Citations

Objective To characterize regional brain atrophy patterns, evaluate their association with regional leucine‐rich glioma‐inactivated‐1 ( LGI1 ) gene expression, and identify clinical predictors of hippocampal atrophy in LGI1 autoimmune encephalitis (AE). Methods This study compared regional brain volumes in LGI1‐AE patients versus age‐ and sex‐matched healthy controls (HC) and Alzheimer's disease (AD) patients. LGI1 gene expression was derived from the Allen Human Brain Atlas. Multivariable linear regression and generalized linear models were utilized. Results A total of 55 LGI1‐AE patients were included. Median age was 68 years (IQR 62.5–74.2 years) at volumetric analysis, after 19 months (IQR 11–45 months) from symptom onset. Compared with HC ( n = 55) and AD patients ( n = 55), LGI1‐AE patients had greater regional atrophy of the hippocampus, insula, caudate, and putamen ( p < 0.002). Longitudinal analyses demonstrated hippocampal and pallidal atrophy after covariate adjustment. Among those who did not fulfill limbic encephalitis criteria and/or had non‐lesional magnetic resonance imaging, limbic and striatal atrophy were identified compared with HC ( p < 0.01). Regions with the greatest differential atrophy corresponded to higher LGI1 gene expression (Spearman ρ = 0.7, p = 0.005). Severe cognitive deficits at diagnosis ( β = −0.56; p = 0.002) and longer disease duration ( β = −0.01; p = 0.001) independently predicted total hippocampal volume ( R 2 = 0.31). Interpretation LGI1‐AE demonstrates a distinct pattern of limbic and striatal atrophy that differs from AD and aligns with regional LGI1 expression, supporting a model of selective network vulnerability. Greater baseline cognitive impairment and longer disease duration independently associated with hippocampal atrophy, underscoring the potential importance of early diagnosis and treatment to limit structural brain injury. ANN NEUROL 2026

Abstract Introduction Quantitative interpretation of pediatric brain [ 18 F]-FDG PET is limited by the lack of age-matched normative reference databases, despite substantial developmental variation in cerebral glucose metabolism. This study aimed to construct a large-scale normative pediatric [ 18 F]-FDG PET database and develop an age-specific z-score mapping framework for quantitative evaluation of pediatric epilepsy. Method Pediatric brain PET data were retrospectively derived from clinically acquired whole-body or dedicated [ 18 F]-FDG PET examinations in participants 0–19 years of age without neurologic disease or visible brain abnormalities. A total of 417 PET scans from 341 participants were stratified into five developmental age groups: infancy, early childhood, middle childhood/school age, adolescence, and late adolescence. SUVR maps were generated using whole-brain atlas and brainstem reference regions. Voxel-wise t-test analyses were performed to characterize age- and sex-related metabolic differences. Age-specific voxel-wise mean and standard deviation maps were generated from the normative cohort and used to calculate age-matched hypometabolic z-score maps in 14 pediatric patients with epilepsy. The resulting abnormalities were qualitatively evaluated for concordance with epileptogenic findings identified on clinical MRI and/or PET by expert image review. Results Voxel-wise analyses demonstrated prominent age-dependent SUVR differences, particularly between infancy and older age groups. With whole-brain atlas normalization, older children showed progressively higher relative SUVR in cortical and cerebellar gray matter regions, whereas infancy showed relatively higher SUVR in the occipital cortex, white matter, thalamus, and brainstem. Brainstem normalization produced distinct developmental patterns, emphasizing higher white matter SUVR in infancy and broader gray matter differences in older groups. Sex-related differences were limited and regionally specific. Whole-brain atlas normalization showed lower variability than brainstem normalization and was selected for z-score mapping. In representative epilepsy cases, age-matched z-score maps identified focal or regional hypometabolism corresponding to malformations of cortical development, focal cortical dysplasia, mesial temporal sclerosis, nonlesional MRI cases, remote insult, tumor-related abnormalities, and Sturge-Weber syndrome. Discussion A large-scale normative pediatric [18F]-FDG PET database can support developmentally appropriate quantitative interpretation of pediatric brain PET. Age-matched z-score mapping may complement visual assessment by highlighting abnormal cerebral glucose metabolism in pediatric epilepsy, although findings should be interpreted with MRI, EEG, seizure semiology, and other presurgical data.

Abstract Seizure forecasting has progressed from theoretical aspiration to a rapidly advancing research domain, yet clinical translation remains limited. Over the past decades, advances in algorithm development, chronic electroencephalography (EEG), wearable sensors, and the characterization of seizure cycles have demonstrated that seizure risk is not random but fluctuates according to identifiable biological rhythms and patient‐specific patterns. Forecasting algorithms have shown promising performance across diverse retrospective datasets, including intracranial EEG, subscalp recordings, wearable physiological signals, and even self‐reported diaries. However, the clinical value of forecasting is contentious. Prospective real‐world validation and regulatory approval of patient‐facing forecasting systems remain rare. This review incorporates perspectives presented at the 5th International Congress on Mobile Health and Digital Technology in Epilepsy (2025). We examine barriers impeding clinical translation and current attempts to address them. Crucially, forecasting performance cannot be evaluated in isolation from intended use. Applications range from low‐risk uses, like scheduling diagnostic monitoring or visualization of historical trends, to higher risk interventions, including medication titration and adaptive neuromodulation. Each application entails distinct performance thresholds, ethical considerations, and regulatory requirements. Translational challenges include reliable seizure annotation, nonstationarity dynamics of biological cycles, and practical constraints for real‐time deployment. Ethical concerns center on miscalibrated reliance on low‐risk states, potential anxiety associated with high‐risk advisories, and the heterogeneity of patient preferences and risk tolerance. Regulatory pathways are likely to depend on clearly defined use cases and clinically meaningful endpoints, which may extend beyond seizure counts to include quality of life, anxiety, locus of control, and other patient‐reported outcomes. Ultimately, translation will require rigorous prospective evaluation against transparent benchmarks, sustainable scientific–commercial partnerships, and integration of probabilistic risk information into clinical workflows. With careful implementation, seizure forecasting may evolve from proof‐of‐concept research into a clinically meaningful component of epilepsy management, and we remain cautiously optimistic.

Abstract Drug-resistant epilepsy affects tens of millions of people worldwide and is associated with considerable morbidity and mortality. Thalamic deep brain stimulation and cortical responsive neurostimulation are proven treatments for focal epilepsy. Both have been used to target a range of thalamic nuclei; yet, the roles of these thalamic nuclei in focal seizure generation remain incompletely understood. Thirteen patients with drug-resistant focal epilepsy undergoing intracranial EEG were consented to undergo investigation of thalamocortical networks. Sampled regions included cortical, mesial temporal, and thalamic brain regions. Visual and spectral analyses were performed to identify seizure onset patterns and correlate thalamic and cortical seizure activity. Thalamic ictal discharges were observed in 89% of seizures. Of these, 56% demonstrated synchronous thalamocortical activity with distinct patterns. These onset patterns included hypersynchronous spiking, low-voltage fast activity, ictal baseline shifts, and broadband suppression. Multiple thalamic nuclei were involved in ictal organization and propagation, with the specific nuclei depending on the cortical seizure network. The thalamus plays a crucial role in focal onset seizure generation and propagation, with distinct seizure onset patterns and nuclei involved. These findings support exploring a broader range of thalamic nuclei in epilepsy neurostimulation and have implications for seizure detection settings in intracranial sensing devices.

Abstract The International League Against Epilepsy (ILAE) is a global organization dedicated to improving the lives of people with epilepsy through education, research, and advocacy. Recognizing the need for broader engagement and multidisciplinary collaboration, the ILAE has established five sections: Young Epilepsy Section (YES), Nursing, Social Work and Social Services (SWSSS), Neurotechnology, and Pharmacy. These sections aim to strengthen professional networks, enhance education, and promote innovation in epilepsy care. YES fosters leadership and participation among early‐career professionals, whereas the Nursing Section addresses gaps in epilepsy‐specific training and competency development. SWSSS emphasizes psychosocial care, advocacy, and health equity, supporting Intersectoral Global Action Plan (IGAP) objectives through culturally sensitive interventions. The Neurotechnology section advances the integration and development of innovative tools for diagnosis and treatment, and the Pharmacy section focuses on optimizing medication management and access to antiseizure medications globally. Collectively, these sections contribute to the WHO IGAP by improving policy, care delivery, research, and public health responses. Looking ahead, ILAE seeks to expand multidisciplinary participation, encourage cross‐sectional collaboration, and maintain its role as a leading force in global epilepsy care. Through these initiatives, ILAE continues to build capacity, foster inclusivity, and drive progress toward equitable, comprehensive epilepsy services worldwide. Plain Language Summary The International League Against Epilepsy (ILAE) is the global organization responsible for all healthcare professionals involved in the care of those with epilepsy. Recognizing the increasing number of different disciplines now contributing to epilepsy care, and the need to develop professionals of the future, the ILAE has recognized “sections” of like‐minded individuals to encourage networking and communication. Together, these sections contribute to the WHO Intersectoral Global Action Plan (IGAP) by improving policy, care delivery, research, and public health responses. Moving forward, the ILAE seeks to expand multidisciplinary participation, encourage collaboration, and maintain its role as a leading force in global epilepsy care.

Background Patients with anti-leucine-rich glioma-inactivated 1 (anti-LGI1) autoimmune encephalitis (AIE) frequently present with faciobrachial dystonic seizures (FBDS), which are often under-detected due to their subtle motor features and lack of consistent EEG correlates. Conventional in-hospital monitoring and patient-reported seizure diaries are limited, particularly during nocturnal periods. In this pilot feasibility study, we evaluated the use of a wearable device (Empatica E4) to objectively monitor FBDS-related motor and autonomic activity in seven patients with anti-LGI1 AIE and four control subjects. Methods Wearable recordings included accelerometry (ACC), electrodermal activity (EDA), photoplethysmography (PPG) and skin temperature. A support vector machine-based algorithm was developed to identify disorder-related events using ACC and EDA features. Analyses were restricted to sleep periods to minimise confounding from voluntary movements. Results Pretreatment recordings from two patients (P1 and P2) showed significantly elevated motor event frequency, ACC magnitude and EDA activity compared to controls. Post-treatment recordings demonstrated marked reductions in these metrics, consistent with patient-reported clinical improvement. Other patients showed variable results depending on treatment timing and signal quality. Conclusion Our pilot study demonstrates the feasibility of using wearable technology for objective, real-world monitoring and assessment of patient status in anti-LGI1 AIE. Nocturnal monitoring offers a low-noise baseline for detecting seizure-related activity and may support earlier diagnosis, more accurate evaluation of treatment response and reduced reliance on resource-intensive inpatient evaluations. Future studies should expand monitoring to daytime periods and validate findings against conventional modalities such as video-EEG and electromyography polygraphy.

Abstract 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.

Abstract Objective This study reports development and validation of a deep learning seizure detection algorithm for two‐channel subscalp electroencephalographic (EEG) recordings. Ultra‐long‐term monitoring of people with epilepsy may produce new insights into the timing and pattern of their seizures and may pave the way for novel therapeutic options. Although EEG is the accepted standard for epilepsy monitoring, ultra‐long‐term EEG recordings generate a massive quantity of data and are not reasonably reviewable in full by human readers. Methods The convolutional neural network‐bidirectional long short‐term memory (CNN‐BiLSTM) hybrid algorithm uses nine layers, operating on channel spectrograms 5 min in length with 50% overlap. Retrospective subscalp EEG data from 16 patients recorded at three centers were available for algorithm development and testing. EEG was recorded for a median of 63 days (range = 9–508), and a median of 17 seizures (range = 0–96) were recorded. Training data were augmented with scalp EEG seizures, and performance was benchmarked against a conventional spectral power classifier algorithm. We also evaluated an intrapatient training–testing approach where the algorithm was trained on the first 45% of data. Results The CNN‐BiLSTM trained on scalp EEG and subscalp EEG achieved an area under the receiver operating characteristic curve (AUROC) of .98 and an area under the precision–recall curve (AUPRC) of .50, which correspond to 94% sensitivity with 1.11 false detections per day. The same model trained on iEEG achieved only AUROC = .94 and AUPRC = .36. The conventional spectral band power detector achieved AUROC = .93 and AUPRC = .38. The CNN‐BiLSTM detector trained on the earliest half of the subscalp EEG data achieved AUROC = .93 and AUPRC = .37, corresponding to 87% sensitivity and 5.9 false detections per day. Significance High sensitivity and specificity are possible in automated seizure detection in two‐channel subscalp EEG data using a CNN‐BiLSTM framework. Performance of the detector is superior using subcutaneous EEG data for training rather than intracranial EEG, but addition of scalp EEG seizures for training was helpful.

Objective: To evaluate the feasibility and effectiveness of wrist-worn wearable devices combined with machine learning (ML) approaches for detecting a diverse array of seizure types beyond generalized tonic–clonic (GTC), including focal, generalized, and subclinical seizures. Materials and Methods: Twenty-eight patients undergoing inpatient video-EEG monitoring at Mayo Clinic were concurrently monitored using Empatica E4 wrist-worn devices. These devices captured accelerometry, blood volume pulse, electrodermal activity, skin temperature, and heart rate. Seizures were annotated by neurologists. The data were preprocessed to experiment with various segment lengths (10 s and 60 s) and multiple feature sets. Three ML strategies, XGBoost, deep learning models (LSTM, CNN, Transformer), and ROCKET, were evaluated using leave-one-patient-out cross-validation. Performance was assessed using area under the receiver operating characteristic curve (AUROC), seizure-wise recall (SW-Recall), and false alarms per hour (FA/h). Results: Detection performance varied by seizure type and model. GTC seizures were detected most reliably (AUROC = 0.86, SW-Recall = 0.81, FA/h = 3.03). Hyperkinetic and tonic seizures showed high SW-Recall but also high FA/h. Subclinical and aware-dyscognitive seizures exhibited the lowest SW-Recall and highest FA/h. MultiROCKET and XGBoost performed best overall, though no single model was optimal for all seizure types. Longer segments (60 s) generally reduced FA/h. Feature set effectiveness varied, with multi-biosignal sets improving performance across seizure types. Conclusions: Wrist-worn wearables combined with ML can extend seizure detection beyond GTC seizures, though performance remains limited for non-motor types. Optimizing model selection, feature sets, and segment lengths, and minimizing false alarms, are key to clinical utility and real-world adoption.