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.

Inference-based time-resolved stability analysis of nonlinear whole-cortex modeling: application to Xenon anaesthesia.

This study characterizes the neurophysiological mechanisms underlying electromagnetic imaging signals using stability analysis. Researchers have proposed that transitions between conscious awake and anaesthetised states, and other brain states more generally, may result from system stability changes. The concept of stability in dynamical systems theory provides a mathematical framework to describe this possibility. In particular, the degree to which a system's trajectory in phase space is affected by small perturbations determines the stability. Previous studies using linear or oscillator-based whole-brain models cannot represent complex cerebrocortical dynamics, or model parameters were pre-assumed or inferred from data but did not change over time. This study proposes a nonlinear neurophysiologically plausible whole-cortex modeling framework to analyze the stability of brain dynamics for the emergence and disappearance of consciousness using time-varying parameters estimated from the data.Clinical relevance- Depth of anaesthesia is typically measured through changes in EEG statistics like the bispectral index and spectral entropy. However, these monitors have been found to fail in preventing awareness during surgery and postoperative recall. Our whole-cortex stability analysis may be useful in measuring anaesthesia levels in clinical settings, as it changes with the level of consciousness and is independent of individual differences and anaesthetic agents. The proposed method can also be used to, for example, identify critical brain regions for consciousness, locate the epileptogenic zone and investigate the dominance of extrinsic or intrinsic factors in brain functions.

Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions.

Recent work presented a framework for space-time-resolved neurophysiological process imaging that augments existing electromagnetic source imaging techniques. In particular, a nonlinear Analytic Kalman filter (AKF) has been developed to efficiently infer the states and parameters of neural mass models believed to underlie the generation of electromagnetic source currents. Unfortunately, as the initialization determines the performance of the Kalman filter, and the ground truth is typically unavailable for initialization, this framework might produce suboptimal results unless significant effort is spent on tuning the initialization. Notably, the relation between the initialization and overall filter performance is only given implicitly and is expensive to evaluate; implying that conventional optimization techniques, e.g. gradient or sampling based, are inapplicable. To address this problem, a novel efficient framework based on blackbox optimization has been developed to find the optimal initialization by reducing the signal prediction error. Multiple state-of-the-art optimization methods were compared and distinctively, Gaussian process optimization decreased the objective function by 82.1% and parameter estimation error by 62.5% on average with the simulation data compared to no optimization applied. The framework took only 1.6[Formula: see text]h and reduced the objective function by an average of 13.2% on 3.75[Formula: see text]min 4714-source channel magnetoencephalography data. This yields an improved method of neurophysiological process imaging that can be used to uncover complex underpinnings of brain dynamics.

Performance of the bispectral index and electroencephalograph derived parameters of anesthetic depth during emergence from xenon and sevoflurane anesthesia.

Many processed EEG monitors (pEEG) are unreliable when non-GABAergic anesthetic agents are used. The primary aim of the study was to compare the response of the Bispectral Index (BIS) during emergence from anesthesia maintained by xenon and sevoflurane. To better understand the variation in response of pEEG to these agents, we also compared several EEG derived parameters relevant to pEEG monitoring during emergence. Twenty-four participants scheduled for lithotripsy were randomized to receive xenon or sevoflurane anesthesia. Participants were monitored with the BIS and had simultaneous raw EEG collected. BIS index values were compared at three key emergence timepoints: first response, eyes open and removal of airway. Two sets of EEG derived parameters, three related to the BIS: relative beta ratio, SynchFastSlow and SynchFastSlow biocoherence, and two unrelated to the BIS: spectral edge frequency and the composite cortical state, were calculated for comparison. BIS index values were significantly lower in the xenon group than the sevoflurane group at each emergence timepoint. The relative beta ratio parameter increased significantly during emergence in the sevoflurane group but not in the xenon group. The spectral edge frequency and composite cortical state parameters increased significantly in both groups during emergence. The BIS index is lower at equivalent stages of behavioural response during emergence from xenon anesthesia when compared to sevoflurane anesthesia, most likely due to differences in how these two agents influence the relative beta ratio. The spectral edge frequency and composite cortical state might better reflect emergence from xenon anaesthesia.Clinical trial number and registry Australia New Zealand Clinical Trials Registry Number: ACTRN12618000916246.

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps.

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm.

Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes of different brain states.

Alpha blocking and 1/fß spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes.

The dynamical and physiological basis of alpha band activity and 1/fß noise in the EEG are the subject of continued speculation. Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/fß noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling (ß) of the power spectrum (1/fß). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/fß noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG.

Comparison of the Spectral Features of the Frontal Electroencephalogram in Patients Receiving Xenon and Sevoflurane General Anesthesia.

BACKGROUND: Depth-of-anesthesia monitoring is often utilized for patients receiving xenon anesthesia. Processed electroencephalogram (EEG) depth-of-anesthesia monitoring relies to a significant extent on frequency domain analysis of the frontal EEG, and there is evidence that the spectral features observed under anesthesia vary significantly between anesthetic agents. The spectral features of the EEG during xenon anesthesia for a surgical procedure have not previously been described. METHODS: Twenty-four participants scheduled for general anesthesia for lithotripsy were randomized to receive either xenon anesthesia or sevoflurane anesthesia. Frontal EEG recordings were obtained from each participant via the Brain Anesthesia Response Monitor (BARM). Twenty-two EEG recordings were suitable for analysis: 11 in participants who received sevoflurane and 11 in participants who received xenon. Spectrograms for the duration of the anesthetic episode were produced for each participant. Group-level spectral analysis was calculated for two 30-second EEG epochs: one recorded at awake baseline and the other during maintenance anesthesia. A linear mixed-effects model was utilized to compare the changes in 5 frequency bands from baseline to maintenance between the 2 groups. RESULTS: The spectrograms of sevoflurane participants illustrate an increase in frontal delta (0.5-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) band power during maintenance anesthesia. In contrast, spectrograms of the xenon participants did not illustrate an increase in alpha power. The results of the linear mixed-effects model indicate that both agents were associated with a significant increase in delta power from baseline to maintenance. There was no significant difference in the magnitude of this increase observed between the agents. In contrast, sevoflurane anesthesia was associated with significantly greater absolute power in the theta, alpha, and beta (13-30 Hz) bands when compared to xenon. In terms of relative power, xenon was associated with a significant increase in delta power compared to sevoflurane, while sevoflurane was associated with greater increases in relative theta, alpha, and beta power. CONCLUSIONS: Both xenon anesthesia and sevoflurane anesthesia were associated with significant increases in delta power. Sevoflurane anesthesia was also associated with increases in theta, alpha, and beta power, while xenon anesthesia was associated with greater consolidation of power in the delta band. Xenon anesthesia and sevoflurane anesthesia are associated with distinct spectral features. These findings suggest that appropriate depth-of-anesthesia monitoring may require the development of agent-specific spectral measures of unconsciousness.

Interictal spike localization for epilepsy surgery using magnetoencephalography beamforming.

OBJECTIVE: Magnetoencephalography (MEG) kurtosis beamforming is an automated localization method for focal epilepsy. Visual examination of virtual sensors, which are source activities reconstructed by beamforming, can improve performance but can be time-consuming for neurophysiologists. We propose a framework to automate the method and evaluate its effectiveness against surgical resections and outcomes. METHODS: We retrospectively analyzed MEG recordings of 13 epilepsy surgery patients who had one-year minimum post-operative follow-up. Kurtosis beamforming was applied and manual inspection was confined to morphological clusters. The region with the Maximum Interictal Spike Frequency (MISF) was validated against prospectively modelled sLORETA solutions and surgical resections linked to outcome. RESULTS: Our approach localized spikes in 12 out of 13 patients. In eight patients with Engel I surgical outcomes, beamforming MISF regions were concordant with surgical resection at overlap level for five patients and at lobar level for three patients. The MISF regions localized to spike onset and propagation modelled by sLORETA in two and six patients, respectively. CONCLUSIONS: Automated beamforming using MEG can predict postoperative seizure freedom at the lobar level but tends to localize propagated MEG spikes. SIGNIFICANCE: MEG beamforming may contribute to non-invasive procedures to predict surgical outcome for patients with drug-refractory focal epilepsy.

Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra.

Alpha blocking, a phenomenon where the alpha rhythm is reduced by attention to a visual, auditory, tactile or cognitive stimulus, is one of the most prominent features of human electroencephalography (EEG) signals. Here we identify a simple physiological mechanism by which opening of the eyes causes attenuation of the alpha rhythm. We fit a neural population model to EEG spectra from 82 subjects, each showing a different degree of alpha blocking upon opening of their eyes. Though it has been notoriously difficult to estimate parameters by fitting such models, we show how, by regularizing the differences in parameter estimates between eyes-closed and eyes-open states, we can reduce the uncertainties in these differences without significantly compromising fit quality. From this emerges a parsimonious explanation for the spectral differences between states: Changes to just a single parameter, pei, corresponding to the strength of a tonic excitatory input to the inhibitory cortical population, are sufficient to explain the reduction in alpha rhythm upon opening of the eyes. We detect this by comparing the shift in each model parameter between eyes-closed and eyes-open states. Whereas changes in most parameters are weak or negligible and do not scale with the degree of alpha attenuation across subjects, the change in pei increases monotonically with the degree of alpha blocking observed. These results indicate that opening of the eyes reduces alpha activity by increasing external input to the inhibitory cortical population.

Source-level Cortical Power Changes for Xenon and Nitrous Oxide-induced Reductions in Consciousness in Healthy Male Volunteers.

BACKGROUND: Investigations of the electrophysiology of gaseous anesthetics xenon and nitrous oxide are limited revealing inconsistent frequency-dependent alterations in spectral power and functional connectivity. Here, the authors describe the effects of sedative, equivalent, stepwise levels of xenon and nitrous oxide administration on oscillatory source power using a crossover design to investigate shared and disparate mechanisms of gaseous xenon and nitrous oxide anesthesia. METHODS: Twenty-one healthy males underwent simultaneous magnetoencephalography and electroencephalography recordings. In separate sessions, sedative, equivalent subanesthetic doses of gaseous anesthetic agents nitrous oxide and xenon (0.25, 0.50, and 0.75 equivalent minimum alveolar concentration-awake [MACawake]) and 1.30 MACawake xenon (for loss of responsiveness) were administered. Source power in various frequency bands were computed and statistically assessed relative to a conscious/pre-gas baseline. RESULTS: Observed changes in spectral-band power (P < 0.005) were found to depend not only on the gas delivered, but also on the recording modality. While xenon was found to increase low-frequency band power only at loss of responsiveness in both source-reconstructed magnetoencephalographic (delta, 208.3%, 95% CI [135.7, 281.0%]; theta, 107.4%, 95% CI [63.5, 151.4%]) and electroencephalographic recordings (delta, 260.3%, 95% CI [225.7, 294.9%]; theta, 116.3%, 95% CI [72.6, 160.0%]), nitrous oxide only produced significant magnetoencephalographic high-frequency band increases (low gamma, 46.3%, 95% CI [34.6, 57.9%]; high gamma, 45.7%, 95% CI [34.5, 56.8%]). Nitrous oxide-not xenon-produced consistent topologic (frontal) magnetoencephalographic reductions in alpha power at 0.75 MACawake doses (44.4%; 95% CI [-50.1, -38.6%]), whereas electroencephalographically nitrous oxide produced maximal reductions in alpha power at submaximal levels (0.50 MACawake, -44.0%; 95% CI [-48.1,-40.0%]). CONCLUSIONS: Electromagnetic source-level imaging revealed widespread power changes in xenon and nitrous oxide anesthesia, but failed to reveal clear universal features of action for these two gaseous anesthetics. Magnetoencephalographic and electroencephalographic power changes showed notable differences which will need to be taken into account to ensure the accurate monitoring of brain state during anaesthesia.

Evidence that alpha blocking is due to increases in system-level oscillatory damping not neuronal population desynchronisation.

The attenuation of the alpha rhythm following eyes-opening (alpha blocking) is among the most robust features of the human electroencephalogram with the prevailing view being that it is caused by changes in neuronal population synchrony. To further study the basis for this phenomenon we use theoretically motivated fixed-order Auto-Regressive Moving-Average (ARMA) time series modelling to study the oscillatory dynamics of spontaneous alpha-band electroencephalographic activity in eyes-open and eyes-closed conditions and its modulation by the NMDA antagonist ketamine. We find that the reduction in alpha-band power between eyes-closed and eyes-open states is explicable in terms of an increase in the damping of stochastically perturbed alpha-band relaxation oscillatory activity. These changes in damping are putatively modified by the antagonism of NMDA-mediated glutamatergic neurotransmission but are not directly driven by changes in input to cortex nor by reductions in the phase synchronisation of populations of near identical oscillators. These results not only provide a direct challenge to the dominant view of the role that thalamus and neuronal population de-/synchronisation have in the genesis and modulation of alpha electro-/magnetoencephalographic activity but also suggest potentially important physiological determinants underlying its dynamical control and regulation.

Parameter estimation and identifiability in a neural population model for electro-cortical activity.

Electroencephalography (EEG) provides a non-invasive measure of brain electrical activity. Neural population models, where large numbers of interacting neurons are considered collectively as a macroscopic system, have long been used to understand features in EEG signals. By tuning dozens of input parameters describing the excitatory and inhibitory neuron populations, these models can reproduce prominent features of the EEG such as the alpha-rhythm. However, the inverse problem, of directly estimating the parameters from fits to EEG data, remains unsolved. Solving this multi-parameter non-linear fitting problem will potentially provide a real-time method for characterizing average neuronal properties in human subjects. Here we perform unbiased fits of a 22-parameter neural population model to EEG data from 82 individuals, using both particle swarm optimization and Markov chain Monte Carlo sampling. We estimate how much is learned about individual parameters by computing Kullback-Leibler divergences between posterior and prior distributions for each parameter. Results indicate that only a single parameter, that determining the dynamics of inhibitory synaptic activity, is directly identifiable, while other parameters have large, though correlated, uncertainties. We show that the eigenvalues of the Fisher information matrix are roughly uniformly spaced over a log scale, indicating that the model is sloppy, like many of the regulatory network models in systems biology. These eigenvalues indicate that the system can be modeled with a low effective dimensionality, with inhibitory synaptic activity being prominent in driving system behavior.

Interictal and ictal source localization for epilepsy surgery using high-density EEG with MEG: a prospective long-term study.

Drug-resistant focal epilepsy is a major clinical problem and surgery is under-used. Better non-invasive techniques for epileptogenic zone localization are needed when MRI shows no lesion or an extensive lesion. The problem is interictal and ictal localization before propagation from the epileptogenic zone. High-density EEG (HDEEG) and magnetoencephalography (MEG) offer millisecond-order temporal resolution to address this but co-acquisition is challenging, ictal MEG studies are rare, long-term prospective studies are lacking, and fundamental questions remain. Should HDEEG-MEG discharges be assessed independently [electroencephalographic source localization (ESL), magnetoencephalographic source localization (MSL)] or combined (EMSL) for source localization? Which phase of the discharge best characterizes the epileptogenic zone (defined by intracranial EEG and surgical resection relative to outcome)? Does this differ for interictal and ictal discharges? Does MEG detect mesial temporal lobe discharges? Thirteen patients (10 non-lesional, three extensive-lesional) underwent synchronized HDEEG-MEG (72-94 channel EEG, 306-sensor MEG). Source localization (standardized low-resolution tomographic analysis with MRI patient-individualized boundary-element method) was applied to averaged interictal epileptiform discharges (IED) and ictal discharges at three phases: 'early-phase' (first latency 90% explained variance), 'mid-phase' (first of 50% rising-phase, 50% mean global field power), 'late-phase' (negative peak). 'Earliest-solution' was the first of the three early-phase solutions (ESL, MSL, EMSL). Prospective follow-up was 3-21 (median 12) months before surgery, 14-39 (median 21) months after surgery. IEDs (n = 1474) were recorded, seen in: HDEEG only, 626 (42%); MEG only, 232 (16%); and both 616 (42%). Thirty-three seizures were captured, seen in: HDEEG only, seven (21%); MEG only, one (3%); and both 25 (76%). Intracranial EEG was done in nine patients. Engel scores were I (9/13, 69%), II (2/13,15%), and III (2/13). MEG detected baso-mesial temporal lobe epileptogenic zone sources. Epileptogenic zone OR [odds ratio(s)] were significantly higher for earliest-solution versus early-phase IED-surgical resection and earliest-solution versus all mid-phase and late-phase solutions. ESL outperformed EMSL for ictal-surgical resection [OR 3.54, 95% confidence interval (CI) 1.09-11.55, P = 0.036]. MSL outperformed EMSL for IED-intracranial EEG (OR 4.67, 95% CI 1.19-18.34, P = 0.027). ESL outperformed MSL for ictal-surgical resection (OR 3.73, 95% CI 1.16-12.03, P = 0.028) but was outperformed by MSL for IED-intracranial EEG (OR 0.18, 95% CI 0.05-0.73, P = 0.017). Thus, (i) HDEEG and MEG source solutions more accurately localize the epileptogenic zone at the earliest resolvable phase of interictal and ictal discharges, not mid-phase (as is common practice) or late peak-phase (when signal-to-noise ratios are maximal); (ii) from empirical observation of the differential timing of HDEEG and MEG discharges and based on the superiority of ESL plus MSL over either modality alone and over EMSL, concurrent HDEEG-MEG signals should be assessed independently, not combined; (iii) baso-mesial temporal lobe sources are detectable by MEG; and (iv) MEG is not 'more accurate' than HDEEG-emphasis is best placed on the earliest signal (whether HDEEG or MEG) amenable to source localization. Our findings challenge current practice and our reliance on invasive monitoring in these patients. 10.1093/brain/awz015_video1 awz015media1 6018582479001.

Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG.

Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.

1/f electrophysiological spectra in resting and drug-induced states can be explained by the dynamics of multiple oscillatory relaxation processes.

Neurophysiological recordings are dominated by arhythmical activity whose spectra can be characterised by power-law functions, and on this basis are often referred to as reflecting scale-free brain dynamics (1/fß). Relatively little is known regarding the neural generators and temporal dynamics of this arhythmical behaviour compared to rhythmical behaviour. Here we used Irregularly Resampled AutoSpectral Analysis (IRASA) to quantify ß, in both the high (5-100 Hz, ßhf) and low frequency bands (0.1-2.5 Hz, ßlf) in MEG/EEG/ECoG recordings and to separate arhythmical from rhythmical modes of activity, such as, alpha rhythms. In MEG/EEG/ECoG data, we demonstrate that oscillatory alpha power dynamically correlates over time with ßhf and similarly, participants with higher rhythmical alpha power have higher ßhf. In a series of pharmaco-MEG investigations using the GABA reuptake inhibitor tiagabine, the glutamatergic AMPA receptor antagonist perampanel, the NMDA receptor antagonist ketamine and the mixed partial serotonergic agonist LSD, a variety of effects on both ßhf and ßlf were observed. Additionally, strong modulations of ßhf were seen in monkey ECoG data during general anaesthesia using propofol and ketamine. We develop and test a unifying model which can explain, the 1/f nature of electrophysiological spectra, their dynamic interaction with oscillatory rhythms as well as the sensitivity of 1/f activity to drug interventions by considering electrophysiological spectra as being generated by a collection of stochastically perturbed damped oscillators having a distribution of relaxation rates.

Recording Brain Electromagnetic Activity During the Administration of the Gaseous Anesthetic Agents Xenon and Nitrous Oxide in Healthy Volunteers.

Anesthesia arguably provides one of the only systematic ways to study the neural correlates of global consciousness/unconsciousness. However to date most neuroimaging or neurophysiological investigations in humans have been confined to the study of γ-Amino-Butyric-Acid-(GABA)-receptor-agonist-based anesthetics, while the effects of dissociative N-Methyl-D-Aspartate-(NMDA)-receptor-antagonist-based anesthetics ketamine, nitrous oxide (N2O) and xenon (Xe) are largely unknown. This paper describes the methods underlying the simultaneous recording of magnetoencephalography (MEG) and electroencephalography (EEG) from healthy males during inhalation of the gaseous anesthetic agents N2O and Xe. Combining MEG and EEG data enables the assessment of electromagnetic brain activity during anesthesia at high temporal, and moderate spatial, resolution. Here we describe a detailed protocol, refined over multiple recording sessions, that includes subject recruitment, anesthesia equipment setup in the MEG scanner room, data collection and basic data analysis. In this protocol each participant is exposed to varying levels of Xe and N2O in a repeated measures cross-over design. Following relevant baseline recordings participants are exposed to step-wise increasing inspired concentrations of Xe and N2O of 8, 16, 24 and 42%, and 16, 32 and 47% respectively, during which their level of responsiveness is tracked with an auditory continuous performance task (aCPT). Results are presented for a number of recordings to highlight the sensor-level properties of the raw data, the spectral topography, the minimization of head movements, and the unequivocal level dependent effects on the auditory evoked responses. This paradigm describes a general approach to the recording of electromagnetic signals associated with the action of different kinds of gaseous anesthetics, which can be readily adapted to be used with volatile and intravenous anesthetic agents. It is expected that the method outlined can contribute to the understanding of the macro-scale mechanisms of anesthesia by enabling methodological extensions involving source space imaging and functional network analysis.

Assessing nitrous oxide effect using electroencephalographically-based depth of anesthesia measures cortical state and cortical input.

Existing electroencephalography (EEG) based depth of anesthesia monitors cannot reliably track sedative or anesthetic states during n-methyl-D-aspartate (NMDA) receptor antagonist based anesthesia with ketamine or nitrous oxide (N2O). Here, a physiologically-motivated depth of anesthesia monitoring algorithm based on autoregressive-moving-average (ARMA) modeling and derivative measures of interest, Cortical State (CS) and Cortical Input (CI), is retrospectively applied in an exploratory manner to the NMDA receptor antagonist N2O, an adjuvant anesthetic gas used in clinical practice. Composite Cortical State (CCS) and Composite Cortical State distance (CCSd), two new modifications of CS, along with CS and CI were evaluated on electroencephalographic (EEG) data of healthy control individuals undergoing N2O inhalation up to equilibrated peak gas concentrations of 20, 40 or 60% N2O/O2. In particular, CCSd has been devised to vary consistently for increasing levels of anesthetic concentration independent of the anesthetic's microscopic mode of action for both N2O and propofol. The strongest effects were observed for the 60% peak gas concentration group. For the 50-60% peak gas levels, individuals showed statistically significant reductions in responsiveness compared to rest, and across the group CS and CCS increased by 39 and 42%, respectively, while CCSd was found to decrease by 398%. On the other hand a clear conclusion regarding the changes in CI could not be reached. These results indicate that, contrary to previous depth of anesthesia monitoring measures, the CS, CCS, and especially CCSd measures derived from frontal EEG are potentially useful for differentiating gas concentration and responsiveness levels in people under N2O. On the other hand, determining the utility of CI in this regard will require larger sample sizes and potentially higher gas concentrations. Future work will assess the sensitivity of CS-based and CI measures to other anesthetics and their utility in a clinical environment.

Towards a Functional Understanding of PGO Waves.

Ponto-Geniculo-Occipital (PGO) waves are biphasic field potentials identified in a range of mammalian species that are ubiquitous with sleep, but can also be identified in waking perception and eye movement. Their role in REM sleep and visual perception more broadly may constitute a promising avenue for further research, however what was once an active field of study has recently fallen into stasis. With the reality that invasive recordings performed on animals cannot be replicated in humans; while animals themselves cannot convey experience to the extent required to elucidate how PGO waves factor into awareness and behavior, innovative solutions are required if significant research outcomes are to ever be realized. Advances in non-invasive imaging technologies and sophistication in imaging methods now offer substantial scope to renew the study of the electrophysiological substrates of waking and dreaming perception. Among these, Magnetoencephalogram (MEG) stands out through its capacity to measure deep brain activations with high temporal resolution. With the current trend in sleep and dream research to produce translational findings of psychopathological and medical significance, in addition to the clear links that PGO wave generation sites share, pharmacologically, with receptors involved in expression of mental illness; there is a strong case to support scientific research into PGO waves and develop a functional understanding of their broader role in human perception.

Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based Depth of Anesthesia Monitoring.

OBJECTIVE: Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature. METHODS: Individuals' brain states are classified by comparing the distribution of linear (auto-regressive moving average-ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a sliding window to distributions of linear model parameters for each brain state. The method is applied to frontal EEG data from 15 subjects undergoing propofol anesthesia and classified by the observers assessment of alertness/sedation (OAA/S) scale. Classification of the OAA/S score was performed using distributions of either ARMA parameters or the benchmark feature, Higuchi fractal dimension. RESULTS: The highest average testing sensitivity of 59% (chance sensitivity: 17%) was found for ARMA (2,1) models and Higuchi fractal dimension achieved 52%, however, no statistical difference was observed. For the same ARMA case, there was no statistical difference if medians are used instead of distributions (sensitivity: 56%). CONCLUSION: The model-based distribution approach is not necessarily more effective than a median/short-term average approach, however, it performs well compared with a distribution approach based on a high performing anesthesia monitoring measure. SIGNIFICANCE: These techniques hold potential for anesthesia monitoring and may be generally applicable for tracking brain states.