Phenotypic heterogeneity presents challenges in providing clinical care to patients with pathogenic SCN8A variants, which underly a wide disease spectrum ranging from neurodevelopmental delays without seizures to a continuum of mild to severe developmental and epileptic encephalopathies (DEEs). An important unanswered question is whether there are clinically important subgroups within this wide spectrum. Using both supervised and unsupervised machine learning (ML) approaches, we previously found statistical support for two and three subgroups associated with loss- and gain- of- function vari-ants, respectively. Here, we test the hypothesis that the unsupervised subgroups (U1–U3) are distinguished by differential contributions of developmental and epileptic components.
We predicted that patients in the U1 and U2 subgroups would differ in timing of developmental delay and seizure onset, with earlier and concurrent onset of both features for the U3 subgroup. Standard statistical procedures were used to test these predictions, as well as to investigate clinically relevant associations among all five subgroups.
Two-population proportion and Kruskal–Wallis tests supported the hypothesis of a reversed order of developmental delay and seizure onset for patients in U1 and U2, and nearly synchronous developmental delay/seizure onset for the U3 (termed DEE) subgroup. Association testing identified subgroup variation in treatment response, frequency of initial seizure type, and comorbidities, as well as different median ages of developmental delay onset for all five subgroups.
Unsupervised ML approaches discern differential developmental and epileptic components among patients with SCN8A-related epilepsy. Patients in U1 (termed developmental encephalopathy) typically gain seizure control yet rarely experience improvements in development, whereas those in U2 (termed epileptic encephalopathy) have fewer if any developmental impairments despite difficulty in achieving seizure control. This understanding improves prognosis and clinical management and provides a framework to discover mechanisms underlying variability in clinical outcome of patients with SCN8A-related disorders.
To explore seizure control and offspring outcomes associated with antiseizure medication (ASM) withdrawal during the first trimester of pregnancy.
Based on a prospective multicenter study in China, pregnancies followed up between 2009 and 2023 at the neurology outpatient clinic of 50 hospitals were included in this study. Information on demographics, epileptic characteristics, treatment during pregnancy, and offspring outcomes was collected. Pregnancies were categorized into an ASM withdrawal group and an ASM continuation group. Balance tests and univariate log-binomial regression analysis were conducted to identify imbalanced factors between groups and potential risk factors for seizure deterioration during pregnancy. Multivariate log-binomial regression was then used to estimate the adjusted effects of ASM withdrawal on seizure deterioration during pregnancy and fetal outcomes. In addition, exploratory subgroup analysis was conducted to identify high-risk patients who should avoid ASM withdrawal.
Of the 695 pregnancies enrolled, 14.2% withdrew ASMs in the first trimester of pregnancy. ASM withdrawal during this period was associated with a risk of seizure deterioration during pregnancy (adjusted risk ratio [aRR] 1.405, 95% confidence interval [CI] 1.009–1.876). Subgroup analysis revealed a significant risk of seizure deterioration in pregnancies with seizures in 9 months (aRR 1.590, 95% CI 1.079–2.344). After adjusting the folic acid dose, no evidence of protective effects on fetus after ASM withdrawal was observed compared to patients with continued treatment, whereas seizure deterioration during pregnancy increased the risk of fetal death (aRR 3.577, 95% CI 1.086–11.651).
ASM withdrawal in the first trimester of pregnancy did not show a protective effect on fetal outcomes but rather resulted in increased seizure frequency during pregnancy. However, this finding requires a larger sample for validation. Furthermore, seizure deterioration during pregnancy was associated with an increased risk of fetal death.
Developmental and epileptic encephalopathies (DEEs) are neurological disorders characterized by developmental impairment and epilepsy. Our study aims to assess disease progression by comparing clinical findings, electroencephalography (EEG), and neuropsychological data from seizure onset to the last follow-up evaluation.
We retrospectively reviewed patients with genetic DEEs who were followed-up at the epilepsy unit of Bambino Gesù Children's Hospital, Rome. We collected information regarding gender, family history, genetic variant, age at onset and at last follow-up, neurological examination, type of seizure, drug resistance, occurrence of status epilepticus, and movement and cognitive and behavioral disorders. We compared EEG background activity, epileptiform abnormalities, and cognitive functions between seizure onset and the last follow-up evaluation using the McNemar-Bowker test (α = 5%).
A total of 160 patients (94 female) were included. Genetic analysis revealed a spectrum of pathogenic variants, with SCN1A being the most prevalent (25%). The median age at seizure onset and at the last follow-up was 0.37 (interquartile range [IQR]: 0.09–0.75) and 8.54 years (IQR: 4.32–14.55), respectively. We documented a statistically significant difference in EEG background activity (p = .017) and cognitive impairment (p = .01) from seizure onset to the last follow-up evaluation. No significant differences were detected for epileptiform abnormalities (p = .2). In addition, high prevalence rates were observed for drug resistance (81.9%), movement disorders (60.6%), behavioral and autism spectrum disorders (45%), neurological deficits (31.3%), and occurrence of status epilepticus (23.1%).
Our study provides evidence that a clinical progression may appear in genetic DEEs, manifesting as development or worsening of cognitive impairment and disruption of EEG background activity. These results highlight the challenging clinical course and the importance of early intervention and personalized care in the management of patients with DEEs.
This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic–clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts.
We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates.
Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90–1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08–26); (2) the linear relationship (slope = .79, R2 = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%).
Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures.
We lack knowledge about prognostic factors of resective epilepsy surgery (RES) in older adults (≥60 years), especially the role of comorbidities, which are a major consideration in managing the care of people with epilepsy (PWE). We analyzed a single-center cohort of 94 older adults (median age = 63.5 years, 52% females) who underwent RES between 2000 and 2021 with at least 6 months of postsurgical follow-up. Three fourths of the study cohort had lesional magnetic resonance imaging and underwent temporal lobectomy. Fifty-four (57%) PWE remained seizure-free during a median follow-up of 3.5 years. Cox proportional hazard multivariable analysis showed that aura (hazard ratio [HR] = .52, 95% confidence interval [CI] = .27–1.00), single ictal electroencephalographic pattern (HR = .33, 95% CI = .17–.660), and Elixhauser Comorbidity Index (HR = 1.05, 95% CI = 1.00–1.10) were independently associated with seizure recurrence at last follow-up. A sensitivity analysis using the Charlson Combined Score (HR = 1.38, 95% CI = 1.03–1.84, p = .027) confirmed the association of comorbidities with worse seizure outcome. Our findings provide a framework for a better informed discussion about RES prognosis in older adults. More extensive, multicenter cohort studies are needed to validate our findings and reduce hesitancy in pursuing RES in suitable older adults.
There are limited treatment options for individuals with drug-resistant idiopathic generalized epilepsy (IGE). Small, limited case series suggest that centromedian thalamus deep brain stimulation (CM-DBS) may be an effective treatment option. The optimal CM-DBS target for IGE is underexamined. Here, we present a retrospective analysis of CM-DBS targeting and efficacy for five patients with drug-resistant IGE. Volume of tissue activated (VTA) overlap with CM nucleus was performed using an open-source toolbox. Median follow-up time was 13 months. Median convulsive seizure frequency reduction was 66%. One patient had only absence seizures, with >99% reduction in absence seizure frequency. Four patients had electrode contacts positioned within the CM nucleus target, all of whom had >50% reduction in primary semiology seizure, with 85% median seizure reduction (p = .004, paired-sample t test). Volumetric “sweet-spot” mapping revealed that best outcomes were correlated with stimulation of the middle ventral CM nucleus. Connectivity strength between the sweet-spot region and central peri-Rolandic cortex was increased significantly relative to other cortical regions (p = 8.6 × 10−4, Mann–Whitney U test). Our findings indicate that CM-DBS can be an effective treatment for patients with IGE, highlight the importance of accurate targeting and targeting analysis, and within the context of prior work, suggest that ideal CM-DBS targets may be syndrome specific.
Although sleep duration and sleep quality are considered to be significant factors associated with epilepsy and seizure risk, findings are inconsistent, and their joint association remains uncertain. This study aimed to determine independent and joint associations of these two modifiable sleep features with seizure recurrence risk in newly treated patients with epilepsy (PWE).
This is a prospective cohort study of newly treated PWE at a comprehensive epilepsy center in northeast China between June 2020 and December 2023. Self-reported sleep duration and sleep quality were collected at baseline. All patients were followed for 12 months for recurrent seizures. Cox proportional hazard regression models were used to estimate the hazard ratios (HRs) of seizure recurrence. Models fitted with restricted cubic spline were conducted to test for linear and nonlinear shapes of each association.
A total of 209 patients were included, and 103 experienced seizure recurrence during follow-up. Baseline short sleep was significantly associated with greater risk of seizure recurrence (adjusted HR = 2.282, 95% confidence interval [CI] = 1.436–3.628, p < .001). Sleep duration (h/day) and recurrent seizure risk showed a significant nonlinear U-shaped association, with a nadir at 8 h/day. Baseline poor sleep quality was significantly associated with greater risk of seizure recurrence (adjusted HR = 1.985, 95% CI = 1.321–2.984, p < .001). Pittsburgh Sleep Quality Index score and seizure recurrence risk exhibited a positive linear association. Participants with a combination of poor quality–short sleep showed the highest risk of seizure recurrence (adjusted HR = 3.13, 95% CI = 1.779–5.507, p < .001) compared to the referent good quality–intermediate sleep group.
Baseline sleep duration and sleep quality were independently and jointly associated with risk of seizure recurrence in newly treated PWE. Our results point to an important potential role of baseline sleep duration and sleep quality in shaping seizure risk.
Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians.
The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age = 34.76 ± 10.55 years, 14 males) and 29 subjects with epilepsy (mean age = 38.95 ± 13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches.
The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier.
Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.
We aimed to investigate sleep in children with drug-resistant epilepsy (DRE), including developmental and epileptic encephalopathies (DEEs). Next, we examined differences in sleep macrostructure and microstructure and questionnaire outcomes between children with well-controlled epilepsy (WCE) and children with DRE. Furthermore, we wanted to identify factors associated with poor sleep outcome in these children, as some factors might be targets to improve epilepsy and neurodevelopmental outcomes.
A cross-sectional study was conducted in children 4 to 18-years-old. Children without epilepsy, with WCE, and with DRE were included. Overnight electroencephalography (EEG), including chin electromyography and electrooculography, to allow sleep staging, was performed. Parents were asked to fill out a sleep questionnaire. Classical five-stage sleep scoring was performed manually, spindles were automatically counted, and slow wave activity (SWA) in the first and last hour of slow wave sleep was calculated.
One hundred eighty-two patients were included: 48 without epilepsy, 75 with WCE, and 59 with DRE. We found that children with DRE have significantly lower sleep efficiency (SE%), less time spent in rapid eye movement (REM) sleep, fewer sleep spindles, and a lower SWA decline over the night compared to children with WCE. Subjectively more severe sleep problems were reported by the caregivers and more daytime sleepiness was present in children with DRE. Least absolute shrinkage and selection operator (LASSO) regression showed that multifocal interictal epileptiform discharges (IEDs), benzodiazepine treatment, and longer duration of epilepsy were associated with lower SE% and lower REM sleep time. The presence of multifocal discharges and cerebral palsy was associated with fewer spindles. Benzodiazepine treatment, drug resistance, seizures during sleep, intellectual disability, and older age were associated with lower SWA decline.
Both sleep macrostructure and microstructure are severely impacted in children with DRE, including those with DEEs. Epilepsy parameters play a distinct role in the disruption REM sleep, spindle count, and SWA decline.
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