Advances in the development of biomarkers for epilepsy

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.

Enigmatic intractable Epilepsy patients have antibodies that bind glutamate receptor peptides, kill neurons, damage the brain, and cause Generalized Tonic Clonic Seizures

Epilepsy affects 1-2% of the world population, is enigmatic in 30% of cases, and is often intractable, unresponsive to antiepileptic drugs, and accompanied by cognitive, psychiatric and behavioral problems. Tests for Autoimmune Epilepsy are not performed routinely, and limited to passive diagnosis of known autoimmune antibodies, without essential functional tests to reveal active pathogenic antibodies. We investigated two young Epilepsy patients with different Epilepsy characteristics, repeated intractable seizures, and enigmatic etiology. We suspected Autoimmune Epilepsy. We found that both patients have elevated IgG antibodies, and three types of glutamate receptor antibodies, to: AMPA-GluR3B, NMDA-NR1 and NMDA-NR2 peptides. In contrast, they lack autoantibodies to: LGI1, CASPR2, GABA-RB1, Amphiphysin, CV2, PNMA1, Ri, Yo, Hu, Recoverin, Soxi and Titin. IgG antibodies of both patients bound and killed human neural cells In vitro. Moreover, In vivo video EEG studies in naive rats revealed that patient's IgG antibodies, infused continually into rat brain, bound neural cells in the hippocampus and cortex, caused neural loss in these brain regions, and induced recurrent Generalized Tonic Clonic Seizures. We assume they can do so also in the patient's brain. This is the first model of human Autoimmune Epilepsy in rats. It can serve for discovery of patient's pathogenic antibodies, and drug development. Tests for autoimmune antibodies that bind glutamate receptor peptides, and functional diagnostic tests, are obligatory in all enigmatic intractable Epilepsy patients. Current diagnosis of Autoimmune Epilepsy is insufficient! If pathogenic antibodies are found, intractable patients must receive available, suitable and potentially life-changing immunotherapies for Autoimmune Epilepsy.

Prognostic models for seizures and epilepsy after stroke, tumors and traumatic brain injury

Epilepsy is a frequent consequence of acute brain injuries, such as stroke, brain tumors, and traumatic brain injury (TBI). Accurate prediction of epilepsy is essential for early intervention and improved patient outcomes. This review evaluates the best-established prognostic models, including the SeLECT and CAVE scores, which estimate the risk of developing seizures and epilepsy following these injuries. The review highlights their clinical applicability, predictive accuracy, and limitations for different etiologies. In addition to providing practical tables for risk estimation, we also offer user-friendly online calculators for these models at www.predictepilepsy.com to facilitate clinical implementation. These tools help identify high-risk patients and support decision-making for follow-up and treatment. Furthermore, we discuss the potential of integrating electrophysiological data, including EEG biomarkers, to further enhance prediction accuracy and patient care. These insights highlight the need for further refinement and validation of predictive models, enabling more personalized treatment strategies and better patient care.

Causal association between cerebral small vessel disease and epilepsy

Cerebral small vessel disease (SVD) is known to increase the risk of epilepsy, but the causal relationship between the two remains unclear. This study utilizes Mendelian Randomization (MR) to assess the genetic link between SVD and epilepsy, incorporating additional analyses including colocalization, gene-set, and pathway analysis to provide further evidence for shared mechanisms. We performed a two-sample MR study using 13 SNPs strongly associated with SVD as instrumental variables to assess their causal relationship with epilepsy. We also conducted colocalization analysis to identify shared genetic variants between SVD and epilepsy, gene-set enrichment analysis to explore common biological pathways, and protein-protein interaction (PPI) network analysis to investigate the molecular mechanisms. The MR analysis revealed a significant causal effect of SVD on epilepsy, with an odds ratio (OR) of 1.29 (95% CI: 1.09-1.53, P < 0.001). Colocalization analysis identified three genomic regions (chr1:100-600 kb, chr3:200-700 kb, and chr5:50-500 kb) with evidence of shared genetic variants. Pathway analysis highlighted endothelial cell signaling, blood-brain barrier function, and neuroinflammatory responses as enriched pathways, linking vascular health to neuronal excitability. The PPI network revealed key proteins involved in both vascular and neuronal processes, including ZNF646, PLEKHG1, and NOTCH3, further supporting shared mechanisms. This study provides strong genetic evidence for an association between SVD and epilepsy, with new insights from colocalization, gene-set, and pathway analyses while recognizing the limitations of Mendelian Randomization in establishing temporal causality. The findings suggest that genetic variants affecting vascular health, neuroinflammation, and blood-brain barrier integrity may contribute to both SVD and the risk of epilepsy. These results strengthen the understanding of the shared biological mechanisms underlying these two conditions and highlight SVD as an important risk factor for epilepsy.

PET imaging identifies anti-inflammatory effects of fluoxetine and a correlation of glucose metabolism during epileptogenesis with chronic seizure frequency

The serotonergic system has shown to be altered during epileptogenesis and in chronic epilepsy, making selective serotonin reuptake inhibitors interesting candidates for antiepileptogenic therapy. In this study, we aimed to evaluate disease-modifying effects of fluoxetine during experimental epileptogenesis. Status epilepticus (SE) was induced by lithium-pilocarpine, and female rats were treated either with vehicle or fluoxetine over 15 days. Animals were subjected to 18F-FDG (7 days post-SE), 18F-GE180 (15 days post-SE) and 18F-flumazenil positron emission tomography (PET, 21 days post-SE). Uptake (18F-FDG), volume of distribution (18F-GE180) and binding potential (18F-flumazenil) were calculated. In addition, hyperexcitability testing and video-EEG monitoring were performed. Fluoxetine treatment did not alter brain glucose metabolism. 18F-GE180 PET indicated lower neuroinflammation in the hippocampus of treated animals (-22.6%, p = 0.042), but no differences were found in GABAA receptor density. Video-EEG monitoring did not reveal a treatment effect on seizure frequency. However, independently of the treatment, hippocampal FDG uptake 7 days after SE correlated with seizure frequency during the chronic phase (r = -0.58; p = 0.015). Fluoxetine treatment exerted anti-inflammatory effects in rats during epileptogenesis. However, this effect did not alter disease outcome. Importantly, FDG-PET in early epileptogenesis showed biomarker potential as higher glucose metabolism correlated to lower seizure frequency in the chronic phase.

Mapping epilepsy biomarkers: a bibliometric and content analysis

Epilepsy, a complex global neurological disorder, has spurred extensive research efforts focused on enhancing diagnostic and therapeutic strategies, with a growing emphasis on the identification of biomarkers. This bibliometric study examines 1,774 publications from 2000 to 2023, revealing a notable increase in research activity, particularly in the past decade. The US, China, and the UK lead contributions, with Asian countries exhibiting growing potential. Keyword co-occurrence analysis reveals a shift towards investigations of neuroinflammatory and genetic biomarkers, as well as emerging areas such as artificial intelligence and epigenetics. Content analysis links specific epilepsy aetiologies to biomarkers, offering promising possibilities for personalised diagnostics and treatments. These findings yield valuable insights into current trends, guiding future research and informing the development of targeted approaches for the diagnosis and treatment of epilepsy.

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

Background

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

Methods

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

Results

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

Conclusion

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

Significance

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers

Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to identify imaging-based markers for the prediction of PTE using machine learning. Specifically, we examined three imaging features: Lesion volumes, resting-state fMRI-based measures of functional connectivity, and amplitude of low-frequency fluctuation (ALFF). We employed three machine-learning methods, namely, kernel support vector machine (KSVM), random forest, and an artificial neural network (NN), to develop predictive models. Our results showed that the KSVM classifier, with all three feature types as input, achieved the best prediction accuracy of 0.78 AUC (area under the receiver operating characteristic (ROC) curve) using nested cross-validation. Furthermore, we performed voxel-wise and lobe-wise group difference analyses to investigate the specific brain regions and features that the model found to be most helpful in distinguishing PTE from non-PTE populations. Our statistical analysis uncovered significant differences in bilateral temporal lobes and cerebellum between PTE and non-PTE groups. Overall, our findings demonstrate the complementary prognostic value of MR-based markers in PTE prediction and provide new insights into the underlying structural and functional alterations associated with PTE.

GLS2 reduces the occurrence of epilepsy by affecting mitophagy function in mouse hippocampal neurons

BACKGROUND

Altered mitophagy has been observed in various neurological disorders, such as epilepsy. The role of mitophagy in causing neuronal damage during epileptic episodes is significant, and recent research has indicated that GLS2 plays a crucial role in regulating autophagy. However, exactly how GLS2 affects epilepsy is still unclear.

AIMS

To investigate the expression and distribution characteristics of GLS2 in epilepsy, and then observed the changes in behavior and electrophysiology caused by overexpression of GLS2 in epileptic mice, and determined whether GLS2 regulated seizure-like changes in the mouse model through the protective mechanism of mitophagy.

RESULTS

The expression of GLS2 in a kainic acid (KA)-induced epileptic mouse model and aglutamate-inducedneuronal excitatory damage in HT22 cells model was downregulation. In brief, overexpression of GLS2 can alleviate epileptic activity. Subsequently, we demonstrated that GLS2 interacts with mitophagy-related proteins in a KA-induced epilepsy mouse model. Mechanistically, overexpression of GLS2 inhibited mitophagy in epileptic mice, downregulating the expression of LC3 and reducing ROS production.

CONCLUSIONS

This study proves the GLS2 expression pattern is abnormal in epileptic mice. The function of mitophagy in hippocampal neurons is affected by GLS2, and overexpression of GLS2 can reduce the occurrence of seizure-like events (SLEs) by altering mitophagy function. Thus, GLS2 might control seizures, and our findings provide a fresh avenue for antiepileptic treatment and offer novel insights into treating and preventing epilepsy.

Epilepsy as a Novel Phenotype of BPTF-Related Disorders

BACKGROUND

Neurodevelopmental disorder with dysmorphic facies and distal limb anomalies (NEDDFL) is associated to BPTF gene haploinsufficiency. Epilepsy was not included in the initial descriptions of NEDDFL, but emerging evidence indicates that epileptic seizures occur in some affected individuals. This study aims to investigate the electroclinical epilepsy features in individuals with NEDDFL.

METHODS

We enrolled individuals with BPTF-related seizures or interictal epileptiform discharges (IEDs) on electroencephalography (EEG). Demographic, clinical, genetic, raw EEG, and neuroimaging data as well as response to antiseizure medication were assessed.

RESULTS

We studied 11 individuals with a null variant in BPTF, including five previously unpublished ones. Median age at last observation was 9 years (range: 4 to 43 years). Eight individuals had epilepsy, one had a single unprovoked seizure, and two showed IEDs only. Key features included (1) early childhood epilepsy onset (median 4 years, range: 10 months to 7 years), (2) well-organized EEG background (all cases) and brief bursts of spikes and slow waves (50% of individuals), and (3) developmental delay preceding seizure onset. Spectrum of epilepsy severity varied from drug-resistant epilepsy (27%) to isolated IEDs without seizures (18%). Levetiracetam was widely used and reduced seizure frequency in 67% of the cases.

CONCLUSIONS

Our study provides the first characterization of BPTF-related epilepsy. Early-childhood-onset epilepsy occurs in 19% of subjects, all presenting with a well-organized EEG background associated with generalized interictal epileptiform abnormalities in half of these cases. Drug resistance is rare.

Mammalian models of status epilepticus - Their value and limitations

Status epilepticus (SE) is a medical and neurologic emergency that may lead to permanent brain damage, morbidity, or death. Animal models of SE are particularly important to study the pathophysiology of SE and mechanisms of SE resistance to antiseizure medications with the aim to develop new, more effective treatments. In addition to rodents (rats or mice), larger mammalian species such as dogs, pigs, and nonhuman primates are used. This short review describes and discusses the value and limitations of the most frequently used mammalian models of SE. Issues that are discussed include (1) differences between chemical and electrical SE models; (2) the role of genetic background and environment on SE in rodents; (3) the use of rodent models (a) to study the pathophysiology of SE and mechanisms of SE resistance; (b) to study developmental aspects of SE; (c) to study the efficacy of new treatments, including drug combinations, for refractory SE; (d) to study the long-term consequences of SE and identify biomarkers; (e) to develop treatments that prevent or modify epilepsy; (e) to study the pharmacology of spontaneous seizures; (4) the limitations of animal models of induced SE; and (5) the advantages (and limitations) of naturally (spontaneously) occurring SE in epileptic dogs and nonhuman primates. Overall, mammalian models of SE have significantly increased our understanding of the pathophysiology and drug resistance of SE and identified potential targets for new, more effective treatments. This paper was presented at the 9th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures held in April 2024.

New epilepsy therapies in development

Epilepsy is a common brain disorder, characterized by spontaneous recurrent seizures, with associated neuropsychiatric and cognitive comorbidities and increased mortality. Although people at risk can often be identified, interventions to prevent the development of the disorder are not available. Moreover, in at least 30% of patients, epilepsy cannot be controlled by current antiseizure medications (ASMs). As a result of considerable progress in epilepsy genetics and the development of novel disease models, drug screening technologies and innovative therapeutic modalities over the past 10 years, more than 200 novel epilepsy therapies are currently in the preclinical or clinical pipeline, including many treatments that act by new mechanisms. Assisted by diagnostic and predictive biomarkers, the treatment of epilepsy is undergoing paradigm shifts from symptom-only ASMs to disease prevention, and from broad trial-and-error treatments for seizures in general to mechanism-based treatments for specific epilepsy syndromes. In this Review, we assess recent progress in ASM development and outline future directions for the development of new therapies for the treatment and prevention of epilepsy.

Electroencephalographic and Behavioral Effects of Intranasal Administration of a Na+, K+-ATPase-Activating Antibody after Status Epilepticus

Status epilepticus (SE) is a medical emergency associated with high mortality and morbidity. Na+, K+-ATPase, is a promising therapeutic target for SE, given its critical role in regulation of neuron excitability and cellular homeostasis. We investigated the effects of a Na+, K+-ATPase-activating antibody (DRRSAb) on short-term electrophysiological and behavioral consequences of pilocarpine-induced SE. Rats were submitted to pilocarpine-induced SE, followed by intranasal administration (2 μg/nostril). The antibody increased EEG activity following SE, namely, EEG power in theta, beta, and gamma frequency bands, assessed by quantitative analysis of EEG power spectra. One week later, DRRSAb-treated animals displayed less behavioral hyperreactivity in pick-up tests and better performance in novel object recognition tests, indicating that the intranasal administration of this Na+, K+-ATPase activator immediately after SE improves behavioral outcomes at a later time point. These results suggest that Na+, K+-ATPase activation warrants further investigation as an adjunctive therapeutic strategy for SE.

Unveiling Colorectal Cancer Biomarkers: Harnessing Biosensor Technology for Volatile Organic Compound Detection

Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such as computed tomography colonography and stool tests are either too costly or less reliable than invasive ones. On the other hand, volatile organic compounds (VOCs) are potentially ideal non-invasive biomarkers for CRC detection and monitoring. The present review is a comprehensive presentation of the current state-of-the-art VOC-based CRC diagnostics, with a specific focus on recent advancements in biosensor design and application. Among them, breath-based chromatography pattern analysis and sampling techniques are overviewed, along with nanoparticle-based optical and electrochemical biosensor approaches. Limitations of the currently available technologies are also discussed with an outlook for improvement in combination with big data analytics and advanced instrumentation, as well as expanding the scope and specificity of CRC-related volatile biomarkers.

A PET-based radiomics nomogram for individualized predictions of seizure outcomes after temporal lobe epilepsy surgery

PURPOSE

To establish and validate a novel nomogram based on clinical characteristics and [18F]FDG PET radiomics for the prediction of postsurgical seizure freedom in patients with temporal lobe epilepsy (TLE).

PATIENTS AND METHODS

234 patients with drug-refractory TLE patients were included with a median follow-up time of 24 months after surgery. The correlation coefficient redundancy analysis and LASSO Cox regression were used to characterize risk factors. The Cox model was conducted to develop a Clinic-PET nomogram to predict the relapse status in the training set (n = 171). The nomogram's performance was estimated through discrimination, calibration, and clinical utility. The prognostic prediction model was validated in the test set (n = 63).

RESULTS

Eight radiomics features were selected to assess the radiomics score (radscore) of the operation side (Lat_radscore) and the asymmetric index (AI) of the radiomics score (AI_radscore). AI_radscor, Lat_radscor, secondarily generalized seizures (SGS), and duration between seizure onset and surgery (Durmon) were significant predictors of seizure-free outcomes. The final model had a C-index of 0.68 (95 %CI: 0.59-0.77) for complete freedom from seizures and time-dependent AUROC was 0.65 at 12 months, 0.65 at 36 months, and 0.59 at 60 months in the test set. A web application derived from the primary predictive model was displayed for economic and efficient use.

CONCLUSIONS

A PET-based radiomics nomogram is clinically promising for predicting seizure outcomes after temporal lobe epilepsy surgery.

Identifying Myoclonic Epilepsy Misdiagnosed as Psychogenic Nonepileptic Seizures: Challenges in Differential Diagnosis

Psychogenic nonepileptic seizures (PNES) and epileptic seizures often present with similar clinical manifestations. This case report describes the diagnostic journey of a 24-year-old female initially diagnosed with PNES but later found to have myoclonic epilepsy upon comprehensive evaluation. The patient presented with recurrent episodes characterized by sudden loss of awareness, jerking movements, and urinary incontinence, often triggered by stressors. Initial assessment, including video-electroencephalography (EEG) monitoring, did not reveal epileptiform activity, leading to the provisional diagnosis of PNES. However, the persistence of symptoms and doubts regarding the diagnosis prompted further investigation, which uncovered generalized spike-and-wave discharges on repeat EEG studies. The diagnosis of myoclonic epilepsy was established based on these findings, and treatment with valproate resulted in a significant reduction in seizure frequency. This case underscores the importance of a thorough evaluation in distinguishing between seizure disorders and psychogenic manifestations, emphasizing the need for collaborations between neurology and psychology disciplines for accurate diagnosis and management.

Hippocampal transcriptomic analyses reveal the potential antiapoptotic mechanism of a novel anticonvulsant agent Q808 on pentylenetetrazol-induced epilepsy in rats

Brain apoptosis is one of the main causes of epileptogenesis. The antiapoptotic effect and potential mechanism of Q808, an innovative anticonvulsant chemical, have never been reported. In this study, the seizure stage and latency to reach stage 2 of pentylenetetrazol (PTZ) seizure rat model treated with Q808 were investigated. The morphological change and neuronal apoptosis in the hippocampus were detected by hematoxylin and eosin (HE) and terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) staining, respectively. The hippocampal transcriptomic changes were observed using RNA sequencing (RNA-seq). The expression levels of hub genes were verified by quantitative reverse-transcription PCR (qRT-PCR). Results revealed that Q808 could allay the seizure score and prolong the stage 2 latency in seizure rats. The morphological changes of neurons and the number of apoptotic cells in the DG area were diminished by Q808 treatment. RNA-seq analysis revealed eight hub genes, including Map2k3, Nfs1, Chchd4, Hdac6, Siglec5, Slc35d3, Entpd1, and LOC103690108, and nine hub pathways among the control, PTZ, and Q808 groups. Hub gene Nfs1 was involved in the hub pathway sulfur relay system, and Map2k3 was involved in the eight remaining hub pathways, including Amyotrophic lateral sclerosis, Cellular senescence, Fc epsilon RI signaling pathway, GnRH signaling pathway, Influenza A, Rap1 signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. qRT-PCR confirmed that the mRNA levels of these hub genes were consistent with the RNA-seq results. Our findings might contribute to further studies exploring the new apoptosis mechanism and actions of Q808.

Transcriptional Signatures of a Dynamic Epilepsy Process Reveal Potential Immune Regulation

Epilepsy is a progression of development and advancement over time. However, the molecular features of epilepsy were poorly studied from a dynamic developmental perspective. We intend to investigate the key mechanisms in the process of epilepsy by exploring the roles of stage-specifically expressed genes. By using time-course transcriptomic data of epileptic samples, we first analyzed the molecular features of epilepsy in different stages and divided it into progression and remission stages based on their transcriptomic features. 34 stage-specifically expressed genes were then identified by the Tau index and verified in other epileptic datasets. These genes were then enriched for immune-related biological functions. Furthermore, we found that the level of immune infiltration and mechanisms at different stages were different, which may result from different types of immune cells playing leading roles in distinct stages. Our findings indicated an essential role of immune regulation as the potential mechanism of epilepsy development.

Potential merits and flaws of large language models in epilepsy care: A critical review

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.

Sources of pharmacokinetic and pharmacodynamic variability and clinical pharmacology studies of antiseizure medications in the pediatric population

Multiple treatment options exist for children with epilepsy, including surgery, dietary therapies, neurostimulation, and antiseizure medications (ASMs). ASMs are the first line of therapy, and more than 30 ASMs have U.S. Food and Drug Administration (FDA) approval for the treatment of various epilepsy and seizure types in children. Given the extensive FDA approval of ASMs in children, it is crucial to consider how the physiological and developmental changes throughout childhood may impact drug disposition. Various sources of pharmacokinetic (PK) variability from different extrinsic and intrinsic factors such as patients' size, age, drug-drug interactions, and drug formulation could result in suboptimal dosing of ASMs. Barriers exist to conducting clinical pharmacological studies in neonates, infants, and children due to ethical and practical reasons, limiting available data to fully characterize these drugs' disposition and better elucidate sources of PK variability. Modeling and simulation offer ways to circumvent traditional and intensive clinical pharmacology methods to address gaps in epilepsy and seizure management in children. This review discusses various physiological and developmental changes that influence the PK and pharmacodynamic (PD) variability of ASMs in children, and several key ASMs will be discussed in detail. We will also review novel trial designs in younger pediatric populations, highlight the role of extrapolation of efficacy in epilepsy, and the use of physiologically based PK modeling as a tool to investigate sources of PK/PD variability in children. Finally, we will conclude with current challenges and future directions for optimizing the efficacy and safety of these drugs across the pediatric age spectrum.

Serum biomarkers in patients with drug-resistant epilepsy: a proteomics-based analysis

Objective

To investigate the serum biomarkers in patients with drug-resistant epilepsy (DRE).

Methods

A total of 9 DRE patients and 9 controls were enrolled. Serum from DRE patients was prospectively collected and analyzed for potential serum biomarkers using TMT18-labeled proteomics. After fine quality control, bioinformatics analysis was conducted to find differentially expressed proteins. Pathway enrichment analysis identified some biological features shared by differential proteins. Protein-protein interaction (PPI) network analysis was further performed to discover the core proteins.

Results

A total of 117 serum differential proteins were found in our study, of which 44 were revised upwards and 73 downwards. The up-regulated proteins mainly include UGGT2, PDIA4, SEMG1, KIAA1191, CCT7 etc. and the down-regulated proteins mainly include ROR1, NIF3L1, ITIH4, CFP, COL11A2 etc. Pathway enrichment analysis identified that the upregulated proteins were mainly enriched in processes such as immune response, extracellular exosome, serine-type endopeptidase activity and complement and coagulation cascades, and the down-regulated proteins were enriched in signal transduction, extracellular exosome, zinc/calcium ion binding and metabolic pathways. PPI network analysis revealed that the core proteins nodes include PRDX6, CAT, PRDX2, SOD1, PARK7, GSR, TXN, ANXA1, HINT1, and S100A8 etc.

Conclusion

The discovery of these differential proteins enriched our understanding of serum biomarkers in patients with DRE and potentially provides guidance for future targeted therapy.

Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance.

Identification of hub genes significantly linked to temporal lobe epilepsy and apoptosis via bioinformatics analysis

Background

Epilepsy stands as an intricate disorder of the central nervous system, subject to the influence of diverse risk factors and a significant genetic predisposition. Within the pathogenesis of temporal lobe epilepsy (TLE), the apoptosis of neurons and glial cells in the brain assumes pivotal importance. The identification of differentially expressed apoptosis-related genes (DEARGs) emerges as a critical imperative, providing essential guidance for informed treatment decisions.

Methods

We obtained datasets related to epilepsy, specifically GSE168375 and GSE186334. Utilizing differential expression analysis, we identified a set of 249 genes exhibiting significant variations. Subsequently, through an intersection with apoptosis-related genes, we pinpointed 16 genes designated as differentially expressed apoptosis-related genes (DEARGs). These DEARGs underwent a comprehensive array of analyses, including enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, prediction of miRNA and transcription factors, and molecular docking analysis.

Results

In the epilepsy datasets examined, we successfully identified 16 differentially expressed apoptosis-related genes (DEARGs). Subsequent validation in the external dataset GSE140393 revealed the diagnostic potential of five biomarkers (CD38, FAIM2, IL1B, PAWR, S100A8) with remarkable accuracy, exhibiting an impressive area under curve (AUC) (The overall AUC of the model constructed by the five key genes was 0.916, and the validation set was 0.722). Furthermore, a statistically significant variance (p < 0.05) was observed in T cell CD4 naive and eosinophil cells across different diagnostic groups. Exploring interaction networks uncovered intricate connections, including gene-miRNA interactions (164 interactions involving 148 miRNAs), gene-transcription factor (TF) interactions (22 interactions with 20 TFs), and gene-drug small molecule interactions (15 interactions involving 15 drugs). Notably, IL1B and S100A8 demonstrated interactions with specific drugs.

Conclusion

In the realm of TLE, we have successfully pinpointed noteworthy differentially expressed apoptosis-related genes (DEARGs), including CD38, FAIM2, IL1B, PAWR, and S100A8. A comprehensive understanding of the implications associated with these identified genes not only opens avenues for advancing our comprehension of the underlying pathophysiology but also bears considerable potential in guiding the development of innovative diagnostic methodologies and therapeutic interventions for the effective management of epilepsy in the future.

Quantification of Hypsarrhythmia in Infantile Spasmatic EEG: A Large Cohort Study

Infantile spasms (IS) is a neurological disorder causing mental and/or developmental retardation in many infants. Hypsarrhythmia is a typical symptom in the electroencephalography (EEG) signals with IS. Long-term EEG/video monitoring is most frequently employed in clinical practice for IS diagnosis, from which manual screening of hypsarrhythmia is time consuming and lack of sufficient reliability. This study aims to identify potential biomarkers for automatic IS diagnosis by quantitative analysis of the EEG signals. A large cohort of 101 IS patients and 155 healthy controls (HC) were involved. Typical hypsarrhythmia and non-hypsarrhythmia EEG signals were annotated, and normal EEG were randomly picked from the HC. Root mean square (RMS), teager energy (TE), mean frequency, sample entropy (SamEn), multi-channel SamEn, multi-scale SamEn, and nonlinear correlation coefficient were computed in each sub-band of the three EEG signals, and then compared using either a one-way ANOVA or a Kruskal-Wallis test (based on their distribution) and the receiver operating characteristic (ROC) curves. The effects of infant age on these features were also investigated. For most of the employed features, significant ( ) differences were observed between hypsarrhythmia EEG and non-hypsarrhythmia EEG or HC, which seem to increase with increased infant age. RMS and TE produce the best classification in the delta and theta bands, while entropy features yields the best performance in the gamma band. Our study suggests RMS and TE (delta and theta bands) and entropy features (gamma band) to be promising biomarkers for automatic detection of hypsarrhythmia in long-term EEG monitoring. The findings of our study indicate the feasibility of automated IS diagnosis using artificial intelligence.

Plasma exosomes proteome profiling discovers protein markers associated with the therapeutic effect of Chaihu-Longgu-Muli decoction on temporal lobe epilepsy

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) uses Chaihu-Longgu-Muli decoction (CLMD) to alleviate disease, clear away heat, calm the mind, and temper excitation. It has been widely used for the therapy of neuropsychiatric disorders including epilepsy, dementia, anxiety, insomnia, and depression for several centuries in China.

AIM OF THE STUDY

This study aims to analyze differentially expressed proteins (DEPs) in the plasma exosomes of patients with temporal lobe epilepsy (TLE) and after the Chaihu-Longgu-Muli Decoction (CLMD) therapy and to explore the biomarkers of TLE and the potential targets of CLMD in treating TLE.

MATERIALS AND METHODS

The plasma exosomes of normal people and patients with TLE before the treatment of oxcarbazepine (OXC) and combined treatment of OXC and CLMD (OXC.CLMD) were harvested. The exosomes were separated from plasma through ultracentrifugation and then identified by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and flow cytometry. The DEPs were analyzed by proteomics and then subjected to gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The protein level of key genes was detected using Western blot. A lithium chloride-pilocarpine-induced epilepsy rat model was established and treated with OXC alone, OXC. CLMD, and CLMD alone (low dose and high dose). Neuronal injury in the hippocampal dentate gyrus and ribosomal protein L6 (RPL6) expression in the brain tissues were detected using H&E staining, Nissl staining, and Western blot.

RESULTS

The proteomic analysis showed several DEPs were present among plasma exosomes in the four groups; DEPs were enriched in epilepsy-related function and pathway. Four key proteins were screened, including RPL6, Nucleolin (NCL), Apolipoprotein A1 (APOA1), and Lactate Dehydrogenase A (LDHA). Among them, RPL6, NCL, and LDHA protein levels were downregulated and APOA1 protein level was upregulated in the plasma exosomes of TLE patients. After OXC and OXC. CLMD treatment, the protein level of RPL6, NCL, and LDHA was increased, and the APOA1 protein level was decreased. Moreover, the RPL6 protein level was further elevated after OXC. CLMD treatment than that after OXC treatment. In the TLE rat model, neuronal degeneration and necrosis in the hippocampal dentate gyrus increased and RPL6 expression level decreased. After the treatment with OXC, OXC. CLMD, and CLMD alone, the degeneration and necrosis of neurons decreased, and the RPL6 expression level was increased; RPL6 upregulation was remarkably obvious after CLMD treatment.

CONCLUSIONS

RPL6, NCL, LDHA and APOA1 are the DEPs in the plasma exosomes of patients with TLE before and after therapy. RPL6 might be a potential biomarker of CLMD in treating TLE.

Circulating microRNAs from plasma as preclinical biomarkers of epileptogenesis and epilepsy

Epilepsy frequently develops as a result of brain insult; however, there are no tools allowing to predict which patients suffering from trauma will eventually develop epilepsy. microRNAs are interesting candidates for biomarkers, as several of them have been described to change their levels in the brains, and in the plasma of epileptic subjects. This study was conducted to evaluate the usefulness of plasma miRNAs as epileptogenesis/epilepsy biomarkers. In our studies, we used a rat model of temporal lobe epilepsy. An epileptogenic insult was status epilepticus evoked by stimulation of the left lateral nucleus of the amygdala. Next, animals were continuously video and EEG monitored for 3 months. Blood was collected at 14, 30, 60, and 90 days after stimulation. Blood plasma was separated and miRNA levels were analyzed. We compared miRNA levels between sham-operated and stimulated animals, and between animals with high and low numbers of seizures. We propose three miRNAs that could be biomarkers of epilepsy: miR-671, miR-9a-3p and miR-7a-5p. According to us, miR-206-5p is a potential biomarker of epileptogenesis, and miR-221-3p is a potential biomarker of epilepsy severity. We think that these five miRNAs can be considered in the future as potential treatment targets.

Imaging biomarkers of sleep-related hypermotor epilepsy and sudden unexpected death in epilepsy: a review

In recent years, imaging has emerged as a promising source of several intriguing biomarkers in epilepsy, due to the impressive growth of imaging technology, supported by methodological advances and integrations of post-processing techniques. Bearing in mind the mutually influencing connection between sleep and epilepsy, we focused on sleep-related hypermotor epilepsy (SHE) and sudden unexpected death in epilepsy (SUDEP), aiming to make order and clarify possible clinical utility of emerging multimodal imaging biomarkers of these two epilepsy-related entities commonly occurring during sleep. Regarding SHE, advanced structural techniques might soon emerge as a promising source of diagnostic and predictive biomarkers, tailoring a targeted therapeutic (surgical) approach for MRI-negative subjects. Functional and metabolic imaging may instead unveil SHE's extensive and night-related altered brain networks, providing insights into distinctions and similarities with non-epileptic sleep phenomena, such as parasomnias. SUDEP is considered a storm that strikes without warning signals, but objective subtle structural and functional alterations in autonomic, cardiorespiratory, and arousal centers are present in patients eventually experiencing SUDEP. These alterations could be seen both as susceptibility and diagnostic biomarkers of the underlying pathological ongoing loop ultimately ending in death. Finally, given that SHE and SUDEP are rare phenomena, most evidence on the topic is derived from small single-center experiences with scarcely comparable results, hampering the possibility of performing any meta-analytic approach. Multicenter, longitudinal, well-designed studies are strongly encouraged.

Socrates: A Novel N-Ethyl-N-nitrosourea-Induced Mouse Mutant with Audiogenic Epilepsy

Epilepsy is one of the common neurological diseases that affects not only adults but also infants and children. Because epilepsy has been studied for a long time, there are several pharmacologically effective anticonvulsants, which, however, are not suitable as therapy for all patients. The genesis of epilepsy has been extensively investigated in terms of its occurrence after injury and as a concomitant disease with various brain diseases, such as tumors, ischemic events, etc. However, in the last decades, there are multiple reports that both genetic and epigenetic factors play an important role in epileptogenesis. Therefore, there is a need for further identification of genes and loci that can be associated with higher susceptibility to epileptic seizures. Use of mouse knockout models of epileptogenesis is very informative, but it has its limitations. One of them is due to the fact that complete deletion of a gene is not, in many cases, similar to human epilepsy-associated syndromes. Another approach to generating mouse models of epilepsy is N-Ethyl-N-nitrosourea (ENU)-directed mutagenesis. Recently, using this approach, we generated a novel mouse strain, soc (socrates, formerly s8-3), with epileptiform activity. Using molecular biology methods, calcium neuroimaging, and immunocytochemistry, we were able to characterize the strain. Neurons isolated from soc mutant brains retain the ability to differentiate in vitro and form a network. However, soc mutant neurons are characterized by increased spontaneous excitation activity. They also demonstrate a high degree of Ca2+ activity compared to WT neurons. Additionally, they show increased expression of NMDA receptors, decreased expression of the Ca2+-conducting GluA2 subunit of AMPA receptors, suppressed expression of phosphoinositol 3-kinase, and BK channels of the cytoplasmic membrane involved in protection against epileptogenesis. During embryonic and postnatal development, the expression of several genes encoding ion channels is downregulated in vivo, as well. Our data indicate that soc mutation causes a disruption of the excitation-inhibition balance in the brain, and it can serve as a mouse model of epilepsy.

Surgical Treatment of Epilepsy with Bilateral MRI Abnormalities

OBJECTIVE

To investigate the surgical outcomes of patients with drug-resistant epilepsy and bilateral brain magnetic resonance imaging (MRI) abnormalities who had undergone various epilepsy surgeries.

METHODS

Patients with drug-resistant epilepsy and bilateral brain abnormalities on MRI who underwent epilepsy surgery at the Severance Children's Hospital between October 2003 and December 2021 were included. The age of seizure onset was 18 years or younger. Engel's classification was used to assess seizure outcomes at 1, 2, and 5 years after surgery.

RESULTS

A total of 40 patients met the inclusion criteria. The median age at surgery was 10.9 years (interquartile range [IQR] 6.9-15.1); the median interval to surgery was 7.1 years (IQR 2.7-11.5). One year after surgery, a favorable outcome of Engel class I-II was observed in 53% (21/40) of patients. At the 2- and 5-year follow-ups, 56% (20/36) and 63% (17/27) of patients showed good postoperative outcomes, respectively.

CONCLUSIONS

Approximately, half of the patients with bilateral brain MRI abnormalities achieved seizure freedom after epilepsy surgery. The existence of bilateral brain MRI abnormalities should not hinder resective epilepsy surgery.

Evaluation of the diagnostic utility on 1.5T and 3.0T 1H magnetic resonance spectroscopy for temporal lobe epilepsy

BACKGROUND

1H magnetic resonance spectroscopy (1H-MRS) can be used to study neurological disorders because it can be utilized to examine the concentrations of related metabolites. However, the diagnostic utility of different field strengths for temporal lobe epilepsy (TLE) remains unclear. The purpose of this study is to make quantitative comparisons of metabolites of TLE at 1.5T and 3.0T and evaluate their efficacy.

METHODS

Our retrospective collections included the single-voxel 1H-MRS of 23 TLE patients and 17 healthy control volunteers (HCs) with a 1.5T scanner, as well as 29 TLE patients and 17 HCs with a 3.0T scanner. Particularly, HCs were involved both the scans with 1.5T and 3.0T scanners, respectively. The metabolites, including the N-acetylaspartate (NAA), creatine (Cr), and choline (Cho), were measured in the left or right temporal pole of brain. To analyze the ratio of brain metabolites, including NAA/Cr, NAA/Cho, NAA/(Cho + Cr) and Cho/Cr, four controlled experiments were designed to evaluate the diagnostic utility of TLE on 1.5T and 3.0T MRS, included: (1) 1.5T TLE group vs. 1.5T HCs by the Mann-Whitney U Test, (2) 3.0T TLE group vs. 3.0T HCs by the Mann-Whitney U Test, (3) the power analysis for the 1.5T and 3.0T scanner, and (4) 3.0T HCs vs. 1.5T HCs by Paired T-Test.

RESULTS

Three metabolite ratios (NAA/Cr, NAA/Cho, and NAA/(Cho + Cr) showed the same statistical difference (p < 0.05) in distinguishing the TLE from HCs in the bilateral temporal poles when using 1.5T or 3.0T scanners. Similarly, the power analysis demonstrated that four metabolite ratios (NAA/Cr, NAA/Cho, NAA/(Cho + Cr), Cho/Cr) had similar distinction abilities between 1.5T and 3.0T scanner, denoting both 1.5T and 3.0T scanners were provided with similar sensitivities and reproducibilities for metabolites detection. Moreover, the metabolite ratios of the same healthy volunteers were not statistically different between 1.5T and 3.0T scanners, except for NAA/Cho (p < 0.05).

CONCLUSIONS

1.5T and 3.0T scanners may have comparable diagnostic potential when 1H-MRS was used to diagnose patients with TLE.

Causal relationship between addictive behaviors and epilepsy risk: A mendelian randomization study

BACKGROUND

Previous studies have reported inconsistent results regarding the potential relationships between addictive behaviors and the risk of epilepsy.

OBJECTIVE

To assess whether genetically predicted addictive behaviors are causally associated with the risk of epilepsy outcomes.

METHODS

The causation between five addictive behaviors (including cigarettes per day, alcoholic drinks per week, tea intake, coffee intake, and lifetime cannabis use) and epilepsy was evaluated by using a two-sample Mendelian Randomization (MR) analysis. The inverse-variance weighted (IVW) method was used as the primary outcome. The other MR analysis methods (MR Egger, weighted median, simulation extrapolation corrected MR-Egger, and Mendelian Randomization Pleiotropy Residual Sum and Outlier (MR-PRESSO)) were performed to complement IVW. In addition, the robustness of the MR analysis results was assessed by leave-one-out analysis.

RESULTS

The IVW analysis method indicated an approximately 20% increased risk of epilepsy per standard deviation increase in lifetime cannabis use (odds ratio [OR], 1.20; 95% confidence interval [CI]), 1.02-1.42, P = 0.028). However, there is no causal association between the other four addictive behaviors and the risk of epilepsy (cigarettes per day: OR, 1.04; 95% CI, 0.92-1.18, P = 0.53; alcoholic drinks per week: OR, 1.31; 95% CI, 0.93-1.84, P = 0.13; tea intake: OR, 1.15; 95% CI, 0.84-1.56, P = 0.39; coffee intake: OR, 0.86; 95% CI, 0.59-1.23, P = 0.41). The other MR analysis methods and further leave-one-out sensitivity analysis suggested the results were robust.

CONCLUSION

This MR study indicated a potential genetically predicted causal association between lifetime cannabis use and higher risk of epilepsy. As for the other four addictive behaviors, no evidence of a causal relationship with the risk of epilepsy was found in this study.

Trpm2 deficiency in microglia attenuates neuroinflammation during epileptogenesis by upregulating autophagy via the AMPK/mTOR pathway

Epilepsy is one of the most common neurological disorders. Neuroinflammation involving the activation of microglia and astrocytes constitutes an important and common mechanism in epileptogenesis. Transient receptor potential melastatin 2 (TRPM2) is a calcium-permeable, non-selective cation channel that plays pathological roles in various inflammation-related diseases. Our previous study demonstrated that Trpm2 knockout exhibits therapeutic effects on pilocarpine-induced glial activation and neuroinflammation. However, whether TRPM2 in microglia and astrocytes plays a common pathogenic role in this process and the underlying molecular mechanisms remained undetermined. Here, we demonstrate a previously unknown role for microglial TRPM2 in epileptogenesis. Trpm2 knockout in microglia attenuated kainic acid (KA)-induced glial activation, inflammatory cytokines production and hippocampal paroxysmal discharges, whereas Trpm2 knockout in astrocytes exhibited no significant effects. Furthermore, we discovered that these therapeutic effects were mediated by upregulated autophagy via the adenosine monophosphate activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) pathway in microglia. Thus, our findings highlight an important deleterious role of microglial TRPM2 in temporal lobe epilepsy.

Effect of Duvelisib, a Selective PI3K Inhibitor on Seizure Activity in Pentylenetetrazole-Induced Convulsions Animal Model

Epilepsy is one of the most common neurological diseases, which is caused by abnormal brain activity. A wide variety of studies have shown the importance of the phosphatidylinositol-3-kinase (PI3K) signaling pathway in epilepsy pathogenesis. Duvelisib (DUV) is a selective inhibitor of PI3K. The present study investigated the anticonvulsant potential of DUV in a rat model of pentylenetetrazole (PTZ)-induced convulsions. Male Wistar rats (200-250 g, 8 weeks old) were injected intraperitoneally (IP) with DUV at different doses of 5 and 10 mg/kg, or vehicle 30 minutes prior to PTZ (70 mg/kg, IP) treatment. Based on Racine's scale, behavioral seizures were assessed. The results showed that pretreatment with DUV prolonged the seizure stages according to the Racine scale, significantly decreased the duration of general tonic-clonic seizure and reduced the number of myoclonic jerks (P < .05). In conclusion, we found that PI3K antagonist DUV significantly reduced PTZ-induced seizures, indicating that DUV exerts an anticonvulsant effect by inhibiting PI3K signaling pathway.

Epilepsy: Mitochondrial connections to the 'Sacred' disease

Over 65 million people suffer from recurrent, unprovoked seizures. The lack of validated biomarkers specific for myriad forms of epilepsy makes diagnosis challenging. Diagnosis and monitoring of childhood epilepsy add to the need for non-invasive biomarkers, especially when evaluating antiseizure medications. Although underlying mechanisms of epileptogenesis are not fully understood, evidence for mitochondrial involvement is substantial. Seizures affect 35%-60% of patients diagnosed with mitochondrial diseases. Mitochondrial dysfunction is pathophysiological in various epilepsies, including those of non-mitochondrial origin. Decreased ATP production caused by malfunctioning brain cell mitochondria leads to altered neuronal bioenergetics, metabolism and neurological complications, including seizures. Iron-dependent lipid peroxidation initiates ferroptosis, a cell death pathway that aligns with altered mitochondrial bioenergetics, metabolism and morphology found in neurodegenerative diseases (NDDs). Studies in mouse genetic models with seizure phenotypes where the function of an essential selenoprotein (GPX4) is targeted suggest roles for ferroptosis in epilepsy. GPX4 is pivotal in NDDs, where selenium protects interneurons from ferroptosis. Selenium is an essential central nervous system micronutrient and trace element. Low serum concentrations of selenium and other trace elements and minerals, including iron, are noted in diagnosing childhood epilepsy. Selenium supplements alleviate intractable seizures in children with reduced GPX activity. Copper and cuproptosis, like iron and ferroptosis, link to mitochondria and NDDs. Connecting these mechanistic pathways to selenoproteins provides new insights into treating seizures, pointing to using medicines including prodrugs of lipoic acid to treat epilepsy and to potential alternative therapeutic approaches including transcranial magnetic stimulation (transcranial), photobiomodulation and vagus nerve stimulation.

ECoG spiking activity and signal dimension are early predictive measures of epileptogenesis in a translational mouse model of traumatic brain injury

The latency between traumatic brain injury (TBI) and the onset of epilepsy (PTE) represents an opportunity for counteracting epileptogenesis. Antiepileptogenesis trials are hampered by the lack of sensitive biomarkers that allow to enrich patient's population at-risk for PTE. We aimed to assess whether specific ECoG signals predict PTE in a clinically relevant mouse model with ∼60% epilepsy incidence. TBI was provoked in adult CD1 male mice by controlled cortical impact on the left parieto-temporal cortex, then mice were implanted with two perilesional cortical screw electrodes and two similar electrodes in the hemisphere contralateral to the lesion site. Acute seizures and spikes/sharp waves were ECoG-recorded during 1 week post-TBI. These early ECoG events were analyzed according to PTE incidence as assessed by measuring spontaneous recurrent seizures (SRS) at 5 months post-TBI. We found that incidence, number and duration of acute seizures during 3 days post-TBI were similar in PTE mice and mice not developing epilepsy (No SRS mice). Control mice with cortical electrodes (naïve, n = 5) or with electrodes and craniotomy (sham, n = 5) exhibited acute seizures but did not develop epilepsy. The daily number of spikes/sharp waves at the perilesional electrodes was increased similarly in PTE (n = 15) and No SRS (n = 8) mice vs controls (p < 0.05, n = 10) from day 2 post-injury. Differently, the daily number of spikes/sharp waves at both contralateral electrodes showed a progressive increase in PTE mice vs No SRS and control mice. In particular, spikes number was higher in PTE vs No SRS mice (p < 0.05) at 6 and 7 days post-TBI, and this measure predicted epilepsy development with high accuracy (AUC = 0.77, p = 0.03; CI 0.5830-0.9670). The cut-off value was validated in an independent cohort of TBI mice (n = 12). The daily spike number at the contralateral electrodes showed a circadian distribution in PTE mice which was not observed in No SRS mice. Analysis of non-linear dynamics at each electrode site showed changes in dimensionality during 4 days post-TBI. This measure yielded the best discrimination between PTE and No SRS mice (p < 0.01) at the cortical electrodes contralateral to injury. Data show that epileptiform activity contralateral to the lesion site has the the highest predictive value for PTE in this model reinforcing the hypothesis that the hemisphere contralateral to the lesion core may drive epileptogenic networks after TBI.

The dysregulation of miRNAs in epilepsy and their regulatory role in inflammation and apoptosis

Epilepsy is a neurological disorder that impacts millions of people worldwide, and it is characterized by the occurrence of recurrent seizures. The pathogenesis of epilepsy is complex, involving dysregulation of various genes and signaling pathways. MicroRNAs (miRNAs) are a group of small non-coding RNAs that play a vital role in the regulation of gene expression. They have been found to be involved in the pathogenesis of epilepsy, acting as key regulators of neuronal excitability and synaptic plasticity. In recent years, there has been a growing interest in exploring the miRNA regulatory network in epilepsy. This review summarizes the current knowledge of the regulatory miRNAs involved in inflammation and apoptosis in epilepsy and discusses its potential as a new avenue for developing targeted therapies for the treatment of epilepsy.

Computational modeling allows unsupervised classification of epileptic brain states across species

Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.

Sestrin 3 promotes oxidative stress primarily in neurons following epileptic seizures in rats

Epilepsy affects approximately 1% of the global population, with 30% of patients experiencing uncontrolled seizures despite treatment. Reactive oxygen species (ROS) and oxidative stress have been implicated in the pathogenesis of epilepsy. Sestrins are stress-inducible proteins that regulate the ROS response. In particular, Sestrin 3 (SESN3) has been implicated in ROS accumulation and the regulation of proconvulsant genes. To investigate the role of SESN3 in epilepsy, we studied its involvement in rat models of acute seizures and temporal lobe epilepsy. Our results showed that downregulation of SESN3 reduced the oxidative stress induced by seizure activity in neuronal cultures. After acute seizure activity, SESN3 protein levels temporarily increased as early as 3 h after the seizure, whereas kainic acid-induced status epilepticus led to a significant and persistent increase in SESN3 protein levels in the cortex and hippocampus for up to 2 weeks post-status epilepticus. In the chronic epilepsy phase, when spontaneous seizures emerge, SESN3 protein expression is significantly increased in both regions 6 and 12 weeks after status epilepticus. Interestingly, immunohistochemical staining showed a predominant increase in the oxidative stress marker 8-OHdG in neurons in both regions after an acute seizure, whereas following status epilepticus, the marker was detected in both neurons and astrocytes. Our findings suggest that SESN3 may contribute to the development and establishment of epilepsy, and could be a potential therapeutic target for more effective treatments.

Biogenesis, Composition and Potential Therapeutic Applications of Mesenchymal Stem Cells Derived Exosomes in Various Diseases

Exosomes are nanovesicles with a wide range of chemical compositions used in many different applications. Mesenchymal stem cell-derived exosomes (MSCs-EXOs) are spherical vesicles that have been shown to mediate tissue regeneration in a variety of diseases, including neurological, autoimmune and inflammatory, cancer, ischemic heart disease, lung injury, and liver fibrosis. They can modulate the immune response by interacting with immune effector cells due to the presence of anti-inflammatory compounds and are involved in intercellular communication through various types of cargo. MSCs-EXOs exhibit cytokine storm-mitigating properties in response to COVID-19. This review discussed the potential function of MSCs-EXOs in a variety of diseases including neurological, notably epileptic encephalopathy and Parkinson's disease, cancer, angiogenesis, autoimmune and inflammatory diseases. We provided an overview of exosome biogenesis and factors that regulate exosome biogenesis. Additionally, we highlight the functions and potential use of MSCs-EXOs in the treatment of the inflammatory disease COVID-19. Finally, we covered a strategies and challenges of MSCs-EXOs. Finally, we discuss conclusion and future perspectives of MSCs-EXOs.

Multi-omics Integration and Epilepsy: Towards a Better Understanding of Biological Mechanisms

The epilepsies are a group of complex neurological disorders characterised by recurrent seizures. Approximately 30% of patients fail to respond to anti-seizure medications, despite the recent introduction of many new drugs. The molecular processes underlying epilepsy development are not well understood and this knowledge gap impedes efforts to identify effective targets and develop novel therapies against epilepsy. Omics studies allow a comprehensive characterisation of a class of molecules. Omics-based biomarkers have led to clinically validated diagnostic and prognostic tests for personalised oncology, and more recently for non-cancer diseases. We believe that, in epilepsy, the full potential of multi-omics research is yet to be realised and we envisage that this review will serve as a guide to researchers planning to undertake omics-based mechanistic studies.

The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research

OBJECTIVE

The quest for epilepsy biomarkers is on the rise. Variables with statistically significant group-level differences are often misinterpreted as biomarkers with sufficient discriminative power. This study aimed to demonstrate the relationship between significant group-level differences and a variable's power to discriminate between individuals.

METHODS

We simulated normal-distributed datasets from hypothetical populations with varying sample sizes (25-800), effect sizes (Cohen's d: .25-2.50), and variability (standard deviation: 10-35) to assess the impact of these parameters on significance and discriminative power. The simulation data were illustrated by assessing the discriminative power of a potential real-case biomarker-the EEG beta band power-to diagnose generalized epilepsy, using data from 66 children with generalized epilepsy and 385 controls. Additionally, we evaluated recently reported epilepsy biomarkers by comparing their effect sizes to our simulation-derived effect size criterion.

RESULTS

Group size affects significance but not discriminative power. Discriminative power is much more related to variability and effect size. Our real data example supported these simulation results by demonstrating that group-level significance does not translate, one to one, into discriminative power. Although we found a significant difference in the beta band power between children with and without epilepsy, the discriminative power was poor due to a small effect size. A Cohen's d of at least 1.25 is required to reach good discriminative power in univariable prediction modeling. Slightly over 60% of the biomarkers in our literature search met this criterion.

SIGNIFICANCE

Rather than statistical significance of group-level differences, effect size should be used as an indicator of a variable's biomarker potential. The minimal required effects size for individual biomarkers-a Cohen's d of 1.25-is large. This calls for multivariable approaches, in which combining multiple variables with smaller effect sizes could increase the overall effect size and discriminative power.

The S100B Protein: A Multifaceted Pathogenic Factor More Than a Biomarker

S100B is a calcium-binding protein mainly concentrated in astrocytes in the nervous system. Its levels in biological fluids are recognized as a reliable biomarker of active neural distress, and more recently, mounting evidence points to S100B as a Damage-Associated Molecular Pattern molecule, which, at high concentration, triggers tissue reactions to damage. S100B levels and/or distribution in the nervous tissue of patients and/or experimental models of different neural disorders, for which the protein is used as a biomarker, are directly related to the progress of the disease. In addition, in experimental models of diseases such as Alzheimer's and Parkinson's diseases, amyotrophic lateral sclerosis, multiple sclerosis, traumatic and vascular acute neural injury, epilepsy, and inflammatory bowel disease, alteration of S100B levels correlates with the occurrence of clinical and/or toxic parameters. In general, overexpression/administration of S100B worsens the clinical presentation, whereas deletion/inactivation of the protein contributes to the amelioration of the symptoms. Thus, the S100B protein may be proposed as a common pathogenic factor in different disorders, sharing different symptoms and etiologies but appearing to share some common pathogenic processes reasonably attributable to neuroinflammation.

Nicorandil Exerts Anticonvulsant Effects in Pentylenetetrazol-Induced Seizures and Maximal-Electroshock-Induced Seizures by Downregulating Excitability in Hippocampal Pyramidal Neurons

N-(2-hydroxyethyl) nicotinamide nitrate (nicorandil), a nitrate that activates adenosine triphosphate (ATP)-sensitive potassium (K_ATP) channels, is generally used in the treatment of angina and offers long-term cardioprotective effects. It has been reported that several K_ATP channel openers can effectively alleviate the symptoms of seizure. The purpose of this study was to investigate the improvement in seizures induced by nicorandil. In this study, seizure tests were used to evaluate the effect of different doses of nicorandil by analysing seizure incidence, including minimal clonic seizure and generalised tonic-clonic seizure. We used a maximal electroshock seizure (MES) model, a metrazol maximal seizure (MMS) model and a chronic pentylenetetrazol (PTZ)-induced seizure model to evaluate the effect of nicorandil in improving seizures. Each mouse in the MES model was given an electric shock, while those in the nicorandil group received 0.5, 1, 2, 3 and 6 mg/kg of nicorandil by intraperitoneal injection, respectively. In the MMS model, the mice in the PTZ group and the nicorandil group were injected subcutaneously with PTZ (90 mg/kg), and the mice in the nicorandil group were injected intraperitoneally with 1, 3 and 5 mg/kg nicorandil, respectively. In the chronic PTZ-induced seizure model, the mice in the PTZ group and the nicorandil group were injected intraperitoneally with PTZ (40 mg/kg), and the mice in the nicorandil group were each given 1 and 3 mg/kg of PTZ at a volume of 200 nL. Brain slices containing the hippocampus were prepared, and cell-attached recording was used to record the spontaneous firing of pyramidal neurons in the hippocampal CA1 region. Nicorandil (i.p.) significantly increased both the maximum electroconvulsive protection rate in the MES model and the seizure latency in the MMS model. Nicorandil infused directly onto the hippocampal CA1 region via an implanted cannula relieved symptoms in chronic PTZ-induced seizures. The excitability of pyramidal neurons in the hippocampal CA1 region of the mice was significantly increased after both the acute and chronic administration of PTZ. To a certain extent, nicorandil reversed the increase in both firing frequency and proportion of burst spikes caused by PTZ (P < 0.05). Our results suggest that nicorandil functions by downregulating the excitability of pyramidal neurons in the hippocampal CA1 region of mice and is a potential candidate for the treatment of seizures.

Dynamics of AMPA receptors regulate epileptogenesis in patients with epilepsy

The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs) contribute to epileptogenesis. Thirty patients with epilepsy and 31 healthy controls are scanned using positron emission tomography with our recently developed radiotracer for AMPARs, [¹¹C]K-2, which measures the density of cell-surface AMPARs. In patients with focal-onset seizures, an increase in AMPAR trafficking augments the amplitude of abnormal gamma activity detected by electroencephalography. In contrast, patients with generalized-onset seizures exhibit a decrease in AMPARs coupled with increased amplitude of abnormal gamma activity. Patients with epilepsy had reduced AMPAR levels compared with healthy controls, and AMPARs are reduced in larger areas of the cortex in patients with generalized-onset seizures compared with those with focal-onset seizures. Thus, epileptic brain function can be regulated by the enhanced trafficking of AMPAR due to Hebbian plasticity with increased simultaneous neuronal firing and compensational downregulation of cell-surface AMPARs by the synaptic scaling.

Understanding Contexts and Challenges of Information Management for Epilepsy Care

Epilepsy is a common chronic neurological disease. People with epilepsy (PWE) and their caregivers face several challenges related to their epilepsy management, including quality of care, care coordination, side effects, and stigma management. The sociotechnical issues of the information management contexts and challenges for epilepsy care may be mitigated through effective information management. We conducted 4 focus groups with 5 PWE and 7 caregivers to explore how they manage epilepsy-related information and the challenges they encountered. Primary issues include challenges of finding the right information, complexities of tracking and monitoring data, and limited information sharing. We provide a framework that encompasses three attributes - individual epilepsy symptoms and health conditions, information complexity, and circumstantial constraints. We suggest future design implications to mitigate these challenges and improve epilepsy information management and care coordination.

miR-30b-5p targeting GRIN2A inhibits hippocampal damage in epilepsy

GRIN2A is associated with epilepsy (EP); however, its regulatory mechanism involving upstream miRNA (miR-30b-5p) has been overlooked. In this study, we aimed to identify the regulatory mechanism of the miR-30b-5p/GRIN2A axis in EP. Hippocampal neurons isolated from mice were incubated in magnesium-free medium for 48 h to establish an in vitro EP model. An in vivo model of EP was constructed by the intraperitoneal injection of atropine into mice. Nissl staining and hematoxylin and eosin staining were used to evaluate pathological injuries in the hippocampal CA1 regions of mice. The CCK8 assay confirmed that miR-30b-5p overexpression restored the suppressed proliferative capacity of hippocampal neurons exposed to magnesium-free conditions. Caspase-3 activity assay revealed that miR-30b-5p overexpression abrogated the increased apoptosis of hippocampal neurons under magnesium-free conditions. In an in vivo model of EP, miR-30b-5p overexpression reversed pathological injuries in the hippocampal CA1 regions of mice and abrogated the increased apoptosis in the EP mouse model. Luciferase assays and western blotting confirmed that miR-30b-5p targeted GRIN2A, thereby inhibiting GRIN2A expression. Overall, miR-30b-5p can protect against cell proliferation and attenuate apoptosis in hippocampal neurons under magnesium-free conditions by targeting GRIN2A.

Personalised virtual brain models in epilepsy

Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.

An exploratory approach to identify microRNAs as circulatory biomarker candidates for epilepsy-associated psychiatric comorbidities in an electrical post-status epilepticus model

Patients with epilepsy have a high risk of developing psychiatric comorbidities, and there is a particular need for early detection of these comorbidities. Here, in an exploratory, hypothesis-generating approach, we aimed to identify microRNAs as potential circulatory biomarkers for epilepsy-associated psychiatric comorbidities across different rat models of epilepsy. The identification of distress-associated biomarkers can also contribute to animal welfare assessment. MicroRNA expression profiles were analyzed in blood samples from the electrical post-status epilepticus (SE) model. Preselected microRNAs were correlated with behavioral and biochemical parameters in the electrical post-SE model, followed by quantitative real-time PCR validation in three additional well-described rat models of epilepsy. Six microRNAs (miR-376a, miR-429, miR-494, miR-697, miR-763, miR-1903) were identified showing a positive correlation with weight gain in the early post-insult phase as well as a negative correlation with social interaction, saccharin preference, and plasma BDNF. Real-time PCR validation confirmed miR-203, miR-429, and miR-712 as differentially expressed with miR-429 being upregulated across epilepsy models. While readouts from the electrical post-SE model suggest different microRNA candidates for psychiatric comorbidities, cross-model analysis argues against generalizability across models. Thus, further research is necessary to compare the predictive validity of rodent epilepsy models for detection and management of psychiatric comorbidities.