Epilepsia最新文献

Objective: Somatic variants causing epilepsy are challenging to detect, as they are only present in a subset of brain cells (e.g., mosaic), resulting in low variant allele frequencies. Traditional methods relying on surgically resected brain tissue are limited to patients undergoing brain surgery. We developed an improved protocol to detect somatic variants using DNA from stereoelectroencephalographic (SEEG) depth electrodes, enabling access to a larger patient cohort and diverse brain regions. This protocol mitigates issues of contamination and low yields by purifying neuronal nuclei using fluorescence-activated nuclei sorting (FANS).

Methods: SEEG depth electrodes were collected upon extraction from 41 brain regions across 17 patients undergoing SEEG. Nuclei were isolated separately from depth electrodes in the affected brain regions (seizure onset zone) and the unaffected brain regions. Neuronal nuclei were isolated using FANS, and DNA was amplified using primary template amplification. Short tandem repeat (STR) analysis and postsequencing allelic imbalance assessment were used to evaluate sample integrity. High-quality amplified DNA samples from affected brain regions, patient-matched unaffected brain regions, and genomic DNA were subjected to whole exome sequencing (WES). A bioinformatic workflow was developed to reduce false positives and to accurately detect somatic variants in the affected brain region.

Results: Based on DNA yield and STR analysis, 14 SEEG-derived neuronal DNA samples (seven affected and seven unaffected) across seven patients underwent WES. From the variants prioritized using our bioinformatic workflow, we chose four candidate variants in MTOR, CSDE1, KLLN, and NLE1 across four patients based on pathogenicity scores and association with phenotype. All four variants were validated using digital droplet polymerase chain reaction.

Significance: Our approach enhances the reliability and applicability of SEEG-derived DNA for epilepsy, offering insights into its molecular basis, facilitating epileptogenic zone identification, and advancing precision medicine.

Objective: Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can be challenging to detect visually on magnetic resonance imaging. Three artificial intelligence models for automated FCD detection are publicly available (MAP18, deepFCD, MELD) but have only been compared on single-center data. Our first objective is to compare them on independent multicenter test data. Additionally, we train and compare three new models and make them publicly available.

Methods: We retrospectively collected FCD cases from four epilepsy centers. We chose three novel models that take two-dimensional (2D) slices (2D-nnUNet), 2.5D slices (FastSurferCNN), and large 3D patches (3D-nnUNet) as inputs and trained them on a subset of Bonn data. As core evaluation metrics, we used voxel-level Dice similarity coefficient (DSC), cluster-level F₁ score, subject-level detection rate, and specificity.

Results: We collected 329 subjects, 244 diagnosed with FCD (27.7 ± 14.4 years old, 54% male) and 85 healthy controls (7.1 ± 2.4 years old, 51% female). We used 118 subjects for model training and kept the remaining subjects as an independent test set. 3D-nnUNet achieved the highest F₁ score of .58, the highest DSC of .36 (95% confidence interval [CI] = .30-.41), a detection rate of 55%, and a specificity of 86%. deepFCD showed the highest detection rate (82%) but had the lowest specificity (0%) and cluster-level precision (.03, 95% CI = .03-.04, F₁ score = .07). MELD showed the least performance variation across centers, with detection rates between 46% and 54%.

Significance: This study shows the variance in performance for FCD detection models in a multicenter dataset. The two models with 3D input data showed the highest sensitivity. The 2D models performed worse than all other models, suggesting that FCD detection requires 3D data. The greatly improved precision of 3D-nnUNet may make it a sensible choice to aid FCD detection.

Objective: The 5xFAD mouse model of Alzheimer disease (AD) recapitulates amyloid-beta (Aβ) deposition and pronounced seizure susceptibility observed in patients with AD. Forty-hertz audiovisual stimulation is a noninvasive technique that entrains gamma neural oscillations and can reduce Aβ pathology and modulate glial expression in AD models. We hypothesized that 40-Hz sensory stimulation would improve seizure susceptibility in 5xFAD mice and this would be associated with reduction of plaques and modulation of glial phenotypes.

Methods: 5xFAD mice and wild-type (WT) littermates received 1 h/day 40-Hz audiovisual stimulation or sham (n = 7-11/group), beginning 2 weeks before and continuing throughout amygdala kindling epileptogenesis. Postmortem analyses included Aβ pathology and morphology of astrocytes and microglia.

Results: 5xFAD mice exhibited enhanced susceptibility to seizures compared to WT, evidenced by fewer stimulations to reach kindling endpoint (incidence rate ratio [IRR] = 1.46, p < .0001) and a trend to higher seizure severity (odds ratio [OR] = .34, p = .059). Forty-hertz stimulation reduced the behavioral severity of the first seizure (OR = 4.04, p = .02) and delayed epileptogenesis, increasing the number of stimulations required to reach kindling endpoint (IRR = .82, p = .01) compared to sham, regardless of genotype. 5xFAD mice receiving sensory stimulation exhibited ~50% reduction in amyloid pathology compared to sham. Furthermore, markers of astrocytes and microglia were upregulated in both genotypes receiving 40-Hz stimulation.

Significance: Forty-hertz sensory entrainment slows epileptogenesis in the mouse amygdala kindling model. Although this intervention improves Aβ pathology in 5xFAD mice, the observed antiepileptogenic effect may also relate to effects on glia, because mice without Aβ plaques (i.e., WT) also experienced antiepileptogenic effects of the intervention.

Objective: The most common medically resistant epilepsy (MRE) involves the temporal lobe (TLE), and children designated as temporal plus epilepsy (TLE+) have a five-times increased risk of postoperative surgical failure. This retrospective, blinded, cross-sectional study aimed to correlate visual and computational analyses of magnetoencephalography (MEG) virtual sensor waveforms with surgical outcome and epilepsy classification (TLE and TLE+).

Methods: Patients with MRE who underwent MEG and iEEG monitoring and had at least 1 year of postsurgical follow-up were included in this retrospective analysis. User-defined virtual sensor (UDvs) beamforming was completed with virtual sensors placed manually and symmetrically in the bilateral amygdalohippocampi, inferior/middle/superior temporal gyri, insula, suprasylvian operculum, orbitofrontal cortex, and temporoparieto-occipital junction. Additionally, MEG effective connectivity was computed and quantified using eigenvector centrality (EC) to identify hub regions. More conventional MEG methods (equivalent current dipole [ECD], standardized low-resolution brain electromagnetic tomography, synthetic aperture magnetometry beamformer), UDvs beamformer, and EC hubs were compared to iEEG.

Results: Eighty patients (38 female, 42 male) with MRE (mean age = 11.3 ± 6.2 years, range = 1.0-31.5) were identified and included. Twenty-five patients (31.3%) were classified as TLE, whereas 55 (68.8%) were TLE+. When modeling the association between MEG method, iEEG, and postoperative surgical outcome (odds of a worse [International League Against Epilepsy (ILAE) class > 2] outcome), a significant result was seen only for UDvs beamformer (odds ratio [OR] = 1.22, 95% confidence interval [CI] = 1.01-1.48). Likewise, when the relationship between MEG method, iEEG, and classification (TLE and TLE+) was modeled, only UDvs beamformer had a significant association (OR = 1.47, 95% CI = 1.13-1.92). When modeling the association between EC hub location and resection/ablation to postoperative surgical outcome (odds of a good [ILAE 1-2] outcome), a significant association was seen (OR = 1.22, 95% CI = 1.05-1.43).

Significance: This study demonstrates a concordance between UDvs beamforming and iEEG that is related to both postsurgical seizure outcome and presurgical classification of epilepsy (TLE and TLE+). UDvs beamforming could be a complementary approach to the well-established ECD, improving invasive electrode and surgical resection planning for patients undergoing epilepsy surgery evaluations and treatments.

Objective: Fenfluramine (FFA), stiripentol (STP), and cannabidiol (CBD) are approved add-on therapies for seizures in Dravet syndrome (DS). We report on the long-term safety and health care resource utilization (HCRU) of patients with DS treated with FFA under an expanded access program (EAP).

Methods: A cohort of 124 patients received FFA for a median of 2.8 years (34.4 months). We compared data on safety and HCRU during FFA treatment with those from a same pre-treatment period. Echocardiography was conducted every 6 months. Information collected included gender, age, and auxological parameters (height, weight, and body mass index [BMI]) at the start (T0) and follow-up (T1); FFA treatment details (start, withdrawal, dosage); adverse events (AEs); and HCRU data including hospital admissions, status epilepticus (SE) episodes, and rescue medication use. We grouped patients by weight: ≤37.4 kg (n = 68, 54.8%) and ≥37.5 kg (n = 56; 45.1%), with FFA dosing adjusted accordingly. Statistical analyses included paired t test, Wilcoxon signed-rank test, Kaplan-Meier analysis, and Bonferroni correction to adjust for multiple testing.

Results: Mean age was 47 months at clinical diagnosis and 81 months at T0. The last follow-up average FFA dose was .5 mg/kg/day, with a median of .4 mg/kg/day. FFA led to a 9.5% reduction in prior treatment load. At last follow-up, 118 of 124 (91.5%) remained on FFA. Rescue medication use decreased significantly from 4.5 to 1, hospitalizations from 1 to 0, and SE episodes from 0-240 to 0-180 (p < .001 for all). Seizure freedom was achieved in 9 of 118 patients (7.6%). AEs occurred in 39 of 124 patients (31.5%), with no cardiac issues or deaths. There was an overall mean reduction in BMI, with no statistical significance, and never requiring FFA withdrawal.

Significance: FFA is well tolerated, without cardiac toxicity, and reduces treatment load and HCRU, suggesting improved patient management. BMI reduction in young children highlights the need for growth and nutritional monitoring.

Objective: This study aimed to investigate two key aspects of scalp high-frequency oscillations (HFOs) in pediatric focal lesional epilepsy: (1) the stability of scalp HFO spatial distribution across consecutive nights, and (2) the variation in scalp HFO rates in response to changes in antiseizure medication (ASM).

Methods: We analyzed 81 whole-night scalp electroencephalography (EEG) recordings from 20 children with focal lesional epilepsy. We used a previously validated automated HFO detector to assess scalp HFO rates (80-250 Hz) during non-rapid eye movement (NREM) sleep. The spatial distribution of HFO rates across consecutive nights was evaluated using Hamming similarity, and changes in ASM were classified as increased, decreased, or stable.

Results: For each patient, we analyzed 3 ± 1 whole-night scalp EEG recordings, with a mean duration of 650 ± 215 min per recording. The distribution of HFO remained stable across consecutive nights, with a Hamming similarity of 88% ± 6%. Four patients had at least one ASM dosage decrease, nine patients had both ASM dosage decreases and increases, two patients had only ASM dosage increases, and five patients had no changes in ASM during the study period. A decrease in ASM dosage was associated with increased HFO rates (from .16 ± .32 to .22 ± .36 HFO/min; p = .03), whereas an increase in ASM dosage led to decreased HFO rates (from .32 ± .54 HFO/min to .22 ± .38 HFO/min; p = .005) when comparing the last night to the first.

Significance: The spatial distribution of scalp HFOs remained consistent across multiple nights, whereas fluctuations in HFO rates correlated with changes in ASM dosage. These findings suggest that scalp HFOs may not only help identify epileptogenic brain tissue but also monitor treatment response.

Objective: At our institute, most pediatric patients undergo epilepsy surgery following a thorough presurgical evaluation without intracranial electroencephalography (EEG). We conducted an initial validation of our noninvasive presurgical strategy by assessing the seizure and developmental outcomes of 135 children.

Methods: All 135 pediatric patients were <15 years old, had undergone curative surgery, and were followed for at least 2 years postoperatively. Presurgical evaluations and postoperative seizure and developmental outcomes were investigated. Thorough noninvasive evaluation included 3-T magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) in all patients. Intracranial EEG was mainly indicated for patients whose MRIs were negative or subtle. We defined Engel class I as favorable and Engel classes II-IV as unfavorable seizure outcomes. Intelligence quotient (IQ) and developmental quotient (DQ) before and 2 years after surgery were used to assess developmental/neuropsychological outcomes.

Results: MRI was positive in 130 of 135 patients (96.3%), including 39 of 40 with focal cortical dysplasia (FCD) type II and 30 of 33 with FCD type I. FDG-PET revealed concordant localizing findings in 119 of 132 patients (90.2%). Ictal single photon emission computed tomography provided concordant localizing information in 85 of 91 patients (93.4%). Intracranial EEG was performed in only 10 of 135 patients (7.4%). Ninety-seven of 135 patients (71.9%) were seizure-free 2 years after surgery. The final seizure-free rate was 99 of 135 (73.3%). Temporal lobe surgery predicted a favorable seizure outcome by multivariate analysis, whereas FCD type I and preoperative IQ/DQ < 70 predicted an unfavorable outcome. The mean IQ change was +1.3 points, and the mean DQ change was +1.0 points. Mean DQ significantly improved following extratemporal surgery (multivariate regression, p < .05), and mean DQ significantly decreased in patients with epileptic spasms (multivariate regression, p < .01).

Significance: Thorough noninvasive presurgical evaluation enables detection of subtle MRI lesions and curative epilepsy surgery without intracranial EEG in most patients, including those with FCD type II and type I, and leads to favorable seizure and developmental/neuropsychological outcomes.

Objective: Traumatic brain injury (TBI) is a significant risk factor for epilepsy, but little work has explored whether risk of epilepsy after TBI may operate through intermediary mechanisms. The objective of this study was to statistically screen for potentially mediating effects among 64 comorbidities for epilepsy risk following TBI among Post-9/11 U.S. veterans.

Methods: This longitudinal matched cohort study used an established algorithm to identify veterans in Department of Defense (DoD) and Veterans Health Administration (VHA) records with a history of the primary exposure, TBI, between 2003 and 2023, who were demographically matched 1:1 with veterans without history of TBI exposure from the same cohort. In the observation time window after index date, mediation models estimated the proportion eliminated of the total TBI-epilepsy relationship by other factors. Cox proportional hazard models were implemented for 64 comorbidities determined using International Classification of Diseases, Ninth/Tenth Revision (ICD-9/10) codes, each individually tested for the potential mediation of epilepsy onset after date of first TBI (index date), adjusting for demographic and military covariates. Age-stratified mediation analyses were conducted. Biologically plausible mechanisms were investigated.

Results: Among N = 292 200 veterans in the TBI and matched groups, 8458 (2.9%) had an epilepsy diagnosis that met study criteria between 2003 and 2023. The adjusted hazard ratio (HR, 95% CI) for epilepsy given TBI was 6.76 [6.33-7.21]. The median duration between TBI documentation and epilepsy diagnosis was 3.3 years. In the observation time after index date (median duration: 12.2 years), Cox proportional hazard models identified the primary meditators of epilepsy risk after TBI as post-concussive symptoms (10.3%), cognitive dysfunction (7.0%), suicidal ideation/attempt (5.1%), overdose and drug abuse (3.8%-4.8%), and stroke (3.8%).

Significance: This study identified neurological conditions and symptoms that may play an intermediary role in the TBI-epilepsy relationship. Specific changes in health status after TBI may present useful targets for future trials and experimental approaches of PTE prevention.

Objective: Idiopathic generalized epilepsy (IGE) in adults comprise juvenile myoclonic epilepsy (JME), juvenile absence epilepsy (JAE), and epilepsy with generalized tonic-clonic seizures alone (EGTCS), which are defined by their seizure types but also cover a broad endophenotype of symptoms. Controversy exists on whether adult IGE is a group of distinct diseases or a clinical spectrum of one disease. Here, we used a deeply phenotyped cohort to test the hypothesis that IGE comprises three distinct clinical entities.

Methods: Patients (>18 years old) with IGE were recruited between 2016 and 2020 at Odense University Hospital and the Danish Epilepsy Center. Complete data were available for basic demographics, imaging, social status, and treatment response. Subjects were offered neuropsychological screening (including symptoms of psychiatric disease). Electroencephalograms (EEGs) were reanalyzed, and missing data were imputed. After selecting the features and normalizing the data, the dataset and an identical randomized dataset were subjected to k-means cluster analysis.

Results: The dataset comprised 502 patients and 22 distinct nonoverlapping clinical features. Elbow and gap analyses revealed an optimal number of clusters of 1; the features with the highest eigenvalues were age at diagnosis and self-reported executive dysfunction. Applying k-means clustering yielded three low-quality clusters as assessed by silhouette score (mean = .400 ± .01). The corresponding mean silhouette score for the randomized control dataset was .390 (±.002). Age at diagnosis was associated with the epileptic discharges on EEG and treatment response. Self-reported executive dysfunction showed associations with psychiatric symptoms and impulsivity. JME, JAE, and EGTCS were loosely associated with the clusters (k = .088, p = .002) but showed a specific distribution within a matrix defined by age at diagnosis and self-reported executive dysfunction.

Significance: IGE in adults is best described as a continuum of symptoms, where age at diagnosis and executive dysfunction are two main factors explaining most of its clinical variability. The seizure-defined syndromes cover different patient groups within the clinical spectrum.