EEG response of dexmedetomidine during drug induced sleep endoscopy.

Introduction: Dexmedetomidine is one of the anesthetics of choice for drug induced sleep endoscopy (DISE), with advantages including limited respiratory depression, analgesia, and decreased incidence of emergence delirium. However, challenges with determining sedation levels and prolonged recovery have limited its usage. An improved understanding of the effect of dexmedetomidine on the level of sedation and the corresponding electroencephalographic (EEG) changes could help overcome these barriers. Methods: Fifty-one patients received dexmedetomidine sedation with Richmond Agitation-Sedation Scale (RASS) score assessment and continuous EEG monitoring via SedLine for DISE. We constructed a pharmacokinetic model to determine continuous dexmedetomidine blood concentration. From the SedLine, we extracted the patient state index (PSI), and from the EEG we calculated the spectral edge frequency 95% (SEF95) and the correlation dimension (CD), a type of fractal dimension used to assess the complexity of a system. These metrics were subsequently compared against one another and with the dexmedetomidine concentration. Results: Our pharmacokinetic model yielded a two-compartment model with volumes of 51.8 L and 106.2 L, with clearances of 69.5 and 168.9 L/h, respectively, and a time to effect of 9 min, similar to prior studies. Based on this model, decreasing RASS score, SEF95, CD, and PSI were all significantly associated with increasing dexmedetomidine concentration (p < 0.001, p = 0.006, p < 0.001 respectively). The CD, SEF95, and PSI better captured the effects of increasing dexmedetomidine concentration as compared to the RASS score. Simulating dexmedetomidine concentration based on titration to target levels derived from CD and PSI confirmed commonly used dexmedetomidine infusion dosages. Conclusion: Dexmedetomidine use for DISE confirmed previous pharmacokinetic models seen with dexmedetomidine. Complex EEG metrics such as PSI and CD, as compared to RASS score and SEF95, better captured changes in brain state from dexmedetomidine and have potential to improve the monitoring of dexmedetomidine sedation.

Evaluation of patient state index, bispectral index, and entropy during drug induced sleep endoscopy with dexmedetomidine.

Multiple electroencephalographic (EEG) monitors and their associated EEG markers have been developed to aid in assessing the level of sedation in the operating room. While many studies have assessed the response of these markers to propofol sedation and anesthetic gases, few studies have compared these markers when using dexmedetomidine, an alpha-2 agonist. Fifty-one patients underwent drug induced sleep endoscopy with dexmedetomidine sedation. Continuous EEG was captured using SedLine (Masimo, Inc), and a playback system was used to extract the bispectral index (BIS) (Medtronic Inc), the patient state index (PSI) (Masimo, Inc), the state and response Entropy (GE Healthcare), and calculate the spectral edge frequency 95% (SEF95). Richmond Agitation-Sedation Scale (RASS) scores were assessed continually throughout the procedure and in recovery. We assessed the correlation between EEG markers and constructed ordinal logistic regression models to predict the RASS score and compare EEG markers. All three commercial EEG metrics were significantly associated with the RASS score (p < 0.001 for all metrics) whereas SEF95 alone was insufficient at characterizing dexmedetomidine sedation. PSI and Entropy achieved higher accuracy at predicing deeper levels of sedation as compared to BIS (PSI: 58.3%, Entropy: 58.3%, BIS: 44.4%). Lightening secondary to RASS score assessment is significantly captured by all three commercial EEG metrics (p < 0.001). Commercial EEG monitors can capture changes in the brain state associated with the RASS score during dexmedetomidine sedation. PSI and Entropy were highly correlated and may be better suited for assessing deeper levels of sedation.

A call for comparing theories of consciousness and data sharing.

Merker, Williford, and Rudrauf make several arguments against the integrated information theory of consciousness; whereas some have merit, their conclusion that the theory should be discarded is premature. Coming years promise advances in the empirical study of consciousness, and only after theories are independently tested with shared data can they be ruled in or out. We propose future research directions.

Offline comparison of processed electroencephalogram monitors for anaesthetic-induced electroencephalogram changes in older adults.

BACKGROUND: Several devices record and interpret patient brain activity via electroencephalogram (EEG) to aid physician assessment of anaesthetic effect. Few studies have compared EEG monitors on data from the same patient. Here, we describe a set-up to simultaneously compare the performance of three processed EEG monitors using pre-recorded EEG signals from older surgical patients. METHODS: A playback system was designed to replay EEG signals into three different commercially available EEG monitors. We could then simultaneously calculate indices from the SedLine® Root (Masimo Inc., Irvine, CA, USA; patient state index [PSI]), bilateral BIS VISTA™ (Medtronic Inc., Minneapolis, MN, USA; bispectral index [BIS]), and Datex Ohmeda S/5 monitor with the Entropy™ Module (GE Healthcare, Chicago, IL, USA; E-entropy index [Entropy]). We tested the ability of each system to distinguish activity before anaesthesia administration (pre-med) and before/after loss of responsiveness (LOR), and to detect suppression incidences in EEG recorded from older surgical patients receiving beta-adrenergic blockers. We show examples of processed EEG monitor output tested on 29 EEG recordings from older surgical patients. RESULTS: All monitors showed significantly different indices and high effect sizes between comparisons pre-med to after LOR and before/after LOR. Both PSI and BIS showed the highest percentage of deeply anaesthetised indices during periods with suppression ratios (SRs) > 25%. We observed significant negative correlations between percentage of suppression and indices for all monitors (at SR >5%). CONCLUSIONS: All monitors distinguished EEG changes occurring before anaesthesia administration and during LOR. The PSI and BIS best detected suppressed periods. Our results suggest that the PSI and BIS monitors might be preferable for older patients with risk factors for intraoperative awareness or increased sensitivity to anaesthesia.

Nonlinear dynamics captures brain states at different levels of consciousness in patients anesthetized with propofol.

The information processing capability of the brain decreases during unconscious states. Capturing this decrease during anesthesia-induced unconsciousness has been attempted using standard spectral analyses as these correlate relatively well with breakdowns in corticothalamic networks. Much of this work has involved the use of propofol to perturb brain activity, as it is one of the most widely used anesthetics for routine surgical anesthesia. Propofol administration alone produces EEG spectral characteristics similar to most hypnotics; however, inter-individual and drug variation render spectral measures inconsistent. Complexity measures of EEG signals could offer better measures to distinguish brain states, because brain activity exhibits nonlinear behavior at several scales during transitions of consciousness. We tested the potential of complexity analyses from nonlinear dynamics to identify loss and recovery of consciousness at clinically relevant timepoints. Patients undergoing propofol general anesthesia for various surgical procedures were identified as having changes in states of consciousness by the loss and recovery of response to verbal stimuli after induction and upon cessation of anesthesia, respectively. We demonstrate that nonlinear dynamics analyses showed more significant differences between consciousness states than spectral measures. Notably, attractors in conscious and anesthesia-induced unconscious states exhibited significantly different shapes. These shapes have implications for network connectivity, information processing, and the total number of states available to the brain at these different levels. They also reflect some of our general understanding of the network effects of consciousness in a way that spectral measures cannot. Thus, complexity measures could provide a universal means for reliably capturing depth of consciousness based on EEG changes at the beginning and end of anesthesia administration.

Do Complexity Measures of Frontal EEG Distinguish Loss of Consciousness in Geriatric Patients Under Anesthesia?

While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration-differences which future work may reveal to improve geriatric patient monitoring.

Remifentanil and Nitrous Oxide Anesthesia Produces a Unique Pattern of EEG Activity During Loss and Recovery of Response.

Nitrous oxide (N2O) and remifentanil (remi) are used along with other anesthetic and adjuvant agents for routine surgical anesthesia, yet the electroencephalogram (EEG) changes produced by this combination are poorly described. N2O administered alone produces EEG spectral characteristics that are distinct from most hypnotics. Furthermore, EEG frequency-derived trends before and after clinically relevant time points vary depending on N2O concentration. Remifentanil typically increases low frequency and decreases high frequency activity in the EEG, but how it influences N2O's EEG effect is not known. Previous attempts to characterize EEG signals of patients anesthetized with N2O using frequency-derived measures have shown conflicts and inconsistencies. Thus, in addition to determining the spectral characteristics of this unique combination, we also test whether a newly proposed characterization of time-delayed embeddings of the EEG signal tracks loss and recovery of consciousness significantly at clinically relevant time points. We retrospectively investigated the effects of remi and N2O on EEG signals recorded from 32 surgical patients receiving anesthesia for elective abdominal surgeries. Remifentanil and N2O (66%) were co-administered during the procedures. Patients were tested for loss and recovery of response (ROR) to verbal stimuli after induction and upon cessation of anesthesia, respectively. We found that the addition of remifentanil to N2O anesthesia improves the ability of traditional frequency-derived measures, including the Bispectral Index (BIS), to discriminate between loss and ROR. Finally, we found that a novel analysis of EEG using nonlinear dynamics showed more significant differences between states than most spectral measures.

Calculations of consciousness: electroencephalography analyses to determine anesthetic depth.

PURPOSE OF REVIEW: Electroencephalography (EEG) was introduced into anesthesia practice in the 1990s as a tool to titrate anesthetic depth. However, limitations in current analysis techniques have called into question whether these techniques improve standard of care, or instead call for improved, more ubiquitously applicable measures to assess anesthetic transitions and depth. This review highlights emerging analytical approaches and techniques from neuroscience research that have the potential to better capture anesthetic transitions to provide better measurements of anesthetic depth. RECENT FINDINGS: Since the introduction of electroencephalography, neuroscientists, engineers, mathematicians, and clinicians have all been developing new ways of analyzing continuous electrical signals. Collaborations between these fields have proliferated several analytical techniques that demonstrate how anesthetics affect brain dynamics and conscious transitions. Here, we review techniques in the following categories: network science, integration and information, nonlinear dynamics, and artificial intelligence. SUMMARY: Up-and-coming techniques have the potential to better clinically define and characterize altered consciousness time points. Such new techniques used alongside traditional measures have the potential to improve depth of anesthesia measurements and enhance an understanding of how the brain is affected by anesthetic agents. However, new measures will be needed to be tested for robustness in real-world environments and on diverse experimental protocols.

Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state.

Cortical activity changes continuously during the course of the day. At a global scale, population activity varies between the 'synchronized' state during sleep and 'desynchronized' state during waking. However, whether local fluctuations in population synchrony during wakefulness modulate the accuracy of sensory encoding and behavioral performance is poorly understood. Here, we show that populations of cells in monkey visual cortex exhibit rapid fluctuations in synchrony ranging from desynchronized responses, indicative of high alertness, to highly synchronized responses. These fluctuations are local and control the trial variability in population coding accuracy and behavioral performance in a discrimination task. When local population activity is desynchronized, the correlated variability between neurons is reduced, and network and behavioral performance are enhanced. These findings demonstrate that the structure of variability in local cortical populations is not noise but rather controls how sensory information is optimally integrated with ongoing processes to guide network coding and behavior.

Image sequence reactivation in awake V4 networks.

In the absence of sensory input, neuronal networks are far from being silent. Whether spontaneous changes in ongoing activity reflect previous sensory experience or stochastic fluctuations in brain activity is not well understood. Here we describe reactivation of stimulus-evoked activity in awake visual cortical networks. We found that continuous exposure to randomly flashed image sequences induces reactivation in macaque V4 cortical networks in the absence of visual stimulation. This reactivation of previously evoked activity is stimulus-specific, occurs only in the same temporal order as the original response, and strengthens with increased stimulus exposures. Importantly, cells exhibiting significant reactivation carry more information about the stimulus than cells that do not reactivate. These results demonstrate a surprising degree of experience-dependent plasticity in visual cortical networks as a result of repeated exposure to unattended information. We suggest that awake reactivation in visual cortex may underlie perceptual learning by passive stimulus exposure.