Epilepsia最新文献

{"title":"Tracking seizure cycles beats a prospective moving average: Commentary on \"Rigorous evaluation of five e-diary alone seizure forecasting tools\".","authors":"Rachel E Stirling, Benjamin H Brinkmann, Dean R Freestone, Philippa J Karoly","doi":"10.1002/epi.70084","DOIUrl":"10.1002/epi.70084","url":null,"abstract":"<p><p>There is debate on the predictive value of multiday seizure cycles versus simple statistical baselines. Multidien seizure cyclicity is a prevalent, patient-specific phenomenon with promise for epilepsy management. We challenge the assertion that cycle tracking is no better than a moving average, which is an inherently retrospective model that lags changes in seizure likelihood. This commentary compared a causal cyclic forecast to a prospectively applied moving average across a large seizure diary cohort (n = 768) and two gold-standard chronic EEG cohorts (n = 24). For the EEG and diary cohorts, cycle tracking demonstrated significantly superior accuracy to the moving average for both hourly and daily forecasts (p < 0.0001), using multiple performance metrics. These results confirm that event-based cyclical models offer more accurate, simulated real-world forecasts. Robust forecasting tools must prioritize the detection and modeling of seizure cycles to move beyond simple baseline performance and provide actionable clinical utility.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":"1582-1586"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145862515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mHealth adherence in pediatric epilepsy: Outcomes of a sequential, multiple assignment, randomized trial (SMART).","authors":"Avani C Modi, Janelle L Wagner, Anup D Patel, Stacy Buschhaus, Matthew Schmidt, Heather Huszti, Tracy A Glauser, Marie Davidian","doi":"10.1002/epi.70097","DOIUrl":"10.1002/epi.70097","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Significance: </strong>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.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":"1975-1991"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seizure forecasting with multiple timescales and features.","authors":"Yueyang Liu, Artemio Soto-Breceda, Philippa Karoly, David B Grayden, Mark J Cook, Dean R Freestone, Daniel Schmidt, Levin Kuhlmann","doi":"10.1002/epi.70076","DOIUrl":"10.1002/epi.70076","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Significance: </strong>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.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":"1778-1791"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146061122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for adaptive neuromodulation in drug-resistant epilepsy.","authors":"Amir Hossein Daraie, Arianna Damiani, Mahsa Khoshkhou, Luis A Sanchez, Rachel J Smith, Shruti H Agashe, Jorge A Gonzalez-Martinez, Jennifer Hopp, Adam S Charles, Sridevi V Sarma, Joon-Yi Kang","doi":"10.1002/epi.70224","DOIUrl":"https://doi.org/10.1002/epi.70224","url":null,"abstract":"<p><p>Drug-resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and responsive neurostimulation offer an important and established therapeutic option that can provide substantial, often life-changing benefits, although clinical response varies widely and typically requires prolonged, iterative optimization. Epilepsy is increasingly understood as a disorder of distributed and dynamic brain networks, in which seizure generation, propagation, and termination reflect interactions among connected regions that evolve over time. In this setting, improving neuromodulation requires precise and patient-specific decisions across the treatment workflow. This perspective reframes neuromodulation as a control problem organized around four interdependent clinical questions: who is most likely to benefit, where to deliver stimulation within an individual's epileptic network, when to apply stimulation relative to evolving seizure risk, and how to configure and adapt stimulation parameters over time. Artificial intelligence (AI) may provide a unifying framework for addressing these questions in a personalized and data-driven manner. AI-guided response prediction could estimate the likelihood of benefit before implantation. AI-driven network analyses could identify optimal stimulation targets based on circuit influence rather than seizure onset alone. AI-based state estimation could enable continuous forecasting of seizure risk across short-term, circadian, and multidien timescales, supporting stimulation during periods of rising vulnerability rather than solely in response to detected seizures. Also, AI-enabled adaptive control could optimize stimulation parameters through state-dependent strategies that balance efficacy, safety, tolerability, and energy constraints as brain dynamics evolve. This review synthesizes biological, clinical, and computational advances to outline a roadmap for safe, interpretable, and individualized AI-guided neuromodulation in DRE.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mortality in functional seizures: Evidence from a large electronic health records dataset.","authors":"Richard A Kanaan, Rok Berlot, Tom Pollak, Markus Reuber, Stoyan Popkirov","doi":"10.1002/epi.70230","DOIUrl":"https://doi.org/10.1002/epi.70230","url":null,"abstract":"<p><strong>Objective: </strong>Several studies have found that people with functional seizures (FS) have increased mortality, approaching that of epilepsy (epileptic seizures [ES]). The small numbers of deaths in these studies make it unclear whether they can be attributed to comorbidities. We used a very large electronic health database to compare mortality in FS and ES, controlling for comorbidity.</p><p><strong>Methods: </strong>We searched the TriNetX database for people with FS and no ES, people with ES and no FS, and people with neither FS nor ES (NS). We compared mortality while controlling for demographics and comorbid conditions.</p><p><strong>Results: </strong>We identified 1 916 787 people with ES, 32 854 people with FS, and 21 053 667 healthy controls. FS had rates of mental and physical comorbidities that were higher than NS and, for most categories, higher than ES. The risk of death (hazard ratio [HR]) in ES compared to FS was 2.99 (95% confidence interval [CI] = 2.81-3.18), which was reduced to 2.07 (95% CI = 1.92-2.24) when matched for 31 demographic and health factors. The risk of death in NS compared to FS was .56 (95% CI = .53-.59); after matching for demographics and mood disorders, this was .48 (95% CI = .44-.53). Matched sensitivity analyses suggest this was particularly pronounced in the year following diagnosis (HR = .39, 95% CI = .33-.47) and in the 5th decade of life (HR = .38, 95% CI = .27-.52).</p><p><strong>Significance: </strong>FS confers approximately doubled mortality over that conferred by its comorbidities, although still only half of that conferred by ES, most prominently after diagnosis and in early middle age.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-assisted detection of epileptic spasms using electroencephalographic-video analysis.","authors":"Lin Wan, Nan Lin, Wen Wang, Zi Liang, Yisu Dong, Haibo He, Jian Chen, Yuying Fan, Tong Liu, Hongjie Wang, Weike Cheng, Guangshuang Lu, Juan Wang, Bo Zhang, Qiang Lu, Guang Yang","doi":"10.1002/epi.70194","DOIUrl":"https://doi.org/10.1002/epi.70194","url":null,"abstract":"<p><strong>Objective: </strong>This study was undertaken to develop and validate an artificial intelligence (AI) diagnostic tool using hybrid electroencephalographic (EEG)-video signals for automatic epileptic spasms (ES) detection.</p><p><strong>Methods: </strong>This retrospective cohort study with internal cross-validation and multicenter external validation was conducted from July 2022 to May 2025. It included 252 patients with ES from Chinese PLA General Hospital and 60 from three other medical centers. We developed a multimodal fusion approach combining video and electrophysiological signals. All EEG data were segmented into continuous 4-s pages. The internal cohort consisted of 212 patients (723.4 h video-EEG, 7348 ES, and 643 215 non-ES segments). Clinical validation involved 100 patients across four datasets (212.2 h, 5709 ES and 185 207 non-ES segments) plus 78 controls without ES (218.1 h, 196 249 segments, used for false alarm rate analysis). Primary outcomes included sensitivity, specificity, accuracy, precision, and F1 score versus electroencephalographer interpretation.</p><p><strong>Results: </strong>In internal cross-validation, the hybrid EEG-video model achieved superior performance compared to current EEG-only models (area under the precision-recall curve = .7334, 95% confidence interval [CI] = .7118-.7539, area under the receiver operating characteristic curve = .9820, 95% CI = .9782-.9856). In the clinical validation dataset, the model demonstrated diagnostic sensitivity (.735, 95% CI = .673-.822) and specificity (.995, 95% CI = .992-.996) comparable to experienced electroencephalographers. With AI assistance, all electroencephalographers showed improved sensitivity trends, with one rater showing a marked improvement from .620 (95% CI = .532-.699) to .792 (95% CI = .757-.821). Notably, specificity did not decline significantly for any of the raters. In samples containing subtle ES, machine-assisted recognition significantly improved sensitivity by 16%-21% for all electroencephalographers (p < .01). The model maintained low false alarm rates (.16‰) across different patient populations including healthy controls and epilepsy patients without ES.</p><p><strong>Significance: </strong>This AI diagnostic tool achieves clinical performance comparable to senior electroencephalographers when applied independently for ES detection, with greater robustness in detecting subtle ES. When used collaboratively with clinicians, it could enhance diagnostic sensitivity while maintaining high specificity. The technology addresses critical diagnostic challenges in pediatric epilepsy care and shows promise to reduce health care inequities in resource-limited settings lacking specialized expertise.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional connectivity in self-limited epilepsy with centrotemporal spikes increases with epilepsy duration and interictal spike exposure.","authors":"Marie E Vasitas, Miguel S Menchaca, Beatrice S Goad, Xiwei She, Christopher Lee-Messer, Zihuai He, Fiona M Baumer","doi":"10.1002/epi.70212","DOIUrl":"10.1002/epi.70212","url":null,"abstract":"<p><strong>Objective: </strong>To determine whether persistent exposure to interictal spikes is associated with progressive changes in functional connectivity in children with self-limited epilepsy with centrotemporal spikes (SeLECTS).</p><p><strong>Methods: </strong>Connectivity was calculated from electroencephalograms (EEGs) of 68 children with SeLECTS and 65 age- and sex-matched controls using the weighted phase lag index. First, we assessed whether connectivity increased with longer epilepsy duration, categorizing duration based on time since first seizure (less vs. greater than 6 months). Second, in a subset of 19 SeLECTS children with repeated EEGs, we assessed whether trajectory of connectivity over time differed based on persistence versus resolution of spikes. Connectivity during sleep and wakefulness was examined separately.</p><p><strong>Results: </strong>During sleep, connectivity was lowest in controls, intermediate in patients with recent onset epilepsy, and highest in those with longer epilepsy duration. Elevations in connectivity were greatest within the right occipital region at onset and became more widespread with longer epilepsy duration, especially increasing in the left temporal region. During wakefulness, connectivity in recent onset epilepsy trended lower than controls, whereas longer duration epilepsy trended higher, such that the two epilepsy groups differed significantly from each other but not from controls. In the cohort of children with repeated EEGs, patients with persistent spikes showed increasing connectivity over time in sleep, whereas those with spike resolution demonstrated decreasing connectivity over time. There were no significant changes in awake connectivity over time regardless of spike persistence or resolution.</p><p><strong>Significance: </strong>Functional connectivity in SeLECTS increases progressively with longer duration of spike exposure, suggesting that ongoing spikes drive neural network alterations. Spikes are a potential treatment target to prevent progressive brain network disruption and preserve cognitive outcomes.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serum NfL, GFAP, and p-tau217 in adults with drug-resistant epilepsy and intellectual disabilities: Signs of ongoing neural injury.","authors":"Hadassa Kwetsie, Inge M W Verberk, Jans S van Ool, Anneke J J Rampen, Rebecca Z Rousset, Nicole I Wolf, Dederieke A M Maes-Festen, Clara D M van Karnebeek, Charlotte E Teunissen, Erik Boot, Agnies M van Eeghen","doi":"10.1002/epi.70218","DOIUrl":"https://doi.org/10.1002/epi.70218","url":null,"abstract":"<p><strong>Objective: </strong>Adults with epilepsy and intellectual disabilities (IDs) may be at increased risk of dementia, but clinical evaluation is complex and use of conventional biomarkers is often considered too invasive. We explored abnormality of serum neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and phosphorylated tau-217 (p-tau217) in these adults, and their associations with clinical outcomes.</p><p><strong>Methods: </strong>Serum biomarker levels were quantified with Single Molecule Array (Simoa) in 68 adults with co-occurring epilepsy and ID at a median age of 52.0 (range 24-76) years. Levels were classified normal/abnormal (>95th percentile) in comparison to reference data of age-matched healthy controls (NfL, GFAP) or Alzheimer's disease (AD)-specific cutoff (p-tau217). Associations with age were assessed with correlations and segmented regression analyses. Associations with adaptive decline, suspected dementia, ID, epilepsy, antiseizure medication, comorbidity, and mortality were explored using chi-square tests, Mann-Whitney U tests, log rank tests, and Cox regression analyses.</p><p><strong>Results: </strong>NfL levels were abnormal in 51.5%, GFAP in 63.2%, and p-tau217 in 2.9%. Age-corrected Z-scores of NfL were still significantly associated with age (r = .31, p = .01). Elevated NfL was associated with significant adaptive decline (χ² = 7.20, p = .007), suspected dementia (χ² = 10.73, p = .001), monthly seizure frequency (χ² = 5.21, p = .03), non-early seizure onset (U = 281, p < .001), carbamazepine use (χ² = 4.38, p = .04), and mortality (Log rank p = .04; hazard ratio [HR] p = .007). GFAP levels were significantly higher in severe ID (median Z = 2.5) compared to mild ID (median Z = 1.6) (U = 196, p = .02). Having abnormal levels of GFAP and NfL simultaneously was the most common biomarker profile in participants with suspected dementia (69.2%).</p><p><strong>Significance: </strong>NfL and GFAP were abnormal in many participants, coinciding with clinical decline. Findings are suggestive for ongoing neural injury, but not necessarily for AD.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mental health impacts experienced by caregivers of people with Dravet syndrome: A systematic literature review.","authors":"Adam Strzelczyk, Mary Anne Meskis, Galia Wilson, Bobby Jacob, Christoph Helmstaedter, Jane von Gaudecker, Veronica Hood, Ceri Hughes, Michael Scott Perry","doi":"10.1002/epi.70171","DOIUrl":"https://doi.org/10.1002/epi.70171","url":null,"abstract":"<p><strong>Objective: </strong>Dravet syndrome (DS) places tremendous burden on caregivers owing to the extent of required assistance and impact on daily living, as well as the risk to the individual with DS of premature mortality from sudden unexpected death in epilepsy and morbidity associated with nonseizure manifestations. This systematic literature review provides an up-to-date characterization of the mental health impacts experienced by caregivers of people with DS.</p><p><strong>Methods: </strong>Databases (1974 to August 29, 2024 in Embase; 1946 to August 29, 2024 in MEDLINE) were searched for records containing keywords relevant to mental health in caregivers of people with DS. The study population comprised caregivers of people with DS with any or no intervention and/or comparator (and excluding pharmacologic interventions affecting caregiver burden-related outcomes) and with mental health outcomes that included depression, anxiety, fatigue, sleep quality, stress, mood, and quality of life scales.</p><p><strong>Results: </strong>Database searches returned 519 records; 20 published articles were included. Most common were cross-sectional studies, with populations from Asia, Australia, Central/South America, Europe, and North America. Study sample sizes ranged from seven to 256 caregivers of people with DS; most caregivers were female. Depression and anxiety were reported in 11 and 10 articles, respectively; the prevalence of depression and anxiety among caregivers ranged 5%-66% and 5.2%-80%, respectively. Some studies used instruments to assess mental health outcomes; Beck Depression Inventory-II for depressive symptoms and the Hospital Anxiety and Depression Scale for symptoms of anxiety and depression were reported in three and two articles, respectively. Factors potentially associated with mental health including sleep quality, fatigue, and stress were commonly reported, with poor sleep quality and fatigue often linked to nighttime monitoring of people with DS.</p><p><strong>Significance: </strong>Physicians should routinely assess the mental health of caregivers of people with DS; future studies should focus on identifying interventions that ease burden on caregivers.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep brain stimulation of the thalamus for intractable epilepsy (FRANCE study): A randomized clinical trial.","authors":"Stéphan Chabardès, Fabrice Bartolomei, Anca Nica, Claire Haegelen, Lorella Minotti, Brigitte Piallat, Chrystele Mosca, Sandrine Massicot, Sylvain Rheims, Marc Guenot, Emmanuel Cuny, Jérome Aupy, Louis Maillard, Sophie Colnat-Coulbois, Corentin Leroy, Romain Carron, Philippe Coubes, Philippe Gélisse, Carine Karachi, Vincent Navarro, Bertrand Devaux, Francine Chassoux, Phlippe Derambure, Nicolas Reyns, Jean Christope Sol, Luc Valton, Denys Fontaine, Veronique Bourg, Philippe Kahane, Sandra David-Tchouda, Jean Regis","doi":"10.1002/epi.70211","DOIUrl":"https://doi.org/10.1002/epi.70211","url":null,"abstract":"<p><strong>Objective: </strong>This study was undertaken to evaluate the efficacy and safety of deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) compared with best medical treatment (BMT) in patients with drug-resistant epilepsy (DRE).</p><p><strong>Methods: </strong>This randomized, open-label, phase 3 controlled trial was conducted across 14 specialized epilepsy and DBS centers. Eligible participants were adults with focal or multifocal DRE who had previously failed vagus nerve stimulation (VNS). Sixty-one patients were randomized 1:1 either to receive continuous bilateral ANT-DBS (n = 30) or to continue BMT, including VNS (n = 31), for 12 months. Afterward, patients in the BMT group were offered delayed ANT-DBS and followed for an additional year. The primary outcome was the change in monthly severe seizure frequency (defined on the modified Chalfont Scale) at 12 months. Safety outcomes were also recorded.</p><p><strong>Results: </strong>Among 67 screened patients, 61 were enrolled. At 12 months, median seizure reduction was greater in the DBS group (-44%, interquartile range [IQR] = -67 to 0) versus the BMT group (-6%, IQR = -56 to 20; p = .09); 44.5% of patients in the DBS group achieved a ≥50% reduction in seizure frequency compared to 27% in the BMT group. Within-group analyses showed significant seizure reductions in the DBS group at both 12 months (-44%, p < .001) and 24 months (-46%, p < .001), as well as in the delayed DBS group at 12 months (-36%, IQR = -73 to -4; p < .001). No significant differences in quality of life were observed between groups, and no major DBS-related adverse events were reported.</p><p><strong>Significance: </strong>Our study suggests a potential benefit rather than demonstrating superiority of ANT-DBS over medical therapy. However, within-group improvements and favorable safety profile support the use of ANT-DBS as a palliative treatment option in patients with DRE who have failed VNS.</p>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}