Epilepsia最新文献

Objective: Pathogenic variants of KCNQ2 lead to a spectrum of disorders including self-limited familial neonatal-infantile epilepsy (SeL(F)NIE), developmental and epileptic encephalopathies (DEEs), and neurodevelopmental disorders (NDDs) with intellectual disability (ID). This study aimed to delineate the clinical progression and underlying pathogenesis of these disorders. Particularly, we unraveled the role of gain-of-function (GoF) variants in neurodevelopmental impairment.

Methods: We conducted a longitudinal study of a 90-patient Chinese cohort with KCNQ2-related disorders, classified into SeL(F)NIE, DEEs, and NDDs subgroups. Clinical phenotyping was integrated with functional analyses (electrophysiology, biochemistry) of five missense variants in homomeric and heteromeric (with Kv7.3/Kv7.5) channel assemblies.

Results: Despite comparable seizure control to SeL(F)NIE (96% vs 100%), the NDDs group exhibited significant cognitive impairment. All deceased patients (8/90) were in the DEEs group. Functional profiling revealed a spectrum from loss-of-function (LoF) to GoF. LoF variants (e.g., S247L in DEEs) were linked to severe epilepsy. Crucially, we identified strong GoF variants (P335A/L in NDDs) in the S6-HelixA domain that confer insensitivity to phosphatidylinositol 4,5-bisphosphate (PIP₂) regulation and are associated with a primary neurodevelopmental phenotype, distinct from the severe epileptic phenotype of established voltage-sensing domain (VSD) GoF variants.

Significance: Our integrated clinical and functional analysis showed that the clinical outcome relies on the functional consequence of a KCNQ2 variant (LoF vs GoF) and its behavior in heteromeric complexes, rather than its mere location. We defined a novel class of strong GoF KCNQ2 variants that are mechanistically and phenotypically distinct, highlighting aberrantly enhanced channel function as a key driver of cognitive dysfunction and presenting new targets for precision medicine.

Objective: Nonadherence to antiseizure medications in pediatric epilepsy affects ~60% of youth. The aim of this multisite two-stage sequential, multiple assignment, randomized trial (SMART) was to evaluate the effectiveness of mHealth intervention strategies for improving adherence in caregivers of young children with epilepsy, particularly those who are underserved. It was hypothesized that participants initially randomized to treatment would exhibit significantly greater improvements in adherence compared to an active control group at the end of stage 1. Secondary outcomes included longitudinal adherence, seizure freedom/severity, and health-related quality of life.

Methods: Participants across four epilepsy centers were recruited (n = 461, mean age = 7.6 ± 3.0 years, 51% males, 62% White non-Hispanic, 71% underserved). Baseline questionnaires were completed, and electronic adherence monitors were provided. Three intervention strategies were embedded in this SMART: (1) active control (mHealth education + automated digital reminders), (2) treatment (mHealth education + automated digital reminders + individualized adherence feedback) in stage 1 continuing in stage 2 regardless of responsiveness in stage 1, and (3) treatment in stage 1 continuing in stage 2 if patient is responsive in stage 1 or augmented by problem-solving if patient is nonresponsive in stage 1. Active intervention was 5 months, with two posttreatment follow-ups. Standard analysis of covariance was conducted for the primary aim. Secondary analyses compared mean change from baseline associated with each intervention strategy.

Results: The treatment group had significantly better mean adherence percentage change from baseline to the end of stage 1 compared to the active control group (13.2% vs. 3.1% change, t = 2.82, p = .005, d = .37). Adherence rates declined over time across all SMART strategies. An increased probability of seizure freedom at 6 and 12 months across all SMART strategies was found.

Significance: This pediatric epilepsy SMART trial demonstrated adherence improvements for the treatment group that did not persist over time. Improvements in seizure freedom that were independent of SMART strategy were also identified. mHealth education, automated digital reminders, and individualized adherence feedback appear to have been efficacious.

Objective: Benzodiazepines (BZDs) are the recommended first-line treatment in early status epilepticus (SE). At least 30% of episodes are resistant, leading to established SE. Chronic BZD use may induce pharmacological tolerance. We hypothesized that chronic exposure to BZDs may reduce their efficacy as first-line treatment in early SE.

Methods: This retrospective case-control study included SE episodes from 2010 to 2023 at a tertiary center in Brussels. Postanoxic and Alcohol- or BZD withdrawal-related SE episodes were excluded, as well as those in which BZDs were not used as first-line treatment. Chronic BZD use was defined as a daily use for at least 6 months. Chronic BZD users (cases) were matched 1:1 with nonusers (controls) for age, sex, etiology, and semiology. Univariable and multivariable comparisons were performed. The primary outcome was evolution of early SE to established SE, defined as the use of second-line treatment.

Results: Ninety-eight cases and 98 controls were included and matched. Chronic use of BZDs was associated with more frequent need for second-line treatment or evolution to established SE (85% vs. 64%, p = .022). Established SE (n = 146) was also associated with longer in-hospital stay (15 vs. 5 days, p < .001) and more complications (Complications Burden Index: 1 vs. 2.5, p < .001). After adjusting for both timing and dose of first-line treatment, multivariable analysis confirmed chronic BZD use as an independent risk factor for established SE (odds ratio = 3.34, 95% confidence interval = 1.66-7.03, p = .001). Among chronic users, no association was observed between modalities of BZD chronic use (dose, half-life time, duration) and the establishment or refractoriness of SE episodes.

Significance: Chronic BZD use is associated with a higher rate of second-line treatment administration in SE, suggesting greater resistance to acute BZD administration. This association should be considered when managing early SE. Chronic BZD use should be considered with extreme caution in patients with epilepsy at risk of SE.

Objective: We previously reported in a prior cohort that fetal exposure to antiseizure medications (ASMs), especially valproate, was associated with reduced right-handedness and poorer verbal versus nonverbal performance in children at age 6 years. Given changes in prescribing practices in epilepsy over the past 2 decades, we enrolled a new cohort of the Maternal Outcomes and Neurodevelopmental Effects of Antiepileptic Drugs (MONEAD) study to determine whether fetal exposure to newer generation ASMs is associated with altered cerebral lateralization in children at age 6 years and to assess exposure-dependent effects on handedness, manual dexterity, and verbal/nonverbal abilities.

Methods: The MONEAD multicenter prospective cohort study followed children of women with epilepsy (WWE) on ASMs and healthy women (HW) until age 6 years. We assessed handedness using the Edinburgh Handedness Inventory, manual dexterity through the Grooved Pegboard Test, and verbal/nonverbal abilities with the Differential Ability Scales-II. Outcomes were analyzed in relation to ASM exposure, controlling for confounders.

Results: Of 1123 screened pregnant women, 351 children born to WWE and 105 to HW were enrolled. The overall distribution of handedness and verbal/nonverbal scores did not differ between groups. However, subgroup analyses revealed that higher third-trimester levetiracetam concentrations were significantly associated with nondextrality (p = .03, 95% confidence interval [CI] = .03-.67). Across the WWE cohort, higher ASM concentrations correlated with poorer manual dexterity (coefficient = 6.76, 95% CI = .40-13.11, p = .037). Additionally, periconceptional folate supplementation was associated with better nonverbal outcomes (coefficient = 2.54, 95% CI = .93-4.15, p = .002) and parental stress was linked to poorer verbal scores (coefficient = -19.74, 95% CI = -28.48 to -11, p < .001).

Significance: This cohort reassuringly showed no overall difference in verbal/nonverbal abilities between WWE and HW. Fetal ASM exposure had exposure-dependent effects on manual dexterity. Modifiable factors, including folate supplementation and parental stress, significantly influenced developmental outcomes, underscoring the importance of proactive screening and providing intervention when appropriate.

Objective: Determining patient-specific thalamic connectivity alterations may be an important step toward personalized surgical and neuromodulation strategies, but no data are available to support this concept in pediatric cohorts. This study investigated thalamocortical structural connectivity profiles in children with focal-onset epilepsy of different seizure-onset zones.

Methods: This neuroimaging, case-control study compared structural connectivity of four thalamic nuclei (anteroventral [AV], centromedian [CM], mediodorsal [MDPf], and pulvinar [PUL]) between 81 children who underwent surgery for focal-onset epilepsy (median age = 12.2 years) and 63 controls (median age = 12.8 years). Using preoperative 3-Tesla diffusion magnetic resonance imaging (dMRI), brain (Lausanne) and thalamic (THOMAS) parcellations combined with tractography generated structural connectomes based on streamline counts. Connectivity strength of each thalamic nucleus was calculated by summing the weights of each connecting brain region.

Results: Patients had higher structural connectivity strengths than controls of the thalamic nuclei (effect size (η²ₚ) = .072; p = .015), differentially involving nucleus regions, but there was no overall difference in nucleus volumes (η²ₚ < .000; p = .968). When comparing patient groups defined by seizure-onset zones, it emerged that reduced AV connectivity strength was specific to the hippocampal sclerosis group, whereas CM, MDPf, and PUL connectivity was similarly high in all the patient groups, including those with frontal or temporal lobe epilepsy. Patients who were seizure-free after surgery had a lower ipsilateral and a higher contralateral connectivity strength (η²ₚ = .111; p = .005) and volumes (η²ₚ = .073; p = .025) of thalamic nuclei compared to those who were not.

Significance: This study provides unique data suggesting that different pediatric focal epilepsies have distinct structural thalamocortical connectivity and volumetric profiles. The structural connectivity and volumetric asymmetries of the thalami have an association with postoperative seizure freedom. More studies are required to further understand the thalamic connectivity signatures that may have implications for precision surgical planning and neuromodulation targeting for focal-onset epilepsy.

Objective: Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is difficult to predict. We explored whether spectral entropy, a fast computable electroencephalographic (EEG) feature, could estimate epileptic activity in intraoperative electrocorticography (ioECoG) and forecast postsurgical outcomes.

Methods: We included people with FCD pathology who underwent ioECoG-assisted resective surgery. We analyzed ioECoG recordings acquired before and after resection. We computed spectral entropy across eight frequency bands (1-500, 1-4, 4-8, 8-12, 12-20, 20-80, 80-250, 250-500 Hz) in 2-s epochs during 1 min, and the mean and SD per electrode. The preresection features were the input for a random forest machine learning model to classify channels covering resected from nonresected tissue. Explainable artificial intelligence was used to select features that positively influenced the model predicting resected tissue. We then related these markers measured after the resection to postsurgical outcome using trend analysis (Jonckheere-Terpstra) across outcome groups Engel IA without medication, Engel IA-D, Engel II, Engel III, and Engel IV.

Results: We analyzed ioECoG data from 37 patients (age range = 0-61 years, 21 patients were ≤16 years), including 2270 preresection and 1278 postresection channels. The random forest model discriminated between resected and nonresected cortex (validation fold area under the curve = .84, 95% confidence interval =.74-.95). Features with the strongest positive Shapley Additive Explanations values included high spectral entropy variability (SEV) in the 80-500-Hz bands. In postresection recordings, higher mean SEV and greater spatial variability of SEV were associated with poorer Engel outcome across several frequency bands. After multiple comparison correction, the positive relationship of high mean SEV (p = .004) and high spatial variability (p = .001) at 250-500 Hz with poor seizure outcome remained statistically significant.

Significance: SEV is a real-time computable invasive EEG marker that represents persisting epileptic activity after resection. SEV may be used to evaluate resection adequacy directly or may reflect residual epileptic activity. This would enable epilepsy surgery guidance and postsurgical counseling and decision-making.

Interictal epileptiform discharges (IEDs) are not just biomarkers but active contributors to cognitive and behavioral dysfunction. Antiseizure medications (ASMs) not only treat seizures but can also modulate IEDs. However, their broader neurocognitive impact remains underexplored. The goal of this review is to synthesize current evidence on ASM effects on IEDs and to examine their therapeutic implications for cognitive and behavioral improvement. A comprehensive literature search was conducted, focusing on studies that reported ASM-related IED modulation and associated cognitive or behavioral measures. ASMs demonstrate variable efficacy in reducing IEDs, with broad-spectrum agents like valproate and lamotrigine showing consistent suppression of IEDs resulting in cognitive benefit, particularly in children. The use of sodium channel blockers, such as lamotrigine and oxcarbazepine, produces cognitive improvements. Additionally, γ-aminobutyric acidergic agents, including clobazam and diazepam, are effective in treating developmental epileptic encephalopathies. Emerging therapies, including cannabidiol and perampanel, show promising IED and behavioral outcomes. Animal studies confirm that ASMs can suppress IEDs, leading to enhanced memory, attention, and social behaviors. Targeted reduction of IEDs may lead to improved cognitive and behavioral outcomes. This can be achieved by testing and recognizing ASMs in carefully designed prospective trials in animals and humans.

Objective: This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to predict individual seizure risk over time, comparing its performance to traditional clinical predictors such as interictal epileptiform discharges (IEDs).

Methods: We conducted a retrospective cohort study including 1014 consecutive routine EEGs from 994 patients recorded at a tertiary epilepsy center. We developed EEGSurvNet, a deep learning model that predicts time to next seizure over a 2-year horizon from a single EEG. Model performance was evaluated on a temporally shifted testing set of 135 EEGs from 115 patients using time-dependent area under the receiver operating characteristic curve (AUROC), AUROC integrated over 2 years (iAUROC), and C-index. We compared the deep survival model to a clinical Cox model incorporating standard risk factors as well as a random model based on baseline seizure risk.

Results: EEGSurvNet achieved a 2-year iAUROC of .69 (95% confidence interval [CI] = .64-.73) and C-index of .66 (95% CI = .60-.73), outperforming both clinical and random models. Performance was highest in the first months following EEG, peaking at 2 months (AUROC = .80). Combining EEGSurvNet with clinical predictors further improved performances (iAUROC = .70, C = .69). Notably, the model showed superior discrimination on EEGs without IEDs (iAUROC = .78 vs. .53). Model interpretation revealed that the temporal-occipital regions and 6-15-Hz frequencies contributed most to risk prediction.

Significance: EEGSurvNet demonstrates that deep learning can extract prognostic information from routine EEG beyond visible epileptiform abnormalities, potentially improving patient counseling and treatment decisions. Future prospective studies are needed to validate these findings and assess their clinical impact.