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In patients with drug-resistant epilepsy, difficulties in identifying the epileptogenic zone are well known to correlate with poorer clinical outcomes post-surgery. The integration of PET and MRI in the presurgical assessment of pediatric patients likely improves diagnostic precision by confirming or widening treatment targets. PET and MRI together offer superior insights compared to either modality alone. For instance, PET highlights abnormal glucose metabolism, while MRI precisely localizes structural anomalies, providing a comprehensive understanding of the epileptogenic zone. Furthermore, both methodologies, whether utilized through simultaneous PET/MRI scanning or the co-registration of separately acquired PET and MRI data, present unique advantages, having complementary roles in lesional and non-lesional cases. Simultaneous FDG-PET/MRI provides precise co-registration of functional (PET) and structural (MR) imaging in a convenient one-stop-shop approach, which minimizes sedation time and reduces radiation exposure in children. Commercially available fusion software that allows retrospective co-registration of separately acquired PET and MRI images is a commonly used alternative. This review provides an overview and illustrative cases that highlight the role of combining 18F-FDG-PET and MRI imaging and shares the authors' decade-long experience utilizing simultaneous PET/MRI in the presurgical evaluation of pediatric epilepsy. [ABSTRACT FROM AUTHOR]

Graphogenic, or writing epilepsy, is a rare type of reflex epilepsy (RE) in which seizures are provoked by the act of writing. RE is a condition that affects between 4% and 7% of all patients diagnosed with epilepsy. In this case study, we provide a case report on a patient whose seizures were generated by the act of writing. We also present evidence that the cortical mechanisms by which writing-induced seizures occur are focal with possible secondary generalization. [ABSTRACT FROM AUTHOR]

Objective: The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans. Methods: Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated. Results: The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p < 0.05), and the trends of parameter changes aligned with the seizure process. Additionally, excitatory parameters of epileptogenic contacts were higher than that of non-epileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%. Conclusion: The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses. [ABSTRACT FROM AUTHOR]

Background: People with epilepsy (PWE) frequently experience sleep disturbances that can severely affect their quality of life. Depression is also a common symptom in the PWE population and can aggravate sleep problems. However, the interplay between epilepsy, depression, and sleep disturbances is not yet fully understood. Our study was designed to investigate the association between epilepsy and sleep disturbances in US adults and to determine whether depressive symptoms play a mediating role in this relationship. Methods: We examined data from the National Health and Nutrition Examination Survey (NHANES) spanning January 1, 2015, to March 2020, before the pandemic.A total of 10,093 participants aged ≥ 20 years with complete data on epilepsy and sleep disturbance were included. Weighted multiple logistic regression and mediation analysis were used to explore the associations among depression, epilepsy, and sleep disturbance. Interaction effects of epilepsy with various covariates were also investigated. Results: Epilepsy was associated with depression and sleep disturbances. Weighted logistic regression analysis revealed a significant association between epilepsy and sleep disturbances (OR = 3.67, 95% CI = 1.68–8.04). Depression partially mediated this relationship, demonstrating a mediation effect of 23.0% (indirect effect = 0.037, P < 0.001). Subgroup analyses revealed variations in the relationship between epilepsy and sleep disturbances among different groups. Furthermore, interaction analyses revealed significant interactions between epilepsy and age (P = 0.049) and hypertension (P = 0.045). Conclusions: Our study utilizing NHANES data confirmed that depression partially mediated the association between epilepsy and sleep disturbance. Additionally, we observed differences in this association across demographic groups. Addressing depressive symptoms in PWE may improve their sleep quality, but further research is needed to explore the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is a global pandemic that has highly impacted the healthcare system and patients, especially patients with epilepsy, due to the fact that the success of their treatment depends on obtaining sustainable access to medical professions, diagnostic services, facilities, and medications. The epidemiology and presence of neuro-pediatric emergencies in the setting of COVID-19 in XXX have not been thoroughly described. This is a barrier to planning and providing quality emergency care within the local health systems. The objective of this study is to provide a comprehensive description of the epidemiology of neurological cases encountered in the pediatric emergency unit. Methods: This is a retrospective study to analyze the flow pattern of Emergency Department (ED) visits among pediatric patients with neuro-related complaints. Participants were filtered, and a total of 108,000 visits were reduced to 960 patients with a neurological provisional diagnosis. Patients were grouped into pre- and post-pandemic visits according to their age group. We identified demographic and clinical variables. Results: The study included 960 patients with a provisional neurological diagnosis, consisting of 542 (56.5%) males and 418 (43.5%) females. The mean age of admission was 5.29 ± 4.19 years. The majority of patients were triaged as "priority 1—resuscitation" (n = 332, 34.6%), and seizures were the most frequent chief complaint (n = 317, 33.0%). Statistical significance was observed for patients with vascular issues (p = 0.013) during the pre-COVID-19 period after adjusting for odds ratio. The most common outcome was discharge (n = 558, 58.1%). The mean length of stay during the pre-COVID-19 pandemic was 16.48 ± 33.53 h, which was significantly longer compared to a mean length of stay of 7.76 ± 7.27 h during the COVID-19 pandemic (P < 0.001). Conclusion: We presented a new epidemiology of pediatric patients with neuro-related ED visits. An increase in seizure diagnosis was observed, as were significant shifts in the length of stay. Demographic changes were less evident in the two periods. Understanding such variation aids in managing this vulnerable population during critical periods. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is a non-communicable chronic brain disease that affects all age groups. There are approximately 50 million epilepsy patients worldwide, which is one of the most common neurological disorder. This study reports the time trends in the burden of epilepsy from 1999 to 2019. Methods: We evaluated the disease burden and its temporal trends of epilepsy using the prevalence and years lived with disability (YLDs), which was estimated based on the Global Burden of Disease (GBD) 2019 study. The age-period- cohort (APC) model was used to estimate the temporal trends of the epilepsy prevalence and YLDs rates, and to analyze the relative risks of age, periods and queues (age/period/queue effects). Results: In the past 30 years, the global age-standardized prevalence rate and age-standardized rate has increased by 29.61% and 27.02%, respectively. Globally, the APC model estimated the net drift of prevalence and YLDs were 0.88% (95% CI: 0.83-0.93) and 0.80% (95% CI: 0.75-0.85) per year. Among 204 countries and territories, the YLDs in 146 and prevalence 164 showed an increasing trend. And the risk of YLDs and prevalence increases with age, with the lowest risk among 0-4 years old and the highest risk among 75-79 years old. Unfavorable increasing period and cohort risks of YLDs and prevalence were observed. Conclusion: Over the past 30 years, the YLDs and prevalence of epilepsy have gradually increased globally and unfavorable increasing period and cohort risks were observed. Emphasizing epilepsy prevention, strengthening epilepsy health education, optimizing older adults epilepsy diagnosis and treatment plans, and actively promoting epilepsy diagnosis and treatment plans can effectively reduce new cases of epilepsy and related disabilities. [ABSTRACT FROM AUTHOR]

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8–13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment. [ABSTRACT FROM AUTHOR]

Background: Anti leucine-rich, glioma inactivated 1 (LGI1) antibody-associated autoimmune encephalitis (AE) is the second most common AE, where the trafficking and recycling of the pathogenic immunoglobulin (IgG) can be controlled by the neonatal crystallizable fragment receptor (FcRn), making the latter as a candidate therapeutic target. Efgartigimod is an antagonist of FcRn, its ability to increase the degradation of IgGs and improve the health and quality of life of patients. ADAPT trail indicated its rapid efficacy and safety on myasthenia gravis. However, there is currently no case reported using efgartigimod for the treatment of anti-LGI1-associated AE. Case description: The patient presented with five episodes of generalized tonic–clonic seizures in the past 2 weeks. The patient had no abnormal signs on magnetic resonance imaging. Electroencephalogram examinations showed an increase in bilateral symmetric or asymmetric slow activity, without any clear epileptic waves. The cerebrospinal fluid (CSF) examination results indicated a slight increase in protein (47 mg/dL). The anti-LGI1 antibody titer in serum was 1:100 and that in CSF was 1:3.2. The treatment with intravenous methylprednisolone 1000 mg once a day combined with levetiracetam tablets failed to completely control the patient's seizures. Thus, 10 mg/kg efgartigimod was administered intravenously once a week for 2 weeks. After 2 weeks of treatment, serum levels of anti-LGI1 antibody and IgG decreased and the patient's epilepsy did not recur in the next 3 months. Conclusions: This is the first case report of using efgartigimod to treat anti-LGI1-associated AE. The combination of efgartigimod and methylprednisolone resulted in favorable outcomes, indicating that this is an optional treatment plan. [ABSTRACT FROM AUTHOR]

Background and Objectives: Cenobamate (CNB) is a new antiseizure medication (ASM) to treat drug-resistant, focal-onset seizures. Data on its use in early therapy lines are not yet available, and clinicians frequently consider CNB to be a later ASM drug choice. We investigated the efficacy and safety of CNB as an early adjunctive treatment in drug-resistant, focal-onset seizures. Methods: The study population were patients with drug-resistant, focal-onset seizures who were initiated with CNB after they did not respond to two or three lifetime ASMs, including all prior and concomitant ASMs. These patients were matched (1:2) by sex, age, and seizure frequency to controls who were initiated with any ASM other than CNB. All participants participated in the Mainz Epilepsy Registry. We evaluated the retention rate after 12 months of CNB and after each new adjunctive ASM in the control group. In addition, seizure freedom and the response rate (reduction of seizure frequency by ≥ 50% from baseline) after 12 months were estimated. Results: We included 231 patients aged 44.4 ± 15.8 years. Of these, 33.3% (n = 77) were on CNB, 19.0% (n = 44) on valproate (VPA), 17.3% (n = 40) on lacosamide (LCS), 16.4% (n = 38) on levetiracetam (LEV), and 13.9% (n = 32) on topiramate (TPM). The highest retention rate after 12 months since the beginning of the early adjunctive therapy was observed on CNB (92.0%), compared with LCS (80.0%), LEV (73.3%), VPA (68.2%), or TPM (62.5%) (p < 0.05). Seizure freedom and response rate were also the best on CNB (19.5% and 71.4%, respectively) compared with other ASMs (8.3% and 52.5%, respectively; p < 0.05). No significant differences in adverse events between CNB and other ASMs were observed. Conclusions: Our study provides evidence that CNB is an effective ASM with a good safety profile in the early therapy lines of drug-resistant, focal-onset seizures. This data should support medical decision making in the management of patients with refractory epilepsy. Clinical Trial ID: NCT05267405. [ABSTRACT FROM AUTHOR]

To describe a rare seizure semiology originating from a hypothalamic hamartoma in a child, along with unusual ictal onset and connectivity pattern, and provide a review of the pathophysiology of epilepsy associated with hypothalamic hamartoma and management. A detailed retrospective chart review and literature search were performed using Pubmed and Embase. We present a case of a three-year-old male who presented with dyscognitive seizures with onset at age 22 months. Stereoelectroencephalography exploration confirmed the onset in hypothalamic hamartoma with rapid propagation to the temporal-parietal-occipital association cortex and precuneus. The patient's epilepsy was cured with laser ablation of the hamartoma. Published literature mostly describes a more anterior frontal or temporal epileptic network with primarily gelastic seizures being the hallmark type of seizures associated with hypothalamic hamartoma. We highlight a rare posterior cortex network with an atypical presentation of focal nonmotor seizures with impaired awareness in the setting of a hypothalamic hamartoma. Stereotactic laser ablation of the hamartoma rendered seizure freedom. Early diagnosis and appropriate treatment can lead to seizure freedom. [ABSTRACT FROM AUTHOR]

Background: Childhood epilepsies are caused by heterogeneous underlying disorders where approximately 40% of the origins of epilepsy can be attributed to genetic factors. The application of next-generation sequencing (NGS) has revolutionized molecular diagnostics and has enabled the identification of disease-causing genes and variants in childhood epilepsies. The objective of this study was to use NGS to identify variants in patients with childhood epilepsy, to expand the variant spectrum and discover potential therapeutic targets. Methods: In our study, 55 children with epilepsy of unknown etiology were analyzed by combining clinical-exome and whole-exome sequencing. Novel variants were characterized using various in silico algorithms for pathogenicity and structure prediction. Results: The molecular genetic cause of epilepsy was identified in 28 patients and the overall diagnostic success rate was 50.9%. We identified variants in 22 different genes associated with epilepsy that correlate well with the described phenotype. SCN1A gene variants were found in five unrelated patients, while ALDH7A1 and KCNQ2 gene variants were found twice. In the other 19 genes, variants were found only in a single patient. This includes genes such as ASH1L, CSNK2B, RHOBTB2, and SLC13A5, which have only recently been associated with epilepsy. Almost half of diagnosed patients (46.4%) carried novel variants. Interestingly, we identified variants in ALDH7A1, KCNQ2, PNPO, SCN1A, and SCN2A resulting in gene-directed therapy decisions for 11 children from our study, including four children who all carried novel SCN1A genetic variants. Conclusions: Described novel variants will contribute to a better understanding of the European genetic landscape, while insights into the genotype-phenotype correlation will contribute to a better understanding of childhood epilepsies worldwide. Given the expansion of molecular-based approaches, each newly identified genetic variant could become a potential therapeutic target. [ABSTRACT FROM AUTHOR]

Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase of transaminase levels while undergoing VPA therapy for 1995 epilepsy patients. The study employed the two-tailed T test, Chi-square test, and binary logistic regression analysis, selecting six clinical parameters, including age, stature, leukocyte count, Total Bilirubin, oral dosage of VPA, and VPA concentration. These variables were used to build a risk prediction model using "H2O" autoML platform, achieving the best performance (AUC training = 0.855, AUC test = 0.789) in the training and testing data set. The model also exhibited robust accuracy (AUC valid = 0.742) in an external validation set, underscoring its credibility in anticipating VPA-induced transaminase abnormalities. The significance of the six variables was elucidated through importance ranking, partial dependence, and the TreeSHAP algorithm. This novel model offers enhanced versatility and explicability, rendering it suitable for clinicians seeking to refine parameter adjustments and address imbalanced data sets, thereby bolstering classification precision. To summarize, the personalized prediction model for VPA-treated epilepsy, established with an autoML model, displayed commendable predictive capability, furnishing clinicians with valuable insights for fostering pharmacovigilance. [ABSTRACT FROM AUTHOR]

Background: Antiseizure medications remain the cornerstone of treatment for epilepsy, although a proportion of individuals with the condition will continue to experience seizures despite appropriate therapy. Treatment choices for epilepsy are based on variables related to both the individual patient and the available medications. Brivaracetam is a third-generation agent antiseizure medication. Methods: We carried out a Delphi consensus exercise to define the role of brivaracetam in clinical practice and to provide guidance about its use as first add-on ASM and in selected clinical scenarios. A total of 15 consensus statements were drafted by an expert panel following review of the literature and all were approved in the first round of voting by panelists. The consensus indicated different clinical scenarios for which brivaracetam can be a good candidate for treatment, including first add-on use. Results: Overall, brivaracetam was considered to have many advantageous characteristics that render it a suitable option for patients with focal epilepsy, including a fast onset of action, favorable pharmacokinetic profile with few drug-drug interactions, broad-spectrum activity, and being well tolerated across a range of doses. Brivaracetam is also associated with sustained clinical response and good tolerability in the long term. Conclusions: These characteristics also make it suitable as an early add-on for the elderly and for patients with post-stroke epilepsy or status epilepticus as highlighted by the present Delphi consensus. [ABSTRACT FROM AUTHOR]

Accurately studying structural connectivity requires precise tract segmentation strategies. The U-Net network has been widely recognized for its exceptional capacity in image segmentation tasks and provides remarkable results in large tract segmentation when high-quality diffusion-weighted imaging (DWI) data are used. However, short tracts, which are associated with various neurological diseases, pose specific challenges, particularly when high-quality DWI data acquisition within clinical settings is concerned. Here, we aimed to evaluate the U-Net network ability to segment short tracts by using DWI data acquired in different experimental conditions. To this end, we conducted three types of training experiments involving 350 healthy subjects and 11 white matter tracts, including the anterior, posterior, and hippocampal commissure, fornix, and uncinated fasciculus. In the first experiment, the model was exclusively trained with high-quality data of the Human Connectome Project (HCP) dataset. The second experiment focused on images of healthy subjects acquired from a local hospital dataset, representing a typical clinical routine acquisition. In the third experiment, a hybrid training approach was employed, combining data of the HCP and local hospital datasets. Then, the best model was also tested in unseen DWIs of 10 epilepsy patients of the local hospital and 10 healthy subjects acquired on a scanner from another company. The outcomes of the third experiment demonstrated a notable enhancement in performance when contrasted with the preceding trials. Specifically, the short tracts within the local hospital dataset achieved Dice scores ranging between 0.60 and 0.65. Similar intervals were obtained with HCP data in the first experiment, and a substantial improvement compared to the scores between 0.37 and 0.50 obtained with the local hospital dataset at the same experiment. This improvement persisted when the method was applied to diverse scenarios, including different scanner acquisitions and epilepsy patients. These results indicate that combining datasets from different sources, coupled with resolution standardization strengthens the neural network ability to generalize predictions across a spectrum of datasets. Nevertheless, short tract segmentation performance is intricately linked to the training composition, to validation, and to testing data. Moreover, curved tracts have intricate structural nature, which adds complexities to their segmenting. Although the network training approach tested herein has provided promising results, caution must be taken when extrapolating its application to datasets acquired under distinct experimental conditions, even in the case of higher-quality data or analysis of long or short tracts. • To study structural connectivity, precise tract segmentation is crucial. • U-Net network is recognized for its exceptional capacity for image segmentation. • High-quality DWI data is a challenge for clinical image acquisitions. • Short tracts pose a greater challenge for neural networks to accurately predict. • Effectiveness of automatic segmentation on clinical images. [ABSTRACT FROM AUTHOR]

Background: Third-generation antiseizure medications, such as brivaracetam, are recognized for their superior safety, tolerability, and pharmacokinetic profiles. However, their potential benefits are often limited in low-income populations because of challenges related to availability and affordability. Objective: We aimed to evaluate the effectiveness and safety of brivaracetam for treating epilepsy in a low-income population, within a real-world setting. Methods: This retrospective cohort study included individuals with epilepsy from a low-income population in Bogotá, Colombia, who were treated with brivaracetam between January 2020 and July 2023. Effectiveness (mean seizure reduction and ≥ 50% seizure reduction) and safety (retention rate and adverse events) were evaluated. Results: A total of 106 individuals were included, with a median age of 33 years (interquartile range: 24–44). Most had focal epilepsy with a median disease duration of 25.4 years (standard deviation: 13.6). The baseline seizure frequency was 4 seizures per month (interquartile range: 2–15) and individuals had previously received a mean of 4.4 (standard deviation: 1.8) antiseizure medications. The mean percentage seizure reduction at 3, 6, and 12 months was 55.3%, 66.9%, and 63.8%, respectively. Additionally, 60%, 63.8%, and 65.9% of individuals achieved a ≥ 50% seizure reduction at 3, 6, and 12 months, respectively. Retention rate at 3 months was 89% (n = 95) and 18.7% (n = 20) reported adverse effects. Conclusions: In a real-world setting, brivaracetam has been shown to be safe and effective for the treatment of epilepsy in individuals from a low-income population. This study suggests that people with epilepsy living in this context can significantly benefit from the use of third-generation antiseizure medications. [ABSTRACT FROM AUTHOR]

Background: Developmental and Epileptic Encephalopathies (DEEs) are defined by drug-resistant seizures and neurodevelopmental disorders. Over 50% of patients have a genetic cause. Studies have shown that patients with DEEs, regardless of genetic diagnosis, experience a central visual function disorder known as Cerebral (cortical) Visual Impairment (CVI). The prevalence of CVI in DEE patients is currently unknown. A quantitative synthesis of existing data on the prevalence rates of this condition would aid in understanding the magnitude of the problem, outlining future research, and suggesting the need for therapeutic strategies for early identification and prevention of the disorder. Methods: The protocol followed the PRISMA-P statement for systematic review and meta-analysis protocols. The review will adhere to the JBI Manual for Evidence Synthesis (Systematic Reviews of Prevalence and Incidence) and use the CoCoPop framework to establish eligibility criteria. We will conduct a comprehensive search of several databases, including MEDLINE, EMBASE, Science Direct, Scopus, PsychINFO, Wiley, Highwire Press, and Cochrane Library of Systematic Reviews. Our primary focus will be determining the prevalence of cerebral visual impairments (Condition) in patients with developmental and epileptic encephalopathy (Population). To ensure clarity, we will provide a narrative summary of the risk of bias in the studies we include. The Cochrane Q statistic will be used to assess heterogeneity between studies. If the quantitative synthesis includes more than 10 studies, potential sources of heterogeneity will be investigated through subgroup and meta-regression analyses. Meta(bias)es analysis will also be performed. The quality of evidence for all outcomes will be evaluated using the Grading of Recommendations Assessment Development and Evaluation (GRADE) working group methodology. Discussion: This protocol outlines a systematic review and meta-analysis to identify, collect, evaluate, and integrate epidemiological knowledge related to the prevalence of CVI in patients with DEEs. To the best of our knowledge, no other systematic review and meta-analysis has addressed this specific issue. The results will provide useful information for understanding the extent of the problem, outlining future research, and suggesting the need for early identification strategies. Systematic review registrations: This Systematic Review Protocol was registered in PROSPERO (CRD42023448910). [ABSTRACT FROM AUTHOR]

Objective Methods Results Interpretation The piriform cortex is considered to be highly epileptogenic. Its resection during epilepsy surgery is a predictor for postoperative seizure freedom in temporal lobe epilepsy. Epilepsy is associated with a dysfunction of the blood–brain barrier. We investigated blood–brain barrier dysfunction in the piriform cortex of people with temporal lobe epilepsy using quantitative T1‐relaxometry.Gadolinium‐based contrast agent was administered ictally and interictally in 37 individuals before undergoing quantitative T1‐relaxometry. Postictal and interictal images were co‐registered, and subtraction maps were created as biomarkers for peri‐ictal (∆qT1interictal‐postictal) and interictal (∆qT1noncontrast‐interictal) blood–brain barrier dysfunction. Values were extracted for the piriform cortex, hippocampus, amygdala, and the whole cortex.In temporal lobe epilepsy (n = 14), ∆qT1noncontrast‐interictal was significantly higher in the piriform cortex than in the whole cortex (p = 0.02). In extratemporal lobe epilepsy (n = 23), ∆qT1noncontrast‐interictal was higher in the hippocampus than in the whole cortex (p = 0.05). Across all individuals (n = 37), duration of epilepsy was correlated with ∆qT1noncontrast‐interictal (ß = 0.001, p < 0.001) in all regions, while the association was strongest in the piriform cortex. Impaired verbal memory was associated with ∆qT1noncontrast‐interictal only in the piriform cortex (p = 0.04). ∆qT1interictal‐postictal did not show differences in any region.Interictal blood–brain barrier dysfunction occurs in the piriform cortex in temporal lobe epilepsy. This dysfunction is linked to longer disease duration and worse cognitive deficits, emphasizing the central role of the piriform cortex in the epileptogenic network of temporal lobe epilepsy. [ABSTRACT FROM AUTHOR]

Objective: Approximately 50 million people worldwide have epilepsy and 8–17% of the deaths in patients with epilepsy are attributed to sudden unexpected death in epilepsy (SUDEP). The goal of the present work was to establish a biomarker for SUDEP so that preventive treatment can be instituted. Approach: Seizure activity in patients with SUDEP and non-SUDEP was analyzed, specifically, the scalp EEG extracted muscle activity (SMA) and the average wavelet phase coherence (WPC) during seizures was computed for two frequency ranges (1–12 Hz, 13–30 Hz) to identify differences between the two groups. Main results: Ictal SMA in SUDEP patients showed a statistically higher average WPC value when compared to non-SUDEP patients for both frequency ranges. Area under curve for a cross-validated logistic classifier was 81%. Significance: Average WPC of ictal SMA is a candidate biomarker for early detection of SUDEP. [ABSTRACT FROM AUTHOR]

Background: Epilepsy and other seizure disorders account for a high disease burden in Germany. As a timely diagnosis and accurate treatment are crucial, improving the management of these disorders is important. Outside of Germany, outpatient long-term video EEGs (ALVEEGs) have demonstrated the potential to support the diagnosis and management of epilepsy and other seizure disorders. This study aims to evaluate the implementation of ALVEEGs as a new diagnostic pathway in eastern parts of Germany to diagnose epilepsy and other seizure disorders and to assess if ALVEEGs are equally effective as the current inpatient-monitoring gold standard, which is currently only available at a limited number of specialized centers in Germany. Methods: ALVEEG is a prospective, multicenter, randomized controlled equivalence trial, involving five epilepsy centers in the eastern states of Germany. Patients will be randomized into either intervention (IG) or control group (CG), using a permuted block randomization. The sample size targeted is 688 patients, continuously recruited over the trial. The IG will complete an ALVEEG in a home setting, including getting access to a smartphone app to document seizure activity. The CG will receive care as usual, i.e., inpatient long-term video-EEG monitoring. The primary outcome is the proportion of clinical questions being solved in the IG compared to the CG. Secondary outcomes include hospital stays, time until video EEG, time until diagnosis and result discussion, patients' health status, quality of life and health competence, and number and form of epilepsy-related events and epileptiform activity. Alongside the trial, a process implementation and health economic evaluation will be conducted. Discussion: The extensive evaluation of this study, including an implementation and health economic evaluation, will provide valuable information for health policy decision-makers to optimize future delivery of neurological care to patients affected by epilepsy and other seizure disorders and on the uptake of ALVEEG into standard care in Germany. Trial registration: German Clinical Trials Register (DRKS00032220), date registered: December 11, 2023. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance In addition to the oscillatory brain activity, the nonoscillatory (scale‐free) components of the background electroencephalogram (EEG) may provide further information about the complexity of the underlying neuronal network. As epilepsy is considered a network disease, such scale‐free metrics might help to delineate the epileptic network. Here, we performed an analysis of the sleep oscillatory (spindle, slow wave, and rhythmic spectral power) and nonoscillatory (H exponent) intracranial EEG using multiple interictal features to estimate whether and how they deviate from normalcy in 38 adults with drug‐resistant epilepsy.To quantify intracranial EEG abnormalities within and outside the seizure onset areas, patients' values were adjusted based on normative maps derived from the open‐access Montreal Neurological Institute open iEEG Atlas. In a subset of 29 patients who underwent resective surgery, we estimated the predictive value of these features to identify the epileptogenic zone in those with a good postsurgical outcome.We found that distinct sleep oscillatory and nonoscillatory metrics behave differently across the epileptic network, with the strongest differences observed for (1) a reduction in spindle activity (spindle rates and rhythmic sigma power in the 10–16 Hz band), (2) a higher rhythmic gamma power (30–80 Hz), and (3) a higher H exponent (steeper 1/f slope). As expected, epileptic spikes were also highest in the seizure onset areas. Furthermore, in surgical patients, the H exponent achieved the highest performance (balanced accuracy of .76) for classifying resected versus nonresected channels in good outcome patients.This work suggests that nonoscillatory components of the intracranial EEG signal could serve as promising interictal sleep candidates of epileptogenicity in patients with drug‐resistant epilepsy. Our findings further advance the understanding of epilepsy as a disease, whereby absence or loss of sleep physiology may provide information complementary to pathological epileptic processes. [ABSTRACT FROM AUTHOR]

Objective Methods Results Interpretation There is currently scarce data on the electroclinical characteristics of epilepsy associated with synapsin 1 (SYN1) pathogenic variations. We examined clinical and electro‐encephalographic (EEG) features in patients with epilepsy and SYN1 variants, with the aim of identifying a distinctive electroclinical pattern.In this retrospective multicenter study, we collected and reviewed demographic, genetic, and epilepsy data of 19 male patients with SYN1 variants. Specifically, we analyzed interictal EEG data for all patients, and electro‐clinical data from 10 epileptic seizures in 5 patients, using prolonged video‐EEG monitoring recordings. Inter‐ictal EEG functional connectivity parameters and frequency spectrum of the 10 patients over 12 years of age, were computed and compared with those of 56 age‐ and sex‐matched controls.The main electroclinical features of epilepsy in patients with SYN1 were (1) EEG background and organization mainly normal; (2) interictal abnormalities are often rare or not visible on EEG; (3) more than 60% of patients had reflex seizures (cutaneous contact with water and defecation being the main triggers) isolated or associated with spontaneous seizures; (4) electro‐clinical semiology of seizures was mainly temporal or temporo‐insulo/perisylvian with a notable autonomic component; and (5) ictal EEG showed a characteristic rhythmic theta/delta activity predominating in temporo‐perisylvian regions at the beginning of most seizures. Comparing patients with SYN1 to healthy subjects, we observed a shift to lower frequency bands in power spectrum of interictal EEG and an increased connectivity in both temporal regions.A distinct epilepsy syndrome emerges in patients with SYN1, with a rather characteristic clinical and EEG pattern suggesting predominant temporo‐insular involvement. ANN NEUROL 2024 [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Among motor vehicle crashes (MVCs), little is known about whether the characteristics and collision features involving drivers with epilepsy differ from those involving drivers without any history of epilepsy. We assessed MVC features and the effect of epilepsy diagnosis on the risk of severe crash‐related injuries among drivers.A total of 33 174 MVC events among people with epilepsy (PWE) and 663 480 MVC events of age‐ and sex‐matched non‐PWE (1:20) were selected. Crash‐related features that involved drivers with and without epilepsy were compared, including driver eligibility, medical history of comorbidities and medications, road and environmental conditions, and accident causes. Cox and logistic regression analyses were used to examine the risks of fatality and severe injury among drivers with and without epilepsy.PWE involved in MVCs were more likely to have lower socioeconomic status, comorbidities, scooter drivers without a qualified driver's license, driving under the influence of alcohol, and be involved in single‐vehicle accidents than non‐PWE. Drivers with epilepsy also had a higher risk of fatality within 30 days of MVC, with an adjusted hazard ratio (aHR) of 1.37 (95% confidence interval [CI], 1.20–1.57) and a higher risk of hospital admission within 3 days after MVC (aHR, 1.33; 95% CI, 1.29–1.38) compared to that of non‐PWE.The characteristics of MVCs of drivers with epilepsy were distinct from those of non‐affected drivers. And higher fatality and injury rates were observed among drivers with epilepsy, which should be considered in further policymaking regarding safe driving of PWE. [ABSTRACT FROM AUTHOR]

The prevalence of epilepsy is highest in the Central Africa subregion most especially in rural settlements. This is further compounded by a high prevalence of treatment gap, from poor drug adherence, and poor road network. One approach to overcoming this burden is to leverage Video-based Directly Observed Therapy (ViDOT) use for stable patients living with epilepsy, harnessing the advancement in mHealth technologies, and widespread adoption of smartphones. ViDOT is a form of telemedicine that is a smartphone-based, treatment approach for monitoring and evaluating remote patients.We performed a narrative review of the existing literature using electronic databases from PubMed and Google Scholar to identify relevant publications related to ViDOT and chronic diseases, in particular epilepsy.The selected articles were assessed for relevance, and key findings were synthesized to provide an overview of the role of ViDOT in addressing the challenges of drug adherence in epilepsy.The review shows that poor drug adherence is a major risk for poor outcomes in patients living with epilepsy. ViDOT has the potential to significantly contribute to improved drug adherence in patients living with epilepsy. Other benefits of ViDOT include the elimination of prolonged waiting times, improvement in access to specialized care, eradication of self-induced stigma and discrimination, diminishing neurologists’ workload, and cost-effectiveness. Challenges include poor network coverage and unstable internet service.Giving a brief psychoeducation about epilepsy, anti-epileptic drug (AED) side effects, and the importance of sticking with the recommended drug use can improve AED adherence and epilepsy outcomes. Information dissemination through ViDOT to people with epilepsy and their caregivers at large is important to promote a healthy life. [ABSTRACT FROM AUTHOR]

Background: During the Coronavirus disease pandemic "COVID-19", epilepsy was one of many chronic neurological diseases in which treatment was neglected. This study aimed to evaluate the impact of the pandemic on people with epilepsy and identify potential predictors of seizure worsening through a face-to-face hospital survey. During the study period, 245 participants were recruited: 124 people with epilepsy (PwE) and 121 people without epilepsy (PwoE) age and sex-matched. Both groups were compared using sociodemographic COVID-19-related questionnaires and Hamilton Anxiety and Depression scales. PwE also completed epilepsy-specific questionnaires. Subsequently, we subdivided the PwE group into people with worsening epilepsy (WPwE) and those without (NWPwE). Results: Compared with PwoE, PwE had significantly higher rates of COVID-19 infection (59.7% versus 41.3%, p = 0.004), and 69.4% of them (86/124) reported WPwE. WPwE had significantly higher rates of COVID-19 infection (75.6% versus. 23.7%, p < 0.0001), emergency room visits (69.8% versus 42.1%, p = 0.004), delayed neurology appointments (69.8% versus 42.1%, p = 0.004), and difficulties accessing medication (69.8% versus 47.4%, p = 0.02) and being less likely to be vaccinated (39.5% versus 68.4%, p = 0.003) than NWPwE. Depression and anxiety rates increased significantly during the pandemic compared with prior pandemics in both PwE and PwoE (p < 0.0001 for each). Moreover, the WPwE showed a significant increase in depression rates (33.7–60.5%, p < 0.0001) and higher mean anxiety scores compared to the NWPwE (p = 0.029). A multivariate binary logistic regression analysis showed that having a COVID-19 infection (AOR: 12.086, p < 0.0001), being laid off (AOR: 0.024, p = 0.001), or having more seizures before the pandemic (AOR: 3.366, p = 0.009) were all strong predictors of seizures getting worse. Conclusions: Nearly 69% of PwE experienced pandemic-related seizures worsening, along with deterioration of mental health. Factors such as personal COVID-19 infection, unemployment, work interruption, and higher pre-COVID seizure frequency were identified as key predictors of seizure worsening. Mitigating these predictors could strengthen resilience among PwE during future widespread crises. Trial registration clinicaltrials.gov, NCT05205590. Registered on October 25, 2021—Retrospectively registered, https://classic.clinicaltrials.gov/ct2/show/NCT05205590 [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures. [ABSTRACT FROM AUTHOR]

Patients with epilepsy face heightened risk of post-ictal cardiorespiratory suppression and sudden unexpected death in epilepsy (SUDEP). Studies have shown that neuroinflammation, mediated by the activation of microglia and astrocytes, may be a cause or consequence of seizure disorders. Kcnj16 (Kir5.1) knockout rats (SSkcnj16-/-) are susceptible to repeated audiogenic seizures and recapitulate features of human SUDEP, including post-ictal ventilatory suppression, which worsens with repeated seizures and seizureinduced mortality. In this study, we tested the hypothesis that repeated seizures cause neuroinflammation within key brainstem regions that contribute to the control of breathing. Audiogenic seizures were elicited once/day for up to 10 days in groups of adult male SSkcnj16-/- rats, from which frozen brainstem biopsies of the pre-Bötzinger complex/nucleus ambiguus (preBötC/NA), Bötzinger complex (BötC), and raphe magnus (RMg) regions were subjected to a cytokine array. Several cytokines/chemokines, including IL-1α and IL-1ß, were increased selectively in preBötC/NA after 3 or 5 days of seizures with fewer changes in other regions tested. In additional groups of male SSkcnj16-/- rats that underwent repeated seizures, we quantified microglial (IBA-1+) cell counts and morphology, specifically within the preBötC/NA region, and showed increased microglial cell counts, area, and volume consistent with microglial activation. To further test the role of inflammation in physiological responses to seizures and seizure-related mortality, additional groups of SSkcnj16-/- rats were treated with anakinra (IL-1R antagonist), ketoprofen (nonselective COX inhibitor), or saline for 3 days before and up to 10 days of seizures (1/day), and breathing was measured before, during, and after each seizure. Remarkably, IL-1R antagonism mitigated changes in post-ictal ventilatory suppression on days 7-10 but failed to prevent seizure-related mortality, whereas ketoprofen treatment exacerbated post-ictal ventilatory suppression compared to other treatment groups but prevented seizure-related mortality. These data demonstrate neuroinflammation and microglial activation within the key brainstem region of respiratory control following repeated seizures, which may functionally but differentially contribute to the pathophysiological consequences of repeated seizures. [ABSTRACT FROM AUTHOR]

Background: The relationship between epilepsy and risk of acute myocardial infarction (AMI) is not fully understood. Evidence from the Stockholm Heart Study indicates that the risk of AMI is increased in people with epilepsy. This study aims to analyze the temporal trends in prevalence, adverse clinical outcomes, and risk factors of AMI in patients with epilepsy (PWE). Methods: Patients aged 18 years or older, diagnosed with epilepsy with or without AMI and hospitalized from January 1, 2008, to December 31, 2017, were identified from the National Inpatient Sample (NIS) database. The Cochran-Armitage trend test and logistic regressions were conducted using SAS 9.4. Odds ratios (ORs) were generated for multiple variables. Results: A total of 8,456,098 inpatients were eligible for our analysis, including 181,826 comorbid with AMI (2.15%). The prevalence of AMI diagnosis in PWE significantly increased from 1,911.7 per 100,000 hospitalizations in 2008 to 2,529.5 per 100,000 hospitalizations in 2017 (Ptrend < 0.001). Inpatient mortality was significantly higher in epilepsy patients with AMI compared to those without AMI (OR = 4.61, 95% CI: 4.54 to 4.69). Factors significantly associated with AMI in PWE included age (=75 years old vs. 18 ~ 44 years old, OR = 3.54, 95% CI: 3.45 to 3.62), atherosclerosis (OR = 4.44, 95% CI: 4.40 to 4.49), conduction disorders (OR = 2.21, 95% CI: 2.17 to 2.26), cardiomyopathy (OR = 2.11, 95% CI: 2.08 to 2.15), coagulopathy (OR = 1.52, 95% CI: 1.49 to 1.54), dyslipidemia (OR = 1.26, 95% CI: 1.24 to 1.27), peptic ulcer disease (OR = 1.23, 95% CI: 1.13 to 1.33), chronic kidney disease (OR = 1.23, 95% CI: 1.22 to 1.25), smoking (OR = 1.20, 95% CI: 1.18 to 1.21), and weight loss (OR = 1.20, 95% CI: 1.18 to 1.22). Conclusion: The prevalence of AMI in PWE increased during the decade. Mortality rates were high among this population, highlighting the need for comprehensive attention to prophylaxis for risk factors and early diagnosis of AMI in PWE by physicians. [ABSTRACT FROM AUTHOR]

Introduction: Pharmacotherapy with antiseizure medications (ASMs) has been a cornerstone for achieving long-term remissions in persons with epilepsy (PWEs). This study aims to determine the prescription patterns and treatment gaps (TGs) among PWEs. Methods: Accordingly, a descriptive cross-sectional study was conducted with 940 PWEs aged =18 years having clinically confirmed diagnosis of epilepsy based on the International League Against Epilepsy (ILAE) criteria. At a scheduled interview with each participant, a previously established questionnaire was used to obtain clinical information relating to epilepsy in terms of the age of onset, etiology, duration of epilepsy, frequency, types, and number of ASMs used. Results: There were fewer male participants [445 (47.4%) vs. 495 (53.6%)] than females, with a higher mean age of onset [(35.19 ± 21.10 vs. 31.58 ± 20.82 years; p = 0.009]. The medication characteristics showed that 336 (35.7%) of the 940 PWEs recruited were not on any ASMs, whereas the remaining 604 (64.3%) patients were on ASMs, with 504 (83.4%) on monotherapy vs. 100 (16.6%) on polytherapy. The PWEs on ASM monotherapy had a higher mean age [40.92 ± 19.40 vs. 33.61 ± 16.51 years; p < 0.001] and higher mean age of onset [34.47 ± 21.80 vs. 25.39 ± 19.78 years; p < 0.001] than those on polytherapy. Furthermore, there were more persons on ASM monotherapy among the participants with seizure duration < 2 years [251 (87.5%) vs. 36 (12.5%)] and seizure duration > 2 years [253 (79.8%) vs 64 (20.2%)]. Conclusion: The majority of the participants receiving ASMs were on monotherapy, with carbamazepine being the most frequently prescribed medication. Furthermore, about a third of the participants had TGs; therefore, healthcare providers should focus on alleviating the TGs among PWEs. [ABSTRACT FROM AUTHOR]

Objective To master the information of patients with convulsion epilepsy in phenobarbital in rural epilepsy prevention and management project of Heilongjiang Province - understand the reasons for the withdrawal, analyze the influencing factors, and provide reference for improving the patient retention rate and work quality. Methods From 2011 to 2020, 3 890 convulsion epilepsy patients received phenobarbital treatment in Heilongjiang Province, of which 807 patients were enrolled. For 807 patients, EpiData, EXCEL and SPSS 19. 0 software were used for data entry, processing and statistical analysis of the influencing factors in the withdrawal group, including analysis of variance, univariate analysis, and multivariate survival analysis. Results 3 890 patients with convulsive epilepsy enrolled in phenobarbital treatment group in rural epilepsy project of Heilongjiang Province, 807 cases dropped out successively, the dropout rate was 20.75%, the primary factors contributing to dropout were relocation, noncompliance with medication, and self - perceived or actual remission. Multivariate Cox regression analysis showed that female patients (HR = 1. 278, 95% CI: 1. 112 - 1. 468) were the risk factors for dropping out, and the probability of dropping out in female patients was 1. 278 times that of male patients. Conclusion The withdrawal rate of phenobarbital epilepsy treatment in rural areas of Heilongjiang Province is relatively high. In the later management work, the publicity and education of epilepsy prevention and treatment knowledge should be strengthened to provide personalized treatment programs and improve the treatment efficiency, especially for female epilepsy patients. [ABSTRACT FROM AUTHOR]

Background: There is no standardized EEG duration guideline for detecting epileptiform abnormalities in patients, and research on this topic is scarce. This study aims to determine an optimal EEG duration for efficient detection of epileptiform abnormalities across different patient groups. Methods: Retrospective analysis was performed on EEG recordings and clinical data of patients with the first seizure and epilepsy. Patients were categorized based on various factors, including the interval time since the last seizure, use of anti-seizure medication (ASM), and seizure frequency. The detection ratio (DR) of epileptiform abnormalities and latency time for their discovery were calculated. Statistical analyses, including chi-square tests, logistic regression, and survival analysis were utilized to illustrate DR and latency times. Results: In whole-night EEG recordings, the DR was 37.6% for the first seizure group and 57.4% for the epilepsy group. Although the maximum latency times were 720 min in both two groups, DR in the first seizure group was distinctly decreased beyond 300 min. Significant factors influencing the DR included the use of ASM in the first seizure group (P < 0.05) and seizure frequency in the epilepsy group (P < 0.001). For epilepsy patients who experience a seizure at least once a month or undergo timely EEG recordings (within 24 h after a seizure), the DR significantly increases, and the maximum latency time is reduced to 600 min (P < 0.001). Additionally, the DR was significantly reduced after 240 min in epilepsy patients who had been seizure-free for more than one year. Conclusions: In this retrospective study, we observed a maximum latency of 720 min for detecting epileptiform abnormalities in whole-night EEG recordings. Notably, epilepsy patients with a higher seizure frequency or timely EEG recordings demonstrated both a higher detection ratio and a shorter maximum latency time. For patients exhibiting a low detection ratio, such as those experiencing their first seizure or those with epilepsy who have been seizure-free for more than a year, a shorter EEG duration is recommended. These findings underscore the importance of implementing customized EEG strategies to meet the specific needs of different patient groups. [ABSTRACT FROM AUTHOR]

This study aims to develop a detection method based on morphological features of spike–wave (SW) patterns in the EEG of epilepsy patients and evaluate the effect of cathodal transcranial direct current stimulation (ctDCS) treatment. The proposed method is based on several simple features describing the shape of SW patterns and their synchronous occurrence on at least two EEG channels. High sensitivity, specificity and selectivity values were achieved for each patient and condition. ctDCS resulted in a significant reduction in the number of detected patterns, a decrease in spike duration and amplitude, and an increased spike mobility. The proposed method allows efficient identification of SW patterns regardless of brain condition, although the recruitment of patterns may be modified by ctDCS. This method can be useful in the clinical evaluation of ctDCS effects. [ABSTRACT FROM AUTHOR]

Background: Previous structural neuroimaging studies linked cerebellar deficits to temporal lobe epilepsy (TLE). The functions of various cerebellar regions are increasingly being valued, and their changes in TLE patients warrant further indepth investigation. In this study, we used the Spatially Unbiased Infratentorial (SUIT) toolbox with a new template to evaluate the cerebellar structural abnormalities in patients with TLE, and further explored the relationship between the changes of different cerebellar regions and cognition. Methods: Thirty-two patients with TLE were compared with 39 healthy controls (HC) matched according to age, gender, handedness, and education level. All participants underwent a high-resolution T1-weighted MRI scan on a 3.0 Tesla scanner. We used a voxel-based morphometry (VBM) approach utilizing the SUIT toolbox to provide an optimized and fine-grained exploration of cerebellar structural alterations associated with TLE. Results: Compared with HC, TLE patients showed a significant reduction in the volume of gray matter in the Left lobule VI and white matter in the Right Crus II. In the TLE patient group, we conducted partial correlation analysis between the volumes of different cerebellar regions and cognitive rating scale scores, such as MMSE and MoCA. The volume of the Left lobule VI (GM) exhibited a positive correlation with the MMSE score, but no significant correlation was found with the MoCA score. On the other hand, there was no significant correlation observed between the volume of the Right Crus II (WM) and the two cognitive scale scores mentioned above. Furthermore, it was observed that the MMSE was more effective than the MoCA in identifying epilepsy patients with cognitive impairment. Conclusion: This study supported previous research indicating that temporal lobe epilepsy (TLE) is linked to structural changes in the cerebellum, specifically affecting the volume of both gray and white matter. These findings offer valuable insights into the neurobiology of TLE and hold potential to inform the development of enhanced diagnostic methods and more effective treatment approaches. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to examine differences in factors influencing quality of life (QOL) in people with extratemporal lobe epilepsy (ETLE) and mesial temporal lobe epilepsy (MTLE). Methods: We obtained data from the medical records of 84 (47 ETLE and 37 MTLE) people with epilepsy. The data included age, sex, employment, seizure frequency, number of antiseizure medication (ASM), Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) score, and Quality of Life in Epilepsy Inventory-31 (QOLIE-31) score. Multiple regression analyses were performed using QOLIE-31 as the dependent variable and age, sex, employment, seizure frequency, number of ASM, and NDDI-E score as the independent variables in ETLE or MTLE. Results: From the results of the multiple regression analyses, QOLIE-31 in ETLE was associated with NDDI-E (β = -0.757, p < 0.001) and employment (β = 0.388, p = 0.008). Meanwhile, QOLIE-31 in MTLE was associated with NDDI-E (β = -0.625, p < 0.001), employment (β = 0.396, p = 0.041), and number of ASMs (β = -0.399, p = 0.018). Conclusion: Overall, our findings indicate that the number of ASMs is potentially an influence on QOL of MTLE but similar effect is not observed in ETLE. [ABSTRACT FROM AUTHOR]

Epilepsy is defined by the abrupt emergence of harmful seizures, but the nature of these regime shifts remains enigmatic. From the perspective of dynamical systems theory, such critical transitions occur upon inconspicuous perturbations in highly interconnected systems and can be modeled as mathematical bifurcations between alternative regimes. The predictability of critical transitions represents a major challenge, but the theory predicts the appearance of subtle dynamical signatures on the verge of instability. Whether such dynamical signatures can be measured before impending seizures remains uncertain. Here, we verified that predictions on bifurcations applied to the onset of hippocampal seizures, providing concordant results from in silico modeling, optogenetics experiments in male mice and intracranial EEG recordings in human patients with epilepsy. Leveraging pharmacological control over neural excitability, we showed that the boundary between physiological excitability and seizures can be inferred from dynamical signatures passively recorded or actively probed in hippocampal circuits. Of importance for the design of future neurotechnologies, active probing surpassed passive recording to decode underlying levels of neural excitability, notably when assessed from a network of propagating neural responses. Our findings provide a promising approach for predicting and preventing seizures, based on a sound understanding of their dynamics. Understanding the sudden regime shifts leading to epileptic seizures is crucial for developing preventive measures. This research reveals that subtle dynamical changes can indicate impending seizures, and highlights the effectiveness of active probing over passive recording in assessing neural excitability in hippocampal circuits. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance To describe the characteristics of psychogenic non‐epileptic (functional) seizures (PNES) in adults with epilepsy and intellectual disability (ID) and to establish differences and risk factors regarding psychosocial functioning between individuals with and without PNES.Adults with ID and epilepsy living in epilepsy care facilities in The Netherlands were screened for PNES by a neurologist. A control group consisting of people with epilepsy and ID, without PNES, was matched according to age, sex, and level of ID. Objective data were retrieved retrospectively from clinical notes of the resident. Standardized questionnaires and tests, adjusted for people with ID, were obtained from participants and their nursing staff. Differences were analyzed using paired t tests, Wilcoxon signed‐rank tests, or McNemar's tests, appropriate for matched case–control studies. Conditional logistic regression identified PNES risk factors.Five hundred forty individuals were screened, of which 42 had PNES (point prevalence 7.8%). In total, 35 cases and 35 controls gave consent. Proxy reports indicated that PNES impacted daily life in 79% by adjusting the individual's schedule, and caused minor injuries in one‐third. Those with PNES were mainly female (69%); had a mild (46%) or moderate (37%) level of ID; showed more symptoms of depression (p = .024), anxiety (p = .030), self‐injurious behavior (p = .015); and experienced more negative life events (p < .001). Clinically relevant predictors of PNES were the number of negative life events (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.12–2.53) and self‐injurious behaviors (OR 5.27, 95% CI .97–28.81).Previously, PNES in individuals with ID and epilepsy were described mainly as a reinforced behavioral pattern, due to limited associations with psychiatric disorders. Our results demonstrate that this population does show individual psychosocial vulnerabilities when measured with instruments adjusted for this population, as indicated by proxy reports from daily caregivers. Viewing PNES as an involuntary response, especially for stress‐prone individuals with ID, could reduce stigma and improve treatment. [ABSTRACT FROM AUTHOR]

Epileptiform abnormalities that arise over the midline can sometimes be confused with normal sleep transients, such as vertex sharp waves, because of their location and their activation during sleep. However, epileptiform transients can be distinguished from sleep architecture by their waveform and their occurrence during wakefulness. Here, we report a 24-year-old man with drug-resistant epilepsy whose seizures began with tonic posturing of the left leg before progressing to bilateral tonic-clonic activity. During presurgical scalp video-EEG monitoring, his interictal background showed focal spike-wave discharges maximal over the vertex (phase reversal at Cz), with a more-well-defined field over the right parasagittal region (C4/F4), that were present during both sleep and awake states. The discharges met the IFCN criteria for focal interictal epileptiform discharges (spiky morphology, duration shorter than background activity, asymmetric waveform, after-going slow wave, and physiologic distribution) and appeared to be distinct from the patient’s vertex sharp waves. Prior to electroclinical seizures, these discharges would increase in prevalence and appear as repetitive spike-wave discharges. When distinguishing epileptiform from nonepileptiform transients, it is critical to consider both their morphology, especially the degree of background disruption and presence of an after-going slow wave, and their variability with state changes. [ABSTRACT FROM AUTHOR]

Objective: There is ongoing debate regarding the association between epilepsy and obesity. Thus, the aim of this study was to examine the correlation between epilepsy and obesity. Method: This study adhered to the PRISMA guidelines for systematic reviews and meta-analyses. On The Prospero website, this study has been successfully registered (CRD42023439530), searching electronic databases from the Cochr-ane Library, PubMed, Web of Sciences and Embase until February 10, 2024.The search keywords included "Epilepsy", "Obesity", "Case-Control Studies", "cohort studies", "Randomized Controlled Trial" and "Cross-Sectional Studies". The medical subject headings(MeSH) of PubMed was utilized to search for relevant subject words and free words, and a comprehensive search strategy was developed. Two reviewers conducted article screening, data extraction and bias risk assessment in strict accordance with the predefined criteria for including and excluding studies. The predefined inclusion criteria were as follows: 1) Inclusion of case-control, cohort, randomized controlled trial, and cross-sectional studies; 2) Segregation of subjects into epileptic patients and healthy controls; 3)Obesity as the outcome measure; 4) Availability of comprehensive data; 5) Publication in English. The exclusion criteria were as follows: 1) Exclusion of animal experiments, reviews, and other types of studies; 2) Absence of a healthy control group; 3) Incomplete data; 4) Unextractable or unconvertible data; 5) Low quality, indicated by an Agency for Healthcare Research and Quality(AHRQ) score of 5 or lower,or a Newcastle-Ottawa Scale (NOS) score less than 3. The subjects included in the study included adults and children, and the diagnostic criteria for obesity were used at different ages. In this study, obesity was defined as having a body mass index(BMI) of 25 kg/m2 or higher in adults and being above the 85th percentile of BMI for age in children. We used obesity as an outcome measure for meta-analysis using RevMan, version 5.3. Results: A meta-analysis was conducted on a total of 17 clinical studies, which involved 5329 patients with epilepsy and 480837 healthy controls. These studies were selected from a pool of 1497 articles obtained from four electronic databases mentioned earlier. Duplicate studies were removed based on the search strategies employed. No significant heterogeneity was observed in the outcome measure of obesity in epileptic patients compared with healthy controls(p = 0.01,I2 = 49%). Therefore, a fixed effects model was utilized in this study. The findings revealed a significant difference in obesity prevalence between patients with epilepsy and healthy controls(OR = 1.28, 95%CI: 1.20–1.38, p<0.01). Conclusion: The results of this meta-analysis indicate that epilepsy patients are more prone to obesity than healthy people, so we need to pay attention to the problem of post-epilepsy obesity clinically. Currently, there is a scarcity of largescale prospective studies. Additional clinical investigations are warranted to delve deeper into whether obesity is a comorbidity of epilepsy and whether obesity can potentially trigger epilepsy. [ABSTRACT FROM AUTHOR]

Background: Drug-resistant epilepsy is defined as failure of seizure control in spite of using 2 or 3 proper antiepileptic drugs in appropriate time. Mineral elements play important roles in neuronal function; it is believed that mineral deficiency may lead to complications through seizure management. In the present study, serum levels of zinc (Zn), copper (Cu), magnesium (Mg), calcium (Ca), and 25-hydroxy vitamin D (Vit D) in drug-resistant-epilepsy (DRE) patients were evaluated and compared with the controlled patients. Methods: In this cross-sectional study, epileptic patients were included and categorized into two groups of DRE and well-controlled patients. Patients' serum samples were analysed to evaluate Zn, Cu, Mg, Ca, and Vit D levels. The primary objective was comparison of serum levels of different trace elements between the groups. Results: Sixty-four epileptic children including 33 DRE and 31 well-controlled children entered the study. The DRE children showed a significantly earlier onset of disease compared to the other group (p = 0.014). Comparing the frequency of developmental delay between the groups, the results showed this complication was significantly more frequent in the DRE group (p < 0.001). Concerning serum elements, the results showed a significantly higher concentration of Zn in the well-controlled group than the DRE group (p = 0.007). On the other hand, no significant differences were observed between the groups regarding the means of Vit D, Ca, Cu, and Mg levels (p > 0.05). Conclusion: The results of the present study delineated that drug-resistant epilepsy patients had earlier onset of disease and were at higher risk of neurodevelopmental delay compared with well-controlled-epilepsy patients. A significant lower serum levels of Zn were also observed in drug-resistant-epilepsy patients. This finding may suggest the role of zinc supplementation in help to better control of drug-resistant seizures, as well as, the importance of serum zinc monitoring in epileptic patients. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Neurobehavioural comorbidities have a detrimental effect on the quality of life of people with epilepsy, yet tracking their impact is challenging as behaviour may vary with seizures and anti‐seizure medication (ASM) side effects. Smartphones have the potential to monitor day‐to‐day neurobehavioural patterns objectively. We present the case of a man in his late twenties with drug‐resistant focal epilepsy in whom we ascertained the effects of ASM withdrawal and a convulsive seizure on his touchscreen interactions.Using a dedicated app, we recorded over 185 days the timestamps of 718,357 interactions. We divided the various smartphone behaviours according to the next‐interval dynamics of the interactions by using a joint interval distribution (JID). During two ASM load transitions, namely before versus during tapering and tapering versus restarting medication, we used cluster‐based permutation tests to compare the JIDs. We also compared the JID of the seizure day to the average of the previous 3 days.The cluster‐based permutation tests revealed significant differences, with accelerated next‐interval dynamics during tapering and a reversal upon medication restart. The day of the convulsion exhibited a marked slowing of next‐interval dynamics compared to the preceding 3 days.Our findings suggest that the temporal dynamics of smartphone touchscreen interactions may help monitor neurobehavioural comorbidities in neurological care. [ABSTRACT FROM AUTHOR]

Background and purpose Methods Results Conclusion Prior studies highlighted the high diagnostic specificity (ranging from 92% to 100%) of clinical signs observed in functional neurological disorders (FNDs). However, these signs are rarely looked for by epileptologists when trying to distinguish between functional dissociative seizure (FDS) and epileptic seizure. The aim of this study was to determine the prevalence of inter‐ictal clinical signs of FND in a cohort of patients with probable FDS. The secondary objective was to compare the prevalence of inter‐ictal FND clinical signs in FDS patients with age‐ and gender‐matched epileptic patients without FDS.Patients diagnosed with FDS seen at two tertiary care centres and epileptic outpatients were included in the study. Each patient underwent a physical examination, searching for inter‐ictal clinical signs of FND.In the FDS group, 79% of patients presented at least one sign of FND, compared to 16.6% of patients with epilepsy (p < 0.001). Moreover, 66.6% of FDS patients presented three or more FND signs, whereas only 4.1% of epileptic patients did (p < 0.001). The median number of FND clinical signs in the FDS group was four (SD 1.7; 5.5). Using the threshold of three signs or more, the specificity of detecting three or more FND signs was 83.3%, with a sensitivity of 79.2%.Inter‐ictal clinical signs of FND are present in patients with FDS and should be looked for during neurological examination. [ABSTRACT FROM AUTHOR]

Introduction: Functional magnetic resonance imaging (fMRI) can be used to assess language and memory function as part of pre-surgical decision making in refractory epilepsy. Although language paradigms are well established, memory paradigms are not widely used in clinical practice due to a lack of evidence for robust and reliable methods. Here, we aim to investigate the clinical utility of the Home Town Walk (HTW) paradigm for personalized treatment decisions in medial temporal lobe epilepsy. Methods: A cohort of 123 consecutive patients having HTW-fMRI as part of routine MRI scans over a 7.5 year period were included in this retrospective study. Of these, 111 patients underwent repeated HTW-fMRI in two scanning sessions one to three days apart. fMRI analysis was performed at the time of the scans using clinically approved software and retrospectively validated using FSL. We assessed the test-retest within subject reliability of activations within the posterior parahippocampal gyri (pPHG) at the individual subject level. Results and discussion: Activations within the pPHG region were observed for 101 patients (91%) in at least one of the fMRI sessions and for 88 patients (79%) in both fMRI sessions, with 82 patients showing overlapping unilateral or bilateral activations and 8 further patients showing overlapping activations in one of the hemispheres but not the other. Reproducibility was evaluated using metrics based on the concordance ratios for size (Rsize) and location (Roverlap) within the pPHG region, as well as the lateralization index (LI) metric to reflect the asymmetry of hemispheric activations, which is of crucial relevance to inform surgery. Test-retest reliability of visuospatial memory LIs, assessed by an intraclass correlation coefficient (ICC) yielded a value of 0.76, indicating excellent between session stability of memory lateralization. Conclusion: The HTW-fMRI paradigm shows reproducible activations in the medial temporal lobes of individual epilepsy patients sufficient to consistently lateralize visuospatial memory function, demonstrating the clinical utility of HTW memory fMRI and its potential for application in the pre-surgical assessment of people with temporal lobe epilepsy. [ABSTRACT FROM AUTHOR]

Introduction: In South Africa, approximately half a million individuals live with epilepsy. This means that half a million families and caregivers are impacted by epilepsy, with a limited number of healthcare providers responsible for treating people living with the condition, as well as their families and caregivers. Methods: This study explored the knowledge-based care and support healthcare providers give families and caregivers. Fifteen participants were purposefully selected from Limpopo and Mpumalanga provinces to participate in the study. Data were collected via an open-ended interview guide divided into two sections: Section A comprised sociodemographic questions, and Section B had questions on epilepsy care and support. Four of the participants were male, and 11 were female, seven were community health workers, five were nurse practitioners, and three were auxiliary nurses. Seven had a grade 12 qualification or lower, and only six had a degree. The data collected was analyzed using thematic analysis, coded by the researcher and co-coded by an independent expert. Two themes emerged from the raw data: epilepsy knowledge and epilepsy support and counselling. From these two themes, three subthemes were identified: psychosocial impact of epilepsy, epilepsy-related training, and counselling and support. Results: The study revealed a gap in professional capacity building and highlighted the need for intentional knowledge sharing and equipping of healthcare providers. Discussion: The findings suggest that equipping community health workers, in particular, may be a better and more efficient way to increase the quality of life for families and caregivers and people living with epilepsy in South Africa. [ABSTRACT FROM AUTHOR]

Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86 M and the number of parameters is 0.57 M. [ABSTRACT FROM AUTHOR]

Epileptic spasms without hypsarrhythmia occur when patients do not display hypsarrhythmia on electroencephalogram (EEG) at the onset and throughout the clinical course. We report three patients of epileptic spasms in patients with early onset, all of whom experienced other types of seizures. We detail three patients (two boys and one girl) of epileptic spasms without hypsarrhythmia, occurring between 1 and 3 months of age, with no abnormalities detected on neurometabolic analysis and brain magnetic resonance imaging. Long-term video-EEG monitoring revealed epileptic spasms with focal onset seizures in two patients, and epileptic spasms followed by generalized tonic-clonic seizures in one patient. Hypsarrhythmia was never observed in repeated EEG examinations. Two patients achieved seizure freedom and improved development through treatment with topiramate alone or in combination with valproate, without requiring hormonal therapies or vigabatrin. The remaining patient achieved seizure freedom following administration of antiseizure medications, including topiramate, after a trial of adrenocorticotropic hormone therapy. We report the cases of three patients with early onset epileptic spasms without hypsarrhythmia. All patients achieved seizure freedom after topiramate treatment. Topiramate may be considered as a relatively effective antiseizure medication for early onset epileptic spasms without hypsarrhythmia. [ABSTRACT FROM AUTHOR]

Although there are established connections between genetic epilepsies and neurodevelopmental disorders like intellectual disability, the presence of cerebral palsy (CP) in genetic epilepsies is undercharacterized. We performed a retrospective chart review evaluating the motor phenotype of patients with genetic epilepsies. Patients were ascertained through a research exome sequencing study to identify genetic causes of epilepsy. We analyzed data from the first 100 individuals with molecular diagnoses. We determined motor phenotype by reviewing medical records for muscle tone and motor function data. We characterized patients according to CP subtypes: spastic diplegic, spastic quadriplegic, spastic hemiplegic, dyskinetic, hypotonic-ataxic. Of 100 individuals with genetic epilepsies, 14% had evidence of possible CP, including 5% characterized as hypotonic-ataxic CP, 5% spastic quadriplegic CP, 3% spastic diplegic CP, and 1% hemiplegic CP. Presence of CP did not correlate with seizure onset age (P = 0.63) or seizure control (P = 0.07). CP occurred in 11% (n = 3 of 27) with focal epilepsy, 9% (n = 5 of 54) with generalized epilepsy, and 32% (n = 6 of 19) with combined focal/generalized epilepsy (P = 0.06). In this retrospective analysis of patients with genetic epilepsies, we identified a substantial portion with CP phenotypes, representing an under-recognized comorbidity. These findings underscore the many neurodevelopmental features associated with neurogenetic conditions, regardless of the feature for which they were ascertained for sequencing. Detailed motor phenotyping is needed to determine the prevalence of CP and its subtypes among genetic epilepsies. These motor phenotypes require clinical management and represent important targeted outcomes in trials for patients with genetic epilepsies. [ABSTRACT FROM AUTHOR]

• In the time–frequency analysis, P25 and N35 showed power increases of high-frequency activities in comparison to baseline activities, while P50 showed a power decrease. • Short-latency of giant SEPs (P25 and N35) reflect evoked paroxysmal depolarization shifts. • Middle latency of giant SEPs (P50) reflects the inhibitory component. Giant somatosensory evoked potentials (SEPs) are observed in patients with cortical myoclonus. Short-latency components (SLC), are regarded as evoked epileptic activities or paroxysmal depolarization shifts (PDSs). This study aimed to reveal the electrophysiological significance of the middle-latency component (MLC) P50 of the SEPs. Twenty-two patients with cortical myoclonus having giant SEPs (patient group) and 15 healthy controls were included in this study. Waveform changes in SEPs before and after perampanel (PER) treatment were evaluated in the patient group. The wide range, time–frequency properties underlying the waveforms were compared between the groups. After PER treatment, SLC was prolonged and positively correlated with PER concentration, whereas MLC showed no correlation with PER concentration. Time–frequency analysis showed a power increase (156 Hz in all patients, 624 Hz in benign adult familial myoclonus epilepsy patients) underlying SLC and a power decrease (156 Hz, 624 Hz) underlying MLC in the patient group. The high-frequency power increase in SLCs and decrease in MLCs clearly reflected PDS and subsequent hyperpolarization, respectively. This relationship was similar to that of interictal epileptiform discharges, suggesting that giant SEPs evoke epileptic complexes of excitatory and inhibitory components. MLCs of giant SEPs reflected inhibitory components. [ABSTRACT FROM AUTHOR]

• Seizure onset zone localization accuracy based on HFO rate was comparable for visual and automated HFO detection. • Optimizing automated HFO detection settings based on visual markings did not increase seizure onset zone localization accuracy. • For many patients, changing detection settings could improve SOZ localization accuracy, but new optimization methods are needed. High frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. We detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. SOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. SOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. Visual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed. [ABSTRACT FROM AUTHOR]

• Bilateral somatosensory evoked spikes (SESs) (previously reported only in children) were detected in spontaneous MEG recordings of 10/30 adult subjects. • The first peak of SESs was considered to be identical to the somatosensory evoked field (SEF) M2 in latency, equivalent current dipole (ECD) position, and ECD orientation. • M2 amplitude of SEFs was significantly greater in subjects with SESs. Somatosensory evoked spikes (SESs) have been reported only in children aged under 14 years and are considered as an age-dependent phenomenon. However, we detected SESs in adult patients with epilepsy using magnetoencephalography (MEG). The present study investigated whether MEG can detect SESs in normal adults. Spontaneous MEG was recorded during measurement of somatosensory evoked fields (SEFs) for bilateral electrical median nerve stimuli in 30 healthy adults. Bilateral SESs were observed in 10 adults but none in the other 20 subjects. SESs consisted of one or two peaks, and the first peak latency corresponded to that of the second peak (M2) of SEFs. The first SES peak was identical to the M2 in isofield map pattern, as well as location and orientation of the equivalent current dipole (ECD). M2 ECD strength in the 10 subjects with SESs was larger (p <0.0001) than in 20 without SESs. All-or-nothing detection of bilateral SESs by MEG in normal adults must depend on the signal-to-noise issue of symmetrical SEFs and background brain activity. Our results further confirm the higher sensitivity of MEG compared to scalp EEG for the detection of focal cortical sources tangential to the scalp such as SESs. [ABSTRACT FROM AUTHOR]

Objective: Cognitive, mood, and behavioral changes are common among persons with epilepsy (PWE), resulting in a complex neuropsychological presentation. Women with epilepsy (WWE) represent a distinct cohort within the broader epilepsy population due to sex and gender-specific factors impacting epilepsy semiology and treatment. However, unique neuropsychological profiles among WWE have not been established. This narrative review aims to further define neuropsychological correlates in WWE and promote meaningful discussion related to enhancing the provision of neuropsychological care within this clinical population. Method: Current literature in PWE examining differences in cognitive function, mental health, and quality of life (QoL) between women and men was critically reviewed, emphasizing considerations for neuropsychological practice. Results: WWE demonstrate a preservation of verbal learning and memory compared to men both pre- and post-surgically, with sex-based, neurobiological mechanisms likely contributing to this association. WWE also have elevated risk for affective disorder psychopathology, suicidality, and traumatic experiences. Epidemiology related to psychotic and bipolar spectrum disorders is less clear, and findings are mixed regarding sex-specific behavioral side effects of antiseizure and psychotropic medication. Finally, hormonal and obstetric factors are highlighted as important contributors to neuropsychological symptoms in WWE, with elevated risk for low QoL and increased stigma associated with greater medical and psychiatric comorbidities compared to men. Conclusions: While emerging literature has begun to characterize the neuropsychological presentation of WWE, future research is needed to define sex and gender differences in neuropsychological sequalae among PWE to ensure consistency and quality of care for WWE. [ABSTRACT FROM AUTHOR]

Purpose: Coherence analysis in electroencephalography (EEG) allows measurement of the degree of consistency of amplitude between pairs of electrodes. Theoretically, disconnective epilepsy surgery should decrease coherence between corresponding areas. The study aimed to evaluate postoperative changes in interhemispheric coherence values after corpus callosotomy (CC). Methods: Non-lesional, drug-resistant, generalized epilepsy patients who underwent total CC were retrospectively collected. To evaluate coherence, we divided the scalp interictal EEG into "baseline" and "discharge" states after excluding periods with artifacts. Interhemispheric coherence values were obtained between eight pairs of symmetrically opposite scalp electrodes in six different frequency bands. We analyzed both pre- and postoperative EEG sessions and calculated the percentage of difference (POD) in coherence values. Results: We collected 13 patients and analyzed 2496 interhemispheric coherence values. Preoperative coherence values differed significantly between baseline and discharge states (p = 0.0003), but postoperative values did not (p = 0.11). For baseline state, coherence values were decreased after CC and median POD was − 22.3% (p < 0.0001). Delta frequency showed the most decreased POD (-44.3%, p = 0.0009). Median POD was lowest in the Fp1-Fp2 pair of electrodes. For discharge state, coherence values were decreased after CC and median POD was − 24.7% (p < 0.0001). Delta frequency again showed the most decreased POD (-55.9%, p = 0.0016). Median POD was lowest in the F7-F8 pair. Conclusion: After total CC, interhemispheric coherence decreased significantly in both baseline and discharge states. The most decreased frequency band was the delta band, which may be used as a representative frequency band in future studies. [ABSTRACT FROM AUTHOR]