Epilepsy Research Research Articles

Rats in Epilepsy Research: A Bibliometric Analysis of Citations Between 1969 and 2020 on Experimental Models in Epilepsy.

Epilepsy stands as a prominent neurological disorder, affecting a substantial number of individuals who, unfortunately, do not respond to conventional antiepileptic medications. To unravel the intricate mechanisms underlying epileptic seizures and explore potential therapeutic avenues, researchers have turned to animal models. Among these models, rats have emerged as one of the cornerstones of epilepsy research. This bibliometric analysis embarks on the crucial task of delving into the role of rat models in deciphering the mysteries of epileptic seizures and, notably, pinpointing the most prevalent models in use. Our study harnessed Scopus' citation tracking feature to review a range of research papers dating from 1969 to 2020, all dedicated to the exploration of epileptic seizures in rats. The citations that emerged from this rigorous process were subjected to thematic coding, primarily centered around the specific epileptic animal models employed, and subsequently, comprehensive descriptive statistics were computed. In this effort, we found a total of 1,318 publications that explore the world of rat studies, accumulating a substantial citation count of 44,824 references. This analysis illuminated the invaluable role that research employing rat models has played in shaping our current clinical understanding of epileptic seizures. Notably, several models have emerged as predominant forces in this field, including those induced by pilocarpine, pentylenetetrazole (PTZ), kainic acid (KA), electric kindling, and electroshock. This bibliometric exploration serves as a resounding reminder of the pivotal position that rat models occupy in advancing our comprehension of epilepsy. These findings resonate strongly, underscoring the continued importance of directing research and development funding toward this debilitating disorder, with the ultimate aim of maximizing the benefits for the patients grappling with this condition. The potential to revolutionize our approach to epilepsy and enhance the quality of life for those affected remains a beacon of hope, illuminated by the contributions of these tireless researchers and their trusty rat companions.

High‐Resolution Recording of Neural Activity in Epilepsy Using Flexible Neural Probes

AbstractEpilepsy, a prevalent neurological disorder, necessitates precise and reliable electrophysiological recording for accurate study and diagnosis. Although traditional stereo‐electroencephalography and micro‐wire probes are widely used in epilepsy research, they are limited by recording site numbers and mechanical properties. This study explores the use of high‐density, ultra‐flexible neural probes in epilepsy monitoring, highlighting their advantages in terms of recording performance, scalability, and tissue compatibility. This work validates the effectiveness of the probes in detecting characteristic epileptic signals across various stages, including resting, preictal, and ictal stages, in two different mouse models of epilepsy. Additionally, the high spatial resolution of the probes allows to capture fine spatial propagation of epilepsy‐associated high‐frequency oscillations. Furthermore, the flexible probes exhibit superior biocompatibility, reducing inflammation and neuronal apoptosis compared to rigid electrodes. The results underscore the promising potential of ultra‐flexible neural probes for advancing epilepsy research, providing a powerful technological platform for future studies in the field.

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders.

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

3 Latent Wechsler Profiles in Presurgical Pediatric Epilepsy

Objective:The Pediatric Epilepsy Research Consortium (PERC) Epilepsy Surgery Database Project is a multisite collaborative that includes neuropsychological evaluations of children presenting for epilepsy surgery. There is some evidence for specific neuropsychological phenotypes within epilepsy (Hermann et al, 2016); however, this is less clear in pediatric patients. As a first step, we applied an empirically-based subtyping approach to determine if there were specific profiles using indices from the Wechsler scales [Verbal IQ (VIQ), Nonverbal IQ (NVIQ), Processing Speed Index (PSI), Working Memory Index (WMI)]. We hypothesized that there would be at least four profiles that are distinguished by slow processing speed and poor working memory as well as profiles with significant differences between verbal and nonverbal reasoning abilities.Participants and Methods:Our study included 372 children (M=12.1 years SD=4.1; 77.4% White; 48% male) who completed an age-appropriate Wechsler measure, enough to render at least two index scores. Epilepsy characteristics included 84.4% with focal epilepsy (evenly distributed between left and right focus) and 13.5% with generalized or mixed seizure types; mean age of onset = 6.7 years, SD = 4.5; seizure frequency ranged from daily to less than monthly; 53% had structural etiology; 71% had an abnormal MRI; and mean number of antiseizure medications was two. Latent profile analysis was used to identify discrete underlying cognitive profiles based on intellectual functioning. Demographic and epilepsy characteristics were compared among profiles.Results:Based on class enumeration procedures, a 3-cluster solution provided the best fit for the data, with profiles characterized by generally Average, Low Average, or Below Average functioning. 32.8% were in the Average profile with mean index scores ranging from 91.7-103.2; 47.6% were in the Low Average profile with mean index ranging from 80.7 to 84.5; and 19.6% were in the Below Average profile with mean index scores ranging from 55.0-63.1. Across all profiles, the lowest mean score was the PSI, followed by WMI. VIQ and NVIQ represented relatively higher scores for all three profiles. Mean discrepancy between indices within a profile was as large as 11.5 IQ points. No demographics or epilepsy characteristics were significantly different across cognitive phenotypes.Conclusions:Latent cognitive phenotypes in a pediatric presurgical cohort were differentiated by general level of functioning; however, across profiles, processing speed was consistently the lowest index followed by working memory. These findings across phenotypes suggest a common relative weakness which may result from a global effect of antiseizure medications and/or the widespread impact of seizures on neural networks even in a largely focal epilepsy cohort; similar to adult studies with temporal lobe epilepsy (Hermann et al, 2007). Future work will use latent profile analysis to examine phenotypes across other domains relevant to pediatric epilepsy including attention, naming, motor, and memory functioning. These findings are in line with collaborative efforts towards cognitive phenotyping which is the aim of our PERC Epilepsy Surgery Database Project that has already established one of the largest pediatric epilepsy surgery cohorts.

38 Fine Motor Skills in Pediatric Frontal Lobe Epilepsy are Associated with Executive Dysfunction and ADHD Symptomatology

Objective:Pediatric patients with frontal lobe epilepsy (FLE) have higher rates of attention deficit hyperactivity disorder (ADHD), as well as executive functioning (EF) and fine motor (FM) challenges. Relations between these constructs have been established in youth with ADHD and are supported by FM and EF skill involvement in frontal-subcortical systems. Still, they are not well understood in pediatric FLE. We hypothesized that poorer FM performance would be related to greater executive dysfunction and ADHD symptomatology in this group.Participants and Methods:47 children and adolescents with FLE (AgeM=12.47, SD=5.18; IQM=84.07; SD=17.56; Age of Seizure OnsetM=6.85, SD=4.64; right-handed: n=34; left-handed: n=10; Unclear: n=3) were enrolled in the Pediatric Epilepsy Research Consortium dataset as part of their phase I epilepsy surgical evaluation. Participants were selected if they had unifocal FLE and completed the Lafayette Grooved Pegboard (GP). Seizure lateralization (left-sided: n=19; right-sided: n=26; bilateral: n=2) and localization were established via data (e.g., EEG, MRI) presented at a multidisciplinary team case conference. Patients completed neuropsychological measures of FM, attention, and EF. Parents also completed questionnaires inquiring about their child’s everyday EF and ADHD symptomatology. Correlational analyses were conducted to examine FM, EF, and ADHD relations.Results:Dominant hand (DH) manual dexterity (GP) was related to parent-reported EF (Behavior Rating Inventory of Executive Function, Second Edition [BRIEF-2]-Global Executive Composite [GEC]: r(15) =-.70, p<.01, d=1.96). While not statistically significant, medium to large effect sizes were found for GP DH and parent-reported inattention (Behavior Assessment System for Children, Third Edition [BASC-3]-Attention Problems: r(12)=-.39, p=.17, d=.85) and hyperactivity/impulsivity (BASC-3-Hyperactivity: r(11)= -.44, p=.13, d=.98), as well as performance-based attention (Conners Continuous Performance Test, Third Edition -Omission Errors: r(12)=-.35, p=.22, d=.41), working memory (Wechsler Intelligence Scale for Children - Fifth Edition [WISC-V]-Digit Span [DS]: r(19)=.38, p=.09, d=.82) and cognitive flexibility (Delis-Kaplan Executive Function System (D-KEFS) Verbal Fluency Category Switching: r(13)=.46, p=.08, d=1.04); this suggests that these relations may exist but that our study was underpowered to detect them. Non-dominant hand (NDH) manual dexterity was related to performance-based working memory (WISC-V-DS: r(19)=.50, p<.01, d=1.12) and cognitive flexibility (D-KEFS-Trails Making Test Number-Letter Switching: r(17)=.64, p<.01, d=1.67). Again, while underpowered, medium to large effect sizes were found for GP NDH and parent-reported EF (BRIEF-2 GEC: r(15) =-.45, p=.07, d=1.01) and performance-based phonemic fluency (D-KEFS-Letter Fluency: r(13)=.31, p=.20, d=.65).Conclusions:Our findings suggest that FM, EF, and ADHD are related in youth with FLE; however, these relations appear to vary by skill and hand. We posit that our findings are due in part to the frontal-cerebellar networks given their anatomic proximity between frontal motor areas and the dorsolateral prefrontal cortex - as well as their shared functional involvement in these networks. Future studies should evaluate the predictive validity of initial FM skills for later executive dysfunction and ADHD symptomatology in FLE. If such relations emerge, contributions of early FM interventions on EF development should be examined. Further replication of these findings with a larger sample is warranted.

Clinical characteristics and outcomes of patients with post-stroke epilepsy: protocol for an individual patient data meta-analysis from the International Post-stroke Epilepsy Research Repository (IPSERR)

IntroductionDespite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies...

26 The Importance of Executive Functioning for Academic Achievement Among a National Sample of Children with Epilepsy

Objective:Children with epilepsy are at greater risk of lower academic achievement than their typically developing peers (Reilly and Neville, 2015). Demographic, social, and neuropsychological factors, such as executive functioning (EF), mediate this relation. While research emphasizes the importance of EF skills for academic achievement among typically developing children (e.g., Best et al., 2011; Spiegel et al., 2021) less is known among children with epilepsy (Ng et al., 2020). The purpose of this study is to examine the influence of EF skills on academic achievement in a nationwide sample of children with epilepsy.Participants and Methods:Participants included 427 children with epilepsy (52% male; MAge= 10.71), enrolled in the Pediatric Epilepsy Research Consortium (PERC) Epilepsy Surgery Database who had been referred for surgery and underwent neuropsychological testing. Academic achievement was assessed by performance measures (word reading, reading comprehension, spelling, and calculation and word-based mathematics) and parent-rating measures (Adaptive Behavior Assessment System (ABAS) Functional Academics and Child Behavior Checklist (CBCL) School Performance). EF was assessed by verbal fluency measures, sequencing, and planning measures from the Delis Kaplan Executive Function System (DKEFS), NEPSY, and Tower of London test. Rating-based measures of EF included the 'Attention Problems’ subscale from the CBCL and 'Cognitive Regulation’ index from the Behavior Rating Inventory of Executive Function (BRIEF-2). Partial correlations assessed associations between EF predictors and academic achievement, controlling for fullscale IQ (FSIQ; A composite across intelligence tests). Significant predictors of each academic skill or rating were entered into a two-step regression that included FSIQ, demographics, and seizure variables (age of onset, current medications) in the first step with EF predictors in the second step.Results:Although zero-order correlations were significant between EF predictors and academic achievement (.29 < r’s < .63 for performance; -.63 < r’s < -.50 for rating measures), partial correlations controlling for FSIQ showed fewer significant relations. For performance-based EF, only letter fluency (DKEFS Letter Fluency) and cognitive flexibility (DKEFS Trails Condition 4) demonstrated significant associations with performance-based academic achievement (r’s > .29). Regression models for performance-based academic achievement indicated that letter fluency (ß = .22, p = .017) and CBCL attention problems (ß = -.21, p =.002) were significant predictors of sight-word reading. Only letter fluency (ß = .23, p =.006) was significant for math calculation. CBCL Attention Problems were a significant predictor of spelling performance (ß = -.21, p = .009) and reading comprehension (ß = -.18, p =.039). CBCL Attention Problems (ß = -.38, p <.001 for ABAS; ß = -.34, p =.002 for CBCL School) and BRIEF-2 Cognitive Regulation difficulties (ß = -.46, p < .001 for ABAS; ß = -.46, p =.013 for CBCL School) were significant predictors of parent-rated ABAS Functional Academics and CBCL School Performance.Conclusions:Among a national pediatric epilepsy dataset, performance-based and ratings-based measures of EF predicted performance academic achievement, whereas only ratings-based EF predicted parent-rated academic achievement, due at least in part to shared method variance. These findings suggest that interventions that increase cognitive regulation, reduce symptoms of attention dysfunction, and promote self-generative, flexible thinking, may promote academic achievement among children with epilepsy.

Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications.

The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

Complementary structural and functional abnormalities to localise epileptogenic tissue

When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.

Cannabinoid treatments in epilepsy and seizure disorders.

Cannabis has been used to treat convulsions and other disorders since ancient times. In the last few decades, preclinical animal studies and clinical investigations have established the role of cannabidiol (CBD) in treating epilepsy and seizures and support potential therapeutic benefits for cannabinoids in other neurological and psychiatric disorders. Here, we comprehensively review the role of cannabinoids in epilepsy. We briefly review the diverse physiological processes mediating the central nervous system response to cannabinoids, including Δ9-tetrahydrocannabinol (Δ9-THC), cannabidiol, and terpenes. Next, we characterize the anti- and proconvulsive effects of cannabinoids from animal studies of acute seizures and chronic epileptogenesis. We then review the clinical literature on using cannabinoids to treat epilepsy, including anecdotal evidence and case studies as well as the more recent randomized controlled clinical trials that led to US Food and Drug Administration approval of CBD for some types of epilepsy. Overall, we seek to evaluate our current understanding of cannabinoids in epilepsy and focus future research on unanswered questions.

Epilepsy surgery in children with genetic etiologies: A prospective evaluation of current practices and outcomes

ObjectiveThis study assesses current practices and outcomes of epilepsy surgery in children with a genetic etiology. It explores the pre-surgical workup, types of surgeries, and post-surgical outcomes in a broad array of disorders. MethodsPatients ≤18 years who completed epilepsy surgery and had a known genetic etiology prior to surgical intervention were extrapolated from the Pediatric Epilepsy Research Consortium (PERC) surgery database, across 18 US centers. Data were assessed univariably by neuroimaging and EEG results, genetic group (structural gene, other gene, chromosomal), and curative intent. Outcomes were based on a modified International League Against Epilepsy (ILAE) outcome score. ResultsOf 81 children with genetic epilepsy, 72 % had daily seizures when referred for surgery evaluation, which occurred a median of 2.2 years (IQR 0.3, 5.2) after developing drug resistance. Following surgery, 68 % of subjects had >50 % seizure reduction, with 33 % achieving seizure freedom [median follow-up 11 months (IQR 6, 17). Seizure freedom was most common in the monogenic structural group, but significant palliation was present across all groups. Presence of a single EEG focus was associated with a greater likelihood of seizure freedom (p=0.02). SignificanceThere are meaningful seizure reductions following epilepsy surgery in the majority of children with a genetic etiology, even in the absence of a single structural lesion and across a broad spectrum of genetic causes. These findings highlight the need for expedited referral for epilepsy surgery and support of a broadened view of which children may benefit from epilepsy surgery, even when the intent is palliative.

Protein profiling in plasma for biomarkers of seizure

PurposeA biochemical way to measure seizures would greatly benefit epilepsy research and clinical follow-up. Short-term biomarkers like lactate exist, and interest in biomarkers representative of longer-term seizure burden is growing. In this exploratory study, we aimed to identify markers in blood plasma that differentiate persons with recent seizures from persons with epilepsy and long-standing seizure freedom. MethodsA proteomic analysis was performed on plasma samples of 120 persons with seizures using the Olink Neuro-exploratory panel. Participants were selected from a regional biobank study in Västra Götaland (Sweden) and categorized into two groups: recent seizure and seizure-free. The panel contained 92 proteins linked to neurological diseases and processes, and levels of these proteins were compared between the patient groups to identify potential markers of seizure activity. ResultsWe identified significant differences in protein levels between the recent seizure and seizure-free patient groups for Cadherin-15 [(CDH15; p = 0.008)], Latent transforming growth factor beta-binding protein 3 [(LTBP3; p = 0.002)], Phosphoethanolamine/phosphocholine phosphatase 1 [(PHOSPHO1; p = 0.011)], and Progestagen associated endometrial protein [(PAEP; p = 0.0005)]. ConclusionThe findings in this study present CDH15, LTBP3, PHOSPHO1 and PAEP as candidate markers of seizure activity. Further confirmatory studies are needed.

PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy.

The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life. The development of wearable solutions for epilepsy is limited by the nonexistence of datasets which could validate these solutions. Three different form factors were developed and deployed, and the signal quality was assessed for all acquired biosignals. The wearable data acquisition was performed during the video-EEG of patients with epilepsy. The results achieved so far include 59 patients from 2 hospitals totaling 2,721 h of wearable data and 348 seizures. Besides the wearable data, the Electrocardiogram of the hospital is also useable, totalling 5,838 h of hospital data. The quality ECG signals collected with the proposed wearable is equated with the hospital system, and all other biosignals also achieved state-of-the-art quality. During the data acquisition, 18 challenges were identified, and are presented alongside their possible solutions. Though this is an ongoing work, there were many lessons learned which could help to predict possible problems in wearable data collections and also contribute to the epilepsy community with new physiological information. This work contributes with original wearable data and results relevant to epilepsy research, and discusses relevant challenges that impact wearable health monitoring.

Dimethyl sulfoxide’s impact on epileptiform activity in a mouse model of chronic temporal lobe epilepsy

In the quest for novel treatments for patients with drug-resistant seizures, poor water solubility of potential drug candidates is a frequent obstacle. Literature indicated that the highly efficient solvent dimethyl sulfoxide (DMSO) may have a confounding influence in epilepsy research, reporting both pro- and antiepileptic effects. In this study, we aim to clarify the effects of DMSO on epileptiform activity in one of the most frequently studied models of chronic epilepsy, the intrahippocampal kainic acid (IHKA) mouse model, and in a model of acute seizures. We show that 100 % DMSO (in a volume of 1.5 µl/g corresponding to 1651 mg/kg) causes a significant short-term anti-seizure effect in epileptic IHKA mice of both sexes, but does not affect the threshold of acute seizures induced by pentylenetetrazol (PTZ). These findings highlight that the choice of solvent and appropriate vehicle control is crucial to minimize undesirable misleading effects and that drug candidates exclusively soluble in 100 % DMSO need to be modified for better solubility already at initial testing.

Applications for Deep Learning in Epilepsy Genetic Research.

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.

Sex and gonadectomy modify behavioral seizure susceptibility and mortality in a repeated low-dose kainic acid systemic injection paradigm in mice.

Sex differences in epilepsy appear driven in part due to effects of gonadal steroids, with varying results in experimental models based on species, strain, and method of seizure induction. Furthermore, removing the main source of these steroids via gonadectomy may impact seizure characteristics differently in males and females. Repeated low-dose kainic acid (RLDKA) systemic injection paradigms were recently shown to reliably induce status epilepticus (SE) and hippocampal histopathology in C57BL/6J mice. Here, we investigated whether seizure susceptibility in a RLDKA injection protocol exhibits a sex difference and whether gonadectomy differentially influences response to this seizure induction paradigm in males and females. Adult C57BL/6J mice were left gonad-intact as controls or gonadectomized (females: ovariectomized, OVX; males: orchidectomized, ORX). At least 2 weeks later, KA was injected ip, every 30 minutes at 7.5 mg/kg or less until the animal reached SE, defined by at least 5 generalized seizures (GS, Racine stage 3 or higher). Parameters of susceptibility to GS induction, SE development, and mortality rates were quantified. No differences in seizure susceptibility or mortality were observed between control males and control females. Gonadectomized mice exhibited increased susceptibility and reduced latency to both GS and SE in comparison to corresponding controls of the same sex, but the effects were stronger in males. In addition, ORX males, but not OVX females, exhibited strongly increased seizure-induced mortality. The RLDKA protocol is notable for its efficacy in inducing SE and seizure-induced histopathology in C57BL/6J mice, the background for many transgenic strains in current use in epilepsy research. The present results indicate that this protocol may be beneficial for investigating the effects of gonadal hormone replacement on seizure susceptibility, mortality, and seizure-induced histopathology, and that gonadectomy unmasks sex differences in susceptibility to seizures and mortality not observed in gonad-intact controls.

A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management.

Epilepsy is a complex neurological disorder affecting millions worldwide, with a substantial number of patients facing drug-resistant epilepsy. This comprehensive review explores innovative therapies for epilepsy management, focusing on their principles, clinical evidence, and potential applications. Traditional antiseizure medications (ASMs) form the cornerstone of epilepsy treatment, but their limitations necessitate alternative approaches. The review delves into cutting-edge therapies such as responsive neurostimulation (RNS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), highlighting their mechanisms of action and promising clinical outcomes. Additionally, the potential of gene therapies and optogenetics in epilepsy research is discussed, revealing groundbreaking findings that shed light on seizure mechanisms. Insights into cannabidiol (CBD) and the ketogenic diet as adjunctive therapies further broaden the spectrum of epilepsy management. Challenges in achieving seizure control with traditional therapies, including treatment resistance and individual variability, are addressed. The importance of staying updated with emerging trends in epilepsy management is emphasized, along with the hope for improved therapeutic options. Future research directions, such as combining therapies, AI applications, and non-invasive optogenetics, hold promise for personalized and effective epilepsy treatment. As the field advances, collaboration among researchers of natural and synthetic biochemistry, clinicians from different streams and various forms of medicine, and patients will drive progress toward better seizure control and a higher quality of life for individuals living with epilepsy.

Can in vitro studies aid in the development and use of antiseizure therapies? A report of the ILAE/AES Joint Translational Task Force.

In vitro preparations (defined here as cultured cells, brain slices, and isolated whole brains) offer a variety of approaches to modeling various aspects of seizures and epilepsy. Such models are particularly amenable to the application of anti-seizure compounds, and consequently are a valuable tool to screen the mechanisms of epileptiform activity, mode of action of known anti-seizure medications (ASMs), and the potential efficacy of putative new anti-seizure compounds. Despite these applications, all disease models are a simplification of reality and are therefore subject to limitations. In this review, we summarize the main types of in vitro models that can be used in epilepsy research, describing key methodologies as well as notable advantages and disadvantages of each. We argue that a well-designed battery of in vitro models can form an effective and potentially high-throughput screening platform to predict the clinical usefulness of ASMs, and that in vitro models are particularly useful for interrogating mechanisms of ASMs. To conclude, we offer several key recommendations that maximize the potential value of in vitro models in ASM screening. This includes the use of multiple in vitro tests that can complement each other, carefully combined with in vivo studies, the use of tissues from chronically epileptic (rather than naïve wild-type) animals, and the integration of human cell/tissue-derived preparations.

Zebrafish as a robust preclinical platform for screening plant-derived drugs with anticonvulsant properties-a review.

Traditionally, selected plant sources have been explored for medicines to treat convulsions. This continues today, especially in countries with low-income rates and poor medical systems. However, in the low-income countries, plant extracts and isolated drugs are in high demand due to their good safety profiles. Preclinical studies on animal models of seizures/epilepsy have revealed the anticonvulsant and/or antiepileptogenic properties of, at least some, herb preparations or plant metabolites. Still, there is a significant number of plants known in traditional medicine that exert anticonvulsant activity but have not been evaluated on animal models. Zebrafish is recognized as a suitable in vivo model of epilepsy research and is increasingly used as a screening platform. In this review, the results of selected preclinical studies are summarized to provide credible information for the future development of effective screening methods for plant-derived antiseizure/antiepileptic therapeutics using zebrafish models. We compared zebrafish vs. rodent data to show the translational value of the former in epilepsy research. We also surveyed caveats in methodology. Finally, we proposed a pipeline for screening new anticonvulsant plant-derived drugs in zebrafish ("from tank to bedside and back again").

THE ASSOCIATION BETWEEN EPILEPSY AND SYNAPTIC PLASTICITY: BIBLIOMETRICS RESEARCH AND VISUAL ANALYSIS USING CITESPACE

Objective: This study used CiteSpace to look at the current state of research on epilepsy and synaptic plasticity and to point out the hotspots and frontiers. Method: We searched Web of Science (WoS) for studies related to epilepsy and synaptic plasticity. CiteSpace was used to construct network maps of cooperation across countries, institutions, and authors to identify frontiers and hotspots in epilepsy and synaptic plasticity research. Results: A total of 1700 studies on epilepsy and synaptic plasticity were retrieved from the WoS. The United States and Baylor College of Medicine were the most prolific nation and institution in this field with 680 and 28 publications, respectively. The most prolific author (11 articles) was Xuefeng Wang. The Journal of Neuroscience published the most articles (71, 6.71%) and had the most co-citations (1557, 4.57%). In this paper, the interaction and mechanism between epilepsy and synaptic plasticity, as well as future research hotspots, are highlighted. Conclusion: CiteSpace can reveal the institutions, leaders, journals, cited papers, and research hotspots involved in epilepsy and synaptic plasticity. To the best of our knowledge, this is the first study that visualizes the relationship between epilepsy and synaptic plasticity and provides references for future research directions.