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An understanding of mechanisms underlying seizure disorders depends critically on the insights provided by model systems. In particular with the development of cellular, molecular, and genetic investigative tools, there has been an explosion of basic epilepsy research. Models of Seizures and Epilepsy brings together, for the first time in 30 years, an overview of the most widely-used models of seizures and epilepsy. Chapters cover a broad range of experimental approaches (from in vitro to whole animal preparations), a variety of epileptiform phenomenology (including burst discharges and seizures), and suggestions for model characterization and validation, such as electrographic, morphologic, pharmacologic, and behavioral features. Experts in the field provide not only technical reviews of these models but also conceptual critiques - commenting on the strengths and limitations of these models, their relationship to clinical phenomenology, and their value in developing a better understanding and treatments. Models of Seizures and Epilepsy is a valuable, practical reference for investigators who are searching for the most appropriate laboratory models for addressing key questions in the field. It also provides an important background for physicians, fellows, and students, offering insight into the potential for advances in epilepsy research. · The first comprehensive description of animal models of epilepsy since the early 1970's· Comprehensive analysis of \"What the models model\" to guide the selection of each model, and what specific questions it will answer· Elegant examples of the use of novel technologies that can be applied in experimental epilepsy research· World expert opinions on the clinical relevance of each model

Background and objectives: while acute ischemic stroke is the leading cause of epilepsy in the elderly population, data about its risk factors have been conflicting. Therefore, the aim of our study is to determine the association of early and late epileptic seizures after acute ischemic stroke with cerebral cortical involvement and electroencephalographic changes. Materials and methods: a prospective cohort study in the Hospital of the Lithuanian University of Health Sciences Kaunas Clinics Department of Neurology was conducted and enrolled 376 acute ischemic stroke patients. Data about the demographical, clinical, radiological, and encephalographic changes was gathered. Patients were followed for 1 year after stroke and assessed for late ES. Results: the incidence of ES was 4.5%, the incidence of early ES was 2.7% and the incidence of late ES was 2.4%. The occurrence of early ES increased the probability of developing late ES. There was no association between acute cerebral cortical damage and the occurrence of ES, including both early and late ES. However, interictal epileptiform discharges were associated with the occurrence of ES, including both early and late ES.

The hypothalamic–pituitary–adrenal axis is known to be involved in the pathogenesis of epilepsy and psychiatric disorders. Epileptic seizures (ESs) and psychogenic non-epileptic seizures (PNESs) are frequently differentially misdiagnosed. This study aimed to evaluate changes in serum cortisol and prolactin levels after ESs and PNESs as possible differential diagnostic biomarkers. Patients over 18 years with ESs (n = 29) and PNESs with motor manifestations (n = 45), captured on video-EEG monitoring, were included. Serum cortisol and prolactin levels as well as hemograms were assessed in blood samples taken at admission, during the first hour after the seizure, and after 6, 12, and 24 h. Cortisol and prolactine response were evident in the ES group (but not the PNES group) as an acute significant increase within the first hour after seizure. The occurrence of seizures in patients with ESs and PNESs demonstrated different circadian patterns. ROC analysis confirmed the accuracy of discrimination between paroxysmal events based on cortisol response: the AUC equals 0.865, with a prediction accuracy at the cutoff point of 376.5 nmol/L 0.811 (sensitivity 86.7%, specificity 72.4%). Thus, assessments of acute serum cortisol response to a paroxysmal event may be regarded as a simple, fast, and minimally invasive laboratory test contributing to differential diagnosis of ESs and PNESs.

Background Electroconvulsive therapy (ECT) is an important treatment for several severe psychiatric conditions, yet its precise mechanism of action remains unknown. Increased inhibition in the brain after ECT seizures, mediated by γ-aminobutyric acid (GABA), has been linked to clinical effectiveness. Case series on epileptic patients report a postictal serum concentration increase of the GABA A receptor agonist allopregnanolone. Serum allopregnanolone remains unchanged after a full ECT series, but possible transient effects directly after a single ECT seizure remain unexplored. The primary aim was to measure serum concentrations of allopregnanolone and its substrate progesterone after one ECT seizure. Secondary aims were to examine whether concentrations at baseline, or postictal changes, either correlate with seizure generalization or predict clinical outcome ratings after ECT. Methods A total of 130 participants (18–85 years) were included. Generalization parameters comprised peak ictal heart rate, electroencephalographic (EEG) seizure duration, and prolactin increase. Outcome measures were ratings of clinical global improvement, perceived health status and subjective memory impairment. Non-parametric tests were used for group comparisons and correlations. The prediction analyses were conducted with binary logistic and simple linear regression analyses. Results Allopregnanolone and progesterone remained unchanged and correlated neither with seizure generalization nor with clinical outcome. In men ( n  = 50), progesterone increased and allopregnanolone change correlated negatively with EEG seizure duration. In a subgroup analysis ( n  = 62), higher baseline allopregnanolone and progesterone correlated with postictal EEG suppression. Conclusions ECT seizures have different physiologic effects than generalized seizures in epilepsy. Progesterone might have implications for psychiatric illness in men.

Psychogenic non-epileptic seizures (PNES) are often misdiagnosed as epileptic seizures (ES), leading to inappropriate treatment and delayed psychological care. To address this challenge, we analysed electroencephalogram (EEG) data from 74 patients (46 PNES, 28 ES) using one-minute preictal and interictal recordings per subject. Nine entropy measures (Sample, Fuzzy, Permutation, Dispersion, Conditional, Phase, Spectral, Rényi, and Wavelet entropy) were evaluated individually to classify PNES from ES using k-nearest neighbours, Naïve Bayes, linear discriminant analysis, logistic regression, support vector machine, random forest, multilayer perceptron, and XGBoost within a leave-one-subject-out cross-validation framework. In addition, a dynamic state, defined as the entropy difference between interictal and preictal periods, was examined. Sample, Fuzzy, Conditional, and Dispersion entropy were higher in PNES than in ES during interictal recordings (not significant), but significantly lower in the preictal (p < 0.05) and dynamic states (p < 0.01). Spatial mapping and permutation-based importance analyses highlighted O1, O2, T5, F7, and Pz as key discriminative channels. Classification performance peaked in the dynamic state, with Fuzzy entropy and support vector machine achieving the best results (balanced accuracy = 72.4%, F1 score = 77.8%, sensitivity = 74.5%, specificity = 70.4%). These results demonstrate the potential of entropy features for differentiating PNES from ES.

Objective Epilepsy Monitoring Units (EMUs) in Veterans Health Administration (VHA) Epilepsy Centers of Excellence (ECoE) are critical for the diagnosis and management of seizure disorders. Whether a shorter length of stay (LOS) in the EMU due to scheduling impacts diagnostic yield is unclear. Methods Data from 7074 EMU visits across 15 VHA EMUs (2012–2024) were analyzed. Based on usual admission schedules, EMUs were divided into “fixed” (typically Monday–Friday) or “flexible” subgroups. Diagnostic outcomes were classified as epileptic seizures (ES), psychogenic non‐epileptic seizures (PNES), other non‐epileptic events, and inconclusive. Diagnostic rates were compared between fixed and flexible sites using cumulative distribution functions and other statistical tests. Readmission data for initially inconclusive cases were also examined. Results Diagnostic outcomes showed the following distribution: 23% ES, 19% PNES, 11% other non‐epileptic events, and 47% inconclusive. Similar distributions were seen between fixed and flexible sites, although a higher proportion of diagnostic admissions were completed earlier in fixed sites and over a longer average LOS at flexible sites. Admissions diagnostic of ES had longer LOS than all other outcomes (4.5 vs. 3.8 days, p < 0.001). Repeat EMU admissions were performed in 10% of patients and were more likely to be diagnostic of ES than PNES or other non‐epileptic events. Significance About half of EMU admissions within VHA were non‐diagnostic with respect to the patients' typical clinical events. ES and PNES were observed at approximately similar rates, although the diagnosis of ES required a longer LOS. Fixed sites did not appear inferior to flexible sites for reaching diagnostic conclusions in our analysis. The higher proportion of earlier diagnoses at fixed sites observed was likely a statistical effect of their predefined shorter admission lengths. Further investigations of EMU resource utilization based on individual goals of monitoring are necessary to better examine and improve efficiency. Plain Language Summary Epilepsy Monitoring Units (EMUs) are specialized hospital units used to diagnose and characterize seizures. This study looked at over 7000 admissions across 15 Veterans Health Administration EMUs to see whether length of stay affected diagnosis rates based on admission scheduling and seizure types. Regardless of whether patients were admitted on a fixed schedule (Monday–Friday) or a flexible schedule, about half of hospitalizations did not capture typical events. Diagnosis of epileptic seizures and psychogenic non‐epileptic seizures occurred at similar rates, though diagnosing epileptic seizures took longer. Findings suggest fixed (shorter) hospital stays may be as effective as longer flexible hospitalizations.

PNESs have a prevalence of 2–33 per 100,000, whereas epilepsy affects approximately 0.5%–1% of the population. 1 Unlike epilepsy, PNES are not caused by neuronal hypersynchrony or cerebral hypoperfusion but rather by complex neuropsychiatric mechanisms. 2 Nonetheless, their semiology often mimics that of epileptic seizures, leading to misdiagnosis in 20%–30% of cases. 3 Symptoms such as foaming at the mouth and urinary incontinence—observed in this case—are typically associated with generalized tonic–clonic seizures. VEEG remains the gold standard for distinguishing PNES from epilepsy, although the likelihood of capturing an event during monitoring is only 50%–70%. CONFLICT OF INTEREST STATEMENT Authors declare no conflict of interests for this article.

Background/aimAs the clinical differentiation between epileptic seizures, psychogenic non-epileptic seizures (PNES), and syncope depends mainly on a detailed report of the event, which may not be available, an objective assessment of a potential biochemical analysis is needed. We aimed to investigate whether serum creatine kinase (CK) could be used to differentiate epileptic seizure from PNES and syncope and to assess the strength of evidence present.MethodsWe directed a retrospective cohort study coupled with a systematic review and meta-analysis of studies that measured CK in patients with epilepsy, PNES, syncope, and healthy controls.ResultsThe cohort study, which traced 202 patients, showed that the CK level was significantly higher 48 h after the event in the epilepsy group versus patients with syncope (p < 0.01) Along with 1086 patients obtained through a database search for meta-analysis, CK level compared to different types of seizures from PNES was higher in epileptic seizure patients with a mean difference of 568.966 mIU/ml (95% CI 166.864, 971.067). The subgroup analysis of CK showed that it was higher in GTCS compared to syncope with a mean difference of 125.39 mIU/ml (95% CI 45.25, 205.52).DiscussionIncreased serum levels of CK have been associated mainly with epileptic seizures in relation to non-epileptic events. However, further studies would try to explore the variation in measurements and any other potential diagnostic marker.ConclusionThe cohort study shows that the CK level in epilepsy seizures is higher after 48 h from the event compared to syncope. Moreover, the meta-analysis results show the present diagnostic utility of CK and its importance to be used in accordance with a detailed report of the event.

[...]the patient also frequently experienced other episodes of paroxysmal walking impairment, which were at first considered as paroxysmal dyskinesias because of the recurrent and sudden symptoms. The long-term video electroencephalogram (EEG) captured the frequent episodes of paroxysmal sudden behavior arrest, but the corresponding EEGs did not show any changes in ongoing activity or epileptiform abnormalities. [...]a video of the paroxysmal episodes of unsteady staggered-like gait without falling did not reveal any dystonic / choreoathetotic movements, which was also inconsistent with PKC as well as other paroxysmal dyskinesias (Table 1). [...]PNES and PPMD were strongly suspected for the episodes. Table 1 Comparison of paroxysmal dyskinesias and our case PKC PED PNKD Our case OMIM # #128200 #612126 #118800 Onset age Childhood - early adulthood Childhood Infancy - childhood Seven years old Symptoms Dystonia, chorea, athetosis Dystonia, choreoathetosis Dystonia, chorea, athetosis Unsteady staggered-like gait Frequency Daily Weekly Weekly Throughout the day Duration Seconds to minutes Minutes to hours Minutes to hours Minutes to hours Triggers Sudden voluntary movement Exercise, exertion Stress, fatigue, caffeine, alcohol, ovulation, menstruation None (from the beginning of walking) Gene PRRT2 SLC2A1 MR1 PRRT2 Treatment Carbamazepine, phenytoin Ketogenic diet Benzodiazepines Psychosocial intervention PED, paroxysmal exercise-induced dyskinesia; PKC, paroxysmal kinesigenic choreoathetosis; PNKD, paroxysmal non-kinesigenic dyskinesia.

Legionellosis caused by Legionella longbeachae is diagnosed mainly by PCR. We report a case of L. longbeachae infection in mainland China, which was diagnosed by metagenomic next-generation sequencing, in a man who developed an epileptic seizure after using moxifloxacin. Metagenomic next-generation sequencing may be a useful tool to detect Legionella spp.