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Background: Neurologic complications contribute significantly to morbidity and mortality in acute liver failure (ALF). However, clinical assessment of neurologic function in this population is often challenging. Continuous electroencephalography (cEEG) is a low-risk, noninvasive diagnostic tool that can monitor real-time cerebral function. We aimed to investigate cEEG findings and prognostic significance of specific EEG features in a cohort of strictly defined patients with ALF., Methods: This was a retrospective, single-center study of adult patients with ALF who underwent cEEG monitoring for at least 6 h between 2013 and 2022. Clinical, laboratory, imaging, and treatment characteristics were evaluated. cEEG variables included background continuity, background frequency, the presence of sporadic epileptiform discharges, rhythmic or periodic patterns, and electrographic or electroclinical seizures. The primary outcome was mortality or transition to end-of-life care during the index admission., Results: A total of 32 patients with ALF were included. 56.3% of patients had rhythmic or periodic patterns, of which the majority were generalized periodic discharges (37.5%). 12.5% of patients had sporadic epileptiform discharges, and 6.3% of patients demonstrated electrographic or clinical seizures. Eighteen (56.3%) patients died or were transitioned to end-of-life care during the index admission. Worsening background continuity or frequency over the course of the cEEG recording was significantly associated with poor outcome (p = 0.001, p = 0.007, respectively), with a 100% mortality rate in patients demonstrating these EEG trends. A worst recorded continuity of suppression, attenuation, and burst-suppression was also associated with poor outcome (p = 0.012). The presence of rhythmic or periodic patterns, sporadic epileptiform discharges, or seizures was not predictive of outcome., Conclusions: Worsening cEEG background continuity or frequency is associated with poor outcome in adults with ALF. cEEG may contribute useful prognostic information in these patients, in conjunction with other laboratory and clinical markers of disease severity., Competing Interests: Declarations. Conflict of interest: The authors declare no conflicts of interest. Ethical approval: The study was approved by the Emory University Institutional Review Board., (© 2025. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)

Background and Objectives: The use of rapid response EEG (rr-EEG) has recently expanded in limited-resource settings and as a supplement to conventional EEG to rapidly detect and treat nonconvulsive status epilepticus. The study objective was to test the accuracy of an rr-EEG's automated seizure burden estimator (ASBE)., Methods: This is a retrospective observational study using multiple blinded reviewers. All consecutive clinical rr-EEG procedures performed between November 2019 and February 2021 at Yale New Haven Hospital, one affiliated community hospital, and one affiliated inner-city regional hospital were included. Three reviewers blindly reviewed each EEG. The reference standard was 2/3 agreement. The co-primary outcome measures were the negative predictive value (NPV) of the ASBE for the detection of electrographic status epilepticus (ESE) or possible ESE (ESE/pESE) (to be used as a screening method to exclude ESE without the need for urgent expert review) and the positive predictive value (PPV, to be used for immediate treatment without requiring urgent expert review). These were assessed using a variety of seizure burden cutoffs determined by the algorithm (>1%, >10%, >20%, >50%, and >90%)., Results: In the first 2 hours, a >10% burden cutoff detected 86% (95% CI 42%-100%) of studies with ESE alone and 88% (68%-97%) with ESE/pESE; this >10% cutoff had a NPV of 99% (97%-100%) for ESE and 98% (95%-100%) for ESE/pESE. The specificity at this threshold was 79% (73%-84%) for ESE and 84% (79%-89%) for ESE/pESE, but the PPV was low at 11% (4%-23%) for ESE and 39% (26%-53%) for ESE/pESE. A >90% burden cutoff was 97% (94%-99%) specific for detecting ESE (PPV 33% [7%-70%]) and 99% (97%-100%) specific for detecting ESE/pESE [PPV 78% (40%-97%)], although the sensitivity dropped significantly to 29% (13%-51%) for ESE/pESE and 43% (10%-82%) for ESE at the >90% threshold., Discussion: The ASBE has high specificity at >90% seizure burden threshold for detecting ESE and ESE/pESE, with good PPV for ESE/pESE, though with only low-to-moderate sensitivity; at this threshold, it can be used to help triage patients for immediate treatment/transfer, urgent expert review, and additional CEEG. A >10% threshold has a high sensitivity, detecting approximately 85% of patients with ESE; at this lower cutoff, it can be used as a screening tool to exclude ESE with >95% NPV., Classification of Evidence: This study provides Class II evidence that ASBE software can reliably exclude ESE (98% negative predictive value using a <10% burden cutoff) without expert review in most patients requiring rapid response EEG.

Purpose: To investigate the utility of a 15-minute online module to improve the self-confidence and knowledge of neurology trainees when screening an EEG., Methods: We developed a fast, convenient, and accessible 15-minute online module to teach basic concepts of EEG screening using a five-step approach. To assess the efficacy of the module among neurology trainees, three surveys were developed. The EEG module and surveys were distributed to neurology trainees at multiple institutions in the United States. Associations between continuous variables were evaluated using t-test and ANOVA test., Results: A total of 199 residents from 7 different adult neurology residency programs participated in the study. We obtained a response rate of 38% (76/199) for EEG survey 1 and 25% (49/199) for the demographic survey, among junior and senior residents; 22 senior residents completed EEG survey 2. The online EEG module improved confidence when interpreting an EEG among junior residents (1.67 vs. 2.56, p < 0.0001). Senior residents improved their EEG survey score after completing the online module (53.9 vs. 68.6%, p < 0.001). Most of the trainees would recommend the online module to other trainees (93.9%) and would consider including it in an introductory course (93.9%)., Conclusions: A brief and accessible online EEG module was easily implemented as an early introduction to EEG screening for junior neurology trainees and to improve EEG screening skills for senior trainees. These findings clarify specific areas where EEG learning may be optimized and reinforce the importance of implementing innovative curricula that are accessible and efficient for all neurology trainees., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Competing Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: We described patterns and trends in ED use among adults with epilepsy in the United States., Methods: Utilizing inpatient and ED discharge data from seven states, we conducted a cross-sectional analysis to identify adult ED visits diagnosed with epilepsy or seizures from 2010 to 2019. Using ED visit counts and estimates of state-level epilepsy prevalence, we calculated ED visit rates overall and by payer, condition, and year., Results: Our data captured 304,935 ED visits with epilepsy as a primary or secondary diagnosis in 2019. Across the seven states, visit rates ranged between 366 and 726 per 1000 and were higher than rates for adults without epilepsy in all states but one. ED visit rates were highest among Medicare and Medicaid beneficiaries (vs commercial or self-pay). Adults with epilepsy were more likely to be admitted as inpatients. Visits for nervous system disorders were 6.3-8.2 times higher among people with epilepsy, and visits for mental health conditions were 1.2-2.6 times higher. Increases in ED visit rates from 2010 to 2019 among people with epilepsy exceeded increases among adults without by 6.0-27.3 percentage points., Conclusion: Adults with epilepsy visit the ED frequently and visit rates have been increasing over time. These results underscore the importance of identifying factors contributing to ED use and designing tailored interventions to improve ambulatory care quality., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The availability and use of broadband internet play an increasingly important role in health care and public health., Objective: This study examined the associations between broadband internet availability and use with drug overdose deaths in the United States., Methods: We linked 2019 county-level drug overdose death data in restricted-access multiple causes of death files from the National Vital Statistics System at the US Centers for Disease Control and Prevention with the 2019 county-level broadband internet rollout data from the Federal Communications Commission and the 2019 county-level broadband usage data available from Microsoft's Airband Initiative. Cross-sectional analysis was performed with the fixed-effects regression method to assess the association of broadband internet availability and usage with opioid overdose deaths. Our model also controlled for county-level socioeconomic characteristics and county-level health policy variables., Results: Overall, a 1% increase in broadband internet use was linked with a 1.2% increase in overall drug overdose deaths. No significant association was observed for broadband internet availability. Although similar positive associations were found for both male and female populations, the association varied across different age subgroups. The positive association on overall drug overdose deaths was the greatest among Hispanic and Non-Hispanic White populations., Conclusions: Broadband internet use was positively associated with increased drug overdose deaths among the overall US population and some subpopulations, even after controlling for broadband availability, sociodemographic characteristics, unemployment, and median household income., (©Ioannis Karakis, Genti Kostandini, Konstantinos Tsamakis, Velma Zahirovic-Herbert. Originally published in the Online Journal of Public Health Informatics (https://ojphi.jmir.org/), 26.06.2024.)

Background: In intensive care units (ICUs), critically ill patients are monitored with electroencephalography (EEG) to prevent serious brain injury. EEG monitoring is constrained by clinician availability, and EEG interpretation can be subjective and prone to interobserver variability. Automated deep-learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, existing uninterpretable (black-box) deep-learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of both trust and adoption by clinicians., Methods: We developed an interpretable deep-learning system that accurately classifies six patterns of potentially harmful EEG activity - seizure, lateralized periodic discharges (LPDs), generalized periodic discharges (GPDs), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), and other patterns - while providing faithful case-based explanations of its predictions. The model was trained on 50,697 total 50-second continuous EEG samples collected from 2711 patients in the ICU between July 2006 and March 2020 at Massachusetts General Hospital. EEG samples were labeled as one of the six EEG patterns by 124 domain experts and trained annotators. To evaluate the model, we asked eight medical professionals with relevant backgrounds to classify 100 EEG samples into the six pattern categories - once with and once without artificial intelligence (AI) assistance - and we assessed the assistive power of this interpretable system by comparing the diagnostic accuracy of the two methods. The model's discriminatory performance was evaluated with area under the receiver-operating characteristic curve (AUROC) and area under the precision-recall curve. The model's interpretability was measured with task-specific neighborhood agreement statistics that interrogated the similarities of samples and features. In a separate analysis, the latent space of the neural network was visualized by using dimension reduction techniques to examine whether the ictal-interictal injury continuum hypothesis, which asserts that seizures and seizure-like patterns of brain activity lie along a spectrum, is supported by data., Results: The performance of all users significantly improved when provided with AI assistance. Mean user diagnostic accuracy improved from 47 to 71% (P<0.04). The model achieved AUROCs of 0.87, 0.93, 0.96, 0.92, 0.93, and 0.80 for the classes seizure, LPD, GPD, LRDA, GRDA, and other patterns, respectively. This performance was significantly higher than that of a corresponding uninterpretable black-box model (with P<0.0001). Videos traversing the ictal-interictal injury manifold from dimension reduction (a two-dimensional representation of the original high-dimensional feature space) give insight into the layout of EEG patterns within the network's latent space and illuminate relationships between EEG patterns that were previously hypothesized but had not yet been shown explicitly. These results indicate that the ictal-interictal injury continuum hypothesis is supported by data., Conclusions: Users showed significant pattern classification accuracy improvement with the assistance of this interpretable deep-learning model. The interpretable design facilitates effective human-AI collaboration; this system may improve diagnosis and patient care in clinical settings. The model may also provide a better understanding of how EEG patterns relate to each other along the ictal-interictal injury continuum. (Funded by the National Science Foundation, National Institutes of Health, and others.).

Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The author has served as a consultant in a UCB health equity advisory board and a GSK research study.

Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Competing Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Overinterpretation of EEG is an important contributor to the misdiagnosis of epilepsy. For the EEG to have a high diagnostic value and high specificity, it is critical to recognize waveforms that can be mistaken for abnormal patterns. This article describes artifacts, normal rhythms, and normal patterns that are prone to being misinterpreted as abnormal. Artifacts are potentials generated outside the brain. They are divided into physiologic and extraphysiologic. Physiologic artifacts arise from the body and include EMG, eyes, various movements, EKG, pulse, and sweat. Some physiologic artifacts can be useful for interpretation such as EMG and eye movements. Extraphysiologic artifacts arise from outside the body, and in turn can be divided into the environments (electrodes, equipment, and cellphones) and devices within the body (pacemakers and neurostimulators). Normal rhythms can be divided into awake patterns (alpha rhythm and its variants, mu rhythm, lambda waves, posterior slow waves of youth, HV-induced slowing, photic driving, and photomyogenic response) and sleep patterns (POSTS, vertex waves, spindles, K complexes, sleep-related hypersynchrony, and frontal arousal rhythm). Breach can affect both awake and sleep rhythms. Normal variants or variants of uncertain clinical significance include variants that may have been considered abnormal in the early days of EEG but are now considered normal. These include wicket spikes and wicket rhythms (the most common normal pattern overread as epileptiform), small sharp spikes (aka benign epileptiform transients of sleep), rhythmic midtemporal theta of drowsiness (aka psychomotor variant), Cigánek rhythm (aka midline theta), 6 Hz phantom spike-wave, 14 and 6 Hz positive spikes, subclinical rhythmic epileptiform discharges of adults (SREDA), slow-fused transients, occipital spikes of blindness, and temporal slowing of the elderly. Correctly identifying artifacts and normal patterns can help avoid overinterpretation and misdiagnosis. This is an educational review paper addressing a learning objective of the International League Against Epilepsy (ILAE) curriculum., (© 2023 The Authors. Epileptic Disorders published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Objective: Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG., Methods: We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings., Results: Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice., Conclusions: Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback., Significance: This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: E. Duhaime and S. Kapur of Centaur Labs developed and have a financial interest in the DiagnosUs app used to collect the data. J. Barfuss, F. Nascimento, I. Karakis, M. Ng, A. Herlopian, A. Lam, D. Maus, J. Halford, S. Cash, M. Westover, and J. Jing report no disclosures., (© 2023 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.)

Purpose: We conducted an observational study to investigate the opinions of neurologists and psychiatrists all around the world who are taking care of patients with seizures [epilepsy and functional seizures (FS)]., Methods: Practicing neurologists and psychiatrists from around the world were invited to participate in an online survey. On 29th September 2022, an e-mail including a questionnaire was sent to the members of the International Research in Epilepsy (IR-Epil) Consortium. The study was closed on 1st March 2023. The survey, conducted in English, included questions about physicians' opinions about FS and anonymously collected data., Results: In total, 1003 physicians from different regions of the world participated in the study. Both neurologists and psychiatrists identified "seizures" as their preferred term. Overall, the most preferred modifiers for "seizures" were "psychogenic" followed by "functional" by both groups. Most participants (57.9%) considered FS more difficult to treat compared to epilepsy. Both psychological and biological problems were considered as the underlying cause of FS by 61% of the respondents. Psychotherapy was considered the first treatment option for patients with FS (79.9%)., Conclusion: Our study represents the first large-scale attempt of investigating physicianś attitudes and opinions about a condition that is both frequent and clinically important. It shows that there is a broad spectrum of terms used by physicians to refer to FS. It also suggests that the biopsychosocial model has gained its status as a widely used framework to interpret and inform clinical practice on the management of patients., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ali A. Asadi-Pooya: Honoraria from Cobel Daruo, Ronak, and RaymandRad; Royalty: Oxford University Press (Book publication); Grant from the National Institute for Medical Research Development. Eugen Trinka has received personal fees from Arvelle/Angelini, Argenx, UCB, Eisai, Bial, Böhringer Ingelheim, Medtronic, Everpharma, Epilog, GSK, Biogen, Takeda, Liva-Nova, Newbridge, Novartis, Sanofi, Sandoz, Sunovion, GW Pharmaceuticals, Marinus; grants from Austrian Science Fund (FWF), Österreichische Nationalbank, European Union, GSK, Biogen, Eisai, Novartis, Red Bull, Bayer, and UCB; other from Neuroconsult Ges.m.b.H., outside this work. Simona Lattanzi has received speaker’s or consultancy fees from Angelini, Eisai, GW Pharmaceuticals, and UCB Pharma, and has served on advisory boards for Angelini, Arvelle Therapeutics, Bial, EISAI, and GW Pharmaceuticals. Mansur Kutlubaev was supported by the Bashkir State Medical University Strategic Academic Leadership Program (PRIORITY-2030). Yamile Calle-López has received fees as a speaker from UCB Pharma. B Mesraoua received Grant from Qatar National Research Fund (QNRF) and HMC Internal Research Grant. Others: none., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: This study investigated the success rate of antiseizure medications (ASMs) withdrawal following MRI Guided Laser Interstitial Thermal Therapy (MRg-LITT) for extra-temporal lobe epilepsy (ETLE), and identified predictors of seizure recurrence., Methods: We retrospectively assessed 27 patients who underwent MRg-LITT for ETLE. Patients' demographics, disease characteristics, and post-surgical outcomes were evaluated for their potential to predict seizure recurrence associated with ASMs withdrawal., Results: The median period of observation post MRg-LITT was 3 years (range 18 - 96 months) and the median period to initial ASMs reduction was 0.5 years (range 1-36 months). ASMs reduction was attempted in 17 patients (63%), 5 (29%) of whom had seizure recurrence after initial reduction. Nearly all patient who relapsed regained seizure control after reinstitution of their ASMs regimen. Pre-operative seizure frequency (p = 0.002) and occurrence of acute post-operative seizures (p = 0.01) were associated with increased risk for seizure recurrence post ASMs reduction. At the end of the observation period, 11% of patients were seizure free without drugs, 52% were seizure free with drugs and 37% still experienced seizures despite ASMs. Compared with pre-operative status, the number of ASMs was reduced in 41% of patients, unchanged in 55% of them and increased in only 4% of them., Conclusions: Successful MRg-LITT for ETLE allows for ASMs reduction in a significant portion of patients and complete ASMs withdrawal in a subset of them. Patients with higher pre-operative seizure frequency or occurrence of acute post operative seizures exhibit higher chances relapse post ASMs reduction., Competing Interests: Declaration of Competing Interest JTW has received research support from and/or has served as a paid consultant for Medtronic (consulting and research support), Neuropace (consulting and research support), ClearPoint/MRI Interventions (consulting), and AiM Medical (consulting). REG has received research support from and/or has served as a paid consultant for Abbott (consulting and research support), Boston Scientific (consulting and research support), SanBio (consulting and research support), Voyager Therapeutics (consulting and research support), Medtronic (consulting and research support), Neuropace (consulting and research support), and Zimmer Biomet (consulting). DLD has received research support from Medtronic, Inc. REM receives research support from Medtronic for the SLATE trial. IK has served as a paid consultant from Glaxo-Smith-Klein. The remaining authors have no conflicts of interest. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines., (Copyright © 2023 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Purpose: Stereotactic laser amygdalohippocampotomy (SLAH) is a minimally invasive surgical treatment for drug-resistant temporal lobe epilepsy (TLE) that has comparable rates of seizure freedom to traditional open resective TLE surgery. The objective of this study was to determine psychiatric outcome (i.e., depression and anxiety changes, psychosis) after SLAH, to explore possible contributory factors to these changes, and to determine the prevalence of de novo psychopathology., Methods: We explored mood and anxiety in 37 adult patients with TLE undergoing SLAH using the Beck psychiatric symptoms scales (i.e., Beck Depression Inventory-II [BDI-II] and Beck Anxiety Inventory [BAI]) preoperatively and 6 months following surgery. Multivariable regression analysis was conducted to identify predictors of worse depression or anxiety symptoms following SLAH. The prevalence of de novo psychopathology following SLAH was also determined., Results: We found a significant decrease in BDI-II (mean decline from 16.3 to 10.9, p = 0.004) and BAI (mean decline from 13.3 to 9.0, p = 0.045) scores following SLAH at the group level. While the rate of resolution of depression (from 62% to 49%) did not achieve statistical significance (p = 0.13, McNemar's), the rate of resolution of anxiety (from 57% to 35%) was statistically significant (p = 0.03, McNemar's). The de novo rate of psychopathology (i.e., new onset depression or anxiety) following SLAH was 1 of 7 (14%). Using a metric of meaningful change rather than complete symptom resolution, 16 of 37 (43%) patients experienced improvement in depression and 6 of 37 (16%) experienced worsening. For anxiety, 14 of 37 (38%) experienced meaningful improvement and 8 of 37 (22%) experienced worsening. Baseline performance on the Beck Scales was the only factor contributing to outcome status., Discussion: In one of the first studies to evaluate psychiatric outcomes after SLAH, we found promising overall trends toward stability or significant improvement in symptom burden at the group level for both depression and anxiety. There was also a significant improvement in clinical anxiety, though the decrease in clinical depression was not significant, likely owing to the limitations of sample size. SLAH may improve overall psychiatric symptoms, similarly to traditional resective TLE surgery, but de novo psychopathology and postoperative psychiatric morbidity remain significant issues, and larger samples are necessary to determine causal contributory factors., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This project was supported by grants received by Dr. Daniel Drane from the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) [K23 NSO49100, K02NS070960, R01NS088748]. Dr. Willie is supported by R01MH120194 and P41EB018783. Dr. Dickey is supported by the Georgia CTSA grants UL1TR002378 and KL2TR002381. Drs. Willie and Gross are paid consultants of Medtronic, Inc., which produces devices (Visualase Laser Interstitial Thermal Therapy System) used in laser surgeries described in this manuscript., (Copyright © 2023. Published by Elsevier Inc.)