Is complexity complicated?

State-related Electroencephalography Microstate Complexity during Propofol- and Esketamine-induced Unconsciousness

BACKGROUND

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).

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

EDITOR’S PERSPECTIVE

Linear thinking does not reflect the newer 21st-century anesthesia concepts. A narrative review

The brain constitutes a good example of a chaotic, nonlinear biological system where large neuronal networks operate chaotically with random connectivity. This critical state is significantly affected by the anesthetic loss of consciousness induced by drugs whose pharmacological behavior has been classically based on linear kinetics and dynamics. Recent developments in pharmacology and brain monitoring during anesthesia suggest a different view that we tried to explore in this article. The concepts of effect-site for hypnotic drugs modeling a maximum effect, electroencephalographic dynamics during induction, maintenance, and recovery from anesthesia are discussed, integrated into this alternative view, and how it may be applied in daily clinical practice.

Propofol-induced Unresponsiveness Is Associated with a Brain Network Phase Transition

Background:

The wakeful brain can easily access and coordinate a large repertoire of different states—dynamics suggestive of “criticality.” Anesthesia causes loss of criticality at the level of electroencephalogram waveforms, but the criticality of brain network connectivity is less well studied. The authors hypothesized that propofol anesthesia is associated with abrupt and divergent changes in brain network connectivity for different frequencies and time scales—characteristic of a phase transition, a signature of loss of criticality.

Methods:

As part of a previously reported study, 16 volunteers were given propofol in slowly increasing brain concentrations, and their behavioral responsiveness was assessed. The network dynamics from 31-channel electroencephalogram data were calculated from 1 to 20 Hz using four phase and envelope amplitude–based functional connectivity metrics that covered a wide range of time scales from milliseconds to minutes. The authors calculated network global efficiency, clustering coefficient, and statistical complexity (using the Jensen–Shannon divergence) for each functional connectivity metric and compared their findings with those from an in silico Kuramoto network model.

Results:

The transition to anesthesia was associated with critical slowing and then abrupt profound decreases in global network efficiency of 2 Hz power envelope metrics (from mean ± SD of 0.64 ± 0.15 to 0.29 ± 0.28 absolute value, P < 0.001, for medium; and from 0.47 ± 0.13 to 0.24 ± 0.21, P < 0.001, for long time scales) but with an increase in global network efficiency for 10 Hz weighted phase lag index (from 0.30 ± 0.20 to 0.72 ± 0.06, P < 0.001). Network complexity decreased for both the 10 Hz hypersynchronous (0.44 ± 0.13 to 0.23 ± 0.08, P < 0.001), and the 2 Hz asynchronous (0.73 ± 0.08 to 0.40 ± 0.13, P < 0.001) network states. These patterns of network coupling were consistent with those of the Kuramoto model of an order–disorder phase transition.

Conclusions:

Around loss of behavioral responsiveness, a small increase in propofol concentrations caused a collapse of long time scale power envelope connectivity and an increase in 10 Hz phase-based connectivity—suggestive of a brain network phase transition.

Temporospatial electroencephalographic analysis of brain network dynamics over a wide range of frequencies and time scales in 16 volunteers receiving slowly increasing concentrations of propofol revealed that transition to unresponsiveness was associated with a sudden rise in alpha frequency network phase synchrony anteriorly, but also a transient surge and then loss of network coupling over long (tens of seconds) time scales. Deep anesthesia was characterized by alpha waveform hypersynchrony and slow-wave power envelope dissynchrony across the whole cortex. These observations suggest that propofol anesthesia is associated with a constellation of changes in network connectivity across frequencies and time scales that are signatures of sharp and sudden transitions in the behavior of networks.