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Metal-organic frameworks (MOFs) are porous, crystalline materials constructed from organic linkers and inorganic nodes with potential utility in gas separations, drug delivery, sensing, and catalysis. Small variations in MOF synthesis conditions can lead to a range of accessible frameworks with divergent chemical or photophysical properties. New meth-ods to controllably access phases with tailored properties would broaden the scope of MOFs that can be reliably prepared for specific applications. Herein, we demonstrate that simply increasing the reaction concentration during the solvother-mal synthesis of M2(dobdc) (M = Mg, Mn, Ni; dobdc4− = 2,5-dioxido-1,4-benzenedicarboxylate), also known as MOF-74, unexpectedly leads to trapping of a kinetic intermediate termed CORN-MOF-1 (CORN = Cornell University). In-depth spec-troscopic, crystallographic, and computational studies support that CORN-MOF-1 has a similar structure to M2(dobdc) but with partially protonated linkers and charge-balancing or coordinated formate groups in the pores. The resultant vari-ation in linker spacings causes CORN-MOF-1 (Mg) to be strongly photoluminescent in the solid state, whereas H4dobdc and Mg2(dobdc) are weakly emissive due to excimer formation. In addition, CORN-MOF-1 variants can be converted into high-quality samples of the thermodynamic M2(dobdc) phases by heating in N,N-dimethylformamide (DMF). Overall, our findings support that high-concentration synthesis provides a straightforward method to identify new kinetic MOF phas-es with different properties from known materials and to produce highly porous samples of MOFs, paving the way for the discovery and gram-scale synthesis of framework materials.

Objective The World Health Organization, International League Against Epilepsy (ILAE), and International Bureau for Epilepsy have called epilepsy a public health imperative, with appropriate emphasis on low‐to‐middle‐income countries (LMIC). Although Canada is a high‐income country (HIC), income is not distributed uniformly. Furthermore, epilepsy data from the national statistical agency explicitly overlook the Arctic by excluding these territories. A common neurologic emergency, status epilepticus (SE) is a life‐threatening manifestation of epilepsy that demands prompt treatment to avoid death and long‐term sequelae. Therefore, we examined the rate of SE in a well‐defined Canadian Arctic region. Methods This study takes epidemiologic advantage of the Kivalliq Region's geographical isolation, which is accessible only by air. All SE patients requiring emergency care are consistently flown 1200‐1900 kilometers to a single designated hospital in a distinct southern part of Canada for further management and electroencephalography (EEG). We conducted a retrospective database and chart review at this “bottleneck” hospital to identify patients with seizure(s) severe enough to justify emergency airborne medical evacuation over a 11.25‐year period from 2009 to 2020. Results We screened 40 392 EEGs to yield 117 distinct medical evacuations for “operational SE” from 99 patients to derive estimated SE incidences of 99.9 evacuations per 100 000/year and 84.5 patients per 100 000/year. The average time from seizure onset to EEG was 3.2 days. Only 16.2% of SE patients had known epilepsy. For “confirmed SE” cases meeting ILAE criteria, or cases with persistently epileptiform EEG despite days of empiric treatment, estimated incidence was 77.7 evacuations per 100 000/year and 64.9 patients per 100 000/year. Significance High SE and epilepsy rates in the Canadian Arctic are consistent with LMIC rather than HIC. Our findings demonstrate the paradox of LMIC‐equivalent epilepsy populations camouflaged within HIC. Our findings also highlight the long‐standing plight of these under‐served and overlooked populations hidden in plain sight.

Highlights • Quantitative burst suppression ratios (QBSR) represent depth of EEG suppression. • Deeper QBSR on serial intermittent EEG did not affect survival in RSE. • Non-suppressive continuous EEG effects on RSE mortality merits further research., Objectives In refractory status epilepticus (RSE), the optimal degree of suppression (EEG burst suppression or merely suppressing seizures) remains unknown. Many centers lacking continuous EEG must default to serial intermittent recordings where uncertainty from lack of data may prompt more aggressive suppression. In this study, we sought to determine whether the quantitative burst suppression ratio (QBSR) from serial intermittent EEG recording is associated with RSE patient outcome. Methods We screened the EEG database to identify non-anoxic adult RSE patients for EEG and chart review. QBSR was calculated per 10-second EEG epoch as the percentage of time during which EEG amplitude was

Background Acute heart failure is a common cause of hospital admission. This study aims to compare continuous infusion and intermittent boluses of furosemide in treating acute heart failure. Methods Electronic searches were performed on PubMed, Medline, Scopus, and EMBASE. English articles comparing intermittent boluses and continuous infusion of furosemide in treating acute heart failure were included. Non-comparative studies or articles, and articles that did not report specific data for acute heart failure patients were excluded. Primary endpoints included post-treatment daily urine output, weight, length of stay, and serum sodium, potassium, and creatinine. Secondary endpoints included other pre-treatment and treatment variables. Post hoc trial sequential analysis (TSA) was performed on selected variables. Results Ten randomized controlled trials were included with a total of 735 patients (371 with intermittent boluses and 364 with continuous infusion). Mean daily urine output (P Conclusion Choice of furosemide regime in acute heart failure remains physician preference. Both bolus and continuous infusion yields satisfactory outcomes.

Background: Health research agendas are often set by researchers or by industry and may not reflect the needs and priorities of end users. This priority-setting partnership (PSP) for epilepsy was undertaken to identify the most pressing unanswered questions about epilepsy and seizures from the perspective of people with epilepsy (PWE) and their care providers. Methods: Using the methodology developed by the James Lind Alliance (JLA), evidence uncertainties were gathered via online surveys from stakeholders across Canada. Submissions were formed into summary questions and checked against existing evidence to determine if they were true uncertainties. Verified uncertainties were then ranked by patients, caregivers, and healthcare providers and a final workshop was held to reach a consensus on the top 10 priorities. Results: The final top 10 list reflects the priority areas of focus for research as identified by the Canadian epilepsy community, including genetic markers for diagnosis and treatment, concerns about living with the long-term effects of epilepsy, and addressing knowledge gaps in etiology and treatment approaches. Conclusion: This project represents the first systematic evidence of patient- and clinician-centered research priorities for epilepsy. The results of this priority-setting exercise provide an opportunity for researchers and funding agencies to align their agendas with the values and needs of the epilepsy community in order to improve clinical outcomes and quality of life (QOL) for PWE.

SUMMARY As continuous brain monitoring becomes a routine part of clinical care, continuous EEG has allowed better detection and characterization of nonconvulsive seizures, and patterns along the ictal-interictal continuum in critically ill patients. However, this increased workload has led many to turn to quantitative EEG whose central tool is the "spectrogram." Although in relatively wide use, many clinicians lack a detailed understanding of how spectrograms relate to the underlying "raw" EEG signal. This article provides an approachable set of first principles to help clinicians understand how spectrograms encode information about the raw EEG and how to interpret spectrograms to efficiently infer underlying EEG patterns.

Introduction Classic EEG markers of sleep homeostasis (process S) usually study the level and amount of slow wave activity (SWA). In recent years, the odds ratio product (ORP) has been described as a novel quantitative measure of sleep depth (and conversely, arousability), which is derived from a four-digit permutation of the power in four respective frequency bands. Therefore, we sought to determine whether the ORP can also be used as a novel quantitative marker of process S. Methods Utilizing C3/C4 electrodes with TP9/TP10 reference, ORP was calculated from 25 nights in 15 patients. We used ORP from REM and NREM1-3 segments/bouts in each person’s night(s) with varying number of data points per REM segment(s) in each night(s). Artifacts and wake segments were discarded from analysis. Average ORP is calculated for an individual bout per night per patient. Generalized Estimating Equation (GEE) modelling used GEEQBOX MATLAB toolbox to identify change in sleep depth over bouts. Resultant beta (β) values represented change in nightly sleep depth over sequential NREM/REM segments per patient. Significance was p≤0.000833. Results From 15 people in the epilepsy monitoring unit, 3 did not have epilepsy. In NREM, the average ORP for C3/C4 is 1.26/1.19 (n=146307/125709). Average number of nightly NREM segments for C3/C4 are 4.64/4.9 (n=1261.27/1282.74 average ORP values per nightly segment). In REM, the average ORP for C3/C4 is 1.44/1.44 (n=47629/43528). Average number of nightly REM segments for C3/C4 are 4.36/4.38 (n=418.68/453.33 average ORP values per nightly segment). Signifying the change in sleep depth the beta values from C3/C4 were consistently positive: REM (epilepsy 0.033/0.018, non-epilepsy 0.096/0.053), NREM (epilepsy 0.047/0.053, non-epilepsy 0.026/0.064). Conclusion As a marker of sleep homeostasis, SWA decreases across a period of sleep. Consistently positive beta values derived from ORP in both REM and NREM are comparable because they demonstrate shallower sleep depth (i.e. greater arousability) across segments over nights as sleep debt is repaid. Also as expected, REM has a higher ORP than NREM. Furthermore, our findings of positive beta values are robust against sleep stage and pathology (i.e. epilepsy). Therefore, these findings may represent a novel quantitative marker of sleep homeostasis that includes REM. Support (If Any)

Objectives: Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as “ictal interictal injury continuum” (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear. Methods: We assembled >200,000 h of EEG from 1,454 hospitalized patients. From these, we collected 9,808 labeled and 120,000 unlabeled 10-second EEG segments. Labels included 6 IIIC patterns. In each AL iteration, a Dense-Net Convolutional Neural Network (CNN) learned vector representations for EEG segments using available labels, which were used to create a 2D embedding map. Nearest-neighbor label spreading within the embedding map was used to create additional pseudo-labeled data. A second Dense-Net was trained using real- and pseudo-labels. We evaluated several strategies for selecting candidate points for experts to label next. Finally, we compared two methods for class balancing within queries: standard balanced-based querying (SBBQ), and high confidence spread-based balanced querying (HCSBBQ). Results: Our results show: 1) Label spreading increased convergence speed for AL. 2) All query criteria produced similar results to random sampling. 3) HCSBBQ query balancing performed best. Using label spreading and HCSBBQ query balancing, we were able to train models approaching expert-level performance across all pattern categories after obtaining ∼7000 expert labels. Conclusion: Our results provide guidance regarding the use of AL to efficiently label large EEG datasets in critically ill patients., SCOPUS: ar.j, info:eu-repo/semantics/published

The COVID-19 pandemic has had a major impact on clinical practice. Safe standards of practice are essential to protect health care workers while still allowing them to provide good care. The Canadian Society of Clinical Neurophysiologists, the Canadian Association of Electroneurophysiology Technologists, the Association of Electromyography Technologists of Canada, the Board of Registration of Electromyography Technologists of Canada, and the Canadian Board of Registration of Electroencephalograph Technologists have combined to review current published literature about safe practices for neurophysiology laboratories. Herein, we present the results of our review and provide our expert opinion regarding the safe practice of neurophysiology during the COVID-19 pandemic in Canada.

Metal-organic frameworks (MOFs) bearing coordinatively unsaturated metal centers, exemplified by the MOF-74 family of frameworks, are promising for applications ranging from gas separations and storage to Lewis acid catalysis. However, the scalable synthesis of MOF-74 analogues remains a significant challenge. Recently, mechanochemistry has emerged as a sustainable strategy for the preparation of MOFs in the solid state with minimal solvent waste. Mechanochemical methods typically rely on metal salts bearing basic anions to deprotonate the conjugate acid of the organic linker and a small amount of organic solvent or water to facilitate liquid assisted grinding. Here, we demonstrate that the liquid exogenous organic base Hunig's base (N,N-diisopropylethylamine) can fulfill both roles, enabling the mechanochemical synthesis of M2(dobdc) analogues (M = Mg, Mn, Co, Ni, Cu, Zn; dobdc4- = 2,5-dioxidobenzene-1,4-dicarboxylate) using metal nitrate salts in only 5 minutes at room temperature. Importantly, we demonstrate that this straightforward method can be generalized to prepare the isomeric framework Mg2(m-dobdc) (m-dobdc4- = 2,4-dioxidobenzene-1,5-dicarboxylate) and the expanded framework Mg2(dobpdc) (dobpdc4- = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) under solvent-free conditions for the first time. The MOFs prepared using this method possess high crystallinities and surface areas, with the Mg2(m-dobdc) prepared herein representing the first reported permanently porous variant of this framework. This new sustainable mechanochemical synthesis of MOF-74 analogues should enable their preparation on a large scale for industrial applications.

Background: Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG.New method: We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters.Results: Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 +/- 4.44 min to label the 30.19 +/- 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency.Comparison with existing methods: Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods.Conclusions: Our results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.

BACKGROUND Psychogenic non-epileptic seizures (PNES) may involve hypoactivity in the right temporoparietal junction (TPJ), suggesting a promising target for therapeutic neuromodulation. In this proof-of-concept case series, we aimed to investigate the tolerability and potential efficacy of high frequency repetitive Transcranial Magnetic Stimulation (rTMS) over the right TPJ to decrease non-epileptic seizure rates. METHODS Seven subjects with video-EEG documented PNES without comorbid epileptic seizures were recruited. The rTMS protocol involved thirty stimulation sessions administered twice per day over three weeks. Each session consisted of three thousand pulses of high frequency (10 Hz) rTMS applied over the right TPJ. Tolerability was monitored throughout treatment. Weekly PNES counts were recorded at baseline, during treatment, and at post treatment intervals as the primary outcome measure. Additional psychometric scales assessing dissociation and functional neurological symptoms were collected at baseline and within 1-week post treatment as secondary outcome measures. RESULTS Treatment with rTMS was well tolerated by all participants. Participants reported a decrease in weekly seizure rates post vs. pre-treatment, which was sustained at 3-month follow-up. Improvement was also reported on the Dissociative Experiences Scale and the Conversion Disorder Subscale of the Screening for Somatoform Symptoms-7 Scale. CONCLUSION High-frequency rTMS over the right TPJ represents a promising treatment for PNES that warrants additional research.

Introduction. In refractory status epilepticus (RSE), seizures are generally managed by anaesthetic sedation. Although sedation depth correlates with EEG suppression level, the optimal degree of suppression is unknown. We sought to assess the relationship between the ‘quantitative burst suppression ratio’ (QBSR) as a marker of EEG suppression, and RSE patient outcomes. Methods. We used Persyst software to calculate QBSR/10-second EEG epoch from non- anoxic adult RSE intensive care unit (ICU) patients over 15 months. QBSR was the percentage of each epoch with EEG amplitude under 3 microvolts/millimetre. Between unfavourable and favourable (defined as surviving to discharge from ICU) groups, we compared median QBSR per-patient (multivariate regression), per-daily EEG recording session (generalized estimating equation), and per-EEG epoch (quasi-least squared model) using Stata 14 or MATLAB software to control for aSTESS (adjusted status epilepticus severity score), sedative number, and gender. Results. 135,765 QBSRs were calculated from 158 EEGs derived from 17 patients. There were 7 favourable group patients who all survived 3 months post-discharge from ICU. Stratifying analyses per-patient, per-session, and per-epoch, high aSTESS is consistently associated with unfavourable outcome (per-patient OR 0.59–0.97, p = 0.033; per-session OR 0.06–0.94, p = 0.041; per-epoch OR 0.67–0.71, p Conclusion. Our findings validate the prognostic value of aSTESS, but do not support an association between RSE outcomes and EEG suppression depth measured by QBSR. However, an association between more sedative use and better outcome on per-epoch analysis may suggest that more aggressive treatment targeted against foreground transients such as seizures may be more beneficial in RSE than quantitative background suppression.

Diagnosis of paroxysmal events in epilepsy patients is often made through video-telemetry electroencephalography in the epilepsy monitoring unit. This case report describes the first-ever diagnosis of exploding head syndrome in a patient with longstanding epilepsy and novel nocturnal events. In this report, we describe the presentation of exploding head syndrome and its prevalence and risk factors. In addition, the prevalence of newly diagnosed sleep disorders through video-telemetry electroencephalography in the epilepsy monitoring unit is briefly reviewed. This report also illustrates the novel use of clobazam for the treatment of exploding head syndrome.

Purpose Rapid eye movement (REM) sleep can help localize the epileptogenic zone in multifocal epilepsy when interictal discharges appear diffuse. However, REM sleep is reputedly rare and easily overlooked in the Epilepsy Monitoring Unit (EMU). The aims of this study are to determine the characteristics of REM sleep in a typical EMU and whether using automated artifact recognition can meaningfully enhance REM sleep detection. Methods Artifact-based REM sleep detection was applied to 581 nights of EMU recording from 100 patients over 12 months. REM sleep had been manually detected at the time of recording. The index of suspicion for manual detection was raised after 6 months. Artifact-based detection was compared with manual detection, and the impact on localization was assessed. Results REM sleep occurred in 77% of EMU nights. Thirty-six patients achieved REM sleep nightly and 62 patients on at least one night. Mean admission was 5.83 days. Mean REM sleep duration was 5.92 minutes over 1.88 mean nightly bouts. Raising the level of suspicion increased manual detection rates from 22.6% to 40.5%. The artifact-based detection rate was 96% and provided additional localizing information in 10% of epilepsy patients. Conclusions REM sleep is common in the EMU, but bouts are few and brief. Capturing these bouts to maximize the yield of REM sleep in the EMU is made possible by automated artifact recognition whose results could enhance localization of the epileptogenic zone.

Temporal lobe epilepsy (TLE) is a common form of epilepsy that presents with complex partial seizures and in some cases with generalization to tonic-clonic activity that are often well controlled with antiepileptic medication in up to two thirds of patients. The remaining one third with “medically i

Study Objectives Rapid eye movement sleep (REM) usually suppresses interictal epileptiform discharges (IED) and seizures. However, breakthrough IEDs in REM sometimes continue. We aimed to determine if the amount of IED and seizures in REM, or REM duration, is associated with clinical trajectories. Methods Continuous electroencephalogram (EEG) recordings from the epilepsy monitoring unit (EMU) were clipped to at least 3 h of concatenated salient findings per day including all identified REM. Concatenated EEG files were analyzed for nightly REM duration and the “REM spike burden” (RSB), defined as the proportion of REM occupied by IED or seizures. Patient charts were reviewed for clinical data, including patient-reported peak seizure frequency. Logistic and linear regressions were performed, as appropriate, to explore associations between two explanatory measures (duration of REM and RSB) and six indicators of seizure activity (clinical trajectory outcomes). Results The median duration of REM sleep was 43.3 (IQR 20.9–73.2) min per patient per night. 59/63 (93.7%) patients achieved REM during EMU admission. 39/59 (66.1%) patients had breakthrough IEDs or seizures in REM with the median RSB at 0.7% (IQR 0%–8.4%). Every 1% increase in RSB was associated with 1.69 (95% CI = 0.47–2.92) more seizures per month during the peak seizure period of one’s epilepsy (p = 0.007). Conclusions Increased epileptiform activity during REM is associated with increased peak seizure frequency, suggesting an overall poorer epilepsy trajectory. Our findings suggest that RSB in the EMU is a useful biomarker to help guide about what to expect over the course of one’s epilepsy.

Importance Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. Objective To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. Design, Setting, and Participants A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. Main Outcomes and Measures SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. Results SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). Conclusions and Relevance In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

Despite growing use of critical care electroencephalography (ccEEG) to detect seizures and status epilepticus in the intensive care unit (ICU), integrating ccEEG findings with traditionally described benign EEG variants (BEVs) is a relatively new concept. BEV-like waveforms are now increasingly encountered in the ICU, and have also been explicitly included in proposed definitions of brief potentially ictal rhythmic discharges (BIRDs) in the ICU, bringing to the fore the question of if and which EEG patterns in critically ill patients can be safely deemed "benign". Though well-characterized as benign in healthy outpatients at low pre-test risk for neurologic disease, the significance of BEVs in the ICU remains largely unknown. Simultaneously, there has been mounting evidence to suggest that certain BEVs can arise from heterogeneous intracranial sources, including some pathologic generators. We conducted an extensive literature review on all known BEVs to assess what is known of BEVs in the ICU. Here we discuss critically ill BEVs and how to interpret them.

Objective To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in this nomenclature. Methods We defined seven spectrogram categories: “Solid Flames”, “Irregular Flames”, “Broadband-monotonous”, “Narrowband-monotonous”, “Stripes”, “Low power”, and “Artifact”. Ten electroencephalographers scored 115 spectrograms and the corresponding raw EEG samples. Gwet's agreement coefficient was used to calculate IRA. Results Solid Flames represented seizures or IIC patterns 69.4% of the time. Irregular Flames represented seizures or IIC patterns 38.7% of the time. Broadband-monotonous primarily corresponded with seizures or IIC (54.3%) and Narrowband-monotonous with focal or generalized slowing (43.8%). Stripes were associated with burst-suppression (37.2%) and generalized suppression (34.4%). Low Power category was associated with generalized suppression (94%). There was “near perfect” agreement for Solid Flames (κ = 94.36), Low power (κ = 92.61), and Artifact (κ = 93.72). There was “substantial agreement” for all other categories (κ = 74.65–79.49). Conclusions This EEG spectrogram nomenclature has high IRA among electroencephalographers. Significance The nomenclature can be a useful tool for EEG screening. Future studies are needed to determine if using this nomenclature shortens time to IIC identification, and how best to use it in practice to reduce time to intervention.

Objective The choice of anesthetic technique in carotid endarterectomy (CEA) has been controversial. This study compared the outcomes of general anesthesia (GA) and local anesthesia (LA) in CEA. Design Systematic review and meta-analysis of comparative studies. Setting Hospitals. Participants Adult patients undergoing CEA with either LA or GA. Interventions The effects of GA and LA on CEA outcomes were compared. Measurements and Main Results PubMed, OVID, Scopus, and Embase were searched to June 2018. Thirty-one studies with 152,376 patients were analyzed. A random effect model was used, and heterogeneity was assessed with the I2 and chi-square tests. LA was associated with shorter surgical time (weighted mean difference –9.15 min [–15.55 to –2.75]; p = 0.005) and less stroke (odds ratio [OR] 0.76 [0.62-0.92]; p = 0.006), cardiac complications (OR 0.59 [0.47-0.73]; p Conclusion The results from this study showed no inferiority of using LA to GA in patients undergoing CEA. Future investigations should be reported more systematically, preferably with randomization or propensity-matched analysis, and thus registries will facilitate investigation of this subject. Anesthetic choice in CEA should be individualized and encouraged where applicable.

Background. The optimal surgery in lesional neocortical temporal lobe epilepsy is unknown. Hippocampal electrocorticography maximizes seizure freedom by identifying normal-appearing epileptogenic tissue for resection and minimizes neuropsychological deficit by limiting resection to demonstrably epileptogenic tissue. We examined whether standardized hippocampal electrocorticography (hippocampography) guides resection for more consistent hippocampectomy than unguided resection in conventional electrocorticography focused on the lesion. Methods. Retrospective chart reviews any kind of electrocorticography (including hippocampography) as part of combined lesionectomy, anterolateral temporal lobectomy, and hippocampectomy over 8 years . Patients were divided into mesial (i.e., hippocampography) and lateral electrocorticography groups. Primary outcome was deviation from mean hippocampectomy length. Results. Of 26 patients, fourteen underwent hippocampography-guided mesial temporal resection. Hippocampography was associated with 2.6 times more consistent resection. The range of hippocampal resection was 0.7 cm in the mesial group and 1.8 cm in the lateral group (p=0.01). 86% of mesial group versus 42% of lateral group patients achieved seizure freedom (p=0.02). Conclusions. By rationally tailoring excision to demonstrably epileptogenic tissue, hippocampography significantly reduces resection variability for more consistent hippocampectomy than unguided resection in conventional electrocorticography. More consistent hippocampal resection may avoid overresection, which poses greater neuropsychological risk, and underresection, which jeopardizes postoperative seizure freedom.

Purpose This study sought to determine the state of quantitative EEG (QEEG) use in Canada, as QEEG may provide a partial solution to the issue of escalating EEG demand against insufficient health care resources. Methods A 10-item survey questionnaire was administered to participants at the annual meeting of the Canadian Association of Electroneurophysiology Technologists, which was held in parallel with the annual meeting of the Canadian Neurological Sciences Federation. Results At least 70% of the Canadian population has QEEG access through academic medical institutions with applicability to adults and children. QEEG was clinically used 50% in real-time and 50% retrospectively in the critical care and epilepsy monitoring units for long-term monitoring and automated seizure detection. QEEG trend use, montage use, and duration were variable. Conclusion To cope with insufficient health care resources, QEEG is in surprisingly frequent clinical use across Canada. There is no consensus on optimal QEEG trends and montages. The relative ubiquity of QEEG affords an excellent opportunity for research as increasing EEG demand outpaces dwindling health care resources into the foreseeable future.

Importance The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias. Objective To assess the reliability of experts in detecting IEDs in routine EEGs. Design, Setting, and Participants This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016. Main Outcomes and Measures Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts. Results Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%). Conclusions and Relevance This study’s findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.

Background Epilepsy and seizure awareness is gradually improving across Canada. With the strategic proposal for a Comprehensive Epilepsy Program in Manitoba (including formation of a new Pediatric Epilepsy Monitoring Unit (EMU)), a provincial strategy has been recommended outlining a path towards improved access to epilepsy care. We sought to qualify the current state of clinician knowledge and comfort towards diagnosis and management of this condition. Methods A qualitative online survey (Survey of Epilepsy and Seizure Awareness in Manitoba: An Evaluation (SESAME)), comprised of 36 short-answer questions, was delivered to primary care and specialist physicians in Manitoba. Results One hundred and eight subjects responded across varying medical disciplines. One hundred and one (93.5%) had previously managed patients with epilepsy, and 87 (80.6%) had previously ordered an electroencephalogram (EEG). A total of 63 (59.4%) had referred to a neurologist, with a lower proportion (30, 28.3%) referring specifically to an epileptologist. Only 36 respondents (33.3%) had heard of the International League Against Epilepsy (ILAE) guidelines. A total of 61 (56.5%) were unaware of invasive EEG techniques. Most (85, 78.7%) understood a role for surgery in treating epilepsy, with 12 (11.1%) unaware of surgical therapies beyond vagal nerve stimulation (VNS). Finally, less than half (44.2%) had heard about the Comprehensive Epilepsy Program in Manitoba, with nearly two-thirds (62.8%) indicating that they would like to have more information on epilepsy management. Conclusions The SESAME successfully identified strong awareness towards epilepsy, with identifiable lapses in knowledge that will benefit from a formal provincial-wide educational curriculum.

Interest in orexin receptor antagonism as a novel mechanism of action against seizures and epilepsy has increased in recent years. Loss of orexinergic activity is associated with rapid eye movement (REM) sleep onset, and REM sleep is generally protective against seizures. This paper discusses the dynamic modulation of seizure threshold by orexin through a postulated "orexi-cortical" axis in which the specific type of orexinergic activity exquisitely regulates sleep-wake states to modify ascending subcortical influences on cortical synchronization with profound subsequent consequences on seizure threshold. This paper also explores the current state of research into experimental orexinergic modulation of seizure threshold and suggests possible future research directions to fully understand the promise and peril of orexinergic manipulation in seizures and epilepsy.

Quantitatively evaluate whether screening with compressed spectral arrays (CSAs) is a practical and time-effective protocol for assisting expert review of continuous EEG (cEEG) studies in hospitalized adults.Three neurophysiologists reviewed the reported findings of the first 30 minutes of 118 cEEGs, then used CSA to guide subsequent review ("CSA-guided review" protocol). Reviewers viewed 120 seconds of raw EEG data surrounding suspicious CSA segments. The same neurophysiologists performed independent page-by-page visual interpretation ("conventional review") of all cEEGs. Independent conventional review by 2 additional, more experienced neurophysiologists served as a gold standard. We compared review times and detection rates for seizures and other pathologic patterns relative to conventional review.A total of 2,092 hours of cEEG data were reviewed. Average times to review 24 hours of cEEG data were 8 (±4) minutes for CSA-guided review vs 38 (±17) minutes for conventional review (p0.005). Studies containing seizures required longer review: 10 (±4) minutes for CSA-guided review vs 44 (±20) minutes for conventional review (p0.005). CSA-guided review was sensitive for seizures (87.3%), periodic epileptiform discharges (100%), rhythmic delta activity (97.1%), focal slowing (98.7%), generalized slowing (100%), and epileptiform discharges (88.5%).CSA-guided review reduces cEEG review time by 78% with minimal loss of sensitivity compared with conventional review.This study provides Class IV evidence that screening of cEEG with CSAs efficiently and accurately identifies seizures and other EEG abnormalities as compared with standard cEEG visual interpretation.

Seizures are known to occur in Creutzfeldt–Jakob disease (CJD). In the setting of a rapidly progressive condition with no effective therapy, determining appropriate treatment for seizures can be difficult if clinical morbidity is not obvious yet the electroencephalogram (EEG) demonstrates a worrisome pattern such as status epilepticus. Herein, we present the case of a 39-year-old man with CJD and electrographic seizures, discuss how this case challenges conventional definitions of seizures, and discuss a rational approach toward treatment. Coincidentally, our case is the first report of CJD in a patient with Stickler syndrome.

Since the formal characterization of sleep stages, there have been reports that seizures may preferentially occur in certain phases of sleep. Through ascending cholinergic connections from the brainstem, rapid eye movement (REM) sleep is physiologically characterized by low voltage fast activity on the electroencephalogram, REMs, and muscle atonia. Multiple independent studies confirm that, in REM sleep, there is a strikingly low proportion of seizures (~1% or less). We review a total of 42 distinct conventional and intracranial studies in the literature which comprised a net of 1458 patients. Indexed to duration, we found that REM sleep was the most protective stage of sleep against focal seizures, generalized seizures, focal interictal discharges, and two particular epilepsy syndromes. REM sleep had an additional protective effect compared to wakefulness with an average 7.83 times fewer focal seizures, 3.25 times fewer generalized seizures, and 1.11 times fewer focal interictal discharges. In further studies REM sleep has also demonstrated utility in localizing epileptogenic foci with potential translation into postsurgical seizure freedom. Based on emerging connectivity data in sleep, we hypothesize that the influence of REM sleep on seizures is due to a desynchronized EEG pattern which reflects important connectivity differences unique to this sleep stage.

Background: Burgeoning EEG demand has largely gone unmet with insufficient supply of manpower and equipment. Quantitative EEG (QEEG) may help compress large volumes of data for expedited review. We sought to determine the current use of QEEG in Canada through a national EEG technologist survey. Methods: A 10-item questionnaire was administered to participants at the 2016 meeting of the Canadian Association of Electroneurophysiology Technologists, which occurred in parallel with the Canadian Neurological Sciences Federation meeting. Results: A response rate of 63% (14/22) represented 12 institutions (11 adult, 6 paediatric) over six provinces with 73% of the national population. Only academic institutions (9/12) used QEEG, representing five provinces with 70% of the national population. Most institutions generated QEEG either real-time or retrospectively in the critical care and epilepsy monitoring units for long-term monitoring and automated seizure detection. The most used trends were spectrographic, seizure detection, and artifact detection. Montage use, QEEG duration, and timebase settings were highly variable. Conclusions: QEEG is in surprisingly frequent use across Canada. There is no consensus on optimal QEEG use, which mirrors uncertainty in the literature. The relative ubiquity of QEEG in Canada offers promise for collaborative multicentre research into unlocking the full potential of QEEG in enhancing patient care.

Being able to confidently ascertain the amount of sleep is critical to the clinical management of epilepsy. Sleep misperception is the phenomenon in which an individual underestimates the amount of time spent asleep. Little is known about sleep misperception in patients with epilepsy. We conducted retrospective chart reviews on individuals who self-identified as having epilepsy in a questionnaire database of patients undergoing polysomnography (PSG) at the Massachusetts General Hospital Sleep Laboratory. Our metric for sleep misperception was the difference between subjective and objective sleep latency (S-O SL) and subjective and objective total sleep time (S-O TST) with subjective values based on questionnaire and objective values based on PSG. We confirmed 64 patients with epilepsy. We then selected age- and sex-matched diagnostic PSG data for comparison from 50 patients with insomnia symptoms but no obstructive sleep apnea (OSA) and another 50 patients with OSA but no insomnia symptoms. In our cohort with epilepsy, the median SL overestimation was 20 min (p0.05), and the median TST underestimation was 45 min (p0.05). Sleep misperception was similar regardless of potential confounding factors such as categorical epilepsy refractoriness, cognitive impairment, or psychiatric comorbidity. Our findings suggest that sleep misperception occurs similarly in patients with epilepsy as in patients without epilepsy with insomnia. Our findings further support the potential clinical utility of objective PSG testing in patients with epilepsy, as this may not only identify occult OSA but also disclose sleep misperception.

Background: Epilepsy/seizure awareness is improving across Canada. With the formation of a Comprehensive Epilepsy Program in Manitoba (including a new Pediatric Epilepsy Monitoring Unit), a provincial strategy has been proposed outlining a path towards improved access to epilepsy care. We now sought to qualify the current state of clinician knowledge and comfort towards diagnosis and management of this condition. Methods: A qualitative online survey, comprised of 36 short-answer questions, was delivered to primary care and specialist physicians in Manitoba. Results:108 subjects responded, across varying medical disciplines. 101 (93.5%) have previously managed epilepsy patients, and 87 (80.6%) have previously ordered an EEG. A total of 63 (59.4%) have referred to a neurologist, with a lower proportion (30, 28.3%) referring specifically to an epileptologist. 36 respondents (33.3%) have heard of the ILAE guidelines, with 43 (63.2%) reporting refractory epilepsy to be -defined by the failure of 3 (or more) medications. 61 (56.5%) were unaware of invasive EEG techniques. Most (85, 78.7%) understood a role for surgery in treating epilepsy, with 12 (11.1%) unaware of surgical therapies beyond VNS. Conclusions: SESAME successfully identified strong awareness towards epilepsy, with small lapses in knowledge that will benefit from a formal provincial-wide educational curriculum.

Introduction Rapid eye movement (REM) sleep typically inhibits seizures and epileptiform activity in persons with epilepsy. In animal models, REM sleep without atonia has been shown to facilitate seizures and epileptiform activity. We sought to rigorously examine muscle tone in the REM sleep of persons with epilepsy for the first time using a novel quantitative electroencephalographic (QEEG) technique. Methods QEEG software (Persyst, USA) analyzed continuously recorded EEG in the University of Manitoba Epilepsy Monitoring Unit (EMU) over a 12-month period to quantify muscle artifact intensity during REM sleep. Additional electrooculogram and muscle electrodes were included for REM sleep identification. QEEG represented muscle artifact as green bars of varying intensity. The maximal degree of greenness ( M ) was calculated as RGB values in order to represent the overall muscle tone of a REM sleep epoch. Results 790 REM sleep epochs were identified from 94 patients (mean age 37.6 years, 66% female). 58 patients were on medications capable of affecting muscle tone. Of patients not on these medications, 30 were epileptic ( n = 272 epochs) and 6 were nonepileptic controls ( n = 56 epochs). While half of epileptic patients were male, all nonepileptic patients were female. Age and gender were not significantly correlated with M . M was significantly lower in epileptic patients ( M = 5.05 vs. 8.68, p M was also calculated for patients outside the 12-month period: a nonepileptic patient with REM sleep behavior disorder ( M = 13.95), and another epileptic patient during a generalized tonic-clonic seizure ( M = 128). Conclusion This study shows for the first time that QEEG can be used to detect and quantify muscle tone in persons with epilepsy. We found that REM sleep muscle tone was significantly lower in epileptic patients than nonepileptic controls. Given the association of REM sleep without muscle atonia and seizure facilitation in animal models, this phenomenon may be a homeostatic protective mechanism against seizures in persons with epilepsy.

Background We are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer’s disease, multiple sclerosis, and stroke. Description ND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2. Term development in ND has primarily extended the OGMS terms ‘disease’, ‘diagnosis’, ‘disease course’, and ‘disorder’. We have imported and utilize over 700 classes from related ontology efforts including the Foundational Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status, and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the representation of Alzheimer’s disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled file athttp://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker. Conclusion ND seeks to provide a formal foundation for the representation of clinical and research data pertaining to neurological diseases. ND will enable its users to connect data in a robust way with related data that is annotated using other terminologies and ontologies in the biomedical domain.