Citations

Abstract Objective This study was undertaken to evaluate the safety and effectiveness of responsive thalamic stimulation as adjunctive therapy for drug‐resistant idiopathic generalized epilepsy (IGE) with generalized tonic–clonic seizures (GTCSs). Methods NAUTILUS is a prospective, multicenter, single‐blind, randomized sham‐controlled pivotal trial. Patients were ≥12 years of age with drug‐resistant IGE and ≥2 GTCSs over a 3‐month baseline. Bilateral depth leads were targeted to the centromedian thalamus. One month later, patients were randomized to Active (responsive stimulation, n = 44) or Sham (no stimulation, n = 43). The effectiveness evaluation period (EEP) began 3 months postimplant through 1 year. After a second GTCS in the EEP, patients transitioned to open‐label active stimulation. The primary safety endpoint was the serious adverse device‐related event (SADE) rate at 84 days postimplant. The primary effectiveness endpoint was time‐to‐second‐GTCS during the EEP. Additional endpoints included median percent change in days with any generalized seizure, GTCS frequency, and responder rate (RR). Results Eighty‐seven patients were implanted across 23 US centers. The SADE rate was significantly below the performance goal (6.9%, p < .0001), with no adverse effects on cognition, mood, or sleep. The prespecified primary effectiveness endpoint was not significant. However, a post hoc mixed‐effects model considering all EEP days demonstrated greater GTCS reduction in the originally randomized Active group (61%) compared to patients originally randomized to Sham (49%, p = .030). Eighteen‐month outcomes included 76.8% median GTCS reduction, 62.5% RR, 40% GTCS‐free at that timepoint, and 77.8% median reduction in days with any generalized seizure. More than 90% of patients and 86% of physicians reported improvement on Global Impression of Change scales. Significance NAUTILUS is the first randomized controlled neuromodulation trial in IGE. Responsive thalamic stimulation provided a clinically meaningful and durable reduction in seizures with an acceptable safety profile, offering a much‐needed option for drug‐resistant IGE.

Thalamic neuromodulation for drug-resistant epilepsy has evolved from ablative thalamotomy to network-guided deep-brain stimulation (DBS) and responsive neurostimulation (RNS). Currently, the only Food and Drug Administration-approved DBS target is the anterior thalamic nucleus, which provides substantial and durable seizure reduction, particularly in limbic and temporal epilepsies. There is growing evidence that the centromedian and pulvinar nuclei are implicated in generalized, multifocal, and posterior neocortical epilepsies. Advanced imaging, probabilistic atlases, and connectomics can be used to refine targeting; however, they are limited by patient specificity, acquisition burden, and interatlas variability. Thalamic stereoelectroencephalography may clarify potential neuromodulation target selection by characterizing ictal recruitment, network dynamics, and biomarkers. Although DBS and RNS each have unique benefits, no modality has demonstrated superior. Pediatric data are heterogeneous and scarcer, but seizure reduction and safety profiles are similar to adults. Further studies are needed to optimize neuromodulation target selection and programming paradigms.

Understanding the evolution of clinical tools and models of care is fundamental to advancing the field of epilepsy. The 2025 American Epilepsy Society Annual Course examined the expanding spectrum of epilepsy care through the integration of multispecialty collaboration, innovative diagnostic modalities, and surgical and nonsurgical therapeutic interventions. The course underscored significant global advancements in epilepsy research and practice. This comprehensive review synthesizes key educational insights from the program, offering a critical perspective on the ongoing transformation of epilepsy care.

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.

Abstract Objective Novel subcutaneous electroencephalography (sqEEG) systems enable prolonged, near‐continuous cerebral monitoring in real‐world conditions. Nevertheless, the feasibility, acceptability and overall clinical utility of these systems remain unclear. We report on the longest observational study using ultra‐long‐term sqEEG to date. Methods We conducted a 15‐month prospective, observational study including 10 adult people with treatment‐resistant epilepsy. After device implantation, patients were asked to record sqEEG, to use an electronic seizure diary, and to complete acceptability and usability questionnaires. sqEEG seizures were annotated visually, aided by automated detection. Individualized temporal patterns of seizure occurrence were assessed via circadian circular statistics and via Fano factor analysis. Results Over a median duration of 438 days, 10 patients recorded a median 18.8 h/day, totaling 71 984 h of real‐world sqEEG data. Adherence and acceptability remained high throughout the study. Although 754 sqEEG seizures were recorded across patients, more than half (52%) of these were not reported in the patient diary. Of the 140 (27%) diary reports not associated with an identifiable sqEEG seizure, the majority (68%) were reported as seizures with preserved awareness. The sqEEG to diary F1 agreement score was highly variable, ranging from .06 to .97. Patient‐specific patterns of circadian seizure occurrence and seizure clustering were found, including several relevant discrepancies between sqEEG and diary. Significance We demonstrate feasibility and high acceptability of ultra‐long‐term (months–years) sqEEG monitoring. These systems help provide real‐world, more objective seizure counting compared to patient diaries. It is possible to objectively monitor individual temporal fluctuations of seizure occurrence.

BACKGROUND AND OBJECTIVES: Stereotactic procedures are used to manage a diverse set of patients across a variety of clinical contexts. The stereotactic devices and software used in these procedures vary between surgeons, but the fundamental principles that constitute safe and accurate execution do not. The aim of this work is to describe these principles to equip readers with a generalizable knowledge base to execute and understand stereotactic procedures. METHODS: A combination of a review of the literature and empirical experience from several experienced surgeons led to the creation of this work. Thus, this work is descriptive and qualitative by nature, and the literature is used to support instead of generate the ideas of this framework. RESULTS: The principles detailed in this work are categorized based on 5 clinical domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. Illustrations and tables are used throughout to convey the concepts in an efficient manner. CONCLUSION: Stereotactic procedures are complex, requiring a thorough understanding of each step of the workflow. The concepts described in this work enable functional neurosurgeons with the fundamental knowledge necessary to provide optimal patient care.

BACKGROUND AND OBJECTIVES: Identifying and characterizing sources of targeting error in stereotactic procedures is essential to maximizing accuracy, potentially improving surgical outcomes. We aim to describe a generic framework which characterizes sources of stereotactic inaccuracy. METHODS: We assembled a list of stereotactic systems: ROSA, Neuromate, Mazor Renaissance, ExcelsiusGPS, Cirq, STarFix (FHC), Nexframe, ClearPoint, CRW, and Leksell. We searched the literature for qualitative and quantitative work identifying and quantifying potential sources of inaccuracy and describing each system's implementation using Standards for Reporting Qualitative Research guidelines. Our literature search spanned 1969 to 2024, and various studies were included, with formats ranging from phantom studies to systematic reviews. Keyword searches were conducted, and the details about each system were used to create a framework for identifying and describing the unique targeting error profile of each system. RESULTS: We describe and illustrate the details of various sources of stereotactic inaccuracies and generate a framework to unify these sources into a single framework. This framework entails 5 domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. This framework was applied to 10 stereotactic systems. CONCLUSION: This framework provides a rubric to analyze the sources of error for any stereotactic system. Illustrations allow the reader to understand sources of error conceptually so that they may apply them to their practice.

Abstract Objective Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri‐ictal periods in performing a feature‐based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. Methods This study assesses the signal quality of wearable data during peri‐ictal phases of generalized tonic–clonic and focal to bilateral tonic–clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri‐ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. Results We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre‐ and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Significance Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist‐worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

AbstractObjectiveEpilepsy management employs self‐reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each.MethodsUsing a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%–100%), false alarm rate (FAR; 0–2/day), and device type (external wearable vs. implant) in each scenario.ResultsThe RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR.SignificanceThe choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.