Functional/Dissociative Seizures (FDS) are presentations that appear like epileptic seizures but occur without abnormal cortical electrical activity in the brain. Abnormal respiratory patterns, particularly hyperventilation, have been frequently observed in people with FDS, suggesting this may be a potential therapeutic target. This scoping review aimed to explore the various applications of breathing techniques in the treatment of FDS, synthesizing evidence from clinical and case-based articles to provide a comprehensive overview of this therapeutic modality. Systematic searches were conducted in MEDLINE Complete, Embase, and PsycINFO databases to identify published case reports, case series, clinical trials, clinician perspectives, and book chapters reporting on the use of breathing as a primary or adjunct intervention in patients with diagnosed FDS. Of the 4123 articles identified, 28 were included in the final synthesis (18 = case reports/series; 10 = clinical trials). The majority of articles integrated breathwork within broader psychotherapeutic approaches, with only two using it as a standalone intervention. Findings provide preliminary support for the feasibility and potential benefit of breathing techniques in FDS management; however, the evidence remains limited, heterogeneous, and indirect. Further controlled studies are needed to clarify the mechanisms and efficacy of breathwork interventions for FDS.

Objective Stereo-electroencephalography (SEEG) is an intracranial EEG methodology used in presurgical epilepsy evaluation to precisely delineate the epileptogenic zone and associated networks. Although established for decades, its global adoption has accelerated due to a favorable safety profile and clinical effectiveness. However, inconsistent terminology limits comparability of findings and multicenter collaboration. This guideline addresses this gap by providing standardized SEEG terminology based on international consensus. Methods A working group of 41 experts appointed by the International Federation of Clinical Neurophysiology (IFCN) Executive Committee used a modified Delphi process to develop standardized terminology. The process included two meetings and four Delphi rounds, facilitated by two members. Proposals required at least a two-thirds majority for approval. Meetings were video-recorded and the consensus process fully documented. Results The SEEG glossary defines key background activities, interictal and ictal patterns (including onset, termination, and postictal features), electrical stimulation responses, and common artifacts. Each definition is illustrated with representative examples in both time-domain and time–frequency representations. Conclusion This IFCN guideline establishes standardized SEEG terminology to promote consistent interpretation and multicenter collaboration. Significance The glossary provides a framework for future studies assessing the clinical relevance of SEEG patterns.

OBJECTIVE:Sinus tachycardia during seizures is commonly reported in inpatient video-EEG monitoring, often with high prevalence estimates. These data are derived from selected populations under controlled conditions. We examined clinically documented tachycardia prevalence and determinants in a large retrospective ambulatory EEG-ECG cohort. METHODS:Methods: Clinically documented tachycardia during electrographically confirmed seizures was analyzed from finalized ambulatory EEG-ECG reports, with explicitly documented non-sinus tachyarrhythmias identified separately. The analysis was limited to tachycardia documented during seizures and did not include systematic peri-ictal arrhythmia analysis. Sinus tachycardia was defined as heart rate >100 bpm in adults, and age-adjusted thresholds for those <18. Associations with seizure type, duration, and patient-level factors were examined. RESULTS:Reports were analyzed from 1368 patients with 6177 EEG-confirmed seizures. Tachycardia was clinically reported in 2065/6177 (33.4%) of seizures and 769/1368 (56.2%) of patients. Most tachycardia events were sinus/presumed sinus, however 7/2065 seizures with tachycardia were explicitly documented non-sinus tachyarrhythmias. It was more frequent in focal than generalized seizures (39.1% vs 24.4%) and was associated with longer seizure duration (median 61 s vs 18 s). Younger age and female sex were associated with tachycardia, though the age effect was modest. Medication effects were not robust after correction. Marked within-patient variability was observed: 30.5% had tachycardia in all seizures, 25.7% in some, and 43.8% in none. CONCLUSIONS:In ambulatory monitoring, clinically documented sinus tachycardia occurs in one third of seizures, substantially lower than some inpatient estimates. Clinically documented ictal sinus tachycardia is associated with seizure duration, is more common in focal seizures, and demonstrates marked variability within individuals.

OBJECTIVE:Tonic-clonic seizures (TCSs) are widely regarded as clinically obvious, yet seizure counts used for treatment decisions and risk counseling often rely on patient or caregiver diaries. We sought to quantify the frequency of unreported TCSs during prolonged ambulatory video-EEG (vEEG) monitoring and examined associations with electrographic-onset subtype and patient characteristics. METHODS:We conducted a retrospective cohort study of routinely collected ambulatory vEEG from a single national Australian service (January 2018-June 2024). Studies were eligible if the patient had epilepsy and at least one objectively captured TCSs. Reporting status was derived from diary entries and post-study questioning, and classified as reported vs unreported. The primary descriptive outcome was patient-level reporting status across captured TCSs: all captured TCSs reported, some captured TCSs reported, or no captured TCSs reported. Event-level reporting status was used for seizure-level descriptive summaries and patient-clustered analyses. Event-level associations were examined using patient-clustered generalized estimating equations, and patient-level subgroup comparisons used nonparametric and categorical tests. RESULTS:Among 130 patients with objectively captured TCSs, 69 of 130 (53.1%) reported all captured TCSs, 41 of 130 (31.5%) reported some but not all captured TCSs, and 20 of 130 (15.4%) reported no TCSs during monitoring. Overall, 61 of 130 patients (46.9%) had at least one unreported TCS. At the event level, 340 of 754 captured TCSs (45.1%) were unreported and identified only on review. Nineteen of 130 patients (14.6%; 95% confidence interval [CI] 9.0-21.9) had no documented prior TCS history in available service records and reported no TCSs during monitoring. Unreported event proportions were similar across focal-onset and generalized-onset TCSs, whereas sleep was associated with higher odds of underreporting. SIGNIFICANCE:In this selected ambulatory vEEG cohort, nearly half of patients with captured TCSs had at least one unreported TCS, including 15.4% who reported none during monitoring. These findings indicate that diary-based histories may underestimate convulsive seizure burden in some monitored patients, with implications for safety decisions, therapeutic escalation, and sudden unexpected death in epilepsy (SUDEP) counseling.

Bidirectional interactions between sleep, seizures, and epilepsy remain incompletely understood. Evidence from animal models and people with focal epilepsy suggest that seizures may engage mechanisms of memory consolidation during post-ictal sleep to reinforce and strengthen synaptic connections within the pathological networks that generates seizures, termed seizure-related consolidation (SRC). Human studies of post-ictal sleep changes supportive of SRC, however, are limited by small sample size and restricted observations of post-ictal sleep. We investigated the interplay between seizures and sleep by analyzing sleep-wake and seizure catalogs derived from continuous local field potential (LFP) recordings in 11 people (6 males and 5 females) with drug-resistant focal epilepsy implanted with novel investigational devices and living in their natural environments. Our findings demonstrate that post-ictal rapid-eye-movement sleep duration is reduced, whereas slow-wave sleep duration, slow-wave LFP spectral power, and waveform slope are increased compared with inter-ictal nights without preceding seizures. The most significant changes localize to the epileptogenic networks generating the participants' habitual seizures. These results reveal parallels between SRC and physiological memory consolidation, providing novel insights into the potential role of post-ictal sleep in strengthening epileptic neural engrams and may have implications for targeted disruption of post-ictal sleep and SRC in focal epilepsy.

Introduction Functional seizures (FS) are events that resemble epileptic seizures, but are not attributed to brain pathology and are instead thought to be due to psychological factors. A small, multisite, open-label, single-arm, pilot trial of a breathing intervention known as breathing control training (BCT) found it to be safe and effective in reducing seizure frequency in FS. We propose a protocol for a study to confirm these results.Methods and analysis A 24-week, multicentre, individually-randomised, assessor-blinded, two-arm, parallel-group efficacy and acceptability trial of BCT versus control (Befriending) in 220 participants ≥16 years of age with FS. Eligible participants will be randomly allocated to receive two sessions of either BCT or Befriending over a 4-week period. Sessions will be delivered by a respiratory physiotherapist at a clinical care site or via telehealth. They will complete assessments prior to commencing treatment and at 4, 12 and 24 weeks after their initial session of BCT/Befriending. The trial will be conducted alongside treatment as usual. An economic evaluation including cost-utility and cost-effectiveness analyses will be carried out from health sector and societal perspectives.Ethics and dissemination The study has been approved by The Austin Health Human Research Ethics Committee (HREC/84335/Austin-2022) and the New Zealand Central Health and Disability Ethics Committee (2022 FULL 12324). Findings will be reported to trial participants and consumers; presented at local, national and international conferences; and disseminated by a peer-reviewed scientific journal.

OBJECTIVE:Factors that can precipitate and/or prolong functional seizure events are often challenging to identify. We aimed to develop a methodology to investigate peri-ictal behaviors in subjects experiencing functional seizures and those around them in their home environment. METHODS:We conducted an iterative, four-phase process to develop a codebook for operationalizing peri-functional seizure behavior that involved observation of 37 functional seizures across 14 participants in the pre-ictal, ictal, and post-ictal phases. First, researchers reviewed and discussed the literature to devise a conceptual framework. Second, an initial codebook was drafted and refined using a test case. Third, a multi-disciplinary panel provided feedback on the coding of three further cases to further refine code definitions. Finally, the drafted codebook was piloted on 20 functional seizure events, and a discrepancy analysis was conducted. Additional exploratory analyses were conducted with the pilot data, including a correlation analysis between the number of individuals present during pre-ictal and ictal phases and seizure duration. RESULTS:The final codebook identifies five main factors for observers to note across the pre-ictal, ictal, and post-ictal phases of a functional seizure: body position, activity, behavior, nature of interaction, and number of bystanders; detailed definitions of the relevant subcodes are provided. Early exploratory analyses found that the more people present during the ictal phase, the longer the functional seizure tended to be (rs = .61, p = .004). SIGNIFICANCE:This is the first study to highlight the feasibility of investigating functional seizure peri-ictal behavior in a home environment, offering unique benefits to capture naturalistic social factors potentially related to seizure activity. The codebook can be validated in larger datasets to improve our understanding of how peri-ictal behavior impacts functional seizures; compare findings across studies; and inform the development of evidence-based seizure management strategies for individuals and their carers.

BACKGROUND:Epilepsy may be associated with cardiac arrhythmias. The specific incidence and types of cardiac arrhythmias in different forms of epilepsy is not clearly defined. METHODS:We evaluated patients aged 16 years or older referred for continuous home-based ambulatory video-electroencephalographic(EEG)-electrocardiographic(ECG) monitoring (AVEEM) across 24 sites in Australia between 2020 and 2023. Data collected included baseline demographics, type of epilepsy according to EEG findings, and cardiac arrhythmias (ictal and non-ictal) during monitoring. Logistic regression was used to evaluate association between potential risk factors and cardiac arrhythmias. RESULTS:A total of 866 patients with focal epilepsy and 274 with generalised epilepsy were identified who underwent AVEEM. Patients with generalised epilepsy were younger (median age 24 years versus 43 years) and more likely female (69 % versus 55 %) compared with focal epilepsy. Patients with focal epilepsy had more cardiac arrhythmias (279/866; 32 %) compared with generalised epilepsy (40/274; 15 %; p = 0.04). In patients with focal epilepsy, there were more cardiac arrhythmias observed in temporal lobe epilepsy (238/688; 35 %) compared with extra-temporal lobe focal epilepsy (16/82; 20 %; p = 0.006). However, on multivariable analysis only increased age (p < 0.001) remained a significant predictor of increased cardiac arrhythmias. Right or left sided origin of focal epilepsy was not associated with a difference in cardiac arrhythmias (p = 0.63). CONCLUSIONS:Patients with focal epilepsy had more cardiac arrhythmias compared with generalised epilepsy that was explained by increased age. This study demonstrates feasibility of longer term ambulatory EEG-ECG monitoring in the outpatient setting for real-world assessment of cardiac arrhythmias in patients with epilepsy.

OBJECTIVE:Sudden unexpected death in epilepsy (SUDEP) is a devastating event, where the role of cardiac arrhythmias is poorly understood. We systematically evaluated the types and timing of cardiac arrhythmias in SUDEP/near-SUDEP patients from published literature. METHODS:A systematic search was performed on PubMed and Embase. Case reports/series were included if cardiac monitoring was documented during SUDEP/near-SUDEP events. Data collected included baseline demographics, seizure types, baseline and peri-event cardiac rhythms, electroencephalograms and respiratory patterns. Onset post-seizure is reported as mean ± standard deviation. RESULTS:Twenty-two studies were included with a total of 74 patients (54 SUDEP, 20 near-SUDEP). Epilepsy types were generalized (49 %), focal (42 %), generalized and focal (1 %) and unknown (8 %). 93 % of patients did not have significant cardiac comorbidities. Twenty-three events (31 %) occurred at night. Normal sinus rhythm was the most common baseline rhythm (89 %); abnormal baseline rhythms included first-degree atrioventricular (AV) block (n = 3) and QTc prolongation (n = 1). 116 ECG rhythms were documented for 74 patients during the peri-event period. 21 patients (28 %) had documented asystole, with an onset of 6.8 ± 12.3 min post-seizure. There were eight episodes of ventricular fibrillation, six of ventricular tachycardia, five of atrial fibrillation/atrial flutter, and two of high-degree AV block captured. The onset of apnea post-seizure was 3.8 ± 3.7 min. CONCLUSION:Cardiac arrhythmias in SUDEP/near-SUDEP are varied. The most common mechanism involves initial respiratory apnea followed by progressive asystole. Some patients have shockable malignant arrhythmias that may benefit from an implantable cardiac defibrillator. Further research is needed to clarify the mechanism and potential preventive measures in SUDEP.

Background Anti-seizure medications (ASMs) are commonly prescribed in epilepsy. However some have been associated with adverse cardiac outcomes including cardiac arrhythmias. Methods We conducted an observational study evaluating patients aged >= 16 years undergoing ambulatory video - electroencephalographic (EEG) - electrocardiographic (ECG) monitoring (AVEEM) between 2020 and 2023 in Australia. Data collected included baseline demographics, type, number and dose of ASMs and cardiac arrhythmias during monitoring. ASMs were not withdrawn while monitored. Average QT interval was calculated and corrected for heart rate (QTc). Logistic regression was used to evaluate association between demographic variables, ASMs and cardiac arrhythmias. Results 3695 patients underwent AVEEM (median age 40 years [interquartile range 26-57], female 64 %). Median AVEEM duration was 6.8 days. 51 % of patients were taking >= 1 ASMs. About 28 % (1029/3695) patients had a cardiac arrhythmia; the most frequent was non-sustained SVT (19 %; 695/3695). On multivariable analysis, carbamazepine (OR 0.72, 95 %CI 0.53-0.98, p = 0.03), lamotrigine (OR 0.57, 95 %CI 0.44-0.73, p <0.001) and lacosamide (OR 0.63, 95 %CI 0.43-0.92, p = 0.02) were associated with fewer cardiac arrhythmias. Their association with cardiac arrhythmias was not dose-dependent. No commonly-prescribed ASMs were associated with increased risk of cardiac arrhythmias. There was no significant association between use of ASMs and dynamic QTc interval change. Conclusions Certain ASMs, namely carbamazepine, lamotrigine and lacosamide, were associated with fewer cardiac arrhythmias and this association was not dose-dependent. No other ASM was associated with cardiac arrhythmias. Further large clinical prospective studies are needed to confirm these findings and to clarify the mechanism for any potential antiarrhythmic properties of ASMs.

Epilepsy is characterized by recurrent, unpredictable seizures that impose significant challenges in dailymanagement and treatment. One emerging area of interest is the identification of seizure cycles, includingmultiday patterns, which may offer insights into seizure prediction and treatment optimization. This studyinvestigated multiday seizure cycles in a Tetanus Toxin (TT) rat model of epilepsy. Six TT-injected rats wereobserved over a 40-day period, with continuous EEG monitoring to record seizure events. Wavelet transformanalysis revealed significant multiday cycles in seizure occurrences, with periods ranging from 4 to 7 daysacross different rats. Synchronization Index (SI) analysis demonstrated variable phase locking, with somerats showing strong synchronization of seizures with specific phases of the cycle. Importantly, the studyrevealed that these seizure cycles are dynamic and evolve over time, with some rats exhibiting shifts in cycleperiods during the recording period. This suggests that the underlying neural mechanisms driving these cyclesmay change as the epileptic state progresses. The identification of stable and evolving multiday rhythms inseizure activity, independent of external factors, highlights a potential intrinsic biological basis for seizuretiming. These findings offer promising avenues for improving seizure forecasting and designing personalized,timing-based therapeutic interventions in epilepsy. Future research should explore the underlying neuralmechanisms and clinical applications of multiday seizure cycles.

OBJECTIVE:Patient self-report is known to be an inaccurate reflection of true seizure frequency in persons with epilepsy. The current study aimed to assess the safety and performance of the Minder system, a bilateral subscalp electroencephalographic (EEG) acquisition system for continuous long-term EEG recording. METHODS:This prospective, multicenter first-in-human study enrolled adult patients with focal or generalized epilepsy and at least two seizures per month. The primary outcome was adverse events (AEs) in the first 6 months of implantation. Secondary analyses determined whether normal neurophysiological signals, interictal discharges, and seizures seen on scalp video-EEG monitoring were identifiable on subscalp recordings, and signals were rated for clarity on subscalp and two-channel scalp EEG recordings (1 = not recognizable, 5 = clear). Subscalp data were reviewed in relation to events reported in 6-month seizures diaries. RESULTS:Twenty-six subjects were implanted between November 2019 and July 2023. No serious device- or implant procedure-related AEs were reported. The most common device-related AEs were mild or moderate postsurgical pain, headache, or scalp pain/paresthesia (9/26, 35%). All sleep spindles, chewing artifacts, interictal discharges, and electrographic seizures observed on scalp recordings (25 seizures from eight patients) were identified on subscalp recordings and given higher clarity ratings compared to two-channel scalp recordings (median seizure clarity rating was 3 for both scalp and subscalp EEG, range = 1-5, p = .0025). Subscalp recordings captured seizures from diverse seizure focus locations, including frontal and mesial temporal seizure foci and hypothalamic hamartoma. Bilateral recording revealed clinically relevant findings not possible with unilateral recordings (6/26 patients, 23%). Findings of potential clinical utility were identified on manual review of 6-month recordings in most patients (23/26, 88%). SIGNIFICANCE:This study demonstrates the safety and performance of the Minder bilateral subscalp EEG acquisition system for long-term seizure monitoring in patients with epilepsy. Bilateral hemisphere coverage captured seizures in a diverse patient group and permitted lateralization of events.

This study evaluates eyewitness reliability in recognizing focal seizures, nonconvulsive generalized seizures, and psychogenic non-epileptic seizure (PNES) using ambulatory electroencephalography (EEG) monitoring with home video. Analysis of 76 patients and 650 seizure events revealed that witnesses, often family or carers in proximity (witness in proximity [WIP]), did not appear to recognize over half of focal and generalized seizures (57.5% and 57.4%, respectively). PNES were more frequently recognized, and motor seizures were significantly more likely to be acknowledged (p = 0.0009). These findings highlight the limitations of eyewitness reports in seizure recognition and reinforce the necessity of objective monitoring tools in clinical epilepsy management.

Objective.Seizure detection algorithms enable clinicians to accurately assess seizure burden for epilepsy diagnosis and long-term management. State-of-the-art algorithms rely on electroencephalography (EEG) data to identify electrographic seizures. Previous research that used non-EEG signals, such as electrocardiography (ECG) and wristband data, were collected in epilepsy monitoring units. We aimed to investigate the feasibility of ECG seizure detection in ambulatory settings.Approach.We developed a patient-independent, machine learning-based seizure detector using ambulatory long-term ECG monitoring data. The model was trained on long-term studies of 47 patients and evaluated pseudoprospectively using event detection on a hold-out test set of 18 patients.Main results.In the hold-out test set, the seizure detector performed better than chance for 14 out of 18 patients. The average sensitivity was 72% and the average specificity was 68% for the whole test cohort. Overall, across training and test sets, the performance was better for patients diagnosed with focal epilepsy and for patients who were identified as responders (had substantial heart rate changes during seizures).Significance.Key contributions of this study include the development of a patient-independent seizure detector using ambulatory data and the introduction of a pseudoprospective evaluation framework, which can benefit chronic ambulatory seizure monitoring.

OBJECTIVE:Seizure control is often assessed using patient-reported seizure frequencies. Despite its subjectivity, self-reporting remains essential for guiding anti-seizure medication (ASM) decisions and ongoing patient investigations. This study aims to compare patient-reported seizure frequencies with electrographic frequencies captured via ambulatory video EEG (avEEG). METHODS:Data from intake forms and seizure diaries were collected from patients undergoing home-based avEEG in Australia (April 2020-April 2022). Intake forms included monthly seizure frequency estimates. Only avEEG-confirmed epilepsy cases were analyzed. Univariate and multivariate analyses compared seizure frequencies reported via EEG, diaries, and surveys. RESULTS:Of 3,407 reports, 853 identified epilepsy cases, with 234 studies analyzed after excluding outliers. Diary-reported frequencies correlated with EEG frequency (p < 0.00001), but survey-reported frequencies did not (p > 0.05). Surveys significantly overestimated true seizure frequency (median = 3.98 seizures/month, p < 0.0001), while diaries showed substantially smaller differences (median = 0.01 seizures/month, p < 0.0001). Carer presence was associated with higher diary-reported frequencies (p = 0.047). Age negatively correlated with survey frequency estimation error (p = 0.016). Multivariate analysis identified age and carer status as significant predictors of residuals. CONCLUSIONS:Most patients overestimate their true seizure frequency, potentially influencing therapeutic decisions and raising concerns about the reliability of some participants and carers to self-report seizures in clinical trials. SIGNIFICANCE:An "over-reporting, over-prescribing" cascade may affect epilepsy treatment and highlights the potential issue of clinical drug trials relying on self-reported seizure rates for primary endpoints.

PURPOSE:Accurate seizure reporting is crucial for assessing treatment efficacy and guiding acute management in developmental and epileptic encephalopathies (DEEs). This study evaluates the sensitivity and positive predictive value (PPV) of seizure diaries compared to ambulatory video-EEG reports. METHODS:This retrospective cohort study (2018-2024) analyzed video-EEG reports from 19 Australian clinics. vEEG data were not re-reviewed. Patients with confirmed DEEs underwent 1-7 days of ambulatory video-EEG and completed seizure diaries. Sensitivity (proportion of true seizures correctly recorded) and PPV (proportion of diary events confirmed as seizures) were compared to neurologist-reported vEEG events. Demographic and clinical data were also collected. RESULTS:The study included 108 recordings from 65 patients with Lennox-Gastaut Syndrome (LGS), 10 with Dravet Syndrome, and 33 with other/unspecified DEEs. The cohort was 51 % female, with a median age of 15 years (range 5-63). In LGS and other DEEs, higher reporting of non-epileptic events correlated with fewer true seizure recordings. While many participants achieved a sensitivity or PPV of 1, few achieved both. No significant group-level differences in sensitivity or PPV were found across diagnostic categories. CONCLUSION:Seizure diaries show variability in accurately capturing seizure activity in DEEs, with over-reporting of non-epileptic events and under-recognition of true seizures. These findings highlight the need for objective tools like video-EEG to improve seizure reporting accuracy.

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.

Objective.Epilepsy affects millions globally, with a significant subset of patients suffering from drug-resistant focal seizures. Understanding the underlying neurodynamics of seizure initiation and propagation is crucial for advancing treatment and diagnostics. In this study, we present a novel, inference-based approach for analyzing the temporal evolution of cortical stability and chaos during focal epileptic seizures.Approach.Utilizing a multi-region neural mass model, we estimate time-varying synaptic connectivity from intracranial electroencephalography (iEEG) data collected from individuals with drug-resistant focal epilepsy.Main results.Our analysis reveals distinct preictal and ictal phases characterized by shifts in cortical stability, heightened chaos in the ictal phase, and highlight the critical role of inter-regional communication in driving chaotic cortical behaviour. We demonstrate that cortical dynamics are consistently destabilized prior to seizure onset, with a transient reduction in instability at seizure onset, followed by a significant increase throughout the seizure.Significance.This work provides new insights into the mechanisms of seizure generation and offers potential biomarkers for predicting seizure events. Our findings pave the way for innovative therapeutic strategies targeting cortical stability and chaos to manage epilepsy.

BACKGROUND AND OBJECTIVES:Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. METHODS:The data were derived from the Human Epilepsy Project study. A total of 347 consecutive adults aged 18-60 years with newly diagnosed focal epilepsy were enrolled within 4 months of starting treatment. The types of mood and anxiety disorders were identified with the Mini International Neuropsychiatric Interview, whereas suicidal ideation (lifetime, current, active, and passive) and suicidal attempts (lifetime and current) were established with the Columbia Suicidality Severity Rating Scale (CSSRS). Statistical analyses included the t test, χ2 statistics, and logistic regression analyses. RESULTS:A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. DISCUSSION:In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.