To investigate the frequency, predictors, and clinical implications of diagnostic reassessment in patients previously diagnosed with childhood-onset epilepsy during the transition period to adult care at a tertiary epilepsy center. We conducted a retrospective cohort study of 317 patients previously diagnosed with childhood-onset epilepsy who underwent diagnostic reassessment between April 2018 and December 2023 at age 16 years or older at a tertiary epilepsy center in Japan. Diagnostic revision was defined as a newly established or corrected epilepsy/epilepsy syndrome diagnosis, identification of structural or genetic/metabolic etiologies, or diagnosis of non-epileptic conditions. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of diagnostic revision. A new or revised diagnosis was established in 60 of the 317 patients (18.9%). Independent predictors of diagnostic revision included exclusively non-motor seizures (adjusted odds ratio [aOR] = 7.610; 95% confidence interval [CI]: 2.660-21.767; p < 0.001) and weekly or monthly seizure frequency (aOR = 3.370; 95% CI: 1.265-8.976; p = 0.015), whereas prior visits to other epilepsy centers were strongly protective (aOR = 0.047; 95% CI: 0.015-0.146; p < 0.001). Sensitivity analyses yielded consistent results. Newly identified etiologies include focal cortical dysplasia, hippocampal sclerosis, and pathogenic genetic variants such as CDKL5, PCDH19, and SYNGAP1. Diagnostic reassessment facilitated antiseizure medication withdrawal in patients with self-limited epilepsy and non-epileptic events. Nearly one in five patients previously diagnosed with childhood-onset epilepsy required diagnostic revision during the transition to adult care. Non-motor seizure semiology and moderate seizure frequency were the major predictors of diagnostic revision, highlighting the diagnostic uncertainty associated with subtle clinical presentations. These findings highlight that systematic reassessment, especially for individuals who have not previously undergone specialized epilepsy center evaluation, is essential for achieving an accurate diagnosis and optimizing management in adulthood. Many people diagnosed with epilepsy in childhood continue medical care into adulthood, but their diagnosis may need to be reviewed later in life. In this study, nearly one in five patients who had been diagnosed with childhood-onset epilepsy required a change or refinement of their diagnosis when reassessed after age 16. Patients with seizures without clear physical movements and those whose seizures occurred weekly or monthly were more likely to need diagnostic review. These findings highlight the importance of reviewing the diagnosis during adolescence, especially for patients who have not previously been evaluated at specialized epilepsy centers.

Characterize electroencephalogram (EEG) dynamics during propofol withdrawal in patients with generalized convulsive status epilepticus (GCSE) and explore their association with functional outcomes. We conducted a retrospective cohort study of adult patients with GCSE who received continuous EEG monitoring and propofol sedation. EEG data were analyzed during two predefined periods: during sedation (0.5-1.5 mg/kg/h) and 4 h after complete sedation withdrawal. Preprocessed EEG signals underwent spectral and complexity analyses, including relative spectral power across frequency bands, alpha-delta ratio, median frequency (MF), spectral edge frequency 85% (SEF85), and sample entropy. Principal component analysis was applied to EEG trajectory vectors for dimensionality reduction; hierarchical clustering was used to identify distinct evolution patterns during sedation withdrawal. Associations between EEG patterns, SE duration, and clinical outcomes were assessed. Twenty-one patients were included. At the population level, EEG features shifted toward faster and more complex activity after sedation withdrawal, with decreased delta power, increased theta, alpha, and beta power, and greater signal complexity. Clustering analysis identified two distinct EEG trajectories: one subgroup (24%) showed marked recovery of cortical dynamics with significant increases in frequency metrics and entropy, while the other (76%) demonstrated minimal changes, with persistent delta-band dominance and low complexity. Longer SE duration was significantly associated with the minimal-change group (p = 0.007). No significant differences in functional outcomes were observed between groups. EEG dynamics during propofol withdrawal in patients with GCSE evolve along distinct trajectories. Two distinct EEG trajectories emerged: one toward normalization and another with persistently slow, low-complexity patterns. The majority of patients did not normalize their EEG after sedation withdrawal. Notably, patients with prolonged GCSE were overrepresented in the group lacking EEG recovery. However, these trajectories could not be correlated with functional prognosis. These findings underscore the potential of early EEG trajectories as markers of cerebral recovery. We studied how brain activity changes after stopping the sedative propofol in adults treated for severe, long-lasting seizures. Most patients showed little improvement in their brain activity after sedation was stopped, while a smaller group showed signs of recovery. Patients whose seizures lasted longer were more likely to have little or no improvement in their brain activity patterns. These early brain activity patterns did not predict long-term functional outcomes but may help identify how the brain is recovering after severe seizures.

To examine the real-world experience, comorbidities, and mental health of patients with epilepsy (PwE) who are currently receiving antiseizure medication (ASM) treatment. A web-enabled survey of PwE was conducted from July-September 2023. Patients were recruited via patient panels or physician referrals. US residents, ≥18 years old, with physician-confirmed diagnosis of epilepsy for ≥1 year, self-reported focal seizures, ≥1 seizure per month, past/present use of ≥2 ASMs, and currently receiving an ASM for ≥1 month, were eligible. Self-reported aspects of the treatment journey and disease burden were examined, including four validated patient-reported outcome measures: Quality of Life in Epilepsy Inventory-10 (QOLIE-10), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Work Productivity and Activity Impairment questionnaire (WPAI). Healthcare resource utilization and perceived levels of caregiver burden were also assessed. Of 170 patients surveyed, 66.5% reported >1 seizure per month, 75.3% rated their seizures as moderate-to-highly severe, and 72.9% reported ≥3 non-seizure symptoms despite ASM treatment. The most common non-seizure symptoms were mood issues (76.5%), fatigue/lack of energy (72.4%), and problems with sleep (68.8%). Anxiety (49.4%), migraines (40.6%), and depression (40.0%) were the top reported physician-diagnosed comorbidities. The mean QOLIE-10 score was 44.5 (SD, 17.5), indicative of a notable impact on QoL. Overall, 63.5% and 46.5% of patients exhibited moderate-to-severe depression and anxiety based on PHQ-9 and GAD-7, respectively. Using the WPAI, 60.6% mean work productivity loss was observed, driven by presenteeism. Patients averaged 9.4 outpatient visits and 2.8 emergency visits and/or hospitalizations annually. Of patients requiring caregivers (60.6%, n = 103), 68.9% agreed day-to-day and emotional demands from their epilepsy negatively impacted their family/caregivers. Collectively, these findings provide a broad perspective of the burden of illness experienced by PwE who are currently receiving treatment and demonstrate unmet needs for additional therapies that can improve patient experience. The primary treatment goals for epilepsy are to maximize seizure control, reduce side effects of medication, and improve quality of life. In our study, US patients with epilepsy currently using antiseizure medications used a survey to report their perspectives on the epilepsy treatment journey and ongoing burdens from the disease. We found that despite receiving treatment, patients still had a high frequency/severity of seizures, were dissatisfied with medication side effects, reported depression, anxiety, and reduced work productivity, and perceived that their disease had negative impacts on caregivers. New medications that improve the experience for patients with epilepsy are needed.

The SCN1A gene is implicated in a broad spectrum of epilepsy phenotypes, ranging from self-limited genetic epilepsy with febrile seizures plus (GEFS+) to severe developmental and epileptic encephalopathies such as Dravet syndrome (DS). While fenfluramine (FFA) has demonstrated strong efficacy in DS, its role in SCN1A-related epilepsies beyond DS has not been thoroughly investigated. We conducted a multicenter observational study including 11 patients with SCN1A-related GEFS+ who received FFA as adjunctive therapy. All patients had previously failed to achieve adequate seizure control with valproate and, in most cases, additional antiseizure medications. FFA was introduced following the DS titration protocol, with a mean dose of 0.39 mg/kg/day. FFA addition led to a mean seizure frequency reduction of 91%, with more than half of the patients achieving complete seizure freedom. Reduced EEG abnormalities were documented in 5/11 patients of the cohort, including complete normalization in 3/11 patients. Furthermore, subjective caregiver reports indicated perceived improvements in patients' alertness and behavioral responses. FFA was well tolerated, with only mild and transient adverse events reported. These findings support the potential role of FFA as an effective and well-tolerated treatment option in patients with SCN1A-related GEFS+. PLAIN LANGUAGE SUMMARY: GEFS+ is a genetic epilepsy frequently caused by changes in the SCN1A gene. In a multicenter real-world study of 11 people with SCN1A-related GEFS+, adding fenfluramine to usual care substantially reduced seizures, with several becoming seizure-free. EEG recordings improved, and caregivers reported better alertness in some patients. Treatment was generally well tolerated, with only mild, temporary side effects.

CDKL5 deficiency disorder (CDD) is a severe neurodevelopmental encephalopathy characterized by early-onset, treatment-resistant epilepsy. Mice lacking CDKL5 display several clinically relevant phenotypes, but spontaneous seizures are not consistently reported, and it is unknown if CDD model mice are susceptible to sensory stimulus-triggered seizures, a well-documented clinical feature of CDD. Here, we tested the hypothesis that CDKL5 deficiency confers susceptibility to audiogenic seizures (AGS). We exposed adult male Cdkl5 knockout, female heterozygous, and wildtype littermates (P80-217) to audiogenic challenges and, in a separate cohort, monitored for spontaneous seizures. Audiogenic stimulation triggered severe, lethal (80%) seizures in Cdkl5 knockout mice. In contrast, heterozygous mice were largely resistant to audiogenic stimulus (92% survival). These findings establish susceptibility to AGS as a highly penetrant phenotype in a CDD mouse model. Furthermore, spontaneous seizures were detected in a subset of Cdkl5 knockout mice during chronic video-EEG monitoring. AGS may provide a translationally relevant screen for investigating hyperexcitability and for evaluating potential therapeutics to prevent seizures in CDD. PLAIN LANGUAGE SUMMARY: CDKL5 deficiency disorder (CDD) is a severe genetic condition causing early-onset seizures. Mice with the same mutation are useful models but don't consistently have epilepsy. We tested if these mice in our lab are sensitive to sound-triggered seizures. We discovered that male CDD mice are highly vulnerable to sound, which triggered severe seizures in most of them. Female CDD mice and normal mice were resistant. This is the first report of sound-triggered seizures in a CDD model and provides a useful new method to study epilepsy in CDD and screen for antiseizure treatments.

Postimplantation assessment of the position of depth EEG electrodes for intracerebral recordings in patients with refractory focal epilepsy can be performed with MRI or with CT after coregistration to a preimplantation MRI. While both methods offer risks and advantages, postimplantation MRI risks depend on the electrode heating profile under different MRI conditions. We aimed to assess the MRI-related heating of Dixi microdeep electrodes at 1.5T in multiple electrode configurations and with varying levels of radiofrequency (RF) power. In vitro tests of heating due to RF power deposition were performed according to the F2182-19e2 standard from the ASTM (American Society for Testing and Materials International). A 10-contact Dixi microdeep electrode was inserted into the gel within the ASTM head-torso phantom, and the temperature was recorded from selected electrode contacts during MRI. Tests were performed with the electrode positioned in various locations in straight and coiled configurations, with coil diameters from 6 to 25 cm. MRI was conducted on a 1.5T Philips Achieva scanner using the transmit-receive body coil. Significant heating was observed for all configurations where more than 12 cm of the electrode was in the RF coil, apart from those with an applied specific absorption rate (SAR) ≤0.16 W/kg and with additional coiling of the electrode lead using a diameter of ≤6 cm. The worst-case configurations, reaching a maximum temperature of 70°C (temperature rise 48°C), occurred where the electrode end was straight or looped with a large-diameter (25 cm) loop. Heating was greatest in the contact furthest from the tip. Dixi microdeep electrodes demonstrate heating levels capable of causing serious injury during MRI, but using a conservative SAR limit of 0.1 W/kg and coiling the electrode lead to a diameter of ≤6 cm appears to reduce the heating risk. Electrodes positioned within the brain for planning epilepsy surgery can heat up during MRI. Using a standard test object mimicking the electrical properties of the human body, we measured heating of Dixi microdeep depth electrodes in different positions and orientations and with varying levels of radiofrequency power. We found substantial heating apart from when the radiofrequency power was greatly restricted or when the lead was tightly coiled. Different electrode contacts showed drastically different heating, and heating levels capable of causing serious injury were measured during MRI.

Neonates have a high incidence of seizures that are frequently difficult to control with conventional first-line anti-seizure medications, which are gamma-aminobutyric acid (GABA) agonists. The reasons for this clinical problem are multifold but are likely related to the unique physiology of the immature nervous system. Specifically, the early and transient neuronal expression of ion transporters that lead to higher concentrations of chloride inside the cell creates an electrochemical gradient that is depolarizing when chloride channels open, as they do when the GABAA receptor is activated. The later expression of chloride exporting transporters eventually leads to a chloride gradient that is hyperpolarizing, but this does not occur uniformly across the brain. The early depolarizing effect of GABAA receptor activity may have important functions in normal brain development but could theoretically impact therapies designed to enhance GABAergic transmission in neonates. In several studies, neonatal status epilepticus induced in the first 2 weeks of rodent life produces no or minimal brain injury in otherwise normal rodents. However, in certain settings, injury may ensue. A model of pilocarpine-induced seizures induced by higher doses of lithium and pilocarpine in P7 rats has demonstrated that widespread cell death can be seen in unmedicated animals experiencing severe seizures. Injury is further enhanced by treatment with either midazolam or phenobarbital. The effect is separate from the enhancement of apoptosis that has been reported with higher doses of the same drugs. Though limited, these data align with other basic studies and clinical reports that raise questions as to whether enhancement of GABA activity is the best approach for treating all neonatal seizures. GABAA receptor agonists are still used in the clinical setting for the treatment of neonatal seizures. Further basic and clinical research studies are needed to understand the short- and long-term effects of common first-line anti-seizure drugs and to investigate viable alternatives. PLAIN LANGUAGE SUMMARY: In the newborn brain, the neurotransmitter GABA, acting through GABAA receptors, which inhibits neurons in the adult brain, can be depolarizing. Status epilepticus has been reported to cause less severe injury in immature rats compared to adults. In certain settings, however, severe neonatal status epilepticus injury could be observed, and drugs that activate GABAA receptors, like phenobarbital and midazolam, can make seizure-associated brain damage worse in newborn rats. More studies are needed to better understand this problem and create better and safer treatments for neonatal seizures.

Sudden unexpected death in epilepsy (SUDEP) affects more than 3000 individuals annually, yet objective and scalable biomarkers to assess risk remain limited. Postictal generalized electroencephalogram suppression (PGES) has been proposed as a potential biomarker, but its quantification is often subjective and variable. Here, we developed and validated an automated Cumulative Sum (CuSUM)-based algorithm to objectively quantify PGES duration in traumatic brain injury (TBI) mouse models of epilepsy. The algorithm was tested across three cohorts: mm TBI, mm TBI, and mm TBI with HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) treatment. Although SUDEP occurred in only a subset of animals in both TBI groups, injury severity influenced the timing of SUDEP onset. More severe injury was associated with earlier SUDEP events and shorter PGES durations, whereas less severe injury was characterized by longer PGES durations (1 mm TBI: ~110 s; 2 mm TBI: ~40 s) and delayed SUDEP onset. SAHA treatment further reduced PGES duration (~30 s), suggesting potential translational relevance for SUDEP. Bland-Altman analysis demonstrated strong agreement between automated and expert measurements. Together, these findings demonstrate that automated PGES quantification provides a reproducible and objective framework for assessing postictal EEG dynamics and their relationship to SUDEP timing in epilepsy models. This approach offered a scalable tool for mechanistic studies and treatment evaluation, supporting future efforts toward multimodal and clinically translatable SUDEP research. PLAIN LANGUAGE SUMMARY: This study developed a simple, automated method to measure postictal generalized electroencephalogram suppression (PGES) in mouse models of brain injury to estimate the risk of SUDEP. It matched expert ratings and showed that injury severity, PGES length, and SUDEP risk relate in unexpected ways. While an epigenetic inhibitor drug shortened PGES, further studies are needed to validate its efficacy in epilepsy models.

Seizure forecasting models require long-term, high-quality data collected in real-life settings using noninvasive or minimally invasive devices, yet, the lack of such systems remains a major barrier to their clinical translation. Here, we aimed to evaluate the signal quality of self-applied at-home EEG monitoring using a wearable system in patients with epilepsy to assess its reliability for future seizure forecasting applications. All EEG recordings were reviewed to identify non-usable data and interictal epileptiform discharges (IEDs). We analyzed power spectral density and the temporal evolution of a signal-to-noise ratio, and applied composite quality criteria for each patient based on spectral profile and the proportion of EEG data discarded. Twelve patients with drug-resistant epilepsy performed self-applied resting-state EEG recordings twice daily over a median period of 173.5 days (min. 12, max. 235). Two-thirds of patients had data of good or moderate quality (N = 3 and 5, respectively), which remained overall stable over time with cap replacement every 2-3 months. IEDs were found in four patients and were concordant with prior in-hospital recordings. Self-applied at-home EEG monitoring can yield clinically relevant insights and may support future seizure forecasting strategies in selected patients, provided patient adherence and the feasibility of regular maintenance follow-up are addressed. PLAIN LANGUAGE SUMMARY: Twelve people whose epilepsy was hard to control with medication recorded a short brain-wave test (electroencephalography, or EEG) at home twice a day for several months. In 8 of the 12 people, most recordings were clear enough to use and stayed steady over time, although some EEG caps needed replacement. In four people, the home EEG showed abnormal spikes between seizures that matched earlier hospital EEGs. This suggests that long-term, self-recorded EEG at home is possible for some patients and may help clinicians track brain activity outside the hospital.

Predicting seizure recurrence following a first unprovoked seizure (FUS) remains a significant clinical challenge, especially when routine clinical magnetic resonance imaging (MRI) and EEG do not reveal abnormalities diagnostic of epilepsy. Here, we incorporate quantitative structural MRI-derived biomarkers into prediction models for seizure recurrence at 12 months and identify brain structural features that are predictive of seizure recurrence. We analyzed a retrospective, multicenter cohort of 197 adult patients with FUS, comprising 83 with seizure recurrence and 114 with no seizure recurrence at 12 months. All participants had normal or nondiagnostic MRI and EEG findings. Morphometric features were extracted from clinical 3 T T1-weighted MRI using FreeSurfer. Machine learning algorithms were trained on combined imaging and clinical features using nested cross-validation for model selection. Performance was compared with a logistic regression model based on clinical features only. The best-performing model, a support vector machine (SVM) trained on a combination of imaging features and clinical factors, achieved an AUC of 0.65 (95% CI: 0.57-0.73), significantly better than chance (p = 0.01 when compared with an AUC of 0.5). In contrast, the logistic regression model trained on clinical factors alone yielded an AUC of 0.57 (95% CI: 0.49-0.65), not statistically different to chance (p = 0.28). Direct comparison between the SVM and the logistic regression clinical factor-only model was not statistically significant (95% CI for the difference in AUC: -0.019 to 0.173, p = 0.11). The most important imaging features for prediction were inter-hemispheric asymmetry of subcortical and cortical gray matter volumes and regional gyral curvatures, particularly in fronto-parietal and limbic regions. Quantitative structural MRI contributes additional information beyond clinical factors for machine learning models predicting seizure recurrence. Changes to cortical folding and gray matter asymmetries in cortical and subcortical regions show potential as prognostic biomarkers of seizure recurrence risk after a FUS. Identifying individuals who will have another seizure after their first unprovoked seizure is difficult when routine brain scans and EEG appear normal. We developed a tool that combines MRI-derived markers with clinical information to predict seizure recurrence. Subtle structural differences in the brain, especially asymmetries between left and right hemispheres and changes to cortical folding, were associated with a higher chance of another seizure within a year. This approach has potential in identifying individuals at risk of seizure recurrence earlier.