Corrigendum to 'Role of electroencephalogram oscillations and the spectrogram in monitoring anaesthesia' [BJA Education 20 (2020) 166-172].

[This corrects the article DOI: 10.1016/j.bjae.2020.01.004.].

Electroencephalographic markers of brain development during sevoflurane anaesthesia in children up to 3 years old.

BACKGROUND: General anaesthetics generate spatially defined brain oscillations in the EEG that relate fundamentally to neural-circuit architecture. Few studies detailing the neural-circuit activity of general anaesthesia in children have been described. The study aim was to identify age-related changes in EEG characteristics that mirror different stages of early human brain development during sevoflurane anaesthesia. METHODS: Multichannel EEG recordings were performed in 91 children aged 0-3 yr undergoing elective surgery. We mapped spatial power and coherence over the frontal, parietal, temporal, and occipital cortices during maintenance anaesthesia. RESULTS: During sevoflurane exposure: (i) slow-delta (0.1-4 Hz) oscillations were present in all ages, (ii) theta (4-8 Hz) and alpha (8-12 Hz) oscillations emerge by ∼4 months, (iii) alpha oscillations increased in power from 4 to 10 months, (iv) frontal alpha-oscillation predominance emerged at ∼6 months, (v) frontal slow oscillations were coherent from birth until 6 months, and (vi) frontal alpha oscillations became coherent ∼10 months and persisted in older ages. CONCLUSIONS: Key developmental milestones in the maturation of the thalamo-cortical circuitry likely generate changes in EEG patterns in infants undergoing sevoflurane general anaesthesia. Characterisation of anaesthesia-induced EEG oscillations in children demonstrates the importance of developing age-dependent strategies to monitor properly the brain states of children receiving general anaesthesia. These data have the potential to guide future studies investigating neurodevelopmental pathologies involving altered excitatory-inhibitory balance, such as epilepsy or Rett syndrome.

On compression and damage evolution in two thermoplastics.

The well-known Taylor cylinder impact test, which follows the impact of a flat-ended cylindrical rod onto a rigid stationary anvil, is conducted over a range of impact speeds for two polymers, polytetrafluoroethylene (PTFE) and polyetheretherketone (PEEK). In previous work, experiments and a model were developed to capture the deformation behaviour of the cylinder after impact. These works showed a region in which spatial and temporal variation of both longitudinal and radial deformation provided evidence of changes in phase within the material. In this further series of experiments, this region is imaged in a range of impacted targets at the Diamond synchrotron. Further techniques were fielded to resolve compressed regions within the recovered polymer cylinders that showed a fracture zone in the impact region. The combination of macroscopic high-speed photography and three-dimensional X-ray imaging has identified the development of failure with these polymers and shown that there is no abrupt transition in behaviours but rather a continuous range of responses to competing operating mechanisms. The behaviours noted in PEEK in these polymers show critical gaps in understanding of polymer high strain-rate response.

The Ageing Brain: Age-dependent changes in the electroencephalogram during propofol and sevoflurane general anaesthesia.

BACKGROUND: Anaesthetic drugs act at sites within the brain that undergo profound changes during typical ageing. We postulated that anaesthesia-induced brain dynamics observed in the EEG change with age. METHODS: We analysed the EEG in 155 patients aged 18-90 yr who received propofol (n=60) or sevoflurane (n=95) as the primary anaesthetic. The EEG spectrum and coherence were estimated throughout a 2 min period of stable anaesthetic maintenance. Age-related effects were characterized by analysing power and coherence as a function of age using linear regression and by comparing the power spectrum and coherence in young (18- to 38-yr-old) and elderly (70- to 90-yr-old) patients. RESULTS: Power across all frequency bands decreased significantly with age for both propofol and sevoflurane; elderly patients showed EEG oscillations ∼2- to 3-fold smaller in amplitude than younger adults. The qualitative form of the EEG appeared similar regardless of age, showing prominent alpha (8-12 Hz) and slow (0.1-1 Hz) oscillations. However, alpha band dynamics showed specific age-related changes. In elderly compared with young patients, alpha power decreased more than slow power, and alpha coherence and peak frequency were significantly lower. Older patients were more likely to experience burst suppression. CONCLUSIONS: These profound age-related changes in the EEG are consistent with known neurobiological and neuroanatomical changes that occur during typical ageing. Commercial EEG-based depth-of-anaesthesia indices do not account for age and are therefore likely to be inaccurate in elderly patients. In contrast, monitoring the unprocessed EEG and its spectrogram can account for age and individual patient characteristics.

Ageing delays emergence from general anaesthesia in rats by increasing anaesthetic sensitivity in the brain.

BACKGROUND: Little is known about ageing-related changes in the brain that affect emergence from general anaesthesia. We used young adult and aged Fischer 344 rats to test the hypothesis that ageing delays emergence from general anaesthesia by increasing anaesthetic sensitivity in the brain. METHODS: Time to emergence was determined for isoflurane (1.5 vol% for 45 min) and propofol (8 mg kg(-1) i.v.). The dose of isoflurane required to maintain loss of righting (LOR) was established in young adult and aged rats. The efficacy of methylphenidate to reverse LOR from general anaesthesia was tested. Separate young adult and aged rats with implanted electroencephalogram (EEG) electrodes were used to test whether ageing increases sensitivity to anaesthetic-induced burst suppression. RESULTS: Mean time to emergence from isoflurane anaesthesia was 47 s [95% CI 33, 60; young adult) compared with 243 s (95% CI 185, 308; aged). For propofol, mean time to emergence was 13.1 min (95% CI 11.9, 14.0; young adult) compared with 23.1 min (95% CI 18.8, 27.9; aged). These differences were statistically significant. When methylphenidate was administered after propofol, the mean time to emergence decreased to 6.6 min (95% CI 5.9, 7.1; young adult) and 10.2 min (95% CI 7.9, 12.3; aged). These reductions were statistically significant. Methylphenidate restored righting in all rats during continuous isoflurane anaesthesia. Aged rats had lower EEG power and were more sensitive to anaesthetic-induced burst suppression. CONCLUSIONS: Ageing delays emergence from general anaesthesia. This is due, at least in part, to increased anaesthetic sensitivity in the brain. Further studies are warranted to establish the underlying causes.

Age-dependency of sevoflurane-induced electroencephalogram dynamics in children.

BACKGROUND: General anaesthesia induces highly structured oscillations in the electroencephalogram (EEG) in adults, but the anaesthesia-induced EEG in paediatric patients is less understood. Neural circuits undergo structural and functional transformations during development that might be reflected in anaesthesia-induced EEG oscillations. We therefore investigated age-related changes in the EEG during sevoflurane general anaesthesia in paediatric patients. METHODS: We analysed the EEG recorded during routine care of patients between 0 and 28 yr of age (n=54), using power spectral and coherence methods. The power spectrum quantifies the energy in the EEG at each frequency, while the coherence measures the frequency-dependent correlation or synchronization between EEG signals at different scalp locations. We characterized the EEG as a function of age and within 5 age groups: <1 yr old (n=4), 1-6 yr old (n=12), >6-14 yr old (n=14), >14-21 yr old (n=11), >21-28 yr old (n=13). RESULTS: EEG power significantly increased from infancy through ∼6 yr, subsequently declining to a plateau at approximately 21 yr. Alpha (8-13 Hz) coherence, a prominent EEG feature associated with sevoflurane-induced unconsciousness in adults, is absent in patients <1 yr. CONCLUSIONS: Sevoflurane-induced EEG dynamics in children vary significantly as a function of age. These age-related dynamics likely reflect ongoing development within brain circuits that are modulated by sevoflurane. These readily observed paediatric-specific EEG signatures could be used to improve brain state monitoring in children receiving general anaesthesia.

Physiologic changes with abdominal wall reconstruction in a porcine abdominal compartment syndrome model.

PURPOSE: Abdominal compartment syndrome (ACS) is a severe complication of ventral hernia repair. The aims of this study were to investigate the effects of intra-abdominal pressure on the physiologic changes of abdominal wall reconstruction and component separation in a porcine model. METHODS: Ventral hernia repair (VHR) was simulated by abdominal fascial imbrication of a 10 × 15 cm defect in 45 Yorkshire pigs assigned to five experimental groups. ACS was simulated by a Stryker endoscopy insufflator with intra-abdominal pressure elevated to 20 mmHg in two groups. Component separation was performed in one of these groups and in one group without ACS. Physiological parameters were measured before and after the procedures and monitored for 4 h. The animals were euthanized for histologic analysis of organ damage. RESULTS: VHR led to an increase in intra-abdominal pressure, bladder pressure, and central venous pressure by an average of 14.89, 13.93, and 14.69 mmHg (p < 0.001) in all animals. Component separation was performed in 25 animals and the three pressures reduced by 9.11, 8.00, 7.89 mmHg (p < 0.001). ACS correlated with higher percentages of large and small bowel necrosis compared to groups without abdominal compartment syndrome. CONCLUSIONS: The results confirm that primary repair of large abdominal wall defects leads to increased intra-abdominal pressure, which can be reduced with component separation. In animals with ACS, component separation may reduce the risk of organ damage. Central venous pressure, bladder pressure, and other physiologic parameters accurately correlated with elevated intra-abdominal pressure and may have utility as markers for diagnosis of ACS.

Algorithm for total face and multiorgan procurement from a brain-dead donor.

Procurement of a facial vascularized composite allograft (VCA) should allow concurrent procurement of all solid organs and ensure their integrity. Because full facial procurement is time-intensive, "simultaneous-start" procurement could entail VCA ischemia over 12 h. We procured a total face osteomyocutaneous VCA from a brain-dead donor. Bedside tracheostomy and facial mask impression were performed preoperative day 1. Solid organ recovery included heart, lungs, liver, kidneys, and pancreas. Facial dissection time was 12 h over 15 h to diminish ischemia while awaiting recipient preparation. Solid organ recovery began at 13.5 h, during midfacial osteotomies, and concluded immediately after facial explantation. Facial thoracic and abdominal teams worked concurrently. Estimated blood loss was 1300 mL, requiring five units of pRBC and two units FFP. Urine output, MAP, pH and PaO2 remained normal. All organs had good postoperative function. We propose an algorithm that allows "face first, concurrent completion" recovery of a complex facial VCA by planning multiple pathways to expedient recovery of vital organs in the event of clinical instability. Beginning the recipient operation earlier may reduce waiting time due to extensive recipient scarring causing difficult dissection.

Comparative RNA editing in autistic and neurotypical cerebella.

Adenosine-to-inosine (A-to-I) RNA editing is a neurodevelopmentally regulated epigenetic modification shown to modulate complex behavior in animals. Little is known about human A-to-I editing, but it is thought to constitute one of many molecular mechanisms connecting environmental stimuli and behavioral outputs. Thus, comprehensive exploration of A-to-I RNA editing in human brains may shed light on gene-environment interactions underlying complex behavior in health and disease. Synaptic function is a main target of A-to-I editing, which can selectively recode key amino acids in synaptic genes, directly altering synaptic strength and duration in response to environmental signals. Here, we performed a high-resolution survey of synaptic A-to-I RNA editing in a human population, and examined how it varies in autism, a neurodevelopmental disorder in which synaptic abnormalities are a common finding. Using ultra-deep (>1000 × ) sequencing, we quantified the levels of A-to-I editing of 10 synaptic genes in postmortem cerebella from 14 neurotypical and 11 autistic individuals. A high dynamic range of editing levels was detected across individuals and editing sites, from 99.6% to below detection limits. In most sites, the extreme ends of the population editing distributions were individuals with autism. Editing was correlated with isoform usage, clusters of correlated sites were identified, and differential editing patterns examined. Finally, a dysfunctional form of the editing enzyme adenosine deaminase acting on RNA B1 was found more commonly in postmortem cerebella from individuals with autism. These results provide a population-level, high-resolution view of A-to-I RNA editing in human cerebella and suggest that A-to-I editing of synaptic genes may be informative for assessing the epigenetic risk for autism.

nSTAT: open-source neural spike train analysis toolbox for Matlab.

Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - generalized linear model (PP-GLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT--an open source neural spike train analysis toolbox for Matlab®. By adopting an object-oriented programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems.

Beamforming approach to phase-amplitude modulation analysis of multi-channel EEG.

Phase-amplitude modulation is a form of cross frequency coupling where the phase of one frequency influences the amplitude of another higher frequency. It has been observed in neurophysiological recordings during sensory, motor, and cognitive tasks, as well as during general anesthesia. In this paper, we describe a novel beamforming procedure to improve estimation of phase-amplitude modulation. We apply this method to 64-channel EEG data recorded during propofol general anesthesia. The method improves the sensitivity of phase-amplitude analyses, and can be applied to a variety of multi-channel neuroscience data where phase-amplitude modulation is present.

A fast iterative greedy algorithm for MEG source localization.

Recent dynamic source localization algorithms for the Magnetoencephalographic inverse problem use cortical spatio-temporal dynamics to enhance the quality of the estimation. However, these methods suffer from high computational complexity due to the large number of sources that must be estimated. In this work, we introduce a fast iterative greedy algorithm incorporating the class of subspace pursuit algorithms for sparse source localization. The algorithm employs a reduced order state-space model resulting in significant computational savings. Simulation studies on MEG source localization reveal substantial gains provided by the proposed method over the widely used minimum-norm estimate, in terms of localization accuracy, with a negligible increase in computational complexity.

A spatially-regularized dynamic source localization algorithm for EEG.

Cortical activity can be estimated from electroencephalogram (EEG) or magnetoencephalogram (MEG) data by solving an ill-conditioned inverse problem that is regularized using neuroanatomical, computational, and dynamic constraints. Recent methods have incorporated spatio-temporal dynamics into the inverse problem framework. In this approach, spatio-temporal interactions between neighboring sources enforce a form of spatial smoothing that enhances source localization quality. However, spatial smoothing could also occur by way of correlations within the state noise process that drives the underlying dynamic model. Estimating the spatial covariance structure of this state noise is challenging, particularly in EEG and MEG data where the number of underlying sources is far greater than the number of sensors. However, the EEG/MEG data are sparse compared to the large number of sources, and thus sparse constraints could be used to simplify the form of the state noise spatial covariance. In this work, we introduce an empirically tailored basis to represent the spatial covariance structure within the state noise processes of a cortical dynamic model for EEG source localization. We augment the method presented in Lamus, et al. (2011) to allow for sparsity enforcing priors on the covariance parameters. Simulation studies as well as analysis of real data reveal significant gains in the source localization performance over existing algorithms.

Characterizing learning by simultaneous analysis of continuous and binary measures of performance.

Continuous observations, such as reaction and run times, and binary observations, such as correct/incorrect responses, are recorded routinely in behavioral learning experiments. Although both types of performance measures are often recorded simultaneously, the two have not been used in combination to evaluate learning. We present a state-space model of learning in which the observation process has simultaneously recorded continuous and binary measures of performance. We use these performance measures simultaneously to estimate the model parameters and the unobserved cognitive state process by maximum likelihood using an approximate expectation maximization (EM) algorithm. We introduce the concept of a reaction-time curve and reformulate our previous definitions of the learning curve, the ideal observer curve, the learning trial and between-trial comparisons of performance in terms of the new model. We illustrate the properties of the new model in an analysis of a simulated learning experiment. In the simulated data analysis, simultaneous use of the two measures of performance provided more credible and accurate estimates of the learning than either measure analyzed separately. We also analyze two actual learning experiments in which the performance of rats and of monkeys was tracked across trials by simultaneously recorded reaction and run times and the correct and incorrect responses. In the analysis of the actual experiments, our algorithm gave a straightforward, efficient way to characterize learning by combining continuous and binary measures of performance. This analysis paradigm has implications for characterizing learning and for the more general problem of combining different data types to characterize the properties of a neural system.

Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation.

Source current estimation from electromagnetic (MEG and EEG) signals is an ill-posed problem that often produces blurry or inaccurately positioned estimates. The two modalities have distinct factors limiting the resolution, e.g., MEG cannot detect radially oriented sources, while EEG is sensitive to accuracy of the head model. This makes combined EEG+MEG estimation techniques desirable, but different acquisition noise statistics, complexity of the head models, and lack of pertinent metrics all complicate the assessment of the resulting improvements. We investigated analytically the effect of including EEG recordings in MEG studies versus the addition of new MEG channels when computing noise-normalized minimum l(2)-norm estimates. Three-compartment boundary-element forward models were constructed using structural MRI scans for four subjects. Singular value analysis of the resulting forward models predicted better performance of the EEG+MEG case in the form of higher matrix rank. MNE inverse operators for EEG, MEG and EEG+MEG were constructed using the sensor noise covariance estimated from data. Metrics derived from the resolution matrices predicted higher spatial resolution in EEG+MEG as compared to MEG due to decreased spread (lower spatial dispersion, higher resolution index) with no reduction in dipole localization error. The effect was apparent in all source locations, with increased magnitude for deep areas such as the cingulate cortex. We were also able to corroborate the results for the somatosensory cortex using median nerve responses.

Delayed orexin signaling consolidates wakefulness and sleep: physiology and modeling.

Orexin-producing neurons are clearly essential for the regulation of wakefulness and sleep because loss of these cells produces narcolepsy. However, little is understood about how these neurons dynamically interact with other wake- and sleep-regulatory nuclei to control behavioral states. Using survival analysis of wake bouts in wild-type and orexin knockout mice, we found that orexins are necessary for the maintenance of long bouts of wakefulness, but orexin deficiency has little impact on wake bouts <1 min. Since orexin neurons often begin firing several seconds before the onset of waking, this suggests a surprisingly delayed onset (>1 min) of functional effects. This delay has important implications for understanding the control of wakefulness and sleep because increasing evidence suggests that different mechanisms are involved in the production of brief and sustained wake bouts. We incorporated these findings into a mathematical model of the mouse sleep/wake network. Orexins excite monoaminergic neurons and we hypothesize that orexins increase the monoaminergic inhibition of sleep-promoting neurons in the ventrolateral preoptic nucleus. We modeled orexin effects as a time-dependent increase in the strength of inhibition from wake- to sleep-promoting populations and the resulting simulated behavior accurately reflects the fragmented sleep/wake behavior of narcolepsy and leads to several predictions. By integrating neurophysiology of the sleep/wake network with emergent properties of behavioral data, this model provides a novel framework for investigating network dynamics and mechanisms associated with normal and pathologic sleep/wake behavior.

A mixed filter algorithm for cognitive state estimation from simultaneously recorded continuous and binary measures of performance.

Continuous (reaction times) and binary (correct/ incorrect responses) measures of performance are routinely recorded to track the dynamics of a subject's cognitive state during a learning experiment. Current analyses of experimental data from learning studies do not consider the two performance measures together and do not use the concept of the cognitive state formally to design statistical methods. We develop a mixed filter algorithm to estimate the cognitive state modeled as a linear stochastic dynamical system from simultaneously recorded continuous and binary measures of performance. The mixed filter algorithm has the Kalman filter and the more recently developed recursive filtering algorithm for binary processes as special cases. In the analysis of a simulated learning experiment the mixed filter algorithm provided a more accurate and precise estimate of the cognitive state process than either the Kalman or binary filter alone. In the analysis of an actual learning experiment in which a monkey's performance was tracked by its series of reaction times, and correct and incorrect responses, the mixed filter gave a more complete description of the learning process than either the Kalman or binary filter. These results establish the feasibility of estimating cognitive state from simultaneously recorded continuous and binary performance measures and suggest a way to make practical use of concepts from learning theory in the design of statistical methods for the analysis of data from learning experiments.