Monitoring consciousness: the current status of EEG-based depth of anaesthesia monitors

Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.

Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra

Support-vector machines (SVMs) can potentially improve patient monitoring during nitrous oxide anaesthesia. By elucidating the effects of low-dose nitrous oxide on the power spectra of multi-channel EEG recordings, we quantified the degree to which these effects generalise across participants. In this single-blind, cross-over study, 32-channel EEG was recorded from 12 healthy participants exposed to 0, 20, 30 and 40% end-tidal nitrous oxide. Features of the delta-, theta-, alpha- and beta-band power were used within a 12-fold, participant-wise cross-validation framework to train and test two SVMs: (1) binary SVM classifying EEG during 0 or 40% exposure (chance = 50%); (2) multi-class SVM classifying EEG during 0, 20, 30 or 40% exposure (chance = 25%). Both the binary (accuracy 92%) and the multi-class (accuracy 52%) SVMs classified EEG recordings at rates significantly better than chance (p < 0.001 and p = 0.01, respectively). To determine the relative importance of frequency band features for classification accuracy, we systematically removed features before re-training and re-testing the SVMs. This showed the relative importance of decreased delta power and the frontal region. SVM classification identified that the most important effects of nitrous oxide were found in the delta band in the frontal electrodes that was consistent between participants. Furthermore, support-vector classification of nitrous oxide dosage is a promising method that might be used to improve patient monitoring during nitrous oxide anaesthesia.

Carving Nature at Its Joints: A Comparison of CEMI Field Theory with Integrated Information Theory and Global Workspace Theory

The quest to comprehend the nature of consciousness has spurred the development of many theories that seek to explain its underlying mechanisms and account for its neural correlates. In this paper, I compare my own conscious electromagnetic information field (cemi field) theory with integrated information theory (IIT) and global workspace theory (GWT) for their ability to 'carve nature at its joints' in the sense of predicting the entities, structures, states and dynamics that are conventionally recognized as being conscious or nonconscious. I go on to argue that, though the cemi field theory shares features of both integrated information theory and global workspace theory, it is more successful at carving nature at its conventionally accepted joints between conscious and nonconscious systems, and is thereby a more successful theory of consciousness.

[Processed EEG for personalized dosing of anesthetics during general anesthesia]

Electroencephalogram (EEG)-guided anesthesia is indispensable in modern operating rooms and has become established as the standard form of monitoring. Many anesthesiologists rely on processed EEG indices in the hope of averting anesthesia-related complications, such as intraoperative awareness, postoperative delirium and other cognitive complications in their patients. This educational review aims to provide information on the five most prevalent monitors used to guide depth of sedation during general anesthesia. This article elucidates the principles underpinning the application of these monitors where known, which are generally based on power in various EEG frequency bands and on the burst suppression pattern. Convinced that EEG-guided anesthesia has the potential of benefitting many surgical patients, it is felt that many basic principles and shortcomings of processed EEG indices need to be better understood in the clinical practice. After discussing the different monitors and clinically relevant data from the literature, the article gives a short practical guidance on how to critically interpret processed EEG information and troubleshooting of confounded indices in the context of clinical situations.

Consciousness: Matter or EMF?

Conventional theories of consciousness (ToCs) that assume that the substrate of consciousness is the brain's neuronal matter fail to account for fundamental features of consciousness, such as the binding problem. Field ToC's propose that the substrate of consciousness is the brain's best accounted by some kind of field in the brain. Electromagnetic (EM) ToCs propose that the conscious field is the brain's well-known EM field. EM-ToCs were first proposed only around 20 years ago primarily to account for the experimental discovery that synchronous neuronal firing was the strongest neural correlate of consciousness (NCC). Although EM-ToCs are gaining increasing support, they remain controversial and are often ignored by neurobiologists and philosophers and passed over in most published reviews of consciousness. In this review I examine EM-ToCs against established criteria for distinguishing between ToCs and demonstrate that they outperform all conventional ToCs and provide novel insights into the nature of consciousness as well as a feasible route toward building artificial consciousnesses.

Are we really unconscious in "unconscious" states? Common assumptions revisited

In the field of consciousness science, there is a tradition to categorize certain states such as slow-wave non-REM sleep and deep general anesthesia as "unconscious". While this categorization seems reasonable at first glance, careful investigations have revealed that it is not so simple. Given that (1) behavioral signs of (un-)consciousness can be unreliable, (2) subjective reports of (un-)consciousness can be unreliable, and, (3) states presumed to be unconscious are not always devoid of reported experience, there are reasons to reexamine our traditional assumptions about "states of unconsciousness". While these issues are not novel, and may be partly semantic, they have implications both for scientific progress and clinical practice. We suggest that focusing on approaches that provide a more pragmatic and nuanced characterization of different experimental conditions may promote clarity in the field going forward, and help us build stronger foundations for future studies.

Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG

This paper proposed a new depth of anaesthesia (DoA) index for the real-time assessment of DoA using electroencephalography (EEG). In the proposed new DoA index, a wavelet transform threshold was applied to denoise raw EEG signals, and five features were extracted to construct classification models. Then, the Gaussian process regression model was employed for real-time assessment of anaesthesia states. The proposed real-time DoA index was implemented using a sliding window technique and validated using clinical EEG data recorded with the most popular commercial DoA product Bispectral Index monitor (BIS). The results are evaluated using the correlation coefficients and Bland-Altman methods. The outcomes show that the highest and the average correlation coefficients are 0.840 and 0.814, respectively, in the testing dataset. Meanwhile, the scatter plot of Bland-Altman shows that the agreement between BIS and the proposed index is 94.91%. In contrast, the proposed index is free from the electromyography (EMG) effect and surpasses the BIS performance when the signal quality indicator (SQI) is lower than 15, as the proposed index can display high correlation and reliable assessment results compared with clinic observations.

A review of methods used to kill laboratory rodents: issues and opportunities

Rodents are the most widely used species for scientific purposes. A critical pre-requisite of their use, based on utilitarian ethical reasoning, is the provision of a humane death when necessary for scientific or welfare grounds. Focussing on the welfare challenges presented by current methods, we critically evaluate the literature, consider emerging methodologies that may have potential for refinement and highlight knowledge gaps for future research. The evidence supports the conclusion that scientists and laboratory personnel should seek to avoid killing laboratory rodents by exposing them to carbon dioxide (CO₂), unless exploiting its high-throughput advantage. We suggest that stakeholders and policymakers should advocate for the removal of CO₂ from existing guidelines, instead making its use conditionally acceptable with justification for additional rationale for its application. With regards to physical methods such as cervical dislocation, decapitation and concussion, major welfare concerns are based on potential inaccuracy in application and their susceptibility to high failure rates. There is a need for independent quality-controlled training programmes to facilitate optimal success rates and the development of specialist tools to improve outcomes and reliability. Furthermore, we highlight questions surrounding the inconsistent inclusion criteria and acceptability of physical methods in international regulation and/or guidance, demonstrating a lack of cohesion across countries and lack of a comprehensive 'gold standard' methodology. We encourage better review of new data and championing of open access scientific resources to advocate for best practice and enable significant changes to policy and legislation to improve the welfare of laboratory rodents at killing.

The role of intracerebral dopamine D1 and D2 receptors in sleep-wake cycles and general anesthesia

Dopamine (DA), a monoamine neurotransmitter, is synthesized and released mainly by neurons in the ventral tegmental area and the substantia nigra (SN) pars compacta of the midbrain. DA and its receptors are essential for the regulation of arousal, movement, cognition, reward, and other neurobiological behaviors. Arousal, locomotion, cognition, reward, and other neurobiological functions are all regulated by dopamine and its receptors. Dopamine receptors can be divided into D1-like receptors (including D1 and D5) or D2-like receptors (containing D2, D3, and D4), with D1 and D2 receptors (D1Rs, and D2Rs) being the most important. Currently, studies indicated that D1Rs and D2Rs are tightly involved with the process of sleep-wake and general anesthesia, but the specific mechanism remains unclear. In this review, we compiled the most recent findings, mainly focusing on the structure, distribution, and signal pathway of D1Rs and D2Rs in the central nervous system, as well as the involvement of D1Rs and D2Rs in sleep-wake and general anesthesia. Thus, the investigations of the D1Rs and D2Rs will benefit not only better knowledge for how sleep-wake control works but also the mechanism of general anesthesia.

A survey of current practices, attitudes and demands of anaesthesiologists regarding the depth of anaesthesia monitoring in China

Background

In this study, we aimed to analyse survey data to explore two different hypotheses; and for this purpose, we distributed an online survey to Chinese anaesthesiologists. The hypothetical questions in this survey include: (1) Chinese anaesthesiologists mainly use the depth of anaesthesia (DoA) monitors to prevent intraoperative awareness and (2) the accuracy of these monitors is the most crucial performance factor during the clinical daily practice of Chinese anaesthesiologists.

Methods

We collected and statistically analysed the response of a total of 12,750 anesthesiologists who were invited to participate in an anonymous online survey. The Chinese Society of Anaesthesiologists (CSA) trial group provided the email address of each anaesthesiologist, and the selection of respondents was random from the computerized system.

Results

The overall response rate was 32.0% (4037 respondents). Only 9.1% (95% confidence interval, 8.2-10.0%) of the respondents routinely used DoA monitors. Academic respondents (91.5, 90.3-92.7%) most frequently used DoA monitoring to prevent awareness, whereas nonacademic respondents (88.8, 87.4-90.2%) most frequently used DoA monitoring to guide the delivery of anaesthetic agents. In total, the number of respondents who did not use a DoA monitor and whose patients experienced awareness (61.7, 57.8-65.6%) was significantly greater than those who used one or several DoA monitors (51.5, 49.8-53.2%). Overall, the crucial performance factor during DoA monitoring was considered by 61.9% (60.4-63.4%) of the respondents to be accuracy. However, most respondents (95.7, 95.1-96.3%) demanded improvements in the accuracy of the monitors for DoA monitoring. In addition, broad application in patients of all ages (86.3, 85.2-87.4%), analgesia monitoring (80.4, 79.2-81.6%), and all types of anaesthetic agents (75.6, 74.3-76.9%) was reported. In total, 65.0% (63.6-66.5%) of the respondents believed that DoA monitors should be combined with EEG and vital sign monitoring, and 53.7% (52.1-55.2%) believed that advanced DoA monitors should include artificial intelligence.

Conclusions

Academic anaesthesiologists primarily use DoA monitoring to prevent awareness, whereas nonacademic anaesthesiologists use DoA monitoring to guide the delivery of anaesthetics. Anaesthesiologists demand high-accuracy DoA monitors incorporating EEG signals, multiple vital signs, and antinociceptive indicators. DoA monitors with artificial intelligence may represent a new direction for future research on DoA monitoring.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01510-7.

Mouse Anesthesia: The Art and Science

There is an art and science to performing mouse anesthesia, which is a significant component to animal research. Frequently, anesthesia is one vital step of many over the course of a research project spanning weeks, months, or beyond. It is critical to perform anesthesia according to the approved research protocol using appropriately handled and administered pharmaceutical-grade compounds whenever possible. Sufficient documentation of the anesthetic event and procedure should also be performed to meet the legal, ethical, and research reproducibility obligations. However, this regulatory and documentation process may lead to the use of a few possibly oversimplified anesthetic protocols used for mouse procedures and anesthesia. Although a frequently used anesthetic protocol may work perfectly for each mouse anesthetized, sometimes unexpected complications will arise, and quick adjustments to the anesthetic depth and support provided will be required. As an old saying goes, anesthesia is 99% boredom and 1% sheer terror. The purpose of this review article is to discuss the science of mouse anesthesia together with the art of applying these anesthetic techniques to provide readers with the knowledge needed for successful anesthetic procedures. The authors include experiences in mouse inhalant and injectable anesthesia, peri-anesthetic monitoring, specific procedures, and treating common complications. This article utilizes key points for easy access of important messages and authors’ recommendation based on the authors’ clinical experiences.

Consciousness among delta waves: a paradox?

A common observation in EEG research is that consciousness vanishes with the appearance of delta (1 - 4 Hz) waves, particularly when those waves are high amplitude. High amplitude delta oscillations are very frequently observed in states of diminished consciousness, including slow wave sleep, anaesthesia, generalised epileptic seizures, and disorders of consciousness such as coma and vegetative state. This strong correlation between loss of consciousness and high amplitude delta oscillations is thought to stem from the widespread cortical deactivation that occurs during the "down states" or troughs of these slow oscillations. Recently, however, many studies have reported the presence of prominent delta activity during conscious states, which casts doubt on the hypothesis that high amplitude delta oscillations are an indicator of unconsciousness. These studies include work in Angelman syndrome, epilepsy, behavioural responsiveness during propofol anaesthesia, postoperative delirium, and states of dissociation from the environment such as dreaming and powerful psychedelic states. The foregoing studies complement an older, yet largely unacknowledged, body of literature that has documented awake, conscious patients with high amplitude delta oscillations in clinical reports from Rett syndrome, Lennox-Gastaut syndrome, schizophrenia, mitochondrial diseases, hepatic encephalopathy, and nonconvulsive status epilepticus. At the same time, a largely parallel body of recent work has reported convincing evidence that the complexity or entropy of EEG and magnetoencephalogram or MEG signals strongly relates to an individual's level of consciousness. Having reviewed this literature, we discuss plausible mechanisms that would resolve the seeming contradiction between high amplitude delta oscillations and consciousness. We also consider implications concerning theories of consciousness, such as integrated information theory and the entropic brain hypothesis. Finally, we conclude that false inferences of unconscious states can be best avoided by examining measures of electrophysiological complexity in addition to spectral power.

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

Background

Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment.

Methods

A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.

Results

The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.

Conclusions

The incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method—with other evaluation methods, such as EEG—is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01285-x.

An Electroencephalogram Metric of Temporal Complexity Tracks Psychometric Impairment Caused by Low-dose Nitrous Oxide

BACKGROUND

Nitrous oxide produces non-γ-aminobutyric acid sedation and psychometric impairment and can be used as scientific model for understanding mechanisms of progressive cognitive disturbances. Temporal complexity of the electroencephalogram may be a sensitive indicator of these effects. This study measured psychometric performance and the temporal complexity of the electroencephalogram in participants breathing low-dose nitrous oxide.

METHODS

In random order, 20, 30, and 40% end-tidal nitrous oxide was administered to 12 participants while recording 32-channel electroencephalogram and psychometric function. A novel metric quantifying the spatial distribution of temporal electroencephalogram complexity, comprised of (1) absolute cross-correlation calculated between consecutive 0.25-s time samples; 2) binarizing these cross-correlation matrices using the median of all channels as threshold; (3) using quantitative recurrence analysis, the complexity in temporal changes calculated by the Shannon entropy of the probability distribution of the diagonal line lengths; and (4) overall spatial extent and intensity of brain complexity, was quantified by calculating median temporal complexity of channels whose complexities were above 1 at baseline. This region approximately overlay the brain's default mode network, so this summary statistic was termed "default-mode-network complexity."

RESULTS

Nitrous oxide concentration correlated with psychometric impairment (r = 0.50, P < 0.001). Baseline regional electroencephalogram complexity at midline was greater than in lateral temporal channels (1.33 ± 0.14 bits vs. 0.81 ± 0.12 bits, P < 0.001). A dose of 40% N2O decreased midline (mean difference [95% CI], 0.20 bits [0.09 to 0.31], P = 0.002) and prefrontal electroencephalogram complexity (mean difference [95% CI], 0.17 bits [0.08 to 0.27], P = 0.002). The lateral temporal region did not change significantly (mean difference [95% CI], 0.14 bits [-0.03 to 0.30], P = 0.100). Default-mode-network complexity correlated with N2O concentration (r = -0.55, P < 0.001). A default-mode-network complexity mixed-effects model correlated with psychometric impairment (r2 = 0.67; receiver operating characteristic area [95% CI], 0.72 [0.59 to 0.85], P < 0.001).

CONCLUSIONS

Temporal complexity decreased most markedly in medial cortical regions during low-dose nitrous oxide exposures, and this change tracked psychometric impairment.

EDITOR’S PERSPECTIVE

Use of Processed Electroencephalography in the Clinical Setting

Purpose of Review

Processed electroencephalography (pEEG) is widely used in clinical practice. Few clinicians utilize the full potential of these devices. This brief review will address the improvements in patient management available from the utilization of all pEEG data.

Recent Findings

Anesthesiologists easily learn to recognize raw pEEG patterns that are consistent with an appropriate level of hypnotic effect. Power distribution within the waveform can be displayed in a visual format that identifies signatures of the principal anesthetic hypnotics. Opinion on the benefit of pEEG data in the mitigation of postoperative neurological impairment remains divided.

Summary

Looking beyond the index number can aid clinical decision making and improve confidence in the benefits of this monitoring modality.

A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End

In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 μW per channel and has the input-referred noise of 0.29 μVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.

Impact of General Anesthesia Guided by State Entropy (SE) and Response Entropy (RE) on Perioperative Stability in Elective Laparoscopic Cholecystectomy Patients-A Prospective Observational Randomized Monocentric Study

Laparoscopic cholecystectomy is one of the most frequently performed interventions in general surgery departments. Some of the most important aims in achieving perioperative stability in these patients is diminishing the impact of general anesthesia on the hemodynamic stability and the optimization of anesthetic drug doses based on the individual clinical profile of each patient. The objective of this study is the evaluation of the impact, as monitored through entropy (both state entropy (SE) and response entropy (RE)), that the depth of anesthesia has on the hemodynamic stability, as well as the doses of volatile anesthetic. A prospective, observational, randomized, and monocentric study was carried out between January and December 2019 in the Clinic of Anesthesia and Intensive Care of the “Pius Brînzeu” Emergency County Hospital in Timișoara, Romania. The patients included in the study were divided in two study groups: patients in Group A (target group) received multimodal monitoring, which included monitoring of standard parameters and of entropy (SE and RE); while the patients in Group B (control group) only received standard monitoring. The anesthetic dose in group A was optimized to achieve a target entropy of 40–60. A total of 68 patients met the inclusion criteria and were allocated to one of the two study groups: group A (N = 43) or group B (N = 25). There were no statistically significant differences identified between the two groups for both demographical and clinical characteristics (p > 0.05). Statistically significant differences were identified for the number of hypotensive episodes (p = 0.011, 95% CI: [0.1851, 0.7042]) and for the number of episodes of bradycardia (p < 0.0001, 95% CI: [0.3296, 0.7923]). Moreover, there was a significant difference in the Sevoflurane consumption between the two study groups (p = 0.0498, 95% CI: [−0.3942, 0.9047]). The implementation of the multimodal monitoring protocol, including the standard parameters and the measurement of entropy for determining the depth of anesthesia (SE and RE) led to a considerable improvement in perioperative hemodynamic stability. Furthermore, optimizing the doses of anesthetic drugs based on the individual clinical profile of each patient led to a considerable decrease in drug consumption, as well as to a lower incidence of hemodynamic side-effects.

Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia

Accurate monitoring of the depth of anesthesia (DoA) is essential for intraoperative and postoperative patient's health. Commercially available electroencephalograph (EEG)-based DoA monitors are recommended only for certain anesthetic drugs and specific age-group patients. This paper presents a machine learning classification processor for accurate DoA estimation irrespective of the patient's age and anesthetic drug. The classification is solely based on six features extracted from EEG signal, i.e., spectral edge frequency (SEF), beta ratio, and four bands of spectral energy (FBSE). A machine learning fine decision tree classifier is adopted to achieve a four-class DoA classification (deep, moderate, and light DoA versus awake state). The feature selection and the classification processor are optimized to achieve the highest classification accuracy for the state of moderate anesthesia required for the surgical operations. The proposed 256-point fast Fourier transform accelerator is implemented to realize SEF, beta ratio, and FBSE that enables minimal latency and high accuracy feature extraction. The proposed DoA processor is implemented using a 65 nm CMOS technology and experimentally verified using field programming gate array (FPGA) based on the EEG recordings of 75 patients undergoing elective surgery with different types of anesthetic agents. The processor achieves an average accuracy of 92.2% for all DoA states, with a latency of 1s The 0.09 mm² DoA processor consumes 140nJ/classification.

Comparative Analysis of Phase Lag Entropy and Bispectral Index as Anesthetic Depth Indicators in Patients Undergoing Thyroid Surgery with Nerve Integrity Monitoring

BACKGROUND

Most depth of anesthesia (DOA) monitors rely on the temporal characteristics of a single-channel electroencephalogram (EEG) and cannot provide spatial or connectivity information. Phase lag entropy (PLE) reflects DOA by calculating diverse connectivity from temporal patterns of phase relationships. The aim of this study was to compare the performance of PLE and bispectral index (BIS) monitors for assessing DOA during anesthesia induction, nerve integrity monitoring (NIM), and anesthesia emergence.

METHODS

Thirty-five patients undergoing elective thyroid surgery with recurrent laryngeal nerve NIM received propofol and remifentanil via target-controlled infusion. After applying PLE and BIS monitors, propofol infusion was initiated at a calculated effect site concentration (Ce) of 2 μg/mL and then increased in 1-μg/mL Ce increments. After propofol Ce reached 5 μ/mL, a remifentanil infusion was begun, and anesthesia induction was considered complete. During NIM, PLE and BIS values were compared at a specific time points from platysma muscle exposure to subcutaneous tissue closure. PLE and BIS values were recorded continuously from preanesthetic state to full recovery of orientation; bias and limits of agreement between monitors were calculated.

RESULTS

PLE and BIS values decreased progressively with increasing propofol Ce during anesthetic induction and increased by stages during emergence. The prediction probabilities of PLE and BIS for detecting propofol Ce changes were 0.750 and 0.756, respectively, during induction and 0.749 and 0.746, respectively, during emergence. No aberrant PLE or BIS values occurred during NIM. Correlation coefficients for BIS and PLE were 0.98 and 0.92 during induction and emergence, respectively. PLE values were significantly higher than BIS values at full recovery of orientation. Estimated bias between monitors was -4.16 ± 8.7, and 95% limits of agreement were -21.21 to 12.89.

CONCLUSION

PLE is a reasonable alternative to BIS for evaluating consciousness and DOA during general anesthesia and during NIM.

TRIAL REGISTRATION

Clinical Research Information Service Identifier: KCT0003490.

Neural Correlates of Anesthesia in Newborn Mice and Humans

Monitoring the hypnotic component of anesthesia during surgeries is critical to prevent intraoperative awareness and reduce adverse side effects. For this purpose, electroencephalographic (EEG) methods complementing measures of autonomic functions and behavioral responses are in use in clinical practice. However, in human neonates and infants existing methods may be unreliable and the correlation between brain activity and anesthetic depth is still poorly understood. Here, we characterized the effects of different anesthetics on brain activity in neonatal mice and developed machine learning approaches to identify electrophysiological features predicting inspired or end-tidal anesthetic concentration as a proxy for anesthetic depth. We show that similar features from EEG recordings can be applied to predict anesthetic concentration in neonatal mice and humans. These results might support a novel strategy to monitor anesthetic depth in human newborns.

Effects of remifentanil and dexmedetomidine on the mother's awareness and neonatal Apgar scores in caesarean section under general anaesthesia

Objective

This study aimed to compare the effects of remifentanil and dexmedetomidine on awareness during the induction of general anaesthesia.

Material and Methods

Ninety patients scheduled for elective caesarean section under general anaesthesia were included and randomly divided into three anaesthesia groups: 2 mg/kg propofol (control group); 2 mg/kg propofol and 1 µg/kg dexmedetomidine (dexmedetomidine group); and 2 mg/kg propofol and 1 µg/kg remifentanil (remifentanil group). All patients received routine monitoring, and Apgar scores at 1 and 5 minutes were recorded. The bispectral index and the isolated forearm technique were used to determine the depth of anaesthesia.

Results

Bispectral index values at skin and uterine incisions and at delivery were similar among the groups. The number of patients who responded positively to the isolated arm technique during the induction period was also similar. One-minute Apgar scores in the control group were significantly lower and 5-minute Apgar scores significantly higher than those in the other groups.

Conclusion

The effects of remifentanil and dexmedetomidine added to propofol on maternal awareness, neonatal Apgar scores, and bispectral index values were similar compared with propofol alone. However, it was observed that remifentanil controlled the haemodynamic responses to sympathetic stimuli in a better manner than dexmedetomidine.

Sleep- and Wake-Like States in Small Networks In Vivo and In Vitro

Wakefulness and sleep are highly complex and heterogeneous processes, involving multiple neurotransmitter systems and a sophisticated interplay between global and local networks of neurons and non-neuronal cells. Macroscopic approaches applied at the level of the whole organism, view sleep as a global behaviour and allow for investigation into aspects such as the effects of insufficient or disrupted sleep on cognitive function, metabolism, thermoregulation and sensory processing. While significant progress has been achieved using such large-scale approaches, the inherent complexity of sleep-wake regulation has necessitated the development of methods which tackle specific aspects of sleep in isolation. One way this may be achieved is by investigating specific cellular or molecular phenomena in the whole organism in situ, either during spontaneous or induced sleep-wake states. This approach has greatly advanced our knowledge about the electrophysiology and pharmacology of ion channels, specific receptors, intracellular pathways and the small networks implicated in the control and regulation of the sleep-wake cycle. Importantly though, there are a variety of external and internal factors that influence global behavioural states which are difficult to control for using these approaches. For this reason, over the last few decades, ex vivo experimental models have become increasingly popular and have greatly advanced our understanding of many fundamental aspects of sleep, including the neuroanatomy and neurochemistry of sleep states, sleep regulation, the origin and dynamics of specific sleep oscillations, network homeostasis as well as the functional roles of sleep. This chapter will focus on the use of small neuronal networks as experimental models and will highlight the most significant and novel insights these approaches have provided.

Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol

On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1) the sedative types and 2) the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9–11 s. To generally illustrate brain activity at repetitive transition points at the loss of consciousness (LOC) and the recovery of consciousness (ROC), patient-controlled sedation was performed using two different sedatives (midazolam (MDZ) and propofol (PPF)) under two surgical conditions. Once consciousness was lost via sedatives, we observed gradually increasing EEG power at lower frequencies (<15 Hz) and decreasing power at higher frequencies (>15 Hz), as well as spatially increased EEG powers in the delta and lower alpha bands, and particularly also in the upper alpha rhythm, at the frontal and parieto-occipital areas over time. During ROC from unconsciousness, these spatio-temporal changes were reversed. Interestingly, the level of consciousness was switched on/off at significantly higher effect-site concentrations of sedatives in the brain according to the use of surgical stimuli, but the spatio-temporal EEG patterns were similar, regardless of the sedative used. We also observed sudden phase shifts in fronto-parietal connectivity at the LOC and the ROC as critical points. fNIRS measurement also revealed mild hemodynamic fluctuations. Compared with general anesthesia, our results provide insights into critical hallmarks of sedative-induced (un)consciousness, which have similar spatio-temporal EEG-fNIRS patterns regardless of the stage and the sedative used.

The Brain of Binge Drinkers at Rest: Alterations in Theta and Beta Oscillations in First-Year College Students with a Binge Drinking Pattern

Background: Previous studies have reported anomalous resting brain activity in the electroencephalogram (EEG) of alcoholics, often reflected as increased power in the beta and theta frequency bands. The effects of binge drinking, the most common pattern of excessive alcohol consumption during adolescence and youth, on brain activity at rest is still poorly known. In this study, we sought to assess the pattern of resting-state EEG oscillations in college-aged binge drinkers (BDs).

Methods: Resting-state brain activity during eyes-open and eyes-closed conditions was recorded from 60 channels in 80 first-year undergraduate students (40 controls and 40 BDs). Cortical sources activity of EEG rhythms was estimated using exact Low-Resolution Electromagnetic Tomography (eLORETA) analysis.

Results: EEG-source localization analysis revealed that BDs showed, in comparison with controls, significantly higher intracranial current density in the beta frequency band over the right temporal lobe (parahippocampal and fusiform gyri) during eyes-open resting state as well as higher intracranial current density in the theta band over the bilateral occipital cortex (cuneus and lingual gyrus) during eyes-closed resting condition.

Conclusions: These findings are in line with previous results observing increased beta and/or theta power following chronic or heavy alcohol drinking in alcohol-dependent subjects and BDs. Increased tonic beta and theta oscillations are suggestive of an augmented cortical excitability and of potential difficulties in the information processing capacity in young BDs. Furthermore, enhanced EEG power in these frequency bands may respond to a neuromaturational delay as a result of excessive alcohol consumption during this critical brain developmental period.

GABAergic ventrolateral pre‑optic nucleus neurons are involved in the mediation of the anesthetic hypnosis induced by propofol

Intravenous anesthetics have been used clinically to induce unconsciousness for seventeen decades, however the mechanism of anesthetic-induced unconsciousness remains to be fully elucidated. It has previously been demonstrated that anesthetics exert sedative effects by acting on endogenous sleep-arousal circuits. However, few studies focus on the ventrolateral pre-optic (VLPO) to locus coeruleus (LC) sleep-arousal pathway. The present study aimed to investigate if VLPO is involved in unconsciousness induced by propofol. The present study additionally investigated if the inhibitory effect of propofol on LC neurons was mediated by activating VLPO neurons. Microinjection, target lesion and extracellular single-unit recordings were used to study the role of the VLPO-LC pathway in propofol anesthesia. The results demonstrated that GABAA agonist (THIP) or GABAA antagonist (gabazine) microinjections into VLPO altered the time of loss of righting reflex and the time of recovery of righting reflex. Furthermore, propofol suppressed the spontaneous firing activity of LC noradrenergic neurons. There was no significant difference observed in firing activity between VLPO sham lesion and VLPO lesion rats. The findings indicate that VLPO neurons are important in propofol-induced unconsciousness, however are unlikely to contribute to the inhibitory effect of propofol on LC spontaneous firing activity.

Protocol for the Reconstructing Consciousness and Cognition (ReCCognition) Study

Important scientific and clinical questions persist about general anesthesia despite the ubiquitous clinical use of anesthetic drugs in humans since their discovery. For example, it is not known how the brain reconstitutes consciousness and cognition after the profound functional perturbation of the anesthetized state, nor has a specific pattern of functional recovery been characterized. To date, there has been a lack of detailed investigation into rates of recovery and the potential orderly return of attention, sensorimotor function, memory, reasoning and logic, abstract thinking, and processing speed. Moreover, whether such neurobehavioral functions display an invariant sequence of return across individuals is similarly unknown. To address these questions, we designed a study of healthy volunteers undergoing general anesthesia with electroencephalography and serial testing of cognitive functions (NCT01911195). The aims of this study are to characterize the temporal patterns of neurobehavioral recovery over the first several hours following termination of a deep inhaled isoflurane general anesthetic and to identify common patterns of cognitive function recovery. Additionally, we will conduct spectral analysis and reconstruct functional networks from electroencephalographic data to identify any neural correlates (e.g., connectivity patterns, graph-theoretical variables) of cognitive recovery after the perturbation of general anesthesia. To accomplish these objectives, we will enroll a total of 60 consenting adults aged 20-40 across the three participating sites. Half of the study subjects will receive general anesthesia slowly titrated to loss of consciousness (LOC) with an intravenous infusion of propofol and thereafter be maintained for 3 h with 1.3 age adjusted minimum alveolar concentration of isoflurane, while the other half of subjects serves as awake controls to gauge effects of repeated neurobehavioral testing, spontaneous fatigue and endogenous rest-activity patterns.

Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging

The definition of the depth of anesthesia (DOA) is still controversial and its measurement is not completely standardized in modern anesthesia. Power spectral analysis is an important method for feature detection in electroencephalogram (EEG) signals. Several spectral parameters derived from EEG have been proposed for measuring DOA in clinical applications. In the present paper, an improved method based on phase-rectified signal averaging (PRSA) is designed to improve the predictive accuracy of relative alpha and beta power, a frequency band power ratio, total power, median frequency (MF), spectral edge frequency 95 (SEF95), and spectral entropy for assessing anesthetic drug effects. Fifty-six patients undergoing general anesthesia in an operating theatre are studied. All EEG signals are continuously recorded from the awake state to the end of the recovery state and then filtered using multivariate empirical mode decomposition (MEMD). All parameters are evaluated using the commercial bispectral index (BIS) and expert assessment of conscious level (EACL), respectively. The ability to predict DOA is estimated using the area under the receiver-operator characteristics curve (AUC). All indicators based on the improved method can clearly discriminate the conscious state from the anesthetized state after filtration (p  <  0.05). A significantly larger mean AUC (p  <  0.05) shows that the improved method performs better than the conventional method to measure the DOA in most circumstances. Especially for raw EEG contaminated by artifacts, when the BIS index is used to indicate the consciousness level, the improvement is 7.37% (p  <  0.05), 9.04% (p  <  0.05), 18.46% (p  <  0.05), 27.73% (p  <  0.05), 14.65% (p  <  0.05), 2.52%, 5.38% and 6.24% (p  <  0.05) for relative alpha and beta power, power ratio, total power, MF, SEF, RE and SE, respectively. However, when the EACL is used to indicate the consciousness level, the improvement is 3.30% (p  <  0.05), 16.69% (p  <  0.05), 15.08% (p  <  0.05), 34.83% (p  <  0.05), 27.78% (p  <  0.05), 5.89% (p  <  0.05), 26.05% (p  <  0.05) and 23.42% (p  <  0.05). Spectral parameters derived from PRSA are more useful to measure the DOA in noisy cases.

Mathematical Frameworks for Oscillatory Network Dynamics in Neuroscience

The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances where this theory is expected to break down, say in the presence of strong coupling, or must be carefully interpreted, as in the presence of stochastic forcing. There are also surprises in the dynamical complexity of the attractors that can robustly appear-for example, heteroclinic network attractors. In this review we present a set of mathematical tools that are suitable for addressing the dynamics of oscillatory neural networks, broadening from a standard phase oscillator perspective to provide a practical framework for further successful applications of mathematics to understanding network dynamics in neuroscience.

Spectral entropy in monitoring anesthetic depth

Monitoring the brain response to hypnotics in general anesthesia, with the nociceptive and hemodynamic stimulus interaction, has been a subject of intense investigation for many years. Nowadays, monitors of depth of anesthesia are based in processed electroencephalogram by different algorithms, some of them unknown, to obtain a simplified numeric parameter approximate to brain activity state in each moment. In this review we evaluate if spectral entropy suitably reflects the brain electric behavior in response to hypnotics and the different intensity nociceptive stimulus effect during a surgical procedure.

Reduced local field potential power in the medial prefrontal cortex by noxious stimuli

Nociceptive signals produced by noxious stimuli at the periphery reach the brain through ascending pathways. These signals are processed by various brain areas and lead to activity changes in those areas. The medial prefrontal cortex (mPFC) is involved in higher cognitive functions and emotional processing. It receives projections from brain areas involved in nociception. In this study, we investigated how nociceptive input from the periphery changes the local field potential (LFP) activity in the mPFC. Three different types of noxious stimuli were applied to the hind paw contralateral to the LFP recording site. They were transcutaneous electrical stimulations, mechanical stimuli and a chemical stimulus (formalin injection). High intensity transcutaneous stimulations (10V to 50V) and noxious mechanical stimulus (pinch) significantly reduced the LFP power during the stimulating period (p<0.05), but not the low intensity subcutaneous stimulations (0.1V to 5V) and other innocuous mechanical stimuli (brush and pressure). More frequency bands were inhibited with increased intensity of transcutaneous electrical stimulation, and almost all frequency bands were inhibited by stimulations at or higher than 30v. Pinch significantly reduced the power for beta band and formalin injection significantly reduced the power of alpha and beta band. Our data demonstrated the noxious stimuli-induced reduction of LFP power in the mPFC, which indicates the active processing of nociceptive information by the mPFC.

Missed Opportunities for Intervention in a Patient With Prolonged Postoperative Delirium

PURPOSE

Postoperative delirium is a common and costly state of brain dysfunction that complicates postsurgical management in some patients. The purpose of this report was to describe a case of prolonged postoperative delirium and to review the appropriate identification and management of this condition.

METHODS

A 56-year-old female patient who presented with newly diagnosed diabetes mellitus and dry gangrene underwent a vascular bypass procedure while under general anesthesia. After extubation, the patient became disoriented and agitated.

FINDINGS

The delirium continued in a hypoactive form for 10 days before it progressed to severe agitation. During the patient's 2-month hospitalization, she underwent 6 additional surgeries. Eventually, the delirium improved with the use of antipsychotic agents, and the patient was discharged to a skilled nursing facility.

IMPLICATIONS

This patient's history, medications, and anesthetic and surgical exposure placed her at high risk for postoperative delirium. Her exceptionally prolonged course of postoperative delirium was likely perpetuated by a multitude of factors, including the continued use of high-risk medications, the stress of repeated surgeries, urinary issues, and infection.

CONCLUSION

In this high-risk patient, a proactive approach to the prevention and treatment of delirium may have avoided or mitigated the prolonged delirium and, potentially, long-term cognitive deficits.

Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures

The widely used electroencephalogram-based indices for depth-of-anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.

Assessing EEG slow wave activity during anesthesia using Hilbert-Huang Transform

Slow waves (<; 1 Hz) are considered to be the most important electroencephalogram (EEG) signature of non-rapid eye movement sleep and have substantial physiological importance. In addition to natural sleep, slow waves can be seen in the EEG during general anesthesia offering great potential for depth of anesthesia monitoring. In this paper, Hilbert-Huang Transform, an adaptive data-driven method designed for the analysis on non-stationary data, was used to investigate the dynamical changes in the EEG slow wave activity during induction of anesthesia with propofol. The method was found to be able to extract stable signal components representing slow wave activity that were consistent between patients. The signal analysis revealed a possible specific structure between different components dependent on the depth of anesthesia on which further studies are needed.

Neural network-based classification of anesthesia/awareness using Granger causality features

This article investigates the signal processing part of a future system for monitoring awareness during surgery. The system uses features from the patients' electrical brain activity (EEG) to discriminate between "anesthesia" and "awareness." We investigate the use of a neural network classifier and Granger causality (GC) features for this purpose. GC captures anesthetic-induced changes in the causal relationships between pairs of signals from different brain areas. The differences in the pairwise causality estimated from the EEG activity are used as features for subsequent classification between "awake" and "anesthetized" states. EEG data from 31 subjects obtained during surgery and maintenance of anesthesia with propofol, sevoflurane, or desflurane, are classified using a neural network with one layer of hidden units. An average accuracy of 96% is obtained.

Anesthesia differentially modulates spontaneous network dynamics by cortical area and layer

Anesthesia is widely used in medicine and research to achieve altered states of consciousness and cognition. Whereas changes to macroscopic cortical activity patterns by anesthesia measured at the spatial resolution of electroencephalography have been widely studied, modulation of mesoscopic and microscopic network dynamics by anesthesia remain poorly understood. To address this gap in knowledge, we recorded spontaneous mesoscopic (local field potential) and microscopic (multiunit activity) network dynamics in primary visual cortex (V1) and prefrontal cortex (PFC) of awake and isoflurane anesthetized ferrets (Mustela putoris furo). This approach allowed for examination of activity as a function of cortical area, cortical layer, and anesthetic depth with much higher spatial and temporal resolution than in previous studies. We hypothesized that a primary sensory area and an association cortical area would exhibit different patterns of network modulation by anesthesia due to their different functional roles. Indeed, we found effects specific to cortical area and cortical layer. V1 exhibited minimal changes in rhythmic structure with anesthesia but differential modulation of input layer IV. In contrast, anesthesia profoundly altered spectral power in PFC, with more uniform modulation across cortical layers. Our results demonstrate that anesthesia modulates spontaneous cortical activity in an area- and layer-specific manner. These finding provide the basis for 1) refining anesthesia monitoring algorithms, 2) reevaluating the large number of systems neuroscience studies performed in anesthetized animals, and 3) increasing our understanding of differential dynamics across cortical layers and areas.

Strain differences in cortical electroencephalogram associated with isoflurane-induced loss of consciousness

INTRODUCTION

Previously observed increased sensitivity to noxious stimulation in the Dahl salt-sensitive rat strain (SS/JrHsdMcwi, abbreviated as SS) compared to Brown Norway rats (BN/NhsdMcwi abbreviated as BN) is mediated by genes on a single chromosome. The current study used behavioral and electrocortical data to determine if differences also exist between SS and BN rats in loss of consciousness.

METHODS

Behavioral responses, including loss of righting, (a putative index of consciousness) and concurrent electroencephalogram recordings, in 12 SS and BN rats were measured during isoflurane at inhaled concentrations of 0, 0.3, 0.6, 0.8, 1.0 and 1.2%.

RESULTS

In SS compared to BN rats, the mean ± SEM EC50 for righting was significantly less (0.65 ± 0.01% vs. 0.74 ± 0.02% inhaled isoflurane) and delta fraction in parietal electroencephalogram was enhanced 50-100% at all isoflurane levels during emergence. The frequency decay constant of an exponential fit of the parietal electroencephalogram spectrum graphed as a function of isoflurane level was three times less steep (mean ± SEM slope -57 ± 13 vs. -191 ± 38) and lower at each level of isoflurane in SS versus BN rats (i.e., shifted toward low frequency activity). Electroencephalogram differences between strains were larger during emergence than induction.

CONCLUSIONS

Sensitivity is higher in SS compared to BN rats leading to unconsciousness at lower levels of isoflurane. This supports using additional strains in this animal model to study the genetic basis for differences in anesthetic action on mechanisms of consciousness. Moreover, induction and emergence appear to involve distinct pathways.

Cross-frequency coupling during isoflurane anaesthesia as revealed by electroencephalographic harmonic wavelet bicoherence

BACKGROUND

Fourier bicoherence has previously been applied to investigate phase coupling in the EEG in anaesthesia. However, there are significant theoretical limitations regarding its sensitivity in detecting transient episodes of inter-frequency coupling. Therefore, we used a recently developed wavelet bicoherence method to investigate the cross-frequency coupling in the EEG of patients under isoflurane anaesthesia; examining the relationship between the patterns of wavelet bicoherence and the isoflurane concentrations.

METHODS

We analysed a set of previously published EEG data, obtained from 29 patients who underwent elective abdominal surgery under isoflurane anaesthesia. Artifact-free, 1 min EEG segments at different isoflurane concentrations were extracted from each subject and the wavelet bicoherence calculated for all pairs of frequencies from 0.5 to 20 Hz.

RESULTS

Isoflurane caused two peaks in the α (6-13 Hz) and slow δ (<1 Hz) regions of the bicoherence matrix diagonal. Higher concentrations of isoflurane shifted the α peak to lower frequencies [11.3 (0.9) Hz at 0.3% to 7.1 (1.2) Hz at 1.5%], as has been previously observed in the power spectra. Outside the diagonal, we also found a significant α peak that was phase-coupled to the slow δ waves; higher concentrations of isoflurane shifted this peak to lower frequencies [10.8 (1.2) to 7.7 (0.7) Hz].

CONCLUSIONS

Isoflurane caused cross-frequency coupling between α and slow δ waves. Increasing isoflurane concentration slowed the α frequencies where the coupling had occurred. This phenomenon of α-δ coupling suggests that slow cortical oscillations organize the higher α band activity, which is consistent with other studies in natural sleep.

Increased electroencephalographic gamma activity reveals awakening from isoflurane anaesthesia in rats

BACKGROUND

Animal studies often require reliable measures for anaesthetic drug effects. Lately, EEG-based depth of anaesthesia estimation has been widely applied to rat models. This study investigated the reliability of different EEG spectral properties in revealing awakening from isoflurane anaesthesia in rats.

METHODS

Adult Wistar rats with previously implanted frontal epidural electrodes were anaesthetized using isoflurane. The anaesthesia was slowly lightened until awakening, as observed by the first spontaneous movement, after which anaesthesia was induced again by increasing the isoflurane concentration. EEG was recorded during the recovery and induction periods, and the spectrograms and 23 quantitative spectral parameters used in the depth of anaesthesia estimation were calculated from the signals.

RESULTS

The awakening was accompanied by a decrease in EEG activity at frequencies below 25 Hz, while the activity at higher frequencies (25-150 Hz) was increased. Whereas the behaviour of parameters used to measure activity in the lower frequencies was subject to variability between animals, the increase in higher frequency activity was more consistent, resulting in a statistically significant change in the relative gamma power parameters at the moment of awakening.

CONCLUSIONS

The increase in frontal relative gamma activity, especially in the 50-150 Hz frequency band, seems to be the most reliable EEG indicator for the awakening of a rat from isoflurane anaesthesia. A number of other spectral measures can also be used to detect this event. However, the role of gamma frequencies in the performance of these parameters is crucial.

Unresponsiveness ≠ unconsciousness

Consciousness is subjective experience. During both sleep and anesthesia, consciousness is common, evidenced by dreaming. A defining feature of dreaming is that, while conscious, we do not experience our environment; we are disconnected. Besides inducing behavioral unresponsiveness, a key goal of anesthesia is to prevent the experience of surgery (connected consciousness), by inducing either unconsciousness or disconnection of consciousness from the environment. Review of the isolated forearm technique demonstrates that consciousness, connectedness, and responsiveness uncouple during anesthesia; in clinical conditions, a median 37% of patients demonstrate connected consciousness. We describe potential neurobiological constructs that can explain this phenomenon: during light anesthesia the subcortical mechanisms subserving spontaneous behavioral responsiveness are disabled but information integration within the corticothalamic network continues to produce consciousness, and unperturbed norepinephrinergic signaling maintains connectedness. These concepts emphasize the need for developing anesthetic regimens and depth of anesthesia monitors that specifically target mechanisms of consciousness, connectedness, and responsiveness.

EEG-based automatic classification of 'awake' versus 'anesthetized' state in general anesthesia using Granger causality

BACKGROUND

General anesthesia is a reversible state of unconsciousness and depression of reflexes to afferent stimuli induced by administration of a "cocktail" of chemical agents. The multi-component nature of general anesthesia complicates the identification of the precise mechanisms by which anesthetics disrupt consciousness. Devices that monitor the depth of anesthesia are an important aide for the anesthetist. This paper investigates the use of effective connectivity measures from human electrical brain activity as a means of discriminating between 'awake' and 'anesthetized' state during induction and recovery of consciousness under general anesthesia.

METHODOLOGY/PRINCIPAL FINDINGS

Granger Causality (GC), a linear measure of effective connectivity, is utilized in automated classification of 'awake' versus 'anesthetized' state using Linear Discriminant Analysis and Support Vector Machines (with linear and non-linear kernel). Based on our investigations, the most characteristic change of GC observed between the two states is the sharp increase of GC from frontal to posterior regions when the subject was anesthetized, and reversal at recovery of consciousness. Features derived from the GC estimates resulted in classification of 'awake' and 'anesthetized' states in 21 patients with maximum average accuracies of 0.98 and 0.95, during loss and recovery of consciousness respectively. The differences in linear and non-linear classification are not statistically significant, implying that GC features are linearly separable, eliminating the need for a complex and computationally expensive non-linear classifier. In addition, the observed GC patterns are particularly interesting in terms of a physiological interpretation of the disruption of consciousness by anesthetics. Bidirectional interaction or strong unidirectional interaction in the presence of a common input as captured by GC are most likely related to mechanisms of information flow in cortical circuits.

CONCLUSIONS/SIGNIFICANCE

GC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery.