Relation of EEG spectrum progression to loss of responsiveness during induction of anesthesia with propofol

Electrophysiological Activity and Brain Network During Recovery of Propofol Anesthesia: A Stereoelectroencephalography-Based Analysis in Patients With Intractable Epilepsy—An Exploratory Research

Background: The oscillations and interactions between different brain areas during recovery of consciousness (ROC) from anesthesia in humans are poorly understood. Reliable stereoelectroencephalography (SEEG) signatures for transitions between unconsciousness and consciousness under anesthesia have not yet been fully identified.

Objective: This study was designed to observe the change of electrophysiological activity during ROC and construct a ROC network based on SEEG data to describe the network property of cortical and deep areas during ROC from propofol-induced anesthetic epileptic patients.

Methods: We analyzed SEEG data recorded from sixteen right-handed epileptic patients during ROC from propofol anesthesia from March 1, 2019, to December 31, 2019. Power spectrum density (PSD), correlation, and coherence were used to describe different brain areas' electrophysiological activity. The clustering coefficient, characteristic path length, modularity, network efficiency, degrees, and betweenness centrality were used to describe the network changes during ROC from propofol anesthesia. Statistical analysis was performed using MATLAB 2016b. The power spectral data from different contacts were analyzed using a one-way analysis of variance (ANOVA) test with Tukey's post-hoc correction. One sample t-test was used for the analysis of network property. Kolmogorov-Smirnov test was used to judge data distribution. Non-normal distribution was analyzed using the signed rank-sum test.

Result: From the data of these 16 patients, 10 cortical, and 22 deep positions were observed. In this network, we observed that bilateral occipital areas are essential parts that have strong links with many regions. The recovery process is different in the bilateral cerebral cortex. Stage B (propofol 3.0-2.5 μg/ml) and E (propofol 1.5 μg/ml-ROC) play important roles during ROC exhibiting significant changes. The clustering coefficient gradually decreases with the recovery from anesthesia, and the changes mainly come from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient are similar. Deep areas tend to form functional modules. The left occipital lobe, the left temporal lobe, bilateral amygdala are essential nodes in the network. Some specific cortical regions (i.e., left angular gyrus, right angular gyrus, right temporal lobe, left temporal lobe, and right angular gyrus) and deep regions (i.e., right amygdala, left cingulate gyrus, right insular lobe, right amygdala) have more significant constraints on other regions.

Conclusion: We verified that the bilateral cortex's recovery process is the opposite, which is not found in the deep regions. Significant PSD changes were observed in many areas at the beginning of stop infusion and near recovery. Our study found that during the ROC process, the modularity and clustering coefficient of the deep area network is significantly improved. However, the changes of the bilateral cerebral cortex were different. Power spectrum analysis shows that low-frequency EEG in anesthesia recovery accounts for a large proportion. The changes of the bilateral brain in the process of anesthesia recovery are different. The clustering coefficient gradually decreased with the recovery from anesthesia, and the changes mainly came from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient were similar. During ROC, the left occipital lobe, the left temporal lobe, bilateral amygdala were essential nodes in the network. The findings of the current study suggest SEEG as an effective tool for providing direct evidence of the anesthesia recovery mechanism.

Handgrip dynamometry for continuous assessment of volitional control during induction of anesthesia: a prospective observational study

PURPOSE

Response to commands is the gold standard to assess the level of consciousness during anesthesia induction but it only provides an intermittent, binary measure with low temporal resolution. To overcome these limitations, we combined the object hold method with handgrip dynamometry to continuously record the force applied to hold a dynamometer as a surrogate measure of the level of consciousness during induction of anesthesia.

METHODS

Fourteen patients scheduled for elective lumbar surgery and 14 age-matched non-anesthetized controls were enrolled. The subjects held the dynamometer with their dominant hand for as long as possible (patients) or until told to stop (controls). After a one-minute baseline, propofol was infused (1.0 mg·kg^-1·min^-1) to the patient group until the subject dropped the dynamometer, which defined the object hold time. Three additional patients were also asked intermittently to squeeze the dynamometer during the propofol infusion to determine any retained ability to exert a strong grip despite any grip changes during induction.

RESULTS

The mean (standard deviation) object hold time was 115 (22) seconds after the start of the propofol infusion. There was a progressive significant linear decrease (R² = 0.98; P < 0.001) in dynamometry-determined handgrip force starting approximately 74 seconds before object drop. Age was inversely related to the object hold time (R² = 0.47, P < 0.01). The three additional propofol induction patients had strong intermittent grip strength despite progressive decreases in the hold force. Of the 17 patients who completed the object hold task (14 with the standard protocol and three with intermittent squeeze requests), 16 (94%; 95% confidence interval, 76 to 99%) did not respond to verbal commands after dropping the dynamometer.

CONCLUSION

Handgrip dynamometry can be used to continuously track volitional control during induction of anesthesia while also reliably showing a gradual loss of consciousness. This method could be useful for studies investigating mechanisms of anesthesia.

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

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

Age-related changes in EEG power spectra in infants during sevoflurane wash-out

BACKGROUND

Few electroencephalography (EEG) data are available in anaesthetized infants. This study aimed to identify EEG characteristics that might warn of awakening (AW) from sevoflurane anaesthesia in infants.

METHODS

Twenty intubated infants [aged 39-77 weeks post-menstrual age (PMA)] were studied after surgery during sevoflurane wash-out. EEG was recorded at the end of surgery and throughout emergence. Changes in EEG time and frequency domains were described.

RESULTS

At the end of surgery, mean end-tidal sevoflurane concentration was 2.3% (range 1.5-3.5) before wash-out and reduced to 0.3% (0.1-0.6) when AW began. On AW, movement artifacts made signals difficult to interpret. Before awakening, most power was within frequencies ≤4 Hz, but trends over time were variable. Summated power in frequencies between 20 and 70 Hz was almost always <5 µV(2). During anaesthesia, there were two common power spectra: infants >52 weeks PMA had obvious summated power in the frequency range 5-20 Hz (P5-20 Hz) (mean 308, median 320, range 110-542 µV(2)), which decreased before awakening began [mean decrease 252 µV(2) (95% CI 153-351)], whereas younger infants had low P5-20 Hz throughout. P5-20 Hz during anaesthesia increased with age; power in this frequency band of ~100 µV(2) separated infants younger and older than 52 weeks PMA.

CONCLUSIONS

During sevoflurane wash-out, decreasing P5-20 Hz might warn of impending AW in infants >3 months old, but not in younger infants.

EEG spectral changes and onset of burst suppression pattern in propofol/remifentanil anesthesia

This paper studies how remifentanil, a commonly used intraoperative opioid, affects the relation of the onset of burst suppression pattern (BSP) and the spectral changes of EEG during anesthesia. The onsets of BSP were detected using both manual and the automatic method proposed from the EEGs of twenty-seven patients who had received different amount of remifentanil with the anesthetic. The spectral changes were determined by calculating the frequency progression patterns of the EEGs. The results showed that remifentanil significantly affects the relation of EEG spectral changes and the onset of BSP. The finding is important since the current EEG-based assessment of the depth of anesthesia basically relies on the analysis of the spectral features and BSP.

Time-frequency properties of electroencephalogram during induction of anesthesia

A method for detailed description of the time-frequency characteristics of electroencephalogram during induction of anesthesia is proposed. The method, based on averaging of time-normalized smoothed pseudo-Wigner-Ville distributions, is applied to data recorded from nine patients undergoing propofol anesthesia. An extensive representation of the frequency progression pattern related to the induction of anesthesia is given and the time-frequency characteristics that are consistent/not consistent between patients are determined. It is also illustrated how four different clinical end-points, generally used in the assessment of the depth of anesthesia, can be related to different phases of the frequency progression pattern. The method presented has importance in providing information about the neurophysiological phenomenon during induction of anesthesia and can therefore be used in the development of new monitoring algorithms.

EEG frequency progression during induction of anesthesia: from start of infusion to onset of burst suppression pattern

The anesthetic infusion with propofol influences EEG activity rather smoothly by changing the amplitude activity in different frequency bands. This results in a frequency progression pattern (FPP) which can be related to the depth of anesthesia. An iterative algorithm is proposed for the estimation of the shape of this pattern. The presented method is applied to the data recorded from the start of the propofol anesthetic infusion to the onset of the burst suppression pattern (BSP) with nine patients. The results reveal the underlying FPP and how the onset of the BSP is related to it. The proposed method offers potential for the development of automatic assessment systems for the depth of anesthesia.

Forecasting the Unresponsiveness to Verbal Command on the Basis of EEG Frequency Progression During Anesthetic Induction With Propofol

The objective of this study is to model the association between the electroencephalogram (EEG) spectral features and the novel r scale representing the sedative effects of the propofol anesthetic drug. On the basis of the r scale, the unresponsiveness to the verbal command (LVC) is forecasted. EEG recordings are taken from a 16-patient study population undergoing propofol anesthetic induction. EEG was filtered into consecutive 4-Hz passbands up to 28 Hz. Of these time-series, the amplitude envelopes were extracted and used as input features to the first and the second-order polynomial multiple linear regression models. The values r epsilon [0.4, 1] were predicted with the R2 value of 0.775 with a cross validation. The LVC times were forecasted with the median error of 5%-7% or equivalently 10-13 s. In contrast, using the median of the measured LVC times of the training population as a forecast, the corresponding error was 12% or 26 s. The results suggest an acceptable correlation between the r scale and the EEG spectrum in the studied range. Moreover, the r values of an individual can be predicted using a population model. The suggested framework enables forecasting the LVC, which may open new possibilities for steering the drug administration.