BackgroundElectromyographic (EMG) artifact is among the most challenging contaminants in scalp EEG: its broadband spectrum overlaps directly with neural activity, and sustained muscle contractions distribute across many independent components (ICs) rather than segregating cleanly—an effect we term EMG smearing.MethodsWe propose ICA-S3M, a two-stage pipeline that first decomposes the recording via independent component analysis (ICA), then applies a switching state-space model to each retained IC. This model separates neural oscillations, modeled as damped oscillators fitted to the recording's own spectral content, from broadband artifact, modeled as an autoregressive (AR(2)) process. The method produces an explicit artifact probability at every time point and requires no training data. We evaluated ICA-S3M against a CNN from EEGdenoiseNet in three settings: simulated EEG with known ground truth; a semi-synthetic real-EEG dataset constructed from 29 participants through 11,025 expert IC–trial classifications, in which a trained rater classified every IC on a per-trial basis across interleaved rest and facial-movement segments; and a naturalistic recording of musical improvisation.ResultsIn simulation, ICA-S3M reduced RRMSE from 0.83 to 0.23 and improved SNR by 12.4 dB, versus 1.2 dB for the CNN. On the semi-synthetic dataset, ICA-S3M significantly outperformed the CNN in 38 of 42 tested scenarios spanning seven metrics, two time periods, and three scalp regions, while leaving clean segments essentially untouched. Critically, the CNN exhibited an “alpha hallucination” failure mode, injecting spurious alpha-band peaks where none exist in the ground truth; we reproduced this with an alpha-free control simulation and observed it again on real scalp recordings.DiscussionICA-S3M avoids this failure by adapting to each recording's own oscillatory structure rather than imposing learned spectral templates. The method offers a principled, interpretable, and training-free alternative for EMG artifact removal in challenging EEG recordings.

BACKGROUND:With estimated global postoperative mortality rates at 1% to 4% leading to approximately 3 million to 12 million deaths per year, an urgent need exists for reliable measures of perioperative risk. Existing approaches suffer from poor performance, place a high burden on clinicians to gather data, or do not incorporate intraoperative data. Previous work demonstrated that intraoperative anesthetics induce prefrontal electroencephalogram (EEG) oscillations in the alpha band (8 to 12 Hz) that correlate with postoperative cognitive outcomes. METHODS:The authors analyzed a retrospective cohort of 1,081 patients undergoing surgery with general anesthesia at Massachusetts General Hospital (Boston, Massachusetts) with intraoperative EEG recordings. The association between EEG alpha power and adverse outcomes was characterized using statistical models that were fitted on propensity weighted data. The primary outcome was postoperative mortality, measured from date of surgery to date of death or last follow-up. Secondary outcomes included mortality within prespecified time windows (30 days, 90 days, 180 days, and 1 yr), hospital and postanesthesia care unit lengths of stay, discharge to long-term care, and 30-day hospital readmission. RESULTS:Alpha power was associated with mortality risk (hazard ratio, 0.92; 95% CI, 0.85 to 0.99; P = 0.039). Within specified time windows, alpha power was associated with 30-day mortality (odds ratio, 0.81; 95% CI, 0.66 to 0.95; P = 0.010), 90-day mortality (odds ratio, 0.68; 95% CI, 0.55 to 0.79; P < 0.001), 180-day mortality (odds ratio, 0.75; 95% CI, 0.66 to 0.83; P < 0.001), and 1-yr mortality (odds ratio, 0.85; 95% CI, 0.79 to 0.91; P < 0.001). Additionally, alpha power was associated with discharge to long-term care (odds ratio, 0.91; 95% CI, 0.86 to 0.96; P < 0.001). We did not find significant associations among alpha power and 30-day readmission and hospital or postanesthesia care unit lengths of stay. CONCLUSIONS:Intraoperative EEG alpha power is independently associated with postoperative mortality and adverse outcomes, suggesting it could represent a broad measure of postoperative physical resilience and provide clinicians with a low-burden, personalized measure of postoperative risk.

EEG data is used in a variety of disciplines for analyzing brain activity. One major difficulty with EEG data collection is the presence of artifacts due to movement, muscular contractions, or electrical stimulation. Previous methods used to remove these artifacts are often agnostic to the underlying electrophysiological processes that generate the artifacts. Our method uses a state space model framework to model and remove the artifacts, and is capable of single-channel inference.Clinical relevance— This method provides a model-based framework to capture and remove artifacts from EEG in real time, further enabling EEG applications where patient movement or electrical stimulation is commonplace.

BACKGROUND:Peripheral nerve blocks have become popular in orthopaedic surgeries to improve acute postoperative pain. However, studies are mixed on their effectiveness in decreasing postoperative opioid consumption. A more comprehensive analysis is necessary to understand if peripheral nerve blocks reduce postoperative opioid exposure and risk for opioid dependence. METHODS:This retrospective cohort study evaluated electronic health record data for adults undergoing orthopaedic surgery with general anaesthesia from 2016 to 2020 at the Massachusetts General Hospital. Linear models were fitted on propensity-weighted data to characterise the association between single injection peripheral nerve blocks and clinical outcomes. Our primary outcomes were maximum pain score and cumulative opioid dose, quantified in morphine milligram equivalents, administered in the PACU. Post-discharge outcomes associated with pain and opioid consumption were also evaluated. RESULTS:Among 22 956 patients, peripheral nerve block administration was associated with lower maximum pain scores and lower probability of opioid administration in the PACU. However, it was associated with higher maximum pain scores and a 22.7% increase in opioid consumption during the hospital stay. Peripheral nerve blocks were associated with an increase in opioid prescriptions at 30 days after discharge, but no increase at 90 or 180 days, and with decreased chronic pain diagnoses 1 yr after operation. CONCLUSIONS:Although single injection peripheral nerve blocks were effective in reducing immediate postoperative pain and opioid consumption, they were associated with greater opioid consumption that could increase the risk for opioid dependence. Standardised protocols to mitigate the risk for rebound pain could help minimise postoperative opioid exposure.

BACKGROUND:Spinal anesthesia is an alternative to general anesthesia in infants. A caveat of spinal anesthesia in infants is short block duration. Clonidine is a common adjunct that prolongs spinal anesthesia. The mechanisms by which clonidine prolongs spinal anesthesia are unknown. Infants under spinal anesthesia appear in a sleep-like state. We hypothesized that infants receiving bupivacaine spinal anesthesia with clonidine may exhibit more sleep spindles on the electroencephalogram (EEG). METHODS:We obtained intraoperative frontal EEG recordings in 73 infants under spinal anesthesia. We compared EEG spectral features of bupivacaine and bupivacaine + clonidine spinal anesthesia using nonparametric multitaper spectral analysis. A recently developed switching state-space modeling approach was then applied to extract and compare spindle features in bupivacaine versus bupivacaine + clonidine spinal anesthesia. We then applied the same model to compare younger versus older infants. RESULTS:There was no difference in the power spectra and sleep spindle detection probability between bupivacaine and bupivacaine + clonidine spinal anesthesia ( P = .51). We found age-related EEG changes in both bupivacaine and bupivacaine + clonidine spinal anesthesia independent of clonidine. Increasing age was associated with decreased spectral power from 0 to 0.6 Hz (median difference -2.9 dB, 95% CI [-5.3, -0.5,]) and increased power from 2 to 15 Hz (median difference 3.4 dB, 95% CI [1.5, 5.2]). Increasing age was also associated with increased spindle strength (R 2 = 0.323, F(2,67) = 15.98, P < 0.001). These EEG findings mirror those found in infants under physiologic sleep. CONCLUSIONS:Our findings suggest that low-dose clonidine does not impact sleep spindle properties in the EEG of infants under spinal anesthesia. The EEG of infants under spinal anesthesia demonstrate age-related changes that mirror quiet physiologic sleep. In addition, the presence of intrathecal clonidine has no effect on the age-related changes in the EEG pattern. Clonidine is an adjunct for spinal anesthesia in infants that appears to prolong anesthetic duration without affecting their EEG patterns of physiologic sleep.

One critical challenge encountered in non-invasive electrophysiological studies is inferring the spatiotemporal dynamics of cortical sources from measured electro/magneto-encephalography (E/MEG) recordings. In this paper, we describe a dynamic inverse solution utilizing state-space oscillator models to enable simultaneous source localization of multiple oscillations under distributed source modeling. Our solution framework incorporates symmetric spatial dependencies with sparse priors and performs efficient state inference and parameter learning via the expectation-maximization algorithm. We show promising simulation results of successful source localization of oscillatory activity that improve over past non-oscillating solutions. We also demonstrate the added ability to simultaneously localize both alpha and slow rhythms from the same recording in both simulated and real resting-state EEG data. This method adds to a growing body of time-domain modeling methods to characterize neural oscillations from electrophysiological data and paves the way for analyzing cortical neural activity with improved precision beyond traditional static inverse solutions.

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.

Select Drug Category Opiates/OpioidsTopic Tolerance/DependenceAbstract Detail Clinical - EpidemiologyAbstract Category Original Research Aim Characterize the relationship between intraoperative opioid administration and postoperative pain and opioid requirements. Methods We conducted a retrospective cohort study of 61,250 adult patients who received non-cardiac surgery under general anesthesia at a quaternary care center between 2016 and 2020. The exposure variable was intraoperative fentanyl and intraoperative hydromorphone average effect site concentration. The primary study outcomes were maximal pain score during post-anesthesia care unit (PACU) stay and cumulative opioids administered in the PACU in morphine milligram equivalents (MME). Secondary outcomes included frequency of uncontrolled pain at 24hours, new instances of chronic pain diagnosis, total opioid use at 24hours and in-hospital, opioid prescriptions at 30, 90, and 180 postoperative days, frequency of new persistent opioid use, maximal pain score in the first 24hours and in-hospital, and incidence of opioid related complications in PACU. We used multivariate propensity weighting to control for confounding and estimated the counterfactual difference in outcomes after administration of an additional 100mcg fentanyl or 500mcg hydromorphone intraoperatively. Results Increased intraoperative fentanyl and intraoperative hydromorphone were both associated with reduced maximum pain scores in the PACU. Both exposures were also associated with a reduced probability and reduced total dosage of opioid administration in the PACU. We found that increased fentanyl administration in particular was associated with lower frequency of uncontrolled pain, decreased chronic pain at 3-months, fewer opioid prescriptions at 30-, 90-, and 180-days, and decreased persistent opioid use, without significant increases in side effects. Conclusions Our results show that intraoperative opioid administration is significantly associated with short- and long-term effects on post-operative pain and opioid outcomes. Contrary to prevailing trends, reduced opioid administration during surgery may have the unintended consequence of increasing postoperative pain and opioid consumption. Our analysis suggests that significant improvements in long-term outcomes might be achieved by optimizing opioid administration during surgery.

BackgroundPreoperative knowledge of surgical risks can improve perioperative care and patient outcomes. However, assessments requiring clinician examination of patients or manual chart review can be too burdensome for routine use.MethodsWe conducted a multicentre retrospective study of 243 479 adult noncardiac surgical patients at four hospitals within the Mass General Brigham (MGB) system in the USA. We developed a machine learning method using routinely collected coding and patient characteristics data from the electronic health record which predicts 30-day mortality, 30-day readmission, discharge to long-term care, and hospital length of stay.ResultsOur method, the Flexible Surgical Set Embedding (FLEX) score, achieved state-of-the-art performance to identify comorbidities that significantly contribute to the risk of each adverse outcome. The contributions of comorbidities are weighted based on patient-specific context, yielding personalised risk predictions. Understanding the significant drivers of risk of adverse outcomes for each patient can inform clinicians of potential targets for intervention.ConclusionsFLEX utilises information from a wider range of medical diagnostic and procedural codes than previously possible and can adapt to different coding practices to accurately predict adverse postoperative outcomes.

Kreuzer, Matthias PhD; García, Paul S. MD, PhD; Gutierrez, Rodrigo MD, PhD; Purdon, Patrick L. PhD Author Information

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.

AbstractThe development of neural circuits has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, periodic EEG power features and aperiodic components were examined from longitudinal EEGs collected from 592 healthy 2–44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Developmental changes in periodic peaks include (1) the presence and then absence of a 9-10 Hz alpha peak between 2-6 months, (2) nonlinear changes in high beta peaks (20-30 Hz) between 4-18 months, and (3) the emergence of a low beta peak (12-20 Hz) in some infants after six months of age. We hypothesized that the emergence of the low beta peak may reflect maturation of thalamocortical network development. Infant anesthesia studies observe that GABA-modulating anesthetics do not induce thalamocortical mediated frontal alpha coherence until 10-12 months of age. Using a small cohort of infants (n = 23) with EEG before and during GABA-modulating anesthesia, we provide preliminary evidence that infants with a low beta peak have higher anesthesia-induced alpha coherence compared to those without a low beta peak.

The development of neural circuits over the first years of life has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, aperiodic and periodic EEG power features were examined from longitudinal EEGs collected from 592 healthy 2–44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Consistent with the transient developmental progression of thalamocortical circuitry, we observe the presence and then absence of periodic alpha and high beta peaks across the three-year period, as well as the emergence of a low beta peak (12-20Hz) after six months of age. We present preliminary evidence that the emergence of the low beta peak is associated with thalamocortical connectivity sufficient for anesthesia-induced alpha coherence. Together, these findings suggest that early age-dependent changes in alpha and beta periodic peaks may reflect the state of thalamocortical network development.

Electroencephalogram signatures associated with anaesthetic-induced loss of consciousness have been widely described in adult populations. A recent study helps verify our understanding of brain dynamics induced by anaesthetics in a paediatric population by describing a specific pattern in terms of an interaction of the phase of delta oscillations and the amplitude of alpha oscillations. This feature has potential translational implications for optimising future monitoring technologies.

IMPORTANCE Opioids administered to treat postsurgical pain are a major contributor to the opioid crisis, leading to chronic use in a considerable proportion of patients. Initiatives promoting opioid-free or opioid-sparing modalities of perioperative pain management have led to reduced opioid administration in the operating room, but this reduction could have unforeseen detrimental effects in terms of postoperative pain outcomes, as the relationship between intraoperative opioid usage and later opioid requirements is not well understood. OBJECTIVE To characterize the association between intraoperative opioid usage and postoperative pain and opioid requirements. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study evaluated electronic health record data from a quaternary care academic medical center (Massachusetts General Hospital) for adult patients who underwent noncardiac surgery with general anesthesia from April 2016 to March 2020. Patients who underwent cesarean surgery, received regional anesthesia, received opioids other than fentanyl or hydromorphone, were admitted to the intensive care unit, or who died intraoperatively were excluded. Statistical models were fitted on the propensity weighted data set to characterize the effect of intraoperative opioid exposures on primary and secondary outcomes. Data were analyzed from December 2021 to October 2022. EXPOSURES Intraoperative fentanyl and intraoperative hydromorphone average effect site concentration estimated using pharmacokinetic/pharmacodynamic models. MAIN OUTCOMES AND MEASURES The primary study outcomes were the maximal pain score during the postanesthesia care unit (PACU) stay and the cumulative opioid dose, quantified in morphine milligram equivalents (MME), administered during the PACU stay. Medium- and long-term outcomes associated with pain and opioid dependence were also evaluated. RESULTS The study cohort included a total of 61 249 individuals undergoing surgery (mean [SD] age, 55.44 [17.08] years; 32 778 [53.5%] female). Increased intraoperative fentanyl and intraoperative hydromorphone were both associated with reduced maximum pain scores in the PACU. Both exposures were also associated with a reduced probability and reduced total dosage of opioid administration in the PACU. In particular, increased fentanyl administration was associated with lower frequency of uncontrolled pain; a decrease in new chronic pain diagnoses reported at 3 months; fewer opioid prescriptions at 30, 90, and 180 days; and decreased new persistent opioid use, without significant increases in adverse effects. CONCLUSIONS AND RELEVANCE Contrary to prevailing trends, reduced opioid administration during surgery may have the unintended outcome of increasing postoperative pain and opioid consumption. Conversely, improvements in long-term outcomes might be achieved by optimizing opioid administration during surgery.

What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.

Editorial| November 2023 Anesthesia-induced Brain Oscillations and Vulnerability to Postoperative Neurocognitive Disorders This article has an Audio Podcast Rodrigo Gutiérrez, M.D., Ph.D.; Rodrigo Gutiérrez, M.D., Ph.D. 1Department of Anesthesia and Perioperative Medicine, Faculty of Medicine, University of Chile, Santiago, Chile. Search for other works by this author on: This Site PubMed Google Scholar Patrick L. Purdon, Ph.D. Patrick L. Purdon, Ph.D. 2Department of Anesthesiology, Perioperative and Pain Medicine, Stanford Medicine, Palo Alto, California. https://orcid.org/0000-0003-0080-3340 Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information This editorial accompanies the article on p. 568. Accepted for publication July 12, 2023. Address correspondence to Dr. Purdon: Anesthesiology November 2023, Vol. 139, 557–559. https://doi.org/10.1097/ALN.0000000000004704 Connected Content Article: Electroencephalographic Biomarkers, Cerebral Oximetry, and Postoperative Cognitive Function in Adult Noncardiac Surgical Patients: A Prospective Cohort Study See also Editorial-Article Electroencephalographic Biomarkers, Cerebral Oximetry, and Postoperative Cognitive Function in Adult Noncardiac Surgical Patients: A Prospective Cohort Study Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Search Site Citation Rodrigo Gutiérrez, Patrick L. Purdon; Anesthesia-induced Brain Oscillations and Vulnerability to Postoperative Neurocognitive Disorders. Anesthesiology 2023; 139:557–559 doi: https://doi.org/10.1097/ALN.0000000000004704 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsAnesthesiology Search Advanced Search Topics: brain, neurocognitive disorders In recent years, several studies established the association among the triad of baseline cognitive performance, intraoperative (anesthetic-induced) oscillation patterns, and postoperative neurocognitive disorders outcomes such as postoperative delirium.1–7 The various studies seem to agree in the observation that anesthetic-induced neural oscillation profiles are the cornerstone linking these associations. It is less clear whether different brain signals at baseline, immediately before the surgery, might be also informative about brain vulnerability and the individual risk to develop postoperative neurocognitive disorders. In the current issue of Anesthesiology, Vlisides et al.8 provide us with information on the potential role of preoperative brain signals. They conducted a single-center observational study including 64 adult patients undergoing surgery under general anesthesia. They measured electroencephalogram (EEG) spectral power at different frequency bands, EEG alpha connectivity, and cerebral oximetry. None of those parameters at baseline were associated with postoperative cognitive function or postoperative delirium. On the... You do not currently have access to this content.