Epilepsia最新文献

Objective: High-field magnetic resonance imaging (MRI) is a standard in the diagnosis of epilepsy. However, high costs and technical barriers have limited adoption in low- and middle-income countries. Even in high-income nations, many individuals with epilepsy face delays in undergoing MRI. Recent advancements in ultra-low-field (ULF) MRI technology, particularly the development of portable scanners, offer a promising solution to the limited accessibility of MRI. In this study, we present and evaluate the imaging capability of ULF MRI in detecting structural abnormalities typically associated with epilepsy and compare it to high-field MRI at 3 T.

Methods: Data collection was conducted within 3 consecutive weeks at the University Hospital Bonn. Inclusion criteria were a minimum age of 18 years, diagnosed epilepsy, and clinical high-field MRI with abnormalities. We used a .064 T Swoop portable MR Imaging System. Both high-field MRI and ULF MRI scans were evaluated independently by two experienced neuroradiologists as part of their clinical routine, comparing pathology detection and diagnosis completeness.

Results: Twenty-three individuals with epilepsy were recruited. One subject presented with a dual pathology. Across the entire cohort, in 17 of 24 (71%) pathologies, an anomaly colocalizing with the actual lesion was observed on ULF MRI. For 11 of 24 (46%) pathologies, the full diagnosis could be made based on ULF MRI. Tumors and posttraumatic lesions could be diagnosed best on ULF MRI, whereas cortical dysplasia and other focal pathologies were the least well diagnosed.

Significance: This single-center series of individuals with epilepsy demonstrates the feasibility and utility of ULF MRI for the field of epileptology. Its integration into epilepsy care offers transformative potential, particularly in resource-limited settings. Further research is needed to position ULF MRI within imaging modalities in the diagnosis of epilepsy.

Objective: EEG patterns and quantitative EEG (qEEG) features have been poorly explored in monogenic epilepsies. Herein, we investigate regional differences in EEG frequency composition in patients with STXBP1 developmental and epileptic encephalopathy (STXBP1-DEE).

Methods: We conducted a retrospective study collecting electroclinical data of patients with STXBP1-DEE and two control groups of patients with DEEs of different etiologies and typically developing individuals matched for age and sex. We performed a (1) visual EEG assessment, (b) qEEG analysis, and (c) electrical source imaging (ESI). We quantified the relative power (RP) of four frequency bands (α β, θ, δ), in two electrode groups (anterior/posterior), and compared their averages and dynamics (standard deviation [SD] over time). The ESI was performed by applying the standard Distributed Source Modeling algorithm.

Results: We analyzed 42 EEG studies in 19 patients with STXBP1-DEE (10 female), with a median age at recordings of 9.6 years (range 9 months to 29 years). The δRP was higher in recordings of STXBP1-DEE (p < .001) compared to both control groups, suggesting the pathogenicity and STXBP1-specificity of these findings. In STXBP1-DEE, the δRP was significantly higher in the anterior electrode group compared to the posterior one (p = .003). There was no correlation between the anterior δRP and the epilepsy focus, age at recordings, and concomitant medications The ESI modeling of this activity showed a widespread involvement of the dorsomesial frontal cortex, suggesting a large corticosubcortical pathologic network. Finally, we identified two groups of recordings: cluster.1 with higher anterior δRP and low dynamics and cluster.2 with lower δRP and higher dynamics. Patients in cluster.1 had a more severe epilepsy and neurological phenotype compared to patients in cluster 2.

Significance: The qEEG analysis showed a predominant frontal slow activity as a specific STXBP1 feature that correlates with the severity of the phenotype and may represent a biomarker for prospective longitudinal studies of STXBP1-DEE.

Objective: This study was undertaken to assess long-term safety, tolerability, and efficacy of lacosamide (LCM) as adjunctive therapy for generalized onset tonic-clonic seizures (GTCS) in patients aged ≥4 years with idiopathic generalized epilepsy (IGE).

Methods: EP0012 (NCT02408549) was a phase 3, multicenter, open-label extension (OLE) trial. Patients were enrolled from SP0982 (NCT02408523). Trial duration was ≥2 years (adults) and ≤5 years (children). The trial consisted of a treatment period, ≤4-week taper period, and 30-day safety follow-up. Safety (primary) variables were incidence of treatment-emergent adverse events (TEAEs), discontinuations due to TEAEs, incidence of onset of absence or myoclonic seizures, and increase in days with absence or myoclonic seizures per 28 days. Efficacy (secondary) variable was percent change in GTCS frequency per 28 days. Kaplan-Meier estimated retention rates and analyses by number of lifetime antiseizure medications (ASMs) were performed post hoc.

Results: Overall, 239 patients (mean age = 27.9 years, 56.1% female, 18.4% children) were enrolled and received ≥1 dose of LCM in this OLE (median treatment duration = 3.2 years); 157 (65.7%) completed the trial, and 82 (34.3%) discontinued. The most common reason for discontinuation (≥10%) was withdrawn consent (30 [12.6%]). Kaplan-Meier estimated retention rate was 87%, 72%, and 60% at 1, 3, and 5 years, respectively. Overall, 222 (92.9%) patients reported TEAEs; 19 (7.9%) discontinued due to TEAEs. Few patients had an increase in number of days with absence or myoclonic seizures, or incidence of new absence or myoclonic seizures. Median percent change in GTCS frequency per 28 days from the combined baseline was -88.6% (range = -100.0 to 465.4, n = 238). Post hoc analyses demonstrated small numerical differences between patients with 1, 2, and ≥3 lifetime ASMs.

Significance: The results support the use of long-term adjunctive LCM for GTCS in patients with IGE. Long-term adjunctive LCM was efficacious and well tolerated independent of the number of ASMs used before LCM initiation.

Clinical trial registration: ClinicalTrials.gov: NCT02408549.

We studied the distribution of germline and somatic variants in epilepsy surgery patients with (suspected) malformations of cortical development (MCD) who underwent surgery between 2015 and 2020 at University Medical Center Utrecht (the Netherlands) and pooled our data with four previously published cohort studies. Tissue analysis yielded a pathogenic variant in 203 of 663 (31%) combined cases. In 126 of 379 (33%) focal cortical dysplasia (FCD) type II cases and 23 of 37 (62%) hemimegalencephaly cases, a pathogenic variant was identified, mostly involving the mTOR signaling pathway. Pathogenic variants in 10 focal epilepsy genes were found in 48 of 178 (27%) FCDI/mild MCD/mMCD with oligodendroglial hyperplasia and epilepsy cases; 36 of these (75%) were SLC35A2 variants. Six of 69 (9%) patients without a histopathological lesion had a pathogenic variant in SLC35A2 (n = 5) or DEPDC5 (n = 1). A germline variant in blood DNA was confirmed in all cases with a pathogenic variant in tissue, with a variant allele frequency (VAF) of ~50%. In seven of 114 patients (6%) with a somatic variant in tissue, mosaicism in blood was detected. More than half of pathogenic somatic variants had a VAF < 5%. Further analysis of the correlation between genetic variants and surgical outcomes will improve patient counseling and may guide postoperative treatment decisions.

Objective: Brain somatic variants in SLC35A2 were recently identified as a genetic marker for mild malformations of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE). The role of SLC35A2 in cortical development and the contributions of abnormal neurons and oligodendrocytes to seizure activity in MOGHE remain largely unexplored.

Methods: Here, we generated a novel Slc35a2 floxed allele, which we used to develop two Slc35a2 conditional knockout mouse lines targeting (1) the Emx1 dorsal telencephalic lineage (excitatory neurons and glia) and (2) the Olig2 lineage (oligodendrocytes). We examined brain structure, behavior, and seizure activity.

Results: Knockout of Slc35a2 from the Emx1 lineage, which targets both cortical neurons and oligodendrocytes, resulted in early lethality and caused abnormal cortical development, increased oligodendroglial cell density, early onset seizures, and developmental delays akin to what is observed in patients with MOGHE. By tracing neuronal development with 5-Ethynyl-2'-deoxyuridine (EdU) birthdating experiments, we found that Slc35a2 deficiency disrupts corticogenesis by delaying radial migration of neurons from the subventricular zone. To discern the contributions of oligodendrocytes to these phenotypes, we knocked out Slc35a2 from the Olig2 lineage. This recapitulated the increased oligodendroglial cell density and resulted in abnormal electroencephalographic activity, but without a clear seizure phenotype, suggesting Slc35a2 deficiency in neurons is required for epileptogenesis.

Significance: This study presents two novel Slc35a2 conditional knockout mouse models and characterizes the effects on brain development, behavior, and epileptogenesis. Together, these results demonstrate a direct causal role for SLC35A2 in MOGHE-like phenotypes, including a critical role in neuronal migration during brain development, and identify neurons as key contributors to SLC35A2-related epileptogenesis.

Objective: Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective histopathology workup scenario.

Methods: Eighty-six new cases with histopathologically confirmed FCD ILAE type Ia (FCDIa), FCDIIa, FCDIIb, mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE), or mild malformations of cortical development were selected, 20 of which had confirmed gene mosaicism.

Results: The classifier always recognized the correct histopathology diagnosis in four or more 1000 × 1000-μm digital tiles in all cases. Furthermore, the final diagnosis overlapped with the largest batch of tiles assigned by the algorithm to one diagnostic entity in 80.2% of all cases. However, 86.2% of all cases revealed more than one diagnostic category. As an example, FCDIIb was identified in all of the 23 patients with histopathologically assigned FCDIIb, whereas the classifier correctly recognized FCDIIa tiles in 19 of these cases (83%), that is, dysmorphic neurons but no balloon cells. In contrast, the classifier misdiagnosed FCDIIb tiles in seven of 23 cases histopathologically assigned to FCDIIa (33%). This mandates a second look by the signing histopathologist to either confirm balloon cells or differentiate from reactive astrocytes. The algorithm also recognized coexisting architectural dysplasia, for example, vertically oriented microcolumns as in FCDIa, in 22% of cases classified as FCDII and in 62% of cases with MOGHE. Microscopic review confirmed microcolumns in the majority of tiles, suggesting that vertically oriented architectural abnormalities are more common than previously anticipated.

Significance: An AI-based diagnostic classifier will become a helpful tool in our future histopathology laboratory, in particular when large anatomical resections from epilepsy surgery require extensive resources. We also provide an open access web application allowing the histopathologist to virtually review digital tiles obtained from epilepsy surgery to corroborate their final diagnosis.

Objective: Determining the pathogenicity of missense variants in clinical genetic tests for individuals with epilepsy is crucial for guiding personalized treatment. However, achieving a definitive pathogenic classification remains challenging, with most missense variants still classified as variants of uncertain significance (VUS) and with the availability of many computational tools which may provide conflicting predictions. Here, we aim to evaluate the performance of state-of-the-art computational tools in pathogenicity prediction of missense variants in epilepsy-associated genes. This will assist in selecting the most appropriate tool and critically assess their use in clinical setting.

Methods: We assessed the performance of nine in silico pathogenicity prediction tools for missense variants in epilepsy-associated genes on three carefully curated data sets. The first two data sets comprise missense variants in epilepsy associated genes that have been uploaded to ClinVar in the last year and were, therefore, not part of the training set of any of the nine considered tools. These two data sets are based on two different lists of epilepsy-associated genes and comprise ~700 and ~ 250 missense variants, respectively. The third data set includes ~400 missense variants within epilepsy-associated genes for which the functional effects have been determined experimentally and are therefore used here to infer pathogenicity. These three data sets represent the best available approximation to blind and independent test sets.

Results: Among the nine assessed tools, AlphaMissense (area under the curve [AUC]: .93, .88, and .95) and REVEL (AUC: .93, .88, and .93) showed the best classification performance, also outperforming other tools in the number of classified variants.

Significance: We show which recently developed prediction tools achieve higher performance in epilepsy-associated genes and should be integrated, therefore, into the American College of Medical Genetics and Genomics/Association of Molecular Pathology (AGMC/AMP) variant classification process. Periodic reevaluation of genetic test results with newly developed or updated tools should be incorporated into standard clinical practice to improve diagnostic yield and better inform precision medicine.

Individuals with epilepsy are at risk of developing preictal, ictal, postictal and interictal psychoses. Antipsychotic drugs (APDs) are the main class of drugs used to treat psychosis and schizophrenia. The efficacy and safety of APDs as a treatment for epileptic psychosis is not well understood. This systematic review aimed to assess the effectiveness and adverse effects of APDs for treating psychosis in people with epilepsy. We adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched MEDLINE, Embase, PsycInfo, and AMED (Allied and Complementary Medicine) from database inception to June 20, 2023. We contacted experts in the field and performed citation searches to identify additional records. Title, abstract, full-text review, and data analysis were conducted in duplicate, with conflicts resolved by discussion among authors. Given the considerable heterogeneity of study designs, meta-analysis was not deemed appropriate; instead, the results were tabulated in a narrative synthesis. The Joanna Briggs Institute Risk of Bias tool and GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework were used to assess study quality. We identified 13 studies with a total of 1180 participants. In the four case series included, the psychotic symptoms of 25 of 28 patients treated with APDs partially improved or fully resolved. Three of the four cohort studies reported an association between antipsychotic use and longer duration of psychotic episodes, two found similar results in both APD and non-APD groups, and two did not report control psychosis outcomes. When reported, seizure frequency was observed to remain unchanged or decrease following APD treatment. The evidence on the effectiveness of antipsychotics in the treatment of psychosis in epilepsy is inconclusive and may reflect confounding by indication. However, most studies suggest that antipsychotics were not associated with a marked worsening in seizure frequency. It remains unclear whether antipsychotics should be used in epilepsy, and well-controlled cohort studies and randomized controlled trials are necessary to draw definitive conclusions.