State-related Electroencephalography Microstate Complexity during Propofol- and Esketamine-induced Unconsciousness.
Zhenhu Liang, Bo Tang, Yu Chang, Jing Wang, Duan Li, Xiaoli Li, Changwei Wei
Anesthesiology May 1, 2024 DOI: 10.1097/ALN.0000000000004896 via PubMed
Summary
AI-generated from the abstractTwo new measures of EEG microstate complexity—type I, quantifying randomness, and type II, quantifying fluctuation complexity—track anesthetic-induced unconsciousness independently of the drug used (propofol or esketamine). In 20 patients, type I complexity increased from wakefulness to unconsciousness and decreased upon recovery, while type II complexity showed the opposite pattern. Both measures changed significantly under both anesthetics, suggesting they reflect the state of consciousness rather than the specific drug. These complexity measures may serve as state-related neural correlates of consciousness during general anesthesia.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 20 |
| Population | Patients with unconsciousness induced by propofol (n=10) or esketamine (n=10) |
| Interventions | propofol esketamine |
| Citations | 13 |
| Key finding | Both type I and type II EEG microstate complexities are drug-independent indicators of anesthetic-induced unconsciousness, changing significantly between wakefulness, unconsciousness, and recovery under propofol and esketamine. |
Abstract
Identifying the state-related "neural correlates of consciousness" for anesthetics-induced unconsciousness is challenging. Spatiotemporal complexity is a promising tool for investigating consciousness. The authors hypothesized that spatiotemporal complexity may serve as a state-related but not drug-related electroencephalography (EEG) indicator during an unconscious state induced by different anesthetic drugs (e.g., propofol and esketamine). The authors recorded EEG from patients with unconsciousness induced by propofol (n = 10) and esketamine (n = 10). Both conventional microstate parameters and microstate complexity were analyzed. Spatiotemporal complexity was constructed by microstate sequences and complexity measures. Two different EEG microstate complexities were proposed to quantify the randomness (type I) and complexity (type II) of the EEG microstate series during the time course of the general anesthesia. The coverage and occurrence of microstate E (prefrontal pattern) and the duration of microstate B (right frontal pattern) could distinguish the states of preinduction wakefulness, unconsciousness, and recovery under both anesthetics. Type I EEG microstate complexity based on mean information gain significantly increased from awake to unconsciousness state (propofol: from mean ± SD, 1.562 ± 0.059 to 1.672 ± 0.023, P < 0.001; esketamine: 1.599 ± 0.051 to 1.687 ± 0.013, P < 0.001), and significantly decreased from unconsciousness to recovery state (propofol: 1.672 ± 0.023 to 1.537 ± 0.058, P < 0.001; esketamine: 1.687 ± 0.013 to 1.608 ± 0.028, P < 0.001) under both anesthetics. In contrast, type II EEG microstate fluctuation complexity significantly decreased in the unconscious state under both drugs (propofol: from 2.291 ± 0.771 to 0.782 ± 0.163, P < 0.001; esketamine: from 1.645 ± 0.417 to 0.647 ± 0.252, P < 0.001), and then increased in the recovery state (propofol: 0.782 ± 0.163 to 2.446 ± 0.723, P < 0.001; esketamine: 0.647 ± 0.252 to 1.459 ± 0.264, P < 0.001). Both type I and type II EEG microstate complexities are drug independent. Thus, the EEG microstate complexity measures that the authors proposed are promising tools for building state-related neural correlates of consciousness to quantify anesthetic-induced unconsciousness.