Tatum, William O.

OBJECTIVE The aim of this study was to compare 3 electrode designs—a novel 22-contact circular grid, standard 6-contact strip, and high-density (HD) grid between 32 and 64 contacts—for the detection of epileptiform activity during awake craniotomy. METHODS This study included patients who underwent functional brain mapping with and without direct electrical stimulation during awake craniotomy. Demographic, clinical, and electrocorticography (ECoG) data were collected. RESULTS A total of 194 patients were included, with 264 instances of electrode use: 113 circular grid, 96 strip electrode, and 55 HD grid. The HD grid (15%) was used less to record ECoG during direct electrical stimulation than the circular grid (80%, p < 0.001) and strip electrodes (69%, p < 0.001). Sporadic interictal epileptiform activity was better detected with circular (45%, p < 0.001) and HD (40%, p = 0.006) grids compared with strip electrodes (16%). Spontaneous seizures were recorded more frequently with the circular grid (16%, p = 0.018) compared with the strip electrode (9.4%) and HD grid (1.8%). Afterdischarges were more frequently detected by the circular grid (69%, p = 0.031) and with circular and HD grids (combined) compared with strip electrodes (67% vs 48%, p = 0.024), at lower median intensity (3 mA vs 5 mA, p < 0.001). Multivariable analyses demonstrated circular grids (p < 0.001) to be more sensitive in detecting spontaneous epileptiform activity than strip and HD grid electrodes, and grids (circular and HD) to be better in recording stimulus-induced epileptiform activity (p = 0.01). CONCLUSIONS This study demonstrates a higher rate of detection of epileptiform activity with circular and standard grids when compared with strip electrodes. The standard HD grid was used less during electrical stimulation, demonstrating the importance of both the number of contacts and design array when considering optimal conditions for ECoG and functional brain mapping.

Background: Electroencephalography (EEG) is increasingly used in intensive care units (ICUs), primarily for seizure detection. However, the complex clinical context of critically ill patients and the dynamic ICU environment can complicate interpretation. Purpose: To highlight common pitfalls in ICU EEG interpretation and strategies to distinguish epileptic seizures from mimics. Research Design: Retrospective case series. Study Sample: Five ICU patients undergoing continuous video EEG (cvEEG) for altered mental status, status epilepticus, or paroxysmal events, with underlying conditions including malignancy, autoimmune encephalitis, neuromodulation therapy, and drug-resistant epilepsy. Results: Physiological artifacts, such as non-epileptic rhythmic movements, and device-related artifacts (e.g., pacemakers, ventilators) can mimic epileptic seizures. Plateau waves from elevated intracranial pressure may produce deficits resembling epileptic seizures. Accurate differentiation requires attention to clinical context, EEG features, and video correlation. Maintaining a broad differential and avoiding diagnostic anchoring are also essential to prevent misdiagnosis and unnecessary antiseizure medication therapy. Conclusions: ICU EEG interpretation is challenged by artifacts and non-epileptic movements that mimic epileptic seizures. Awareness of these issues, along with a thoughtful, multidisciplinary approach, is critical for improving diagnostic accuracy and optimizing patient outcomes during cvEEG monitoring.

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 Purpose Cenobamate is approved for treating focal epilepsy and has potential for treating tumor‐related epilepsy (TRE) based on potential anti‐tumor effects, though there is little data on efficacy and safety in this patient population. Methods This was a retrospective, multi‐center study of patients with TRE who were prescribed cenobamate as adjunctive treatment between January 2020 and November 2024 at Mayo Clinic in Florida, Minnesota, and Arizona. Drug efficacy was assessed, and factors that were predictive of response to treatment were analyzed. Results A total of 17 patients were included, with 12 being drug resistant. The median seizure frequency for the entire group before starting cenobamate treatment was 12 (IQR = 4–64) per month, and after treatment at last follow‐up was 3 (IQR = 1–15) per month, with an overall 56.3% reduction. Four patients (23.5%) were seizure‐free, and 7 (41.1%) patients were responders (at least 50% seizure reduction). No adverse events were reported for patients on concurrent chemotherapy. Responders had significantly fewer seizures per month compared to non‐responders (5.72 ± 10.29 vs. 27.33 ± 30.17; p = .030). No clinical factors were predictive of responsiveness (or lack thereof) to cenobamate. Conclusions This study demonstrates that adjunctive therapy with cenobamate treatment for TRE is a feasible and potentially effective option. Clinical history, including drug resistance, did not predict drug efficacy. No adverse effects were reported in those treated with chemotherapy. Further studies are needed to validate treatment response and safety.

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.