Epilepsy Patients Research Articles

High-resolution multimodal profiling of human epileptic brain activity via explanted depth electrodes.

The availability and integration of electrophysiological and molecular data from the living brain is critical to understand and diagnose complex human disease. Intracranial stereo electroencephalography (SEEG) electrodes used for identifying the seizure focus on epilepsy patients could enable the integration of such multimodal data. Here, we report MoPEDE (Multimodal Profiling of Epileptic Brain Activity via Explanted Depth Electrodes), a method that recovers extensive protein-coding transcripts, including cell-type markers, DNA methylation and short variant profiles from explanted SEEG electrodes matched with electrophysiological and radiological data allowing for high-resolution reconstructions of brain structure and function. We find gene expression gradients that correspond with the neurophysiology-assigned epileptogenicity index but also outlier molecular fingerprints in some electrodes, potentially indicating seizure generation or propagation zones not detected during electroclinical assessments. Additionally, we identify DNA methylation profiles indicative of transcriptionally permissive or restrictive chromatin states and SEEG-adherent differentially expressed and methylated genes not previously associated with epilepsy. Together, these findings validate that RNA profiles and genome-wide epigenetic data from explanted SEEG electrodes offer high-resolution surrogate molecular landscapes of brain activity. The MoPEDE approach has the potential to enhance diagnostic decisions and deepen our understanding of epileptogenic network processes in the human brain.

Efficacy and safety of transcranial direct current stimulation (tDCS) in treatment of refractory epilepsy: an updated systematic review and meta-analysis of randomized sham-controlled trials.

Despite the currently available treatment, one-third of epilepsy patients continue to experience seizures. Transcranial direct current stimulation (tDCS) has emerged as a potential neuromodulation approach for the non-invasive treatment of refractory epilepsy. This study aims to provide a comprehensive investigation of the efficacy and safety of tDCS in patients with drug-resistant epilepsy. The following databases were searched from inception until June 2023; PubMed, Scopus, Embase, WOS, EBSCO, Cochrane Central, and Ovid MEDLINE. Pooled mean difference was calculated for change in seizure frequency (SF), and number of Interictal epileptiform discharges (IEDs) at different follow-up intervals. We included nine parallel randomized sham-controlled trials with a total of 267 patients. Active tDCS patients had a significantly lower SF per month at 4 and 8 weeks (MD = -4.06, 95% CI [-6.01 to -2.12], p < 0.0001), and (MD = -2.66, 95% CI [-5.09 to -0.23], p = 0.03), respectively. However, weekly SF showed no statistically significant results at 4 weeks of follow-up. The IEDs were observed to significantly decline at 2, 4, and 8 weeks of follow-up. The reported adverse events were mild including mild itching and erythematous rash that resolved spontaneously. In conclusion, tDCS significantly reduced monthly SF and the number of IEDs. Future large RCTs with standard clear informed parameters are still required.

Serum High Mobility Group Box 1 (HMGB1) Protein Levels and Cognitive Function in Epilepsy Patients: A Cross-Sectional Study

Background: Epilepsy is a neurological disease with a high incidence rate. Cognitive decline is one of the consequences of recurrent seizures. Neuroinflammation is closely related to the development of epilepsy and cognitive impairment. An increase in the expression and translocation of High Mobility Group Box 1 (HMGB1) from the nucleus to the extracellular space has been observed in epilepsy patients and experimental animal models. This study aimed to investigate the relationship between serum HMGB1 levels and cognitive function in epilepsy patients. Methods: This cross-sectional observational study involved 45 epilepsy patients. Cognitive function was assessed using the Indonesian version of the Montreal Cognitive Assessment (MoCA-Ina), and serum HMGB1 levels were measured using the ELISA technique. The relationship between cognitive function and HMGB1 levels was analyzed using the Kruskal-Wallis test, with a significance level set at p &lt; 0.05. Results: The mean age of the participants was 28.5 years, with a higher proportion of females. The mean serum HMGB1 level was 22.6 ng/ml. No significant relationship was found between serum HMGB1 levels and cognitive function in epilepsy patients (p = 0.188). Conclusion: Serum HMGB1 protein levels were not associated with cognitive function in this sample of epilepsy patients.

Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis

Objectives: Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. Methods: In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3–5 seizures, categorized into three distinct groups. Results: The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. Conclusions: The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy.

Interictal EEG spikes increase perfusion in low-grade epilepsy-associated tumors: a pediatric arterial spin labeling study.

Arterial spin labeling (ASL), a noninvasive magnetic resonance (MRI) perfusion sequence, holds promise in the presurgical evaluation of pediatric lesional epilepsy patients, including those with low-grade epilepsy-associated tumors (LEATs). The interpretation of ASL-derived perfusion patterns, however, presents challenges. Our study aims to elucidate these perfusion changes in children with LEATs, exploring their correlations with clinical, electroencephalography (EEG), and anatomical MRI findings. Our cohort included 15 children with LEAT-associated focal lesional epilepsy who underwent single-delay pseudo-continuous ASL imaging; eight were imaged under sedation. We assessed perfusion images both qualitatively and quantitatively, focusing on LEAT-related perfusion changes, as indicated by the asymmetry index (AI) and regional cerebral blood flow (rCBF). ASL revealed LEAT-related perfusion changes in all but two patients: 12 LEATs were hypoperfused and one was hyperperfused relative to the contralateral brain parenchyma (CBP). LEATs showed significantly lower perfusion compared to CBP (median: 38.7 vs. 59.1mL/100g/min for LEAT and CBP, respectively; p value = 0.004, Wilcoxon-Mann-Whitney), regardless of sedation. Notably, elevated AI and rCBF values correlated with interictal spikes on EEG (median: -0.008 and 0.84 vs -0.27 and 0.58, respectively), but not to other clinical, EEG, or MRI variables (p value = 0.036, Wilcoxon-Mann-Whitney). By highlighting the connection between LEAT and brain perfusion, and by correlating perfusion characteristics and epileptogenicity, our research enhanced our understanding of pediatric epilepsy associated with LEATs. Also, by proving the robustness of these findings to sedation we confirmed the importance of adding ASL to epilepsy protocols to as a valuable tool to supplement anatomical imaging.

EPCO-11. BIASES IN HEALTHY BRAIN SINGLE-CELL DATASETS: FINDING THE BEST COMPARISON FOR GLIOBLASTOMA STUDIES

Abstract Tumor cells undergo metabolic alterations that support their proliferation and simultaneously modify immune populations, resulting in an immunosuppressive tumor microenvironment. This study aims to compare the metabolic expression of immune cells within the tumor microenvironment to those in a healthy environment. However, there is a lack of single-cell transcriptomic data for normal brain cells, necessitating an analysis of different methods to compare immune cells in tumors with those in a healthy brain. To address this, we explored cell-to-cell metabolic variability using public single-cell RNA-seq (scRNA-seq) data of glioblastoma (n=338564), and from immune cells in the brains of post-mortem individuals (n=14177), fetuses (n=40000), and epilepsy patients (n=466), as well as from immune blood cells (n=329762). We employed ssGSEA to generate activity scores for 70 key metabolic pathways from KEGG for each individual cell. Known marker genes were utilized to identify normal cell populations (macrophages, T cells, oligodendrocytes) in datasets lacking cell type annotations. Our results revealed that blood cells exhibited high metabolic activity in macrophages but showed moderate activity in other cell types. Immune blood cells also demonstrated high oxidative phosphorylation (OXPHOS) but low fatty acid activity. Fetal brain cells displayed a lack of heterogeneity and showed high fatty acid activity with low OXPHOS activity. Brain cells from post-mortem individuals displayed heterogeneity but exhibited low metabolic activity across nearly all cells and pathways. Additionally, the hierarchy of pathway activity differed from the other datasets. Epilepsy data revealed a metabolic activity pattern similar to tumor-infiltrating immune cells, with comparable activity hierarchy and heterogeneity, although the sample size was limited for detailed comparison. Understanding the differences between types of datasets is important to choose the most appropriate comparison, allowing us to observe the metabolic alterations in immune cells caused by glioblastoma and to develop better immunotherapy strategies.

SURG-19. THE SYNDROME OF THE SUPPLEMENTARY MOTOR AREA: CLINICAL COURSE AND PREDICTORS OF OUTCOME AFTER RESECTION OF PREMOTOR LESIONS

Abstract OBJECTIVE Supplementary motor area (SMA) syndrome frequently occurs after resection of premotor lesions. Interhemispheric connectivity including the contralateral SMA might be crucially involved in its development and recovery. We aimed to identify predictors of outcome by quantifying preoperative structural alterations in the non-affected contralateral hemisphere. METHODS We retrospectively searched our database for patients developing SMA syndrome after resection of premotor lesions between 2013 and 2023. Clinical characteristics and imaging data were collected. Structural network analysis was performed using the previously validated morphometric inverse divergence network. RESULTS 27 patients with a mean age of 39.2 ± 17.7 years developed SMA syndrome, including 13/27 patients (48.1%) with primary brain tumors and 14/27 patients (51.9%) with intractable lesional epilepsy. Postoperative SMA syndrome was commonly characterized by contralateral upper extremity hemiparesis (19/27 patients, 70.4%) and aphasia in case of dominant hemisphere involvement (13/13 patients, 100%). Tumor patients were less likely to fully recover from SMA syndrome after 7 days compared to epilepsy patients (2/13 [15.4%] versus 9/14 [64.3%] patients, p=0.018). Most patients (23/27, 85.2%) experienced full recovery of SMA syndrome after a median follow-up of 5 months (IR 3-16 months), with no difference between patients with tumors or epilepsy (p=0.999). Preoperative contralateral intra-hemispheric connectivity was similar between patients receiving tumor or focal resection (0.183 vs 0.177, p=0.295). However, imaging analyses demonstrated baseline affection of SMA connectivity in patients with brain tumors as characterized by reduced cortical thickness of the contralateral frontal lobe and the cingulate gyrus compared to epilepsy patients. CONCLUSION While full recovery from SMA syndrome is common, SMA syndrome is frequently less severe and more transient in patients with epilepsy. This might be due to affection of the contralateral SMA network in tumor patients due to the infiltrative nature of the disease. Imaging analyses might assist in identifying network affections at baseline.

Transcranial direct current stimulation in the management of epilepsy: a meta-analysis and systematic review

BackgroundTranscranial direct current stimulation (tDCS) has recently become a novel and non-invasive treatment option for refractory epilepsy. Previous systematic reviews have suggested that tDCS may be effective in treating epilepsy, this study presents the first meta-analysis on its effectiveness.MethodsWe searched PubMed, Embase, Cochrane Library, and Web of Science for relevant randomized controlled trials (RCTs) from database inception to May 2024. The Cochrane risk of bias tool RoB2.0 was used to assess the risk of bias. Primary outcomes included changes in seizure frequency from baseline and the proportion of patients with a ≥50% reduction in seizure frequency.ResultsOf the 608 studies initially identified, 14 were finally included. The pooled results from the random-effects model indicated that tDCS significantly reduced seizure frequency (WMD 0.41, 95% CI 0.24, 0.59). Further subgroup analysis revealed that tDCS significantly reduced seizure frequency in temporal lobe epilepsy, and seizure frequency was more alleviated in studies that had treatment sessions of fewer than 5 times, and followed up within 2 months' post-treatment. Only four studies provided data on patients with a ≥50% reduction in seizure frequency, showing no significant difference (RR 2.96, 95% CI 0.85, 10.32). In the systematic review, three studies analyzed cognitive function changes after tDCS treatment, but none reported significant improvements. The most common side effect during tDCS treatment was transient tingling, and no patients required additional life-support measures due to side effects.ConclusionThe current meta-analysis on available trials indicates that tDCS can effectively reduce seizure frequency in the short term and is well-tolerated. However, its impact on cognitive improvement in epilepsy patients requires further investigation.Systematic review registrationhttps://inplasy.com/inplasy-2024-6-0033/, identifier INPLASY202460033.

Temporal Lobe Epilepsy Focus Detection Based on the Correlation Between Brain MR Images and EEG Recordings with a Decision Tree

Background/Objectives: In this study, a medical decision support system is presented to assist physicians in epileptic focus detection by correlating MRI and EEG data of temporal lobe epilepsy patients. Methods: By exploiting the asymmetry in the hippocampus in MRI images and using voxel-based morphometry analysis, gray matter reduction in the temporal and limbic lobes is detected, and epileptic focus prediction is realized. In addition, an epileptic focus is also determined by calculating the asymmetry score from EEG channels. Finally, epileptic focus detection was performed by associating MRI and EEG data with a decision tree. Results: The results obtained from the proposed algorithm provide 100% overlap with the physician’s finding on the EEG data. Conclusions: MRI and EEG correlation in epileptic focus detection was improved compared with physicians. The proposed algorithm can be used as a medical decision support system for epilepsy diagnosis, treatment, and surgery planning.

Previously defined variants of uncertain significance may play an important role in epilepsy and interactions between certain variants may become pathogenic.

Epilepsy is a chronic neurological disorder related to various etiologies, and the prevalence of active epilepsy is estimated to be between 4 and 10 per 1000 individuals having a significant role in genetic mutations. Next-Generation Sequencing (NGS) panels are utilized for genetic testing, but a substantial proportion of the results remain uncertain and are not considered directly causative of epilepsy. This study aimed to reevaluate pediatric patients diagnosed with epilepsy who underwent genetic investigation using NGS panels, focusing on inconclusive variant findings or multiple variants of uncertain significance (VUSs). A subgroup of pediatric patients aged 0-25 years, diagnosed with epilepsy, who underwent genetic investigation with an NGS epilepsy panel at the Child Neurology Unit, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, between 2018 and 2022 through Invitae, was reevaluated. Patients with inconclusive variant findings or multiple VUSs in their test results were included. Genetic data were analyzed to identify potentially pathogenic variants and frequent genetic combinations. Two unrelated potentially pathogenic variants were identified in the SCN9A and QARS1 genes. A frequent genetic combination, RANBP2&RYR3, was also observed among other combinations. The RANBP2 gene consistently co-occurred with RYR3 variants in uncertain results, suggesting potential pathogenicity. Analysis of unaffected parents' data revealed certain combinations inherited from different parents, suggesting specific gene combinations as possible risk factors for the disease. This study highlights the importance of reevaluating genetic data from pediatric epilepsy patients with inconclusive variant findings or multiple VUSs. Identification of potentially pathogenic variants and frequent genetic combinations, such as RANBP2&RYR3, could aid in understanding the genetic basis of epilepsy and identifying potential hotspots. We have performed a retrospective analysis on a subpopulation of pediatric patients diagnosed with epilepsy. We found that specific genetic variants were repeatable, indicating their potential pathogenicity to the disease.

Một số đặc điểm về tuân thủ sử dụng thuốc kháng động kinh của người bệnh động kinh tại Trung tâm Thần kinh Bệnh viện Bạch Mai

Epilepsy is a common disease, affecting about 65 million people in the world, accounting for 1% of the global burden of disease. Many aspects are affected such as: Economic, social and especially the quality of life. Some researches have shown that the quality of life of people with epilepsy is much lower than that of healthy people. Treatment of epilepsy is necessary. However, epilepsy treatment is difficult with a high rate of seizure recurrence. One of the important causes of seizure recurrence is the medication non-adherence that even making the disease more difficult to control. Objective: Describe the characteristics of medication adherence in epilepsy patients at the Bach Mai neurology center and analyze some related factors. Subjects and methods: Cross-sectional descriptive study on patients who were diagnosed and treated for outpatient epilepsy, came for follow-up examination at the neurology center of Bạch Mai hospital. Patient classification based on medication adherence characteristics and patients beliefs about drug use, through MMAS-8 scale and BMQ scale. Results: Out of 193 patients, 80.5% of them often had difficulty remembering to take medication, 11.4% felt bothered when adhering to treatment. 0% of patients achieved a high level of compliance, 27.5% at average level and 72.5% at low level). Conclusion: The majority of patients have a moderate level of knowledge about epilepsy, with most of them understanding that epilepsy can be cured, the available treatment methods, and the necessity of adhering to medication. However, they also recognize the serious issue of using stimulants. Despite this, none of the patients achieved a high level of adherence, with most showing low adherence to treatment, primarily due to occasionally forgetting to take their medication.

Imaging blood-brain barrier dysfunction in drug-resistant epilepsy: A multi-center feasibility study.

Blood-brain barrier dysfunction (BBBD) has been linked to various neurological disorders, including epilepsy. This study aims to utilize dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify and compare brain regions with BBBD in patients with epilepsy (PWE) and healthy individuals. We scanned 50 drug-resistant epilepsy (DRE) patients and 58 control participants from four global specialized epilepsy centers using DCE-MRI. The presence and extent of BBBD were analyzed and compared between PWE and healthy controls. Both greater brain volume and higher number of brain regions with BBBD were significantly present in PWE compared to healthy controls (p < 10-7). No differences in total brain volume with BBBD were observed in patients diagnosed with either focal seizures or generalized epilepsy, despite variations in the affected regions. Overall brain volume with BBBD did not differ in PWE with MRI-visible lesions compared with non-lesional cases. BBBD was observed in brain regions suspected to be related to the onset of seizures in 82% of patients (n = 39) and was typically identified in, adjacent to, and/or in the same hemisphere as the suspected epileptogenic lesion (n = 10). These findings are consistent with pre-clinical studies that highlight the role of BBBD in the development of DRE and identify microvascular stabilization as a potential therapeutic strategy.

Abnormal metabolites in the dorsolateral prefrontal cortex of female epilepsy patients with migraine without aura.

Epilepsy and migraine without aura (MWoA) are often comorbid, but the exact mechanisms are unclear. Magnetic resonance spectroscopy (1H-MRS) may help to understand the neurometabolic mechanisms in patients with epilepsy comorbid with MWoA (EWM). In this prospective cross-sectional study, we recruited 64 female patients, including 24 with EWM, 20 with epilepsy, and 20 with MWoA, as well as 20 age-level-matched and educational-level-matched female healthy controls from our hospital between August 2021 and November 2022. A single-voxel point-resolved spectroscopy sequence was used to acquire spectra of the bilateral dorsolateral prefrontal cortices (DLPFCs). Metabolites were quantified by linear combination model software, and the values were corrected for the partial volume effect of cerebrospinal fluid. MRS data comparisons were performed with multivariate analyses of variance. Correlation analyses were calculated between metabolites and main clinical data. The results showed that N-acetyl aspartate (NAA) was asymmetrical between the bilateral DLPFCs. Both NAA and myoinositol were significantly reduced in EWM than in healthy controls. Choline-containing compounds (Cho) were higher in MWoA than in the other three groups. Correlation analyses revealed that NAA of the right DLPFC and Cho of the bilateral DLPFCs in EWM were negatively related to migraine frequency. In addition, glutamate and glutamine (Glu and Gln, Glx) of the right DLPFC in EWM were negatively correlated with migraine severity. Our findings suggested that comorbid epilepsy and MWoA in female patients can lead to a synergistic reduction of both NAA and myoinositol, reflecting more serious injuries of neurons and glial cells.

Self-Supervised Data-Driven Approach Defines Pathological High-Frequency Oscillations in Human.

Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial EEG (iEEG) recordings and whether this mechanism-driven distinction could be simulated by a deep generative model. In a retrospective cohort of 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686,410 HFOs across 18,265 brain contacts. To learn morphological characteristics, each event was transformed into a time-frequency plot and input into a variational autoencoder. We characterized latent space clusters containing morphologically defined putative pathological HFOs (mpHFOs) using interpretability analysis, including latent space disentanglement and time-domain perturbation. mpHFOs showed strong associations with expert-defined spikes and were predominantly located within the seizure onset zone (SOZ). Discovered novel pathological features included high power in the gamma (30-80 Hz) and ripple (>80 Hz) bands centered on the event. These characteristics were consistent across multiple variables, including institution, electrode type, and patient demographics. Predicting 12-month postoperative seizure outcomes using the resection ratio of mpHFOs outperformed unclassified HFOs (F1=0.72 vs. 0.68) and matched current clinical standards using SOZ resection (F1=0.74). Combining mpHFO data with demographic and SOZ resection status further improved prediction accuracy (F1=0.83). Our data-driven approach yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.

Reward recalibrates rule representations in human amygdala and hippocampus intracranial recordings

Adaptive behavior requires the ability to shift responding within (intra-dimensional) or between (extra-dimensional) stimulus dimensions when reward contingencies change. Studies of shifting in humans have focused mainly on the prefrontal cortex and/ or have been restricted to indirect measures of neural activity such as fMRI and lesions. Here, we demonstrate the importance of the amygdala and hippocampus by recording local field potentials directly from these regions intracranially in human epilepsy patients. Reward signals were coded in the high frequency gamma activity (HFG; 60-250 Hz) of both regions and synchronised via low frequency (3-5 Hz) phase-locking only after a shift when patients did not already know the rule and it signalled to stop shifting (“Win-Stay”). In contrast, HFG punishment signals were only seen in the amygdala when the rule then changed and it signalled to start shifting (“Lose-Shift”). During decision-making, hippocampal HFG was more inhibited on non-shift relative to shift trials, suggesting a role in preventing interference in rule representation and amygdala HFG was sensitive to stimulus novelty. The findings expand our understanding of human amygdala-hippocampal function and shifting processes, the disruption of which could contribute to shifting deficits in neuropsychiatric disorders.

Herbal Extracts Exhibit Anti-Epilepsy Properties

Epilepsy refers to a group of persistent neurological illnesses characterized by seizures. It is a chronic brain disease that affects 50 million people in the world, being one of the most common neurological disorders. Developing countries account for over 90% of all epilepsy patients. Epileptic seizures are caused by aberrant, excessive, or hyper synchronized neuronal activity in the brain. The cause of most epileptic seizures is unknown. However, some people develop epilepsy as a result of a brain injury, stroke, brain tumor, or drug and alcohol abuse. Anti-seizure drugs remain the mainstay in the treatment of epilepsy and about 70% of epileptics become seizure-free when antiseizure medications are taken effectively. However, some of these medications have significant pharmacological interactions and undesirable side effects. Traditional utilization of plants extracts for treatment of epilepsy is widely practiced. Many plants extracts and herbal formulations have been studied to determine their efficacy in treatment of epilepsy. This paper presents a review on herbal extracts that have shown antiepileptic activity in animal models published in the last ten years (between 2014 and 2024). The study found that plant extracts from some 138 plant species belonging to 54 different plant families were evaluated for antiepileptic efficacy. The most studied plants belong to the Asteraceae family (19%) followed by Fabaceae (9%), Apiaceae (8%), Lamiaceae (8%), Apocynaceae (7%), Cucurbitaceae (3%), Euphorbiaceae (3%) and Rutaceae (3%). In most cases, the studies focused only on the crude extracts without any attempts to identify the antiepileptic compounds from the plants. The most commonly used model in the antiepileptic assays were found to be pentylenetetrazole and maximal electroshock models. The findings from this study confirm that plant extracts have significant efficacy that needs to be explored for antiepileptic formulation and drugs development. It is also necessary to perform bioassay guided phytochemical evaluation to isolate and characterize the antiepileptic principles.

GC-MS uncovers unique metabolic markers of drug-resistant epilepsy in capillary but not venous dried blood spots

This study compared the effectiveness of capillary dried blood spots (DBS) versus venous DBS in detecting metabolic changes related to drug-resistant epilepsy (DRE). DBS samples were collected from 142 epilepsy patients (58 drug-resistant, 84 drug-responsive) via venipuncture or fingerstick capillary sampling. Metabolomic analysis using gas chromatography-mass spectrometry compared DBS metabolite profiles between the two groups. While venous DBS profiles showed no distinct patterns, capillary DBS profiles revealed clustering patterns in principal components analysis, with the first two principal components explaining 14.5%, and 13.5% of the total variance, respectively. Orthogonal PLS-DA confirmed group discrimination (R2Y=0.989, Q2=0.742). Drug-resistant patients exhibited elevated capillary DBS levels of glutamine, pyruvic acid, and serine, and decreased palmitic acid compared to drug-responsive patients. Pathway analysis revealed disruptions in amino acid metabolism, neurotransmission, and cellular energy regulation. Elevated glutamine levels may contribute to an imbalance between excitatory glutamate and inhibitory GABA neurotransmission, key factors in epileptogenesis and drug resistance. Capillary DBS, likely enriched with arterial blood supply to the brain, appears to better capture central nervous system metabolic disturbances compared to venous DBS containing systemic contributions. This minimally invasive capillary DBS approach offers effective metabolic profiling of brain conditions like DRE, for monitoring disease progression and treatment response, enhancing personalized patient management in epilepsy.

Generation of a human induced pluripotent stem cell (iPSC) line from a patient with a CDKL5 gene mutation

CDKL5(Cyclin-dependent kinase-like 5) encodes a kinase that may influence components of molecular pathways associated with MeCP2 and regulates axon outgrowth, dendritic morphogenesis, and synapse formation early in life. Here, we report an induced pluripotent stem cell (iPSC) line generated from an epilepsy patient harboring the CDKL5 c.2684C > T (p.P895L) variant. Peripheral blood mononuclear cells from the patient were successfully reprogrammed into iPSCs that expressed pluripotency markers, a normal karyotype, and the capacity to differentiate into the three germ layers in vivo. This iPSC line is a potential resource for studying the pathogenic mechanisms of epilepsy.

Employing Convolutional Neural Networks And Explainable Artificial intelligence for the Detection of Seizures from Electroencephalogram Signal

A seizure is a rapid, uncontrolled burst of electrical activity in the brain. Epilepsy is a neurological disorder caused by repeated seizures. Effective management requires early detection. This research aims to create a convolutional neural network (CNN) and explainable artificial intelligence (XAI) integrated system for epileptic seizure detection, using techniques such as Shapley additive explanation (SHAP) for better interpretability. The relevant data is acquired from a variety of electroencephalogram (EEG) dataset to facilitate the development of the model. To identify the EEG data, the proposed system combines deep learning models with feature extraction technique. Model visualization and XAI approaches like feature importance analysis from SHAP values offer clear insights into the model's decision-making process. Evaluation criteria like specificity and accuracy are used to assess the models' performance. This framework's objective is to create simple seizure detection systems that assist early epilepsy patient identification and individualized treatment plans. This research aims in opening the door to improved patient care and treatment by developing the field of epilepsy seizure detection.

Academic performance of pediatric epileptic patients at King Abdulaziz University Hospital

ABSTRACT Background: Epilepsy is a prevalent neurological condition affecting children worldwide, with a particularly high incidence in Saudi Arabia. Children with epilepsy can experience poor school performance due to cognitive deficits and frequent absences. This study investigates the impact of seizures on school attendance among children with epilepsy. Methods: This cross-sectional study was conducted at King Abdulaziz University Hospital (KAUH) in Jeddah, Saudi Arabia, including pediatric epilepsy patients from 2016 to 2022. Data were collected via an online questionnaire and analyzed using IBM SPSS. Factors examined included seizure frequency, medication use, comorbidities, teacher awareness, and absenteeism. Results: The study included 207 pediatric epilepsy patients, predominantly Saudi nationals (87%). Most parents had higher education backgrounds (70%). Participants’ mean age was 9.66 years, with 57% diagnosed before the age of 5. Most (45%) patients took daily medication with 57% experiencing no side effects. Over half (56%) of the population reported focus and memory problems, significantly related to age at diagnosis (P &lt; 0.001). Comorbidities were reported by 35%, with a significant relationship to absenteeism (P &lt; 0.01). Teacher awareness was reported in 58% of cases. Regarding absenteeism, over half (57%) were absent fewer than 10 days during the past academic year, with 41% attributing absenteeism to non-seizure reasons. Seizure attacks at home caused 22% of absences. A significant relationship existed between the etiology of absenteeism and seizures (P &lt; 0.001). Bullying at school due to seizures was reported, with a significant relationship to absenteeism (P = 0.02). Conclusion: This study highlights the complex relationship between epilepsy, school attendance, and associated factors among pediatric patients in Saudi Arabia, shedding light on the importance of poor academic experience to improve the quality of life for children with epilepsy.