Epilepsia最新文献

Objective: In epilepsy, daily treatment provides only symptomatic seizure control, leaving a significant unmet need for a treatment that affects the underlying predisposition to seizures. Here, in a first-of-its-kind study, we test the hypothesis that intermittent treatment of seizure clusters with diazepam in the kainic acid post-status epilepticus rat model of acquired epilepsy has an enduring effect on the seizure cluster phenotype, suggestive of potential disease modification.

Methods: Following kainic acid-induced status epilepticus, rats with epilepsy were monitored for occurrence of seizure clusters (≥2 seizures in 24 h) for a 3-week baseline period before entering a 6-week treatment period using a previously established multidose regimen of diazepam (n = 7) or vehicle (n = 9) upon identification of a seizure cluster. In a subsequent 2-week outcome period during which no rats received diazepam, we evaluated changes in seizure cluster size, burden (cluster size × severity), duration, and other phenotype parameters.

Results: A total of 3396 seizures and 216 seizure clusters were included for analysis. During the outcome period, time between seizures in a cluster (also interseizure interval [ISI]) was significantly longer in the diazepam group (log ISI = .25 longer, SE = .08, p < .0001), and the proportion of clustered seizures with an ISI of ≤30 min increased in the outcome period in the vehicle group (p = .023) but was stable in the diazepam group. Despite the occurrence of rebound seizures during the treatment period, improvement in several phenotypical parameters, including severity and proportion of seizures in a cluster, supported a positive impact of intermittent diazepam treatment on seizure cluster biology.

Significance: Changes in several seizure cluster phenotypical parameters were suggestive of an enduring disease-modifying effect of diazepam, despite an apparent rebound effect of intermittent diazepam treatment on seizure frequency. Further study is warranted using a model incorporating a background antiseizure medication regimen to potentially attenuate the unexpected rebound seizures.

Objective: Responsive neurostimulation (RNS) offers an effective and safe treatment for people living with refractory focal-onset epilepsy. Many RNS candidates need intracranial electroencephalography (IEEG) monitoring to locate seizure-onset zones (SOZs), which is a difficult procedure with variable yields. High-frequency oscillations (HFOs) are a common ictal-onset feature, and our study seeks to explore ictal HFOs as a marker for targeting RNS.

Methods: We screened patients who underwent IEEG then RNS implantation at our center from 2015 to 2022. While blinded to outcomes, we analyzed IEEG using standard clinical software to detect ictal HFO-onset patterns at the previously identified onsets. Then we examined RNS lead placement in relation to the SOZs and seizure control outcomes after 2 years post-implantation.

Results: A total of 62 patients received RNS during the study period. Excluding those patients with insufficient follow-up and combined surgeries (RNS + resection/ablation), 33 patients remained. At 2 years, 26 (~78.8%) had ≥50% seizure reduction, and 7 (~21.2%) were free of debilitating seizures (super-responders). We excluded an additional 8 patients due to having skipped IEEG and early explantation, leaving 25 study subjects. Ictal HFOs were present in all SOZs for 17 patients, and 14 patients (including all 7 super-responders) had RNS leads implanted in all of these SOZs. When ictal HFOs were absent or not covered by RNS leads, the patients did not achieve seizure freedom. There was a statistically significant association between targeting SOZs with ictal HFO onsets and outcomes (Fisher's exact test p-value 0.0078).

Significance: Our results suggest that ictal HFOs provide additional localization value. Ictal HFOs represent very local epileptiform activity that may reflect closer proximity to epileptogenic tissue than conventional features such as spiking and slower rhythms. Because stimulation acts by focal tissue activation, increased targeting precision may be more important in RNS than surgeries such as resection or ablation.

Objective: The US Food and Drug Administration (FDA) issued a warning that lamotrigine slows cardiac conduction, leading to arrhythmias in people with heart disease (HD). This is the first study to investigate the within-person changes in electrocardiographic (ECG) metrics and prevalence of ECG abnormalities when on versus off lamotrigine, in people with and without a history of HD.

Methods: The cohort includes 237 people with ECG both on and off lamotrigine, stratified by a history of HD (n = 97). The within-person percent change in heart rate and ECG metrics (PR, QRS, and heart rate-corrected QT [QT_c] intervals) on versus off lamotrigine is compared within and between groups. Linear mixed effect regression models with adjustments for covariates assess the association between within-subject changes in ECG metrics and pathologies when on versus off lamotrigine.

Results: PR interval (atrioventricular conduction) is significantly longer on- versus off lamotrigine (mean = 3.1% increase). There is a significant increase in the PR within the no HD (3.5%), all HD (2.8%), and structural HD groups (4.1%) when on versus off lamotrigine, but there is no difference between groups. Regression models confirm that lamotrigine is associated with an increase in PR interval in the overall cohort, as well as those with and without HD. Lamotrigine is not associated with an increased prevalence of clinically pathological PR, QRS, or QT_c prolongation, or abnormal ECG interpretations. Subanalysis in people with lamotrigine serum levels in the therapeutic range indicate that each 1-μg/mL increase in concentration is associated with a 2.3% increase in PR interval.

Significance: Lamotrigine leads to an increase in PR interval in the general population and people with HD (population in FDA warning). However, lamotrigine is not associated with increased odds of PR prolongation or any ECG abnormalities. Despite in vitro data predicting cardiac ECG abnormalities, particularly in people with HD, lamotrigine is not associated with the development of pathological ECG findings.

Objective: Antiseizure medication (ASM) may affect autonomic nervous system (ANS) activity in patients with epilepsy (PWE). We examined the relationship between ASM dosage and multimodal correlations among ANS signals recorded from wearables in pediatric PWE.

Methods: We evaluated evening (ASM intake from 5 p.m. to 3 a.m.) multimodal recordings (heart rate [HR], electrodermal activity [EDA], temperature [TEMP], and respiratory rate [RR]) from wearables (Empatica E4) worn by pediatric PWE during long-term monitoring at Boston Children's Hospital between 2015 and 2021. Within-patient comparisons were performed in two groups: patients with both high- and no-dose ASM days, and with both high- and low-dose ASM days. Multimodal interactions were assessed using principal component and canonical correlation analysis, and repeated measure analyses of variance with time and dose as factors.

Results: Of the 52 patients (median age = 12.8 years), 34 total patients had both high- and low-dose ASM days, and 24 total patients had both high- and no-dose days. An interaction between dose and time emerged in the high- versus no-dose comparison (p = .002), indicating divergent trajectories of multimodal autonomic correlations across medication states; correlations increased on high-dose days and decreased on no-dose days. EDA increased (p = .003) and HR decreased (p = .036) from baseline to peak window for patients with both high- and low-dose days. No time effects or dose-time interactions were found for TEMP and RR. Subanalyses by ASM mechanism of action showed no differential effects on individual ANS measures. Cox modeling showed a dose effect on time to seizure (chi-squared = 6.98, p = .031), with higher hazard on low- versus high-dose days (p < .01) and no difference between no- and high-dose days (p = .626).

Significance: ASM dosage was related to multimodal autonomic correlations, suggesting central autonomic regulation and seizure vulnerability. Wearable-based monitoring of these correlations could support seizure risk assessment and inform personalized treatment strategies.

High-frequency oscillations (HFOs) were discovered more than 20 years ago, and since then they have been studied intensively in the context of epilepsy. HFOs encompass a broad spectrum of oscillations, typically ranging from 80 Hz to several kHz, that include both normal and pathological oscillations, documented in people with epilepsy and animal models. HFOs have drawn considerable attention due to their prominent roles in epileptogenesis, ictogenesis, and functional organization of epileptic tissue. We provide historical background on HFOs in epilepsy and summarize the current state of knowledge, synthesizing clinical and basic science content from the Third International Workshop on HFOs in Epilepsy. Over the years, the field has evolved from single-center analysis of HFOs on invasive electroencephalographic recordings to recent multicenter studies and meta-analysis, which have tempered the conviction or hope that HFOs are uniform, "one event fits all," stand-alone biomarkers. Instead, association of HFOs with other electrophysiological phenomena such as interictal spikes, seizures, and signal features like entropy have highlighted new ways to identify epileptogenic tissue. Advances in recording and analytical tools have significantly expanded their potential applications in both clinical and basic science settings. Several recent publications focus on how scalp HFOs can illuminate disease propensity, severity, and therapy responses. Moreover, it was recently discovered that HFOs are also present in experimental models of Alzheimer's disease, and research is ongoing regarding their relations to the HFOs found in epilepsy. Together, these developments highlight that HFOs represent an evolving research area, with significant inroads made over the years. Yet, key gaps in knowledge remain, and we propose five benchmark areas that warrant future research and advancement.

Objective: Pathogenic variants in γ-aminobutyric acid type A (GABA_A) receptor genes have been associated with a wide spectrum of neurological disorders. We aimed to delineate the clinical trajectories associated with gain-of-function (GoF) and loss-of-function (LoF) variants in GABRB2 and GABRB3, and to develop a risk-prediction model for gross motor dysfunction based on age at seizure onset.

Methods: Clinical data, including seizure onset, epilepsy syndromes, cognitive outcomes, and gross motor function classification system (GMFCS), were collected through direct interviews, physician reports, and literature review. Kruskal-Wallis, Mantel-Cox and non-parametric analysis of variance (ANOVA) with Dunn's corrected post hoc tests were used for statistical comparisons. A logistic ordinal regression model was developed to predict GMFCS outcomes based on age at seizure onset.

Results: We analyzed a cohort of 117 individuals with pathogenic GABRB2 (n = 49) and GABRB3 (n = 68) variants. Fifty-three individuals carried GoF variants and 64 carried LoF variants. The GoF group was associated with earlier seizure onset, higher seizure frequency, and lower rates of seizure freedom. Gross motor dysfunction was markedly worse in the GoF group, with 64% classified as GMFCS IV or V (non-ambulation), compared to 7.5% in the LoF group. An inverse correlation was found between age at seizure onset and GMFCS severity in the GoF, but not the LOF group. The risk model predicted a >90% likelihood of non-ambulation for individuals with GoF variants and seizure onset before 1 month of age, decreasing to ~35% with seizure onset after 20 months.

Significance: We found a clear genotype-phenotype correlation in GABRB2- and GABRB3-related disorders, demonstrating that GoF variants are associated with a more severe neurodevelopmental trajectory. The age at seizure onset serves as a biomarker for predicting motor outcomes in individuals with GoF variants. These findings provide guidance regarding prognosis, need for early intervention, and data for comparison of efficacy in targeted therapeutic interventions for GABA_A receptor-related disorders.

Objective: The thalamus is a key hub in seizure propagation, and its nuclei are emerging targets for neuromodulation. However, the contributions of individual nuclei to epileptic networks remain unclear, particularly in children, who are less studied than adults. We investigated structural and functional thalamic alterations across different pediatric focal epilepsies and their associations with clinical features and postsurgical outcomes.

Methods: We retrospectively studied children with temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), and posterior quadrant epilepsy (PQE) and healthy controls. The thalamus was segmented into four nuclei groups (anterior, lateral, medial, pulvinar) using the THOMAS pipeline on T1-weighted magnetic resonance imaging (MRI) to estimate volumes. Functional connectivity was assessed with functional MRI using node strength, capturing total thalamic connectivity across the brain. We compared patients with controls and evaluated associations with hippocampal sclerosis, history of focal to bilateral tonic-clonic seizures, and postsurgical seizure freedom.

Results: Among 136 children with focal epilepsy (81 TLE, 36 FLE, 19 PQE; mean age = 13.0 years) and 70 controls (mean age = 13.4 years), ipsilateral thalamic volume reductions were observed in the following: anterior and lateral nuclei and pulvinar in TLE, anterior and lateral nuclei in FLE, and pulvinar in PQE (Cohen d = .52-.70, all Bonferroni-corrected p < .05). In contrast, medial nuclei volume increase was associated with history of seizure generalization (partial η² = .06). Functional connectivity was bilaterally reduced across epilepsy groups (partial η² = .03), most consistently in the pulvinar (Cohen d = .25-.68). Within TLE, hippocampal sclerosis was associated with increased anterior nucleus connectivity (partial η² = .17), distinguishing it from other pathologies.

Significance: We demonstrate both shared and syndrome-specific thalamic abnormalities in pediatric focal epilepsy. Common patterns included ipsilateral thalamic volume loss, indicating cumulative disease burden, and reduced bilateral functional connectivity, particularly in the pulvinar, likely reflecting thalamocortical decoupling. These findings advance understanding of seizure networks beyond the epileptogenic zone and provide a foundation for personalized thalamic-targeted neuromodulation strategies.

Objective: This study was undertaken to assess the relationship between the severity of depression and anxiety symptoms and epilepsy-related variables and cognitive burden in people with epilepsy (PwE), as assessed using EpiTrack.

Methods: We prospectively enrolled a cohort of PwE who underwent EpiTrack and evaluation by Generalized Anxiety Disorder-7 (GAD-7) and Beck Depression Inventory-II (BDI-II) scales. We assessed the correlation strength between EpiTrack, GAD-7, BDI-II, and the other clinical variables. Analysis of variance and covariance assessed the existence of GAD-7/BDI-II differences between PwE with and without epilepsy-related cognitive impairment. Hierarchical regression analysis (HRA) and logistic regression were performed to characterize the association between BDI-II and GAD-7 and the presence/severity of epilepsy-related cognitive burden. Mediation analysis was performed to evaluate the relationship between EpiTrack and the other variables applied.

Results: We enrolled 100 PwE (42 ± 18 years old). EpiTrack inversely correlated with BDI-II (-.33, p = .003, not with GAD-7), seizures (-.34, p < .001), epileptiform abnormalities (34, p < .001), and pharmacological burden (.21, p = .002). BDI-II correlated with seizure frequency (.27, p = .02). PwE with cognitive impairment had significantly higher BDI-II scores, independently of age, seizure frequency, epileptiform abnormalities, and pharmacological load (p = .02-.04). BDI-II effect on EpiTrack was independent from seizures. The addition of BDI-II in HRA and logistic regression provided a significant increase of the R² value (p = .004) and of area under the curve (p = .02).

Significance: More severe depressive symptoms are strongly associated with worse cognitive performance in PwE, independently of the other epilepsy-related variables. Depressive symptoms could either forecast the occurrence of epilepsy-related cognitive impairment or arise as a consequence of cognitive dysfunction in PwE. We confirmed the association between epilepsy severity and epilepsy-related cognitive impairment.

Objectives: Cenobamate is an antiseizure medication (ASM) with proven effectiveness in individuals with highly refractory epilepsy. This study investigated the effectiveness and tolerability of cenobamate in different treatment lines and a less refractory setting.

Methods: This was a multicenter, retrospective, observational study. Adults with focal epilepsy, 2-6 prior ASMs, and ≥12 months between cenobamate initiation and database closure were included. Data from patients' charts were collected and included in the Drug-Resistant Epilepsy Registry of the Spanish Epilepsy Society using Research Electronic Data Capture. Primary effectiveness endpoints included changes in seizure frequency at 3, 6, and 12 months after cenobamate initiation according to different treatment lines. Primary safety endpoints included the percentage of patients with adverse events (AEs).

Results: In total, 486 patients were included (mean age 42.8 years). The mean number of prior ASMs was 4.4 (2-6); the mean number of concomitant ASMs at cenobamate initiation was 2.5 (1-5). At 12 months, treatment retention was 92% and median dose was 200 mg. One-year seizure-freedom rates were 32.5%, 33.3%, 21.4%, 24.2%, and 11.4% in those treated with 2-6 prior ASMs, respectively. The proportions of patients achieving ≥50%, ≥75%, ≥90%, and 100% reductions in seizure frequency at 12 months were significantly higher in patients with 2-3 (earlier treatment lines) vs 4-6 prior ASMs (later treatment lines; p ≤ .004). Improvements in seizure severity were reported in 71.9% of patients during follow-up (82.5% and 62.1% in those with 2 and 6 prior ASMs, respectively). At 12 months, 56.4% of patients had reported AEs, with somnolence the most frequent, and 4.9% had AEs requiring cenobamate discontinuation.

Significance: Cenobamate was effective and well tolerated in patients who had tried 2-6 prior ASMs in a real-world setting. High effectiveness was confirmed at 1 year, with better outcomes in earlier users.

Objective: Forecasting epileptic seizures is a difficult task. Studies of seizure prediction have investigated many different EEG features, but none of them have been useful enough to be applied in clinical practice beyond trials. Moreover, most of these features have been applied to short-term intracranial EEG (iEEG) recordings, limiting the possibility of reliable statistical evaluation. This paper proposes a machine learning algorithm to forecast an epileptic seizure 2-4 mins before seizure. This allows patients to seek help, or stimulation devices to work.

Methods: This paper investigates a large subset of features from the past and present to unravel which features and feature analysis methods will yield the best performance on long-term iEEG recordings (from 14 patients with focal epilepsy) and thus provide the most reliable step toward clinical utility. Specifically, this study implements a multiple long-time scale cycle feature analysis framework for seizure forecasting that considers the state-of-the-art time series features of critical slowing down (autocorrelation and variance) as well as interictal epileptiform discharge (IED) / spike rate, High Frequency Activity (HFA), seven different univariate features, and three Neural Mass Model (NMM) features based on brain dynamics.

Results: Seizure phase histograms of all the features are then analyzed to investigate each feature's potential for seizure forecasting by evaluating corresponding synchronization indices (SI) on fast (40 minutes to 2 days) and slow (2+ days) wideband time scales. Out of all combinations considered, the overall performance comparison across patients highlights that 'autocorrelation + variance + NMM + spike rate' features achieve the highest average AUC of 0.83, showcasing its performance in forecasting seizures.

Significance: A model is proposed that has a similar performance compared to the state-of-the-art method, without the need of selecting the best channel prior to model building. Light is also shed on the comparative performance on long-term recordings of many of the seizure forecasting features considered in the past.