Citations

Abstract Neuromodulation is approved for the treatment of drug‐resistant epilepsy. It has been increasingly utilized over the past two decades with the approval of deep brain stimulation (DBS) and responsive neurostimulation (RNS) in addition to vagus nerve stimulation (VNS)—particularly in patients who are not deemed to be good resective surgical candidates or in case of patient preference. In this seminar in epileptology article, we address learning objective 3.7 “Demonstrate knowledge of indications, limitations and risks for vagal nerve stimulation and other neuromodulation techniques” of the International League Against Epilepsy curriculum for epileptologists. We will review the indications for neuromodulation, device programming, surgical considerations for implantation, side effects, effects on sleep, and mood. This is complemented by case examples that provide perspective for trainees, along with an outlook on future directions for neuromodulation in epilepsy. This article aims to serve as a learning resource for trainees in neurology, clinical neurophysiology, and epileptology by providing guidance in how to navigate the landscape of neuromodulation in epilepsy.

Abstract This paper provides a practical guide to applying the updated seizure classification in clinical settings. The updated classification, published by the International League Against Epilepsy in 2025, builds on the operational classification introduced in 2017. It aims to enhance clarity, clinical relevance, and consistency in seizure terminology across various healthcare settings. The classification system distinguishes between four main seizure classes: focal, generalized, unknown whether focal or generalized, and unclassified. The basic version allows for application even in resource‐limited or primary care environments, while the expanded version offers detailed semiological descriptors and supports advanced diagnostic and surgical decision‐making. Consciousness, defined through awareness (recall) and responsiveness, is now considered a classifier. The paper also highlights the importance of distinguishing observable from non‐observable features and integrates semiology into the expanded classification with attention to temporal sequence and somatotopic detail. Generalized seizures are categorized into seizure types having a direct impact on syndrome diagnosis and treatment decisions. Epileptic spasms are given special attention because of their unique presentation and the urgency of early intervention, particularly in infants. To support clinical implementation, this paper includes a comprehensive table of semiological descriptors, definitions of generalized seizure types, and a series of real‐world case vignettes illustrating the application of the updated seizure classification. Supplementary figures and videos further support the educational aims of the paper. This practical guide is intended for healthcare professionals managing patients with epilepsy, providing a clear, structured approach to seizure classification that is adaptable to varying levels of clinical expertise and diagnostic resources.

Abstract The accurate interpretation of scalp EEG remains an instrumental diagnostic component of epilepsy care. Knowledge of what constitutes normal EEG findings, non‐epileptiform abnormalities, and epileptiform patterns—both ictal and interictal—is essential for appropriate patient management. The International League Against Epilepsy (ILAE) has developed an educational curriculum containing learning objectives necessary to accurately diagnose and manage people with epilepsy. In this Seminar in Epileptology, we address the learning objectives related to assessment and interpretation of EEG background activity, normal sleep patterns, provocation methods, interictal abnormalities, and ictal patterns, as well as a brief discussion on relevant advances in scalp EEG analysis and future perspectives on this topic.

Abstract The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic–clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource‐limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.

Abstract Precise localization of the epileptogenic zone is pivotal for planning minimally invasive surgeries in drug-resistant epilepsy. Here, we present a graph neural network (GNN) framework that integrates interictal intracranial EEG features, electrode topology, and MRI features to automate epilepsy surgery planning. We retrospectively evaluated the model using leave-one-patient-out cross-validation on a dataset of 80 drug-resistant epilepsy patients treated at St. Anne's University Hospital (Brno, Czech Republic), comprising 31 patients with good postsurgical outcomes (Engel I) and 49 with poor outcomes (Engel II–IV). The GNN predictions demonstrated a significantly better (P < 0.05, Mann–Whitney-U test) area under the precision-recall curve in patients with good outcomes (area under the precision-recall curve: 0.69) compared with those with poor outcomes (area under the precision-recall curve: 0.33), indicating that the model captures clinically relevant targets in successful cases. In patients with poor outcomes, the graph neural network proposed alternative intervention sites that diverged from the original clinical plans, highlighting its potential to identify alternative therapeutic targets. We show that topology-aware GNNs significantly outperformed (P < 0.05, Wilcoxon signed-rank test) traditional neural networks while using the same intracranial EEG features, emphasizing the importance of incorporating implantation topology into predictive models. These findings uncover the potential of GNNs to automatically suggest targets for epilepsy surgery, which can assist the clinical team during the planning process.

Purpose: These American Clinical Neurophysiology Society technical standards suggest best practices for electrical stimulation mapping (ESM) with subdural and stereotactic depth electrodes for seizure induction and mapping of brain function. Methods: A working group was convened from American Clinical Neurophysiology Society membership with expertise in ESM. PubMed searches were performed to identify pertinent peer-reviewed literature. Recurrent meetings reviewed progress, built consensus by discussion, and developed evidence-based recommendations to the extent possible. Recommendations: Stimulators used for ESM should have sufficient dynamic range, ability to interrupt a stimulus train, and ictal disrupt mechanism(s). Charge density should be calculated for the specific electrodes and ESM settings, the maximum safe limits being 52 to 57 μC/cm 2 /phase for subdural electrodes and not established for stereotactic depth electrodes. Subdural ESM for functional mapping is typically performed at 50-Hz pulse frequency, 200- to 300-µs pulse width, 2- to 8-s train duration, and 1- to 20-mA current strength. Stereo ESM is commonly performed using 2 different pulse frequencies: 1 Hz (300–500 µs pulse width, train duration up to 30 s, and often a constant current of 3–5 mA), and 50 Hz (100–500 µs pulse width, train duration 2-8 s, and 0.5–10 mA current intensity). Conclusions: This guideline provides technical standards for the performance of ESM, which will likely evolve over time with advances in technology and additional evidence (also see Graphical Abstract).

Abstract We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2–6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80–500 Hz), ripple (80–250 Hz) and fast ripple (250–500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001 and P < 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2–6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples.

AbstractDirect cortical stimulation has been applied in epilepsy for nearly a century and has experienced a renaissance, given unprecedented opportunities to probe, excite, and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug‐resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, and is further complicated by epilepsy being characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 Conference (International Conference on Technology and Analysis for Seizures), we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on the usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimizing use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long‐term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive electroencephalographic recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker‐informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities.

AbstractObjectiveHigh‐frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high‐frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy‐to‐use novel interictal biomarker of the epileptic tissue.MethodsWe evaluated relative entropy and high‐frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure‐free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision‐recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions.ResultsRelative entropy in the ripple band (80–250 Hz) achieved an average AUROC of .85. The normalized high‐frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high‐frequency oscillations, relative entropy did not require any patient‐level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency.SignificanceAlthough both relative entropy and high‐frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy‐to‐use promising biomarker of the epileptogenic zone.

Abstract In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80–250 Hz) and in the fast ripple (250–500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient’s HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.