrium. These findings suggest that a single measure of connectivity may not be a reliable correlate of surgical anesthesia depth.Supplemental Digital Content is available in the text.