Electroencephalogram signatures of loss and recovery of consciousness from propofol. Proc Natl Acad Sci U S A. 2013;110:E1142-E1151. [PMID: 23487781 DOI: 10.1073/pnas.1221180110]

Spatiospectral dynamics of electroencephalography patterns during propofol-induced alterations of consciousness states

Altered consciousness induced by anesthetics is characterized by distinct spatial and spectral neural dynamics that are readily apparent in the human electroencephalogram. Despite considerable study, we remain uncertain which brain regions and neural oscillations are involved, as well as how they are impacted when consciousness is disrupted. The experimental data was obtained from the open-access dataset, which contains pre-processed EEG data recorded from 20 healthy participants during propofol sedation. Using unsupervised machine learning methods (i.e., non-negative matrix factorization, NMF), we investigated the spatiospectral dynamic evolution of brain activity from awake to sedation and back induced by propofol in healthy research volunteers. Our methods yielded six dynamical patterns that continuously reflect the neural activity changes in specific brain regions and frequency bands under propofol sedation. Temporal dynamic analyses showed that differences in alpha oscillation patterns were less pronounced in response group than drowsy group, with hemispheric asymmetry in posterior occipital lobe over the course of the sedation procedure. We designed an index 'hemispheric lateralization modulation of alpha [HLM(α)]' to measure asymmetry during awake state and predicting individual variability in propofol-induced alterations of consciousness states, obtaining prediction AUC of 0.8462. We present an alpha modulation index which characterizes how these patterns track the transition from awake to sedation as a function of increasing dosage. Our study reveals dynamics indices that track the evolution of neurophysiological of propofol on brain circuits. Analyzing the spatiospectral dynamics influenced by propofol provides valuable understanding of the mechanisms of these agents and strategies for monitoring and precisely controlling the level of consciousness in patients under sedation and general anesthesia.

Driving brain state transitions via Adaptive Local Energy Control Model

The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.

Neural Oscillations in the Somatosensory and Motor Cortex Distinguish Dexmedetomidine-Induced Anesthesia and Sleep in Rats

BACKGROUND

Anesthesia is featured by behavioral and physiological characteristics such as decreased sensory and motor function, loss of consciousness, etc. Some anesthetics such as dexmedetomidine (DEX), induce electroencephalogram signatures close to non-rapid eye movement sleep. Studies have shown that sleep is primarily driven by the activation of subcortical sleep-promoting neural pathways.

AIMS

However, the neuronal level electrophysiology features of anesthesia and how they differ from sleep is still not fully understood.

MATERIALS AND METHODS

In the present study, we recorded neuronal activity simultaneously from somatosensory cortex (S1) and motor cortex (M1) during awake, sleep, and DEX-induced anesthesia in rats.

RESULTS

The results show that DEX increased local field potential (LFP) power across a relatively wide band (1-25 Hz) in both S1 and M1. The coherence between S1 LFP and M1 LFP increased significantly in the delta and alpha bands. Power spectrum analysis during DEX-induced anesthesia revealed relatively high power in the delta and alpha bands, but low power in the theta and beta bands. Overall, the firing rate of individual neurons decreased after DEX. Correlation analysis of firing rate and LFP power indicate that more neurons were correlated, either positively or negatively, with LFPs during DEX-induced anesthesia compared to sleep.

DISCUSSION

Although these results showed enhancement of cortical LFP power in both DEX-induced anesthesia and sleep, different patterns of spike-field correlation suggest that the two states may be regulated by different cortical mechanisms.

CONCLUSION

Distinguishing anesthesia from sleep with neural oscillations could lead to more personalized, safer, and more effective approaches to managing consciousness in medical settings, with the potential for broad applications in neuroscience and clinical practice.

Regulation of states of consciousness by supramammillary nucleus glutamatergic neurones during sevoflurane anaesthesia in mice

BACKGROUND

The supramammillary nucleus (SuM), located in the caudal hypothalamus, includes wake-promoting glutamatergic neurones. Their potential role in regulating states of consciousness during general anaesthesia remains unknown.

METHODS

We used in vivo fibre photometry, c-Fos staining, chemogenetic and optogenetic manipulations, and electroencephalography/electromyography to explore the roles of glutamatergic SuM neurones (SuMVglut2 neurones) at different phases of sevoflurane anaesthesia. Rabies-mediated retrograde and anterograde tract tracing were used to investigate the monosynaptic glutamatergic inputs from the medial septum (MS) to SuM. Their roles in sevoflurane anaesthesia were investigated by in vivo fibre photometry and optogenetic manipulations.

RESULTS

The population activity of SuMVglut2 neurones decreased at loss of consciousness but increased during recovery of consciousness under sevoflurane anaesthesia. Their activity also decreased during suppression but increased during bursts in sevoflurane-induced burst-suppression oscillations. Activating SuMVglut2 neurones chemogenetically or optogenetically decreased sensitivity to sevoflurane, induced behavioural arousal and cortical activation during continuous steady-state anaesthesia, and stable burst-suppression oscillations under sevoflurane. In contrast, chemogenetic or optogenetic inhibition of SuMVglut2 neurones increased sensitivity to sevoflurane or intensified cortical inhibition during sevoflurane anaesthesia. Retrograde and anterograde tracing verified monosynaptic projections from MSVglut2 neurones to SuMVglut2 neurones. The activity of MSVglut2 SuM terminals increased during loss of consciousness but recovered during recovery of consciousness. Optogenetic activation or inhibition of MSVglut2 SuM terminals induced cortical activation or inhibition, respectively, during sevoflurane anaesthesia.

CONCLUSIONS

Activation of SuMVglut2 neurones or the glutamatergic septo-supramammillary circuit induces behavioural arousal and cortical activation during sevoflurane anaesthesia.

Clinical and intracranial electrophysiological signatures of post-operative and post-ictal delirium

OBJECTIVES

(1) Gain insight into the mechanisms of postoperative delirium (POD). (2) Determine mechanistic overlap with post-ictal delirium (PID). Epilepsy patients undergoing intracranial electrophysiological monitoring can experience both POD and PID, and thus are suitable subjects for these investigations.

METHODS

POD was assessed daily after surgery. PID was assessed following seizures. Resting state data were collected following delirium assessments, during a control period, and during sleep. Slow-wave activity (SWA: 1-4 Hz) and resting state functional connectivity were compared between different time points and according to delirium status.

RESULTS

POD was present in 6 of 20 participants. Post-operatively, SWA was globally elevated in all participants but highest in POD+ participants. POD+ participants exhibited altered functional connectivity compared to POD-. These differences persisted even after resolution of delirium. PID was present in 7 of 15 participants and was predicted by seizures involving prefrontal cortex. PID+ participants exhibited higher post-ictal SWA versus PID-; no differences in functional connectivity were observed. Post-operative and post-ictal SWA was comparable to sleep in some participants.

CONCLUSIONS

Elevated SWA may predispose patients to both post-operative and post-ictal delirium and may indicate overlapping mechanisms.

SIGNIFICANCE

Delirium treatments focused on SWA may be most effective for ameliorating cognitive symptoms.

A novel electroencephalographic evaluation of noxious stimulation during isoflurane anesthesia in dogs

In veterinary clinical medicine, evaluating the balance between nociception and antinociception presents a great challenge for anesthesiologists during canine surgeries. Heart rate (HR) and mean arterial pressure (MAP) are suitable indexes for monitoring noxious stimuli during anesthesia. Frontal electroencephalography (EEG) records, including processed parameters, are recommended for evaluating nociceptive balance in anesthetized unconscious human patients, which is unexplored in veterinary medicine. Therefore, the objective is to explore the response of processed EEG parameters to noxious stimulation and elucidate the impact of noxious stimulation on frontal cortical activity in dogs anesthetized with 1.5% isoflurane. Fourteen dogs were included and underwent frontal EEG monitoring, measuring the patient state index (PSI) and spectral edge frequency (SEF) before and after administering noxious stimulation using the towel clamp method on the tail of each 1.5% isoflurane-anesthetized dog. As the noxious stimulation was applied, there was a simultaneous increase in PSI, HR, and MAP, with PSI exhibiting a drastic response. SEF, especially on the left side, also increased with noxious stimulation. In EEG power spectral analysis, the delta band was decreased, and the alpha and beta bands showed an increase following noxious stimulation, with a more profound elevation of beta band on the left side. This study suggests that noxious stimulation brings asymmetric frontal cortical arousal, changing brain activity by suppressing delta wave and augmenting alpha and beta waves. Consequently, PSI seems to be a potential indicator for detecting stimuli in canine isoflurane anesthesia.

Electroencephalogram-Guided General Anesthesia in a Pediatric Patient With Alexander's Disease: A Case Report

In this case, the electroencephalogram (EEG) was used to guide anesthesia care for a pediatric patient with Alexander's Disease undergoing serial intrathecal injections. Previous procedures using a standard maintenance propofol dose of up to 225 µg/kg/min led to postanesthetic recovery times of over 6 hours, requiring a neurology consult for noncoherence. The EEG assisted in guiding maintenance propofol dosing to 75 µg/kg/min, decreasing postanesthetic wash-off and postanesthesia care unit (PACU) recovery time by 50%. This highlights the potential impact of astrocyte dysfunction on anesthetic sensitivity and robustness of EEG as a biomarker of anesthetic effect, including for pediatric patients with rare neurodevelopmental diseases.

Electroencephalogram monitoring during anesthesia and critical care: a guide for the clinician

Perioperative anesthetic, surgical and critical careinterventions can affect brain physiology and overall brain health. The clinical utility of electroencephalogram (EEG) monitoring in anesthesia and intensive care settings is multifaceted, offering critical insights into the level of consciousness and depth of anesthesia, facilitating the titration of anesthetic doses, and enabling the detection of ischemic events and epileptic activity. Additionally, EEG monitoring can aid in predicting perioperative neurocognitive disorders, assessing the impact of systemic insults on cerebral function, and informing neuroprognostication. This review provides a comprehensive overview of the fundamental principles of electroencephalography, including the foundations of processed and quantitative electroencephalography. It further explores the characteristic EEG signatures associated wtih anesthetic drugs, the interpretation of the EEG data during anesthesia, and the broader clinical benefits and applications of EEG monitoring in both anesthetic practice and intensive care environments.

Arousal effects on oscillatory dynamics in the non-human primate brain

Arousal states are thought to influence many aspects of cognition and behavior by broadly modulating neural activity. Many studies have observed arousal-related modulations of alpha (~8 to 15 Hz) and gamma (~30 to 50 Hz) power and coherence in local field potentials across relatively small groups of brain regions. However, the global pattern of arousal-related oscillatory modulation in local field potentials is yet to be fully elucidated. We simultaneously recorded local field potentials in numerous cortical and subcortical regions in the primate brain and assessed oscillatory activity and inter-regional coherence associated with arousal state. In high arousal states, we found a uniquely strong and coherent gamma oscillation between the amygdala and basal forebrain. In low arousal rest-like states, a relative increase in coherence at alpha frequencies was present across sampled brain regions, with the notable exception of the medial temporal lobe. We consider how these patterns of activity may index arousal-related brain states that support the processing of incoming sensory stimuli during high arousal states and memory-related functions during rest.

Xenon anaesthesia is associated with a reduction in frontal electroencephalogram peak alpha frequency

Background

Administration of conventional anaesthetic agents is associated with changes in electroencephalogram (EEG) oscillatory dynamics, including a reduction in the peak alpha frequency. Computational models of neurones can reproduce such phenomena and are valuable tools for investigating their underlying mechanisms. We hypothesised that EEG data acquired during xenon anaesthesia in humans would show similar changes in peak alpha frequency and that computational neuronal models of recognised cellular actions of xenon would be consistent with the observed changes.

Methods

EEG recordings were obtained for 11 participants from a randomised controlled trial of xenon anaesthesia and for 21 participants from a volunteer study of xenon administration. The frontal peak alpha frequency was calculated for both cohorts at awake baseline and during xenon administration. In silico simulations with two computational models of neurones were performed to investigate how xenon antagonism of hyperpolarisation-activated cyclic nucleotide-gated channel 2 (HCN2) and glutamatergic excitatory neurotransmission would influence peak alpha frequency.

Results

Compared with awake baseline, frontal peak alpha frequency was significantly lower during xenon administration in the randomised controlled trial cohort, median (inter-quartile range) frequency 7.73 Hz (7.27-8.08 Hz) vs 8.81 Hz (8.35-9.03 Hz), P=0.012, and the volunteer cohort, 8.69 Hz (8.34-8.98 Hz) vs 9.41 Hz (9.11-9.92 Hz), P=0.001. In silico simulations with both computational models suggest that antagonism of HCN2 and glutamatergic excitatory neurotransmission are associated with a reduction in peak alpha frequency.

Conclusions

Xenon administration is associated with a reduction of peak alpha frequency in the frontal EEG. In silico simulations utilising two computational models of neurones suggest that these changes are consistent with antagonism of HCN2 and glutamatergic excitatory neurotransmission.

Clinical trial registration

The Australian New Zealand Clinical Trials Registry: ANZCTR number 12618000916246.

Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone

BACKGROUND

While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.

METHODS

In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data. This framework is utilized during intraoperative monitoring (IOM) while implanting electrodes and postoperatively in the epilepsy monitoring unit (EMU) before the respective surgery.

RESULTS

Our framework demonstrates a remarkable ability to eliminate pseudo-HFOs in heavily corrupted neural data, achieving accuracy levels comparable to those obtained through expert visual inspection. It not only enhances SOZ localization accuracy of IOM to a level comparable to EMU but also successfully captures sHFO clusters within IOM recordings, exhibiting high specificity to the primary SOZ.

CONCLUSIONS

These findings suggest that intraoperative HFOs, when processed with computational intelligence, can be used as early feedback for SOZ tailoring surgery to guide electrode repositioning, enhancing the efficacy of the overall invasive therapy.

The characteristics of auditorial event-related potential under propofol sedation associated with preoperative cognitive performance in glioma patients

Background

Glioma patients often experience neurocognitive deficits, particularly mild cognitive impairment (MCI), which affects their perioperative safety. The use of auditory event-related potentials (AERPs) might be a promising method for reflecting perioperative cognitive function in patients, even under unresponsive sedation. In this study, we aimed to investigate the relationships between the AERP under sedation and preoperative cognitive performance in glioma patients.

Methods

Patients with primary supratentorial gliomas who were scheduled for elective craniotomy under general anesthesia were included in this prospective observational study. The patients were categorized into MCI and non-MCI groups based on their preoperative Montreal Cognitive Assessment (MoCA) scores. AERP characteristics, including mismatch negativity (MMN), P300, and event-related spectral perturbation (ERSP) in the theta bands, were analyzed under different propofol-induced sedation conditions. Differences in these parameters between groups and their relationships with preoperative cognitive performance were subsequently investigated.

Results

Twenty-nine eligible patients were included in the analysis. Compared to that in the non-MCI group, the average amplitude of the MMN component evoked by the novel stimulus significantly decreased during the recovery period in the MCI group (-3.895 ± 1.961 μV vs. -1.617 ± 1.831 μV, p = 0.003). Theta-ERSPs also differed between the two groups under standard (0.021 ± 0.658 μV2/Hz vs. 0.515 ± 0.622 μV2/Hz, p = 0.048) and novel (0.212 ± 0.584 μV2/Hz vs. 0.823 ± 0.931 μV2/Hz, p = 0.041) stimulation conditions under light sedation. After correcting for age, education level, site of lesion, WHO pathological grade and combined symptomatic epilepsy as confounders, the frontal theta-ERSP induced by standard and novel stimuli under light sedation was inversely related to the preoperative MoCA score (standard stimuli: β = -0.491, p = 0.011; novel stimuli: β = -0.594, p = 0.007), as was the average MMN amplitude induced by novel stimuli during the recovery period (β = -0.356, p = 0.035).

Conclusion

The AERP neural response characteristics of glioma patients during propofol sedation were associated with preoperative cognitive performance, which might be a potential neurophysiological indicator for monitoring perioperative cognitive function, especially theta-ERSP.

Sequential Deactivation Across the Hippocampus-Thalamus-mPFC Pathway During Loss of Consciousness

How consciousness is lost in states such as sleep or anesthesia remains a mystery. To gain insight into this phenomenon, concurrent recordings of electrophysiology signals in the anterior cingulate cortex and whole-brain functional magnetic resonance imaging (fMRI) are conducted in rats exposed to graded propofol, undergoing the transition from consciousness to unconsciousness. The results reveal that upon the loss of consciousness (LOC), there is a sharp increase in low-frequency power of the electrophysiological signal. Additionally, fMRI signals exhibit a cascade of deactivation across a pathway including the hippocampus, thalamus, and medial prefrontal cortex (mPFC) surrounding the moment of LOC, followed by a broader increase in brain activity across the cortex during sustained unconsciousness. Furthermore, sliding window analysis demonstrates a temporary increase in synchrony of fMRI signals across the hippocampus-thalamus-mPFC pathway preceding LOC. These data suggest that LOC may be triggered by sequential activities in the hippocampus, thalamus, and mPFC, while wide-spread activity increases in other cortical regions commonly observed during anesthesia-induced unconsciousness may be a consequence, rather than a cause of LOC. Taken together, the study identifies a cascade of neural events unfolding as the brain transitions into unconsciousness, offering insight into the systems-level neural mechanisms underpinning LOC.

Spectral peak analysis and intrinsic neural timescales as markers for the state of consciousness

Resting state EEG in patients with disorders of consciousness (DOC) is characterized by an increase of power in the delta frequency band and a concurrent decrease in the alpha range, equivalent to a weakening or disappearance of the alpha peak. Prolongation of Intrinsic Neural Timescales (INTs) is also associated with DOCs. Together, this raises the question whether the decreased alpha peak relates to the prolonged INTs and, importantly, how that can be used for diagnosing the state of consciousness in DOC individuals. Analyzing resting state EEG recordings from both healthy subjects and DOC patients, we measure INTs through autocorrelation window (ACW) and utilize peak analysis to quantify the weakening of the alpha peak. First, we replicate previous findings of prolonged ACW in DOC patients. We then identify significantly lower alpha peak measures in DOC compared to controls. Interestingly, spectral peaks shift from the alpha to the theta range in several DOC subjects while such change is absent in healthy controls. Next, our study reveals a close relationship between ACW and alpha peak in both healthy and DOC subjects, a correlation that holds for theta peaks in DOC. Further, the prolonged ACW correlates with the state of consciousness, as quantified by the Coma Recovery Scale-Revised (CRS-R), and mediates the relationship between theta peak and CRS-R. Finally, through split analyses and machine learning, we show that ACW and alpha peak measures conjointly distinguish healthy controls and DOC patients with high accuracy (95.5%). In conclusion, we demonstrate that the prolongation of ACW, together with spectral peak measures, holds promise to serve as additional EEG biomarkers for diagnosing the state of consciousness in DOC subjects.

Decreasing brain activity caused by acute administration of ketamine and alcohol - A randomized, controlled, observer-blinded experimental study

Introduction

Substance abuse is a major public health problem. In recent years, ketamine, which is a parenteral anesthetic, has been consumed increasingly as an illicit drug together with alcohol, although little is known of how this association alters brain activity. The present study investigated the influence of progressive doses of ketamine, associated with alcohol, on electrophysiological activity.

Methods

For this, 72 late-adolescent (8-10-week-old) male Wistar rats received either ketamine only, at low (10 mg/kg), intermediate (20 mg/kg) or high (30 mg/kg) doses via intraperitoneal injection, or alcohol (2 mL/100 g) via oral gavage followed by ketamine (at low, intermediate, and high doses). Electroencephalograms (EEG) and electromyographic recordings were obtained 5 min after the final application of the drug.

Results

When administered alone, ketamine resulted in an increase in delta, theta, beta, and gamma brainwaves, with a more pronounced effect being detected at the highest dose (30 mg/kg) in the case of the delta, beta, and gamma waves. The amplitude of the alpha brainwaves was reduced at all doses of ketamine, but less intensively at the highest dose. When administered alone, alcohol reduced all the brainwaves, with the reduction in the alpha waves being exacerbated by ketamine at all doses, and that of the theta and beta waves being boosted at the lowest dose. The intermediate dose of ketamine (20 mg/kg) reverted the alcohol-induced reduction in the theta and gamma waves, whereas the high dose increased delta, theta, beta, and gamma bandpower.

Discussion

Overall, then, while ketamine enhances the depressant effects of alcohol on the alpha brainwave at all doses, a low dose intensified this effect on the theta and beta 175 waves, whereas a high dose produces neuronal hyperexcitability in the theta and 176 gamma bandpower.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patient's capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Convergent effects of different anesthetics on changes in phase alignment of cortical oscillations

Many anesthetics cause loss of responsiveness despite having diverse underlying molecular and circuit actions. To explore the convergent effects of these drugs, we examined how anesthetic doses of ketamine and dexmedetomidine affected oscillations in the prefrontal cortex of nonhuman primates. Both anesthetics caused increases in phase locking in the ventrolateral and dorsolateral prefrontal cortex, within and across hemispheres. However, the nature of the phase locking varied. Activity in different subregions within a hemisphere became more anti-phase with both drugs. Local analyses within a region suggested that this finding could be explained by broad cortical distance-based effects, such as large traveling waves. By contrast, homologous areas across hemispheres became more in-phase. Our results suggest that both anesthetics induce strong patterns of cortical phase alignment that are markedly different from those in the awake state, and that these patterns may be a common feature driving loss of responsiveness from different anesthetic drugs.

Recovery of consciousness after acute brain injury: a narrative review

BACKGROUND

Disorders of consciousness (DoC) are frequently encountered in both, acute and chronic brain injuries. In many countries, early withdrawal of life-sustaining treatments is common practice for these patients even though the accuracy of predicting recovery is debated and delayed recovery can be seen. In this review, we will discuss theoretical concepts of consciousness and pathophysiology, explore effective strategies for management, and discuss the accurate prediction of long-term clinical outcomes. We will also address research challenges.

MAIN TEXT

DoC are characterized by alterations in arousal and/or content, being classified as coma, unresponsive wakefulness syndrome/vegetative state, minimally conscious state, and confusional state. Patients with willful modulation of brain activity detectable by functional MRI or EEG but not by behavioral examination is a state also known as covert consciousness or cognitive motor dissociation. This state may be as common as every 4th or 5th patient without behavioral evidence of verbal command following and has been identified as an independent predictor of long-term functional recovery. Underlying mechanisms are uncertain but intact arousal and thalamocortical projections maybe be essential. Insights into the mechanisms underlying DoC will be of major importance as these will provide a framework to conceptualize treatment approaches, including medical, mechanical, or electoral brain stimulation.

CONCLUSIONS

We are beginning to gain insights into the underlying mechanisms of DoC, identifying novel advanced prognostication tools to improve the accuracy of recovery predictions, and are starting to conceptualize targeted treatments to support the recovery of DoC patients. It is essential to determine how these advancements can be implemented and benefit DoC patients across a range of clinical settings and global societal systems. The Curing Coma Campaign has highlighted major gaps knowledge and provides a roadmap to advance the field of coma science with the goal to support the recovery of patients with DoC.

Unconscious classification of quantitative electroencephalogram features from propofol versus propofol combined with etomidate anesthesia using one-dimensional convolutional neural network

Objective

Establishing a convolutional neural network model for the recognition of characteristic raw electroencephalogram (EEG) signals is crucial for monitoring consciousness levels and guiding anesthetic drug administration.

Methods

This trial was conducted from December 2023 to March 2024. A total of 40 surgery patients were randomly divided into either a propofol group (1% propofol injection, 10 mL: 100 mg) (P group) or a propofol-etomidate combination group (1% propofol injection, 10 mL: 100 mg, and 0.2% etomidate injection, 10 mL: 20 mg, mixed at a 2:1 volume ratio) (EP group). In the P group, target-controlled infusion (TCI) was employed for sedation induction, with an initial effect site concentration set at 5-6 μg/mL. The EP group received an intravenous push with a dosage of 0.2 mL/kg. Six consciousness-related EEG features were extracted from both groups and analyzed using four prediction models: support vector machine (SVM), Gaussian Naive Bayes (GNB), artificial neural network (ANN), and one-dimensional convolutional neural network (1D CNN). The performance of the models was evaluated based on accuracy, precision, recall, and F1-score.

Results

The power spectral density (94%) and alpha/beta ratio (72%) demonstrated higher accuracy as indicators for assessing consciousness. The classification accuracy of the 1D CNN model for anesthesia-induced unconsciousness (97%) surpassed that of the SVM (83%), GNB (81%), and ANN (83%) models, with a significance level of p < 0.05. Furthermore, the mean and mean difference ± standard error of the primary power values for the EP and P groups during the induced period were as follows: delta (23.85 and 16.79, 7.055 ± 0.817, p < 0.001), theta (10.74 and 8.743, 1.995 ± 0.7045, p < 0.02), and total power (24.31 and 19.72, 4.588 ± 0.7107, p < 0.001).

Conclusion

Large slow-wave oscillations, power spectral density, and the alpha/beta ratio are effective indicators of changes in consciousness during intravenous anesthesia with a propofol-etomidate combination. These indicators can aid anesthesiologists in evaluating the depth of anesthesia and adjusting dosages accordingly. The 1D CNN model, which incorporates consciousness-related EEG features, represents a promising tool for assessing the depth of anesthesia.

Clinical Trial Registration

https://www.chictr.org.cn/index.html.

Propofol disrupts the functional core-matrix architecture of the thalamus in humans

Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. Here we show a significant shift in this geometry during deep sedation, marked by a transmodal-deficient geometry. This alteration is closely linked to the spatial variations in the matrix cell composition within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core-matrix functional architecture in understanding the neural mechanisms of states of consciousness.

Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO2) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69.27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the feasibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

Cyclic Alternating EEG Patterns: From Sleep to Encephalopathy

SUMMARY

In the 2021 version of the Standardized Critical Care EEG Terminology, the American Clinical Neurophysiology Society introduced new definitions, including for the cyclic alternating pattern of encephalopathy (CAPE). CAPE refers to changes in background EEG activity, with two patterns alternating spontaneously in a regular manner. CAPE shares remarkable similarities with the cyclic alternating pattern, a natural EEG phenomenon occurring in normal non-rapid eye movement sleep, considered the main electrophysiological biomarker of sleep instability. This review explores similarities and differences between cyclic alternating pattern and CAPE and, leveraging the existing expertise on cyclic alternating pattern, aims to extend knowledge on CAPE. A standardized assessment of CAPE features is key to ascertain its prevalence and clinical significance among critically ill patients and to encompass the impact of confounding factors such as anesthetic and sedative agents. Although the preservation of non-rapid eye movement sleep-related elements has a well-known prognostic value in the critical care setting, the clinical importance of cyclic oscillating patterns and the prognostic significance of CAPE remain to be elucidated.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Machine learning of brain-specific biomarkers from EEG

BACKGROUND

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS).

METHODS

We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses.

FINDINGS

Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals.

INTERPRETATION

We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML.

FUNDING

All authors have been working for F. Hoffmann-La Roche Ltd.

Applied human neuroscience: Fostering and designing inclusive environments with the 3E-Cognition perspective

The conventional medical paradigm often focuses on deficits and impairments, failing to capture the rich tapestry of experiences and abilities inherent in neurodiversity conditions. In this article, we introduce the 3E-Cognition perspective, offering a paradigm shift by emphasizing the dynamic interplay between the brain, body, and environment in shaping cognitive processes. The perspective fosters a more inclusive and supportive understanding of neurodiversity, with potential applications across various domains such as education, workplace, and healthcare. We begin by introducing the 3E-Cognition principles: embodied, environmentally scaffolded, and enactive. Then, we explore how the 3E-Cognition perspective can be applied to create inclusive environments and experiences for neurodiverse individuals. We provide examples in the realms of education, workplace, and healthcare. In all of these domains, spaces, methodologies, epistemologies, and roles that cater to diverse needs and strengths can be designed using the 3E principles. Finally, we discuss the challenges and benefits of implementing the 3E-Cognition perspective. We focus on the need for technological advancements and research in complex real-world scenarios; we suggest mobile brain/body imaging is a possible solution. We furthermore highlight the importance of recognizing and valuing the diverse manners of experiencing and interacting with the world, the promotion of diverse well-being, and the facilitation of innovation and creativity. Thus, we conclude that the 3E-Cognition perspective offers a groundbreaking approach to understanding and supporting neurodiversity: by embracing the inherent interconnectedness of the brain, body, and environment, we can create a more inclusive and supportive world.

Repurposing General Anesthetic Drugs to Treat Depression: A New Frontier for Anesthesiologists in Neuropsychiatric Care

During the last 100 years, the role of anesthesiologists in psychiatry has focused primarily on facilitating electroconvulsive therapy and mitigating postoperative delirium and other perioperative neurocognitive disorders. The discovery of the rapid and sustained antidepressant properties of ketamine, and early results suggesting that other general anesthetic drugs (including nitrous oxide, propofol, and isoflurane) have antidepressant properties, has positioned anesthesiologists at a new frontier in the treatment of neuropsychiatric disorders. Moreover, shared interest in understanding the biologic underpinnings of anesthetic drugs as psychotropic agents is eroding traditional academic boundaries between anesthesiology and psychiatry. This article presents a brief overview of anesthetic drugs as novel antidepressants and identifies promising future candidates for the treatment of depression. The authors issue a call to action and outline strategies to foster collaborations between anesthesiologists and psychiatrists as they work toward the common goals of repurposing anesthetic drugs as antidepressants and addressing mood disorders in surgical patients.

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyze temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different anesthetics and levels of anesthesia. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting-state fMRI structure. We then track how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks is preserved, their temporal dynamics are highly affected by anesthesia. Networks express for longer durations and co-activate in an anesthetic-specific configuration. Additionally, hierarchical brain organization is disrupted with a consciousness-level-signature role of the default mode network. In conclusion, large-scale brain network temporal dynamics capture differences in anesthetic-specific consciousness-level, paving the way towards a clinical translation of these cortical signature.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100's to 1000's. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Globus pallidus externus drives increase in network-wide alpha power with propofol-induced loss-of-consciousness in humans

States of consciousness are likely mediated by multiple parallel yet interacting cortico-subcortical recurrent networks. Although the mesocircuit model has implicated the pallidocortical circuit as one such network, this circuit has not been extensively evaluated to identify network-level electrophysiological changes related to loss of consciousness (LOC). We characterize changes in the mesocircuit in awake versus propofol-induced LOC in humans by directly simultaneously recording from sensorimotor cortices (S1/M1) and globus pallidus interna and externa (GPi/GPe) in 12 patients with Parkinson disease undergoing deep brain stimulator implantation. Propofol-induced LOC is associated with increases in local power up to 20 Hz in GPi, 35 Hz in GPe, and 100 Hz in S1/M1. LOC is likewise marked by increased pallidocortical alpha synchrony across all nodes, with increased alpha/low beta Granger causal (GC) flow from GPe to all other nodes. In contrast, LOC is associated with decreased network-wide beta coupling and beta GC from M1 to the rest of the network. Results implicate an important and possibly central role of GPe in mediating LOC-related increases in alpha power, supporting a significant role of the GPe in modulating cortico-subcortical circuits for consciousness. Simultaneous LOC-related suppression of beta synchrony highlights that distinct oscillatory frequencies act independently, conveying unique network activity.

GABAergic neurons of anterior thalamic reticular nucleus regulate states of consciousness in propofol- and isoflurane-mediated general anesthesia

BACKGROUND

The thalamus system plays critical roles in the regulation of reversible unconsciousness induced by general anesthetics, especially the arousal stage of general anesthesia (GA). But the function of thalamus in GA-induced loss of consciousness (LOC) is little known. The thalamic reticular nucleus (TRN) is the only GABAergic neurons-composed nucleus in the thalamus, which is composed of parvalbumin (PV) and somatostatin (SST)-expressing GABAergic neurons. The anterior sector of TRN (aTRN) is indicated to participate in the induction of anesthesia, but the roles remain unclear. This study aimed to reveal the role of the aTRN in propofol and isoflurane anesthesia.

METHODS

We first set up c-Fos straining to monitor the activity variation of aTRNPV and aTRNSST neurons during propofol and isoflurane anesthesia. Subsequently, optogenetic tools were utilized to activate aTRNPV and aTRNSST neurons to elucidate the roles of aTRNPV and aTRNSST neurons in propofol and isoflurane anesthesia. Electroencephalogram (EEG) recordings and behavioral tests were recorded and analyzed. Lastly, chemogenetic activation of the aTRNPV neurons was applied to confirm the function of the aTRN neurons in propofol and isoflurane anesthesia.

RESULTS

c-Fos straining showed that both aTRNPV and aTRNSST neurons are activated during the LOC period of propofol and isoflurane anesthesia. Optogenetic activation of aTRNPV and aTRNSST neurons promoted isoflurane induction and delayed the recovery of consciousness (ROC) after propofol and isoflurane anesthesia, meanwhile chemogenetic activation of the aTRNPV neurons displayed the similar effects. Moreover, optogenetic and chemogenetic activation of the aTRN neurons resulted in the accumulated burst suppression ratio (BSR) during propofol and isoflurane GA, although they represented different effects on the power distribution of EEG frequency.

CONCLUSION

Our findings reveal that the aTRN GABAergic neurons play a critical role in promoting the induction of propofol- and isoflurane-mediated GA.

Intraoperative pediatric electroencephalography monitoring: an updated review

Intraoperative electroencephalography (EEG) monitoring under pediatric anesthesia has begun to attract increasing interest, driven by the availability of pediatric-specific EEG monitors and the realization that traditional dosing methods based on patient movement or changes in hemodynamic response often lead to imprecise dosing, especially in younger infants who may experience adverse events (e.g., hypotension) due to excess anesthesia. EEG directly measures the effects of anesthetics on the brain, which is the target end-organ responsible for inducing loss of consciousness. Over the past ten years, research on anesthesia and computational neuroscience has improved our understanding of intraoperative pediatric EEG monitoring and expanded the utility of EEG in clinical practice. We now have better insights into neurodevelopmental changes in the developing pediatric brain, functional connectivity, the use of non-proprietary EEG parameters to guide anesthetic dosing, epileptiform EEG changes during induction, EEG changes from spinal/regional anesthesia, EEG discontinuity, and the use of EEG to improve clinical outcomes. This review article summarizes the recent literature on EEG monitoring in perioperative pediatric anesthesia, highlighting several of the topics mentioned above.

Prevalence of clinical electroencephalography findings in stroke patients with delirium

OBJECTIVE

Delirium is an acute cognitive disorder associated with multiple electroencephalographic (EEG) abnormalities in non-neurological patients, though specific EEG characteristics in patients with stroke remain unclear. We aimed to compare the prevalence of EEG abnormalities in stroke patients during delirium episodes with periods that did not correspond to delirium.

METHODS

We retrospectively analyzed clinical EEG reports for stroke patients who received daily delirium assessments as part of a prospective study. We compared the prevalence of EEG features corresponding to patient-days with vs. without delirium, including focal and generalized slowing, and focal and generalized epileptiform abnormalities (EAs).

RESULTS

Among 58 patients who received EEGs, there were 192 days of both EEG and delirium monitoring (88% [n = 169] corresponding to delirium). Generalized slowing was significantly more prevalent on days with vs. without delirium (96% vs. 57%, p = 0.03), as were bilateral or generalized EAs (38% vs. 13%, p = 0.03). In contrast, focal slowing (53% vs. 74%, p = 0.11) and focal EAs were less prevalent on days with delirium (38% vs. 48%, p = 0.37), though these differences were not statistically significant.

CONCLUSIONS

We found a higher prevalence of generalized but not focal EEG abnormalities in stroke patients with delirium.

SIGNIFICANCE

These findings may reinforce the diffuse nature of delirium-associated encephalopathy, even in patients with discrete structural lesions.

Age-dependent coupling characteristics of bilateral frontal EEG during desflurane anesthesia

Objectives.The purpose of this study is to investigate the age dependence of bilateral frontal electroencephalogram (EEG) coupling characteristics, and find potential age-independent depth of anesthesia monitoring indicators for the elderlies.Approach.We recorded bilateral forehead EEG data from 41 patients (ranged in 19-82 years old), and separated into three age groups: 18-40 years (n= 12); 40-65 years (n= 14), >65 years (n= 15). All these patients underwent desflurane maintained general anesthesia (GA). We analyzed the age-related EEG spectra, phase amplitude coupling (PAC), coherence and phase lag index (PLI) of EEG data in the states of awake, GA, and recovery.Main results.The frontal alpha power shows age dependence in the state of GA maintained by desflurane. Modulation index in slow oscillation-alpha and delta-alpha bands showed age dependence and state dependence in varying degrees, the PAC pattern also became less pronounced with increasing age. In the awake state, the coherence in delta, theta and alpha frequency bands were all significantly higher in the >65 years age group than in the 18-40 years age group (p< 0.05 for three frequency bands). The coherence in alpha-band was significantly enhanced in all age groups in GA (p< 0.01) and then decreased in recovery state. Notably, the PLI in the alpha band was able to significantly distinguish the three states of awake, GA and recovery (p< 0.01) and the results of PLI in delta and theta frequency bands had similar changes to those of coherence.Significance.We found the EEG coupling and synchronization between bilateral forehead are age-dependent. The PAC, coherence and PLI portray this age-dependence. The PLI and coherence based on bilateral frontal EEG functional connectivity measures and PAC based on frontal single-channel are closely associated with anesthesia-induced unconsciousness.

Sequential deactivation across the thalamus-hippocampus-mPFC pathway during loss of consciousness

How consciousness is lost in states such as sleep or anesthesia remains a mystery. To gain insight into this phenomenon, we conducted concurrent recordings of electrophysiology signals in the anterior cingulate cortex and whole-brain functional magnetic resonance imaging (fMRI) in rats exposed to graded propofol, undergoing the transition from consciousness to unconsciousness. Our results reveal that upon the loss of consciousness (LOC), as indicated by the loss of righting reflex, there is a sharp increase in low-frequency power of the electrophysiological signal. Additionally, simultaneously measured fMRI signals exhibit a cascade of deactivation across a pathway including the hippocampus, thalamus, and medial prefrontal cortex (mPFC) surrounding the moment of LOC, followed by a broader increase in brain activity across the cortex during sustained unconsciousness. Furthermore, sliding window analysis demonstrates a temporary increase in synchrony of fMRI signals across the hippocampus-thalamus-mPFC pathway preceding LOC. These data suggest that LOC might be triggered by sequential activities in the hippocampus, thalamus and mPFC, while wide-spread activity increases in other cortical regions commonly observed during anesthesia-induced unconsciousness might be a consequence, rather than a cause of LOC. Taken together, our study identifies a cascade of neural events unfolding as the brain transitions into unconsciousness, offering critical insight into the systems-level neural mechanisms underpinning LOC.

Anesthesia and the neurobiology of consciousness

In the 19th century, the discovery of general anesthesia revolutionized medical care. In the 21st century, anesthetics have become indispensable tools to study consciousness. Here, I review key aspects of the relationship between anesthesia and the neurobiology of consciousness, including interfaces of sleep and anesthetic mechanisms, anesthesia and primary sensory processing, the effects of anesthetics on large-scale functional brain networks, and mechanisms of arousal from anesthesia. I discuss the implications of the data derived from the anesthetized state for the science of consciousness and then conclude with outstanding questions, reflections, and future directions.

Substance specific EEG patterns in mice undergoing slow anesthesia induction

The exact mechanisms and the neural circuits involved in anesthesia induced unconsciousness are still not fully understood. To elucidate them valid animal models are necessary. Since the most commonly used species in neuroscience are mice, we established a murine model for commonly used anesthetics/sedatives and evaluated the epidural electroencephalographic (EEG) patterns during slow anesthesia induction and emergence. Forty-four mice underwent surgery in which we inserted a central venous catheter and implanted nine intracranial electrodes above the prefrontal, motor, sensory, and visual cortex. After at least one week of recovery, mice were anesthetized either by inhalational sevoflurane or intravenous propofol, ketamine, or dexmedetomidine. We evaluated the loss and return of righting reflex (LORR/RORR) and recorded the electrocorticogram. For spectral analysis we focused on the prefrontal and visual cortex. In addition to analyzing the power spectral density at specific time points we evaluated the changes in the spectral power distribution longitudinally. The median time to LORR after start anesthesia ranged from 1080 [1st quartile: 960; 3rd quartile: 1080]s under sevoflurane anesthesia to 1541 [1455; 1890]s with ketamine. Around LORR sevoflurane as well as propofol induced a decrease in the theta/alpha band and an increase in the beta/gamma band. Dexmedetomidine infusion resulted in a shift towards lower frequencies with an increase in the delta range. Ketamine induced stronger activity in the higher frequencies. Our results showed substance-specific changes in EEG patterns during slow anesthesia induction. These patterns were partially identical to previous observations in humans, but also included significant differences, especially in the low frequencies. Our study emphasizes strengths and limitations of murine models in neuroscience and provides an important basis for future studies investigating complex neurophysiological mechanisms.

Lower alpha frequency of intraoperative frontal EEG is associated with postoperative delirium: A secondary propensity-matched analysis

BACKGROUND

Postoperative delirium (POD) is a serious complication of surgery, especially in the elderly patient population. It has been proposed that decreasing the amount of anesthetics by titrating to an EEG index will lower POD rate, but clear evidence is missing. A strong age-dependent negative correlation has been reported between the peak oscillatory frequency of alpha waves and end-tidal anesthetic concentration, with older patients generating slower alpha frequencies. We hypothesized, that slower alpha oscillations are associated with a higher rate of POD.

METHOD

Retrospective analysis of patients` data from a prospective observational study in cardiac surgical patients approved by the Bernese Ethics committee. Frontal EEG was recorded during Isoflurane effect-site concentrations of 0.7 to 0.8 and peak alpha frequency was measured at highest power between 6 and 17 Hz. Delirium was assessed by chart review. Demographic and clinical characteristics were compared between POD and non-POD groups. Selection bias was addressed using nearest neighbor propensity score matching (PSM) for best balance. This incorporated 18 variables, whereas patients with missing variable information or without an alpha oscillation were excluded.

RESULT

Of the 1072 patients in the original study, 828 were included, 73 with POD, 755 without. PSM allowed 328 patients into the final analysis, 67 with, 261 without POD. Before PSM, 8 variables were significantly different between POD and non-POD groups, none thereafter. Mean peak alpha frequency was significantly lower in the POD in contrast to non-POD group before and after matching (7.9 vs 8.9 Hz, 7.9 vs 8.8 Hz respectively, SD 1.3, p < 0.001).

CONCLUSION

Intraoperative slower frontal peak alpha frequency is independently associated with POD after cardiac surgery and may be a simple intraoperative neurophysiological marker of a vulnerable brain for POD. Further studies are needed to investigate if there is a causal link between alpha frequency and POD.

Electroencephalogram Biomarkers from Anesthesia Induction to Identify Vulnerable Patients at Risk for Postoperative Delirium

BACKGROUND

Postoperative delirium is a common complication in elderly patients undergoing anesthesia. Even though it is increasingly recognized as an important health issue, the early detection of patients at risk for postoperative delirium remains a challenge. This study aims to identify predictors of postoperative delirium by analyzing frontal electroencephalogram at propofol-induced loss of consciousness.

METHODS

This prospective, observational single-center study included patients older than 70 yr undergoing general anesthesia for a planned surgery. Frontal electroencephalogram was recorded on the day before surgery (baseline) and during anesthesia induction (1, 2, and 15 min after loss of consciousness). Postoperative patients were screened for postoperative delirium twice daily for 5 days. Spectral analysis was performed using the multitaper method. The electroencephalogram spectrum was decomposed in periodic and aperiodic (correlates to asynchronous spectrum wide activity) components. The aperiodic component is characterized by its offset (y intercept) and exponent (the slope of the curve). Computed electroencephalogram parameters were compared between patients who developed postoperative delirium and those who did not. Significant electroencephalogram parameters were included in a binary logistic regression analysis to predict vulnerability for postoperative delirium.

RESULTS

Of 151 patients, 50 (33%) developed postoperative delirium. At 1 min after loss of consciousness, postoperative delirium patients demonstrated decreased alpha (postoperative delirium: 0.3 μV2 [0.21 to 0.71], no postoperative delirium: 0.55 μV2 [0.36 to 0.74]; P = 0.019] and beta band power [postoperative delirium: 0.27 μV2 [0.12 to 0.38], no postoperative delirium: 0.38 μV2 [0.25 to 0.48]; P = 0.003) and lower spectral edge frequency (postoperative delirium: 10.45 Hz [5.65 to 15.04], no postoperative delirium: 14.56 Hz [9.51 to 16.65]; P = 0.01). At 15 min after loss of consciousness, postoperative delirium patients displayed a decreased aperiodic offset (postoperative delirium: 0.42 μV2 (0.11 to 0.69), no postoperative delirium: 0.62 μV2 [0.37 to 0.79]; P = 0.004). The logistic regression model predicting postoperative delirium vulnerability demonstrated an area under the curve of 0.73 (0.69 to 0.75).

CONCLUSIONS

The findings suggest that electroencephalogram markers obtained during loss of consciousness at anesthesia induction may serve as electroencephalogram-based biomarkers to identify at an early time patients at risk of developing postoperative delirium.

EDITOR’S PERSPECTIVE

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

Nociception Effect on Frontal Electroencephalogram Waveform and Phase-Amplitude Coupling in Laparoscopic Surgery

BACKGROUND

Electroencephalographic pattern changes during anesthesia reflect the nociception-analgesia balance. Alpha dropout, delta arousal, and beta arousal with noxious stimulation have been described during anesthesia; however, data on the reaction of other electroencephalogram signatures toward nociception are scarce. Analyzing the effects of nociception on different electroencephalogram signatures may help us find new nociception markers in anesthesia and understand the neurophysiology of pain in the brain. This study aimed to analyze the electroencephalographic frequency pattern and phase-amplitude coupling change during laparoscopic surgeries.

METHODS

This study evaluated 34 patients who underwent laparoscopic surgery. The electroencephalogram frequency band power and phase-amplitude coupling of different frequencies were analyzed across 3 stages of laparoscopy: incision, insufflation, and opioid stages. Repeated-measures analysis of variance with a mixed model and the Bonferroni method for multiple comparisons were used to analyze the changes in the electroencephalogram signatures between the preincision and postincision/postinsufflation/postopioid phases.

RESULTS

During noxious stimulation, the frequency spectrum showed obvious decreases in the alpha power percentage after the incision (mean ± standard error of the mean [SEM], 26.27 ± 0.44 and 24.37 ± 0.66; P < .001) and insufflation stages (26.27 ± 0.44 and 24.40 ± 0.68; P = .002), which recovered after opioid administration. Further phase-amplitude analyses showed that the modulation index (MI) of the delta-alpha coupling decreased after the incision stage (1.83 ± 0.22 and 0.98 ± 0.14 [MI × 10 3 ]; P < .001), continued to be suppressed during the insufflation stage (1.83 ± 0.22 and 1.17 ± 0.15 [MI × 10 3 ]; P = .044), and recovered after opioid administration.

CONCLUSIONS

Alpha dropout during noxious stimulation is observed in laparoscopic surgeries under sevoflurane. In addition, the modulation index of delta-alpha coupling decreases during noxious stimulation and recovers after the administration of rescue opioids. Phase-amplitude coupling of the electroencephalogram may be a new approach for evaluating the nociception-analgesia balance during anesthesia.

Topographical Features of Pediatric Electroencephalography during High Initial Concentration Sevoflurane for Inhalational Induction of Anesthesia

BACKGROUND

High-density electroencephalographic (EEG) monitoring remains underutilized in clinical anesthesia, despite its obvious utility in unraveling the profound physiologic impact of these agents on central nervous system functioning. In school-aged children, the routine practice of rapid induction with high concentrations of inspiratory sevoflurane is commonplace, given its favorable efficacy and tolerance profile. However, few studies investigate topographic EEG during the critical timepoint coinciding with loss of responsiveness-a key moment for anesthesiologists in their everyday practice. The authors hypothesized that high initial sevoflurane inhalation would better precipitate changes in brain regions due to inhomogeneities in maturation across three different age groups compared with gradual stepwise paradigms utilized by other investigators. Knowledge of these changes may inform strategies for agent titration in everyday clinical settings.

METHODS

A total of 37 healthy children aged 5 to 10 yr underwent induction with 4% or greater sevoflurane in high-flow oxygen. Perturbations in anesthetic state were investigated in 23 of these children using 64-channel EEG with the Hjorth Laplacian referencing scheme. Topographical maps illustrated absolute, relative, and total band power across three age groups: 5 to 6 yr (n = 7), 7 to 8 yr (n = 8), and 9 to 10 yr (n = 8).

RESULTS

Spectral analysis revealed a large shift in total power driven by increased delta oscillations. Well-described topographic patterns of anesthesia, e.g., frontal predominance, paradoxical beta excitation, and increased slow activity, were evident in the topographic maps. However, there were no statistically significant age-related changes in spectral power observed in a midline electrode subset between the groups when responsiveness was lost compared to the resting state.

CONCLUSIONS

High initial concentration sevoflurane induction causes large-scale topographic effects on the pediatric EEG. Within the minute after unresponsiveness, this dosage may perturb EEG activity in children to an extent where age-related differences are not discernible.

EDITOR’S PERSPECTIVE

The Association of Preoperative Diabetes With Postoperative Delirium in Older Patients Undergoing Major Orthopedic Surgery: A Prospective Matched Cohort Study

BACKGROUND

Postoperative delirium (POD) is a common form of postoperative brain dysfunction, especially in the elderly. However, its risk factors remain largely to be determined. This study aimed to investigate whether (1) preoperative diabetes is associated with POD after elective orthopedic surgery and (2) intraoperative frontal alpha power is a mediator of the association between preoperative diabetes and POD.

METHODS

This was a prospective matched cohort study of patients aged 60 years or more, with a preoperative diabetes who underwent elective orthopedic surgery. Nondiabetic patients were matched 1:1 to diabetic patients in terms of age, sex, and type of surgery. Primary outcome was occurrence of POD, assessed using the 3-minute Diagnostic Confusion Assessment Method (3D-CAM) once daily from 6 pm to 8 pm during the postoperative days 1-7 or until discharge. Secondary outcome was the severity of POD which was assessed for all participants using the short form of the CAM-Severity. Frontal electroencephalogram (EEG) was recorded starting before induction of anesthesia and lasting until discharge from the operating room. Intraoperative alpha power was calculated using multitaper spectral analyses. Mediation analysis was used to estimate the proportion of the association between preoperative diabetes and POD that could be explained by intraoperative alpha power.

RESULTS

A total of 138 pairs of eligible patients successfully matched 1:1. After enrollment, 6 patients in the diabetes group and 4 patients in the nondiabetes group were excluded due to unavailability of raw EEG data. The final analysis included 132 participants with preoperative diabetes and 134 participants without preoperative diabetes, with a median age of 68 years and 72.6% of patients were female. The incidence of POD was 16.7% (22/132) in patients with preoperative diabetes vs 6.0% (8/134) in patients without preoperative diabetes. Preoperative diabetes was associated with increased odds of POD after adjustment of age, sex, body mass index, education level, hypertension, arrhythmia, coronary heart disease, and history of stroke (odds ratio, 3.2; 95% confidence interval [CI], 1.4-8.0; P = .009). The intraoperative alpha power accounted for an estimated 20% (95% CI, 2.6-60%; P = .021) of the association between diabetes and POD.

CONCLUSIONS

This study suggests that preoperative diabetes is associated with an increased risk of POD in older patients undergoing major orthopedic surgery, and that low intraoperative alpha power partially mediates such association.

Differential effects of sevoflurane and desflurane on frontal intraoperative electroencephalogram dynamics associated with postoperative delirium

STUDY OBJECTIVE

Intraoperative electroencephalogram (EEG) patterns associated with postoperative delirium (POD) development have been studied, but the differences in EEG recordings between sevoflurane- and desflurane-induced anesthesia have not been clarified. We aimed to distinguish the EEG characteristics of sevoflurane and desflurane in relation to POD development.

DESIGN AND PATIENTS

We collected frontal four-channel EEG data during the maintenance of anesthesia from 148 elderly patients who received sevoflurane (n = 77) or desflurane (n = 71); 30 patients were diagnosed with delirium postoperatively. The patients were divided into four subgroups based on anesthetics and delirium status: sevoflurane delirium (n = 17), sevoflurane non-delirium (n = 60), desflurane delirium (n = 13), and desflurane non-delirium (n = 58). We compared spectral power, coherence, and pairwise phase consistency (PPC) between sevoflurane and desflurane, and between non-delirium and delirium groups for each anesthetic.

MAIN RESULTS

In patients without POD, the sevoflurane non-delirium group exhibited higher EEG spectral power across 8.5-35 Hz (99.5% CI bootstrap analysis) and higher PPC from alpha to gamma bands (p < 0.005) compared to the desflurane non-delirium group. Conversely, in patients with POD, no significant EEG differences were observed between the sevoflurane and desflurane delirium groups. For the sevoflurane-induced patients, the sevoflurane delirium group had significantly lower power within 7.5-31.5 Hz (99.5% CI bootstrap analysis), reduced coherence over 8.9-23.8 Hz (99.5% CI bootstrap analysis), and lower PPC values in the alpha band (p < 0.005) compared with the sevoflurane non-delirium group. For the desflurane-induced patients, there were no significant differences in the EEG patterns between delirium and non-delirium groups.

CONCLUSIONS

In normal patients without POD, sevoflurane demonstrates a higher power spectrum and prefrontal connectivity than desflurane. Furthermore, reduced frontal alpha power, coherence, and connectivity of intraoperative EEG could be associated with an increased risk of POD. These intraoperative EEG characteristics associated with POD are more noticeable in sevoflurane-induced anesthesia than in desflurane-induced anesthesia.

Analysis of Amplitude Modulation of EEG Based on Holo-Hilbert Spectrum Analysis During General Anesthesia

OBJECTIVE

The study aims to investigate the relationship between amplitude modulation (AM) of EEG and anesthesia depth during general anesthesia.

METHODS

In this study, Holo-Hilbert spectrum analysis (HHSA) was used to decompose the multichannel EEG signals of 15 patients to obtain the spatial distribution of AM in the brain. Subsequently, HHSA was applied to the prefrontal EEG (Fp1) obtained during general anesthesia surgery in 15 and 34 patients, and the α-θ and α-δ regions of feature (ROFs) were defined in Holo-Hilbert spectrum (HHS) and three features were derived to quantify AM in ROFs.

RESULTS

During anesthetized phase, an anteriorization of the spatial distribution of AMs of α-carrier in brain was observed, as well as AMs of α-θ and α-δ in the EEG of Fp1. The total power ([Formula: see text]), mean carrier frequency ([Formula: see text]) and mean amplitude frequency ([Formula: see text]) of AMs changed during different anesthesia states.

CONCLUSION

HHSA can effectively analyze the cross-frequency coupling of EEG during anesthesia and the AM features may be applied to anesthesia monitoring.

SIGNIFICANCE

The study provides a new perspective for the characterization of brain states during general anesthesia, which is of great significance for exploring new features of anesthesia monitoring.

Effect of Repeated Exposure to Sevoflurane on Electroencephalographic Alpha Oscillation in Pediatric Patients Undergoing Radiation Therapy: A Prospective Observational Study

BACKGROUND

Pharmacological tolerance is defined as a decrease in the effect of a drug over time, or the need to increase the dose to achieve the same effect. It has not been established whether repeated exposure to sevoflurane induces tolerance in children.

METHODS

We conducted an observational study in children younger than 6 years of age scheduled for multiple radiotherapy sessions with sevoflurane anesthesia. To evaluate the development of sevoflurane tolerance, we analyzed changes in electroencephalographic spectral power at induction, across sessions. We fitted individual and group-level linear regression models to evaluate the correlation between the outcomes and sessions. In addition, a linear mixed-effect model was used to evaluate the association between radiotherapy sessions and outcomes.

RESULTS

Eighteen children were included and the median number of radiotherapy sessions per child was 28 (interquartile range: 10 to 33). There was no correlation between induction time and radiotherapy sessions. At the group level, the linear mixed-effect model showed, in a subgroup of patients, that alpha relative power and spectral edge frequency 95 were inversely correlated with the number of anesthesia sessions. Nonetheless, this subgroup did not differ from the other subjects in terms of age, sex, or the total number of radiotherapy sessions.

CONCLUSIONS

Our results suggest that children undergoing repeated anesthesia exposure for radiotherapy do not develop tolerance to sevoflurane. However, we found that a group of patients exhibited a reduction in the alpha relative power as a function of anesthetic exposure. These results may have implications that justify further studies.

Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses

High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.

Intraoperative EEG-based monitors: are we looking under the lamppost?

PURPOSE OF REVIEW

While electroencephalogram (EEG)-based depth of anesthesia monitors have been in use clinically for decades, there is still a major debate concerning their efficacy for detecting awareness under anesthesia (AUA). Further utilization of these monitors has also been discussed vividly, for example, reduction of postoperative delirium (POD).It seems that with regard to reducing AUA and POD, these monitors might be applicable, under specific anesthetic protocols. But in other settings, such monitoring might be less contributive and may have a 'built-it glass ceiling'.Recent advances in other venues of electrophysiological monitoring might have a strong theoretical rationale, and early supporting results, to offer a breakthrough out of this metaphorical glass ceiling. The purpose of this review is to present this possibility.

RECENT FINDINGS

Following previous findings, it might be concluded that for some anesthesia protocols, the prevailing depth of anesthesia monitors may prevent incidences of AUA and POD. However, in other settings, which may involve other anesthesia protocols, or specifically for POD - other perioperative causes, they may not. Attention-related processes measured by easy-to-use real-time electrophysiological markers are becoming feasible, also under anesthesia, and might be applicable for more comprehensive prevention of AUA, POD and possibly other perioperative complications.

SUMMARY

Attention-related monitoring might have a strong theoretical basis for the prevention of AUA, POD, and potentially other distressing postoperative outcomes, such as stroke and postoperative neurocognitive disorder. There seems to be already some initial supporting evidence in this regard.

Alpha anteriorization and theta posteriorization during deep sleep

Brain states (wake, sleep, general anesthesia, etc.) are profoundly associated with the spatiotemporal dynamics of brain oscillations. Previous studies showed that the EEG alpha power shifted from the occipital cortex to the frontal cortex (alpha anteriorization) after being induced into a state of general anesthesia via propofol. The sleep research literature suggests that slow waves and sleep spindles are generated locally and propagated gradually to different brain regions. Since sleep and general anesthesia are conceptualized under the same framework of consciousness, the present study examines whether alpha anteriorization similarly occurs during sleep and how the EEG power in other frequency bands changes during different sleep stages. The results from the analysis of three polysomnography datasets of 234 participants show consistent alpha anteriorization during the sleep stages N2 and N3, beta anteriorization during stage REM, and theta posteriorization during stages N2 and N3. Although it is known that the neural circuits responsible for sleep are not exactly the same for general anesthesia, the findings of alpha anteriorization in this study suggest that, at macro level, the circuits for alpha oscillations are organized in the similar cortical areas. The spatial shifts of EEG power in different frequency bands during sleep may offer meaningful neurophysiological markers for the level of consciousness.