Akio Ikeda

Current theories of semantic representation are agnostic about how fine-grained neurophysiological activity corresponds to representation of multidimensional semantic information. By applying an innovative multivariate technique (representational similarity learning; RSL), we adjudicated three hypotheses: (1) that multidimensional semantic structure is represented within a single frequency range (e.g., gamma/high gamma); (2) that each semantic dimension is independently represented within a different frequency range; and (3) that multidimensional semantic information is “transfrequency” (at least some information emerges only when multiple frequencies are considered together). RSL was applied to time-frequency power and phase data extracted from electrocorticography (ECoG) grid electrodes on the surface of human ventral anterior temporal lobe (vATL). We found significant decoding of graded, multidimensional semantic information from a wide range of frequencies (4 – 200 Hz), but not from individual frequency bands, providing clear evidence that multidimensional semantic information is coded in a transfrequency fashion.

Educational initiatives that address the gap between basic/preclinical and clinical practices are important to effectively translate basic science discoveries to benefit patients. The ILAE Neurobiology Commission conducted a pilot project aimed at exposing basic and preclinical scientists engaged in epilepsy research to general clinical issues pertaining to the diagnosis and care of people with epilepsy. This aim was addressed through a two‐week‐long, on‐site clinical training program for 50 basic scientists in 21 epilepsy centers across 18 countries in the six ILAE regions (with a maximum of 3 basic scientists per center). The learning objectives and the training module were discussed and defined by the project organizing committee, which consisted of Neurobiology Commission members and a team of epileptologists representing different geographical regions. The training activities were conducted at each epilepsy center under the local supervision of clinical tutors. Each basic scientist was exposed to 50.3 ± 23.3 (range 16–89) hours of intensive and dedicated clinical training, coordinated by 2–3 tutors per center, assisted by 6.8 ± 3.6 colleagues. A structured test consisting of 17 general clinical epilepsy questions was completed by the trainees before and after the training activity. The learning assessment was based on the comparison between responses to the exit and entry tests. After the on‐site clinical exposure, the proportion of correct answers increased to 87% compared to 61% in the entry test. Structured post‐training questionnaires demonstrated very high satisfaction of trainees and all involved tutors across the different aspects of the training module. This global pilot study demonstrated that on‐site attendance by basic scientists in specialized clinical settings up‐scaled their knowledge of clinical epileptology and facilitated networking with clinicians. Expansion of this pilot to further centers should be considered to understand how exposure to clinical practice affects research direction and quality of translational epilepsy research. Plain Language Summary: Epilepsy research has long benefitted from collaboration between scientists and clinicians. Early exposure of researchers to people with epilepsy and their care teams may strengthen future impact. This pilot study tested a two‐week immersive experience where small teams of basic scientists shadowed clinicians during their work at hospitals around the world. Questionnaires showed high satisfaction among both groups. Results support expanding such training, with the backing of the International League Against epilepsy and aligned centers, to build understanding, interest, and long‐term commitment, ensuring bench research is informed by and translates to clinical practice and improved quality of life for patients.

The artificial intelligence (AI) revolution is upon us. It will inevitably form a central component of epilepsy workflows and patient advocacy. Therefore, it behooves us as health care providers to ride the crest of this wave and guide its direction for the benefit of all people with epilepsy. Emerging AI‐based solutions include decision support tools, automated interpretation of electroencephalography (EEG) and brain imaging, and wearable devices that detect seizures and improve patient safety. Pipelines, including decentralized approaches and federated learning, are now being built that will democratize access and facilitate the next generation of AI tools for the global epilepsy community. Despite this, enduring issues remain incompletely addressed. For example, AI requires high volumes of data, leading to concerns about ethical ownership, stewardship, and privacy. Few AI‐based tools have progressed from derivation to validation stages, and only rare exceptions undergo real‐world evaluation. Inadvertent harmful algorithmic and decision allocation biases also continue to represent major risks to the global epilepsy population. Additional barriers include geographical disparities in computing resources, proprietary ownership of electronic health records, EEG, and brain‐imaging platforms, and greenhouse gas emissions related to the demanding power requirements of AI. Therefore, to fully avail ourselves of the benefits of AI, we assert that ethical, equitable, and effective AI for epilepsy requires collaboration from the entirety of the global epilepsy community. Fundamental to this is early and deliberate engagement of people from low‐ and middle‐income countries to ensure that AI‐based solutions do not exacerbate existing global disparities. Ultimately, we advocate for “decision intelligence” approaches to the development of AI‐based epilepsy solutions, which involves early engagement of all interest‐holders to ensure that the correct questions are addressed and the right technical approaches are deployed to maximize value for the global epilepsy community.

Zusammenfassung: Die Internationale Liga gegen Epilepsie (ILAE) hat die operationale Klassifikation epileptischer Anfälle 2017 auf der Grundlage der damals entwickelten Rahmenbedingungen aktualisiert. In diese Überarbeitung wurden die publizierten Erfahrungen mit der Umsetzung der 2017er-Klassifikation einbezogen. Eine 37 Personen zählende Arbeitsgruppe wurde vom ILAE-Exekutivausschuss eingesetzt. Die internationalen Expertinnen und Experten aus allen ILAE-Regionen wandten ein modifiziertes Delphi-Verfahren an, bei dem für jeden Vorschlag ein Konsens von mehr als zwei Dritteln erforderlich war. Nach Veröffentlichung auf der ILAE-Homepage mit dem Ersuchen, Kommentare zu dem Entwurf einzugeben, ernannte der Exekutivausschuss 7 zusätzliche Sachverständige für die Arbeitsgruppe zur Überarbeitung des Positionspapieres, um die eingegangenen Kommentare zu diskutieren und gegebenenfalls einzubeziehen. Die aktualisierte Klassifikation behält die Hauptklassen von Anfällen bei: fokal, generalisiert, unbekannt (ob fokal oder generalisiert) und nicht klassifiziert. Taxonomische Regeln unterscheiden zwischen Klassifikatoren , die biologische Klassen widerspiegeln und sich direkt auf die klinische Behandlung auswirken, und Deskriptoren , die andere wichtige Anfallsmerkmale angeben. Fokale Anfälle und Anfälle unbekannten Ursprungs werden darüber hinaus nach dem Bewusstseinszustand des Patienten während des Anfalls klassifiziert, je nachdem ob eine Bewusstseinsstörung vorliegt oder nicht. Die Bewertung, ob eine Bewusstseinsstörung vorliegt, wird durch Gewahrsein (engl. „awareness“) und Reaktionsfähigkeit (engl. „responsiveness“) während eines Anfalls und erhaltene Erinnerung (engl. „recall“) nach einem Anfall klinisch operationalisiert. Wenn der Bewusstseinszustand nicht bestimmbar ist, wird der Anfall unter dem übergeordneten Begriff, d. h. der Hauptanfallsklasse (fokaler Anfall oder Anfall unbekannten Ursprungs) klassifiziert. Generalisierte Anfälle werden in Absencen, generalisierte tonisch-klonische Anfälle und andere generalisierte Anfälle eingeteilt, wobei jetzt auch der negative Myoklonus als Anfallstyp beschrieben wird. Anfälle werden in der Grundversion als solche mit oder ohne beobachtbare Manifestationen beschrieben, während eine erweiterte Version die chronologische Abfolge der Anfallssemiologie verwendet. Diese aktualisierte Klassifikation umfasst 4 Hauptklassen und nur noch 21 Anfallstypen (und nicht mehr 63 wie in der 2017 Klassifikation; Anm. d. Übersetzer). Besonderer Wert wurde auf die Übersetzbarkeit in andere Sprachen jenseits von Englisch gelegt. Ziel ist es, eine gemeinsame Sprache für alle im Bereich Epilepsie tätigen Gesundheitsfachkräfte zu schaffen – von ressourcenarmen Gebieten bis hin zu hoch spezialisierten Zentren – und leicht zugängliche Begriffe für Patientinnen und Patienten sowie Betreuungspersonen bereitzustellen.

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.