Epilepsia

BackgroundPatients with drug-resistant epilepsy, including Dravet syndrome (DS), are frequently prescribed multiple antiseizure medications (ASMs). Nevertheless, people with DS often have inadequate seizure control, and there is an ongoing unmet clinical need to identify novel therapeutics. As a proof-of-principle study to further validate and characterize the Scn1aA1783V/WT mouse model and identify a drug screening paradigm with face, construct, and predictive value, we assessed the efficacy of subchronic administration of stiripentol add-on to clobazam and valproic acid at clinically relevant doses using the Scn1aA1783V/WT mouse model. MethodsWe evaluated the efficacy of STP add-on to CLB and VPA using hyperthermia-induced and video-EEG monitoring of spontaneous seizure tests following a 14-day treatment. VPA was delivered via osmotic minipump, while STP and CLB were administered via food pellets delivered through automatic feeders. Bioanalytical assays were performed to evaluate drug concentrations in plasma and brain using liquid chromatography-tandem mass spectrometry. ResultsSTP, CLB, N-desmethylclobazam, and VPA all yielded plasma concentrations within the human therapeutic plasma concentrations range. STP added to CLB and VPA significantly elevated the seizing temperatures in the hyperthermia-induced seizure assay. CLB, VPA, and STP coadministration significantly reduced spontaneous seizure frequency compared to CLB and VPA combined. SignificanceThis research lays the groundwork for exploring effective add-on compounds to CLB and VPA in treating DS. The study further highlights the utility of the Scn1aA1783V/WT mice in discovering therapies for DS-associated pharmacoresistant seizures. Key pointsO_LIIntegrating pharmacokinetic studies to guide the selection of doses in preclinical studies to achieve target concentrations comparable to the human therapeutic range is crucial in successfully translating animal drug development studies to clinical use. C_LIO_LISTP add-on to CLB and VPA significantly reduced spontaneous seizure frequency in Scn1aA1783V/WT mice. C_LIO_LIA triple-drug polytherapy approach that mimics the clinical treatment paradigm will be an essential preclinical drug screening strategy for identifying novel investigational compounds for Dravet syndrome. C_LI

Inhibitors of voltage-gated sodium channels (NaVs) are important anti-epileptic drugs, but the contribution of specific channel isoforms is unknown since available inhibitors are nonselective. We created a series of compounds with diverse selectivity profiles enabling block of NaV1.6 alone or together with NaV1.2. Mice with a heterozygous gain-of-function mutation (N1768D/+) in Scn8a (encoding NaV1.6) responded with a tonic-clonic seizure to a mild 6 Hz stimulus that was innocuous to wild-type mice. Pharmacologic inhibition of NaV1.6 in Scn8aN1768D/+ mice prevented seizures. Inhibitors were also effective in a direct current maximal electroshock seizure assay in wild-type mice. NaV1.6 inhibition correlated with efficacy in both models, even without inhibition of other CNS NaV isoforms. Our data suggest NaV1.6 inhibition is a driver of efficacy for NaV inhibitor anti-seizure medicines. Selective NaV1.6 inhibitors may provide targeted therapies for human Scn8a developmental and epileptic encephalopathies and better tolerated treatments for idiopathic epilepsies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/551823v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@39c09borg.highwire.dtl.DTLVardef@19413b8org.highwire.dtl.DTLVardef@9aa226org.highwire.dtl.DTLVardef@b9207_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectivesAffecting around 50 million people, men and women likewise, epilepsies are among the most common neurological diseases worldwide. Despite special challenges in the medical treatment of women with epilepsy, previous research has mainly focused on males, in particular preclinical animal studies, leaving a gap that needs to be urgently addressed. The intrahippocampal kainic acid (IHKA) mouse model of temporal lobe epilepsy (TLE) as one of the most frequently studied models in males is used for screening of novel antiepileptic therapies. In this study we investigate the IHKA model of TLE in female mice, in particular drug-resistance of hippocampal paroxysmal discharges. Furthermore, we provide evidence for anti-seizure effects of dimethyl sulfoxide (DMSO) in epileptic, but not naIve mice. MethodsAfter injecting KA unilaterally into the hippocampus of female mice, we monitored the development of epileptiform activity in in-vivo EEG recordings, evaluated responsiveness to the commonly prescribed antiseizure drugs (ASDs) lamotrigine (LTG), oxcarbazepine (OXC) and levetiracetam (LEV) and assessed typical neuropathological alterations of the hippocampus. Moreover, the effect of different doses of DMSO was tested in the IHKA chronic epilepsy model as well as on the PTZ-induced acute seizure threshold in both female and male mice. ResultsIn the IHKA model, female mice replicated the key features of human TLE (EEG and neuropathological changes). Importantly, hippocampal paroxysmal discharges (HPDs) in female mice did not respond to commonly prescribed ASDs, thus representing a suitable model of drug-resistant seizures. The solvent DMSO caused a significant short-term reduction of HPDs, but did not affect the threshold of acute seizures. SignificanceBy characterizing the drug-resistance of HPDs in the IHKA model of TLE in female mice we have laid a foundation for future research addressing sex-specific aspects. Considering the special issues complicating the therapeutic management of women, inclusion of females in the quest for novel treatment strategies is imperative. The observed effect of DMSO on epileptiform activity underlines that its application in epilepsy research is problematic and that the choice of solvent and appropriate vehicle control is crucial.

Antiseizure medicines (ASMs) cause both acute and chronic behavioral side effects in individuals with epilepsy. While clinical and preclinical studies often focus on chronic effects, the acute dose-related impact of ASMs on behavior is underreported, especially in rodent temporal lobe epilepsy (TLE) models. Investigating the acute effects of both therapeutic and behaviorally impairing doses may inform clinically relevant adverse effects, such as sedation, hyperactivity, and impaired coordination, which are essential for evaluating drug safety and tolerability. This study investigated the acute effects of anticonvulsant doses of carbamazepine (CBZ), valproic acid (VPA), levetiracetam (LEV), and cenobamate (CNB) on locomotor activity and exploratory behavior in rats 8-13 weeks after kainic acid-induced status epilepticus to elicit confirmed spontaneous recurrent seizures consistent (SRS) with TLE. Behavioral outcomes were quantified using an automated open field task (OFT) in both epileptic and non-epileptic (naive) rats. Our findings revealed that anticonvulsant doses of CNB affected locomotor behavior while other ASMs did not alter exploratory behavior. However, the motor impairing doses of CBZ and CNB equally suppressed exploratory behavior, likely due to sedative effects, in both epileptic and non-epileptic rats. LEV was unique, showing no sedative effects even at high doses, while VPA exhibited an anxiolytic effect in SRS rats and a sedative effect in naive rats at high dose. This study provides essential insight into the efficacy and tolerability profiles of a diversity of FDA-approved ASMs in a clinically relevant TLE model. Thus, SRS may influence ASM tolerability in preclinical TLE models that may inform clinical translation.

ABSTRACT/SUMMARYO_ST_ABSObjectiveC_ST_ABSCentral to the development of novel antiseizure medications (ASMs) is testing of anticonvulsant activity in preclinical models. While various well-established models exist, their predictive validity across the spectrum of clinical epilepsies has been less clear. We sought to establish the translational concordance of commonly used preclinical models to define models with the highest predictive clinical validity for focal onset seizures (FOS). MethodsThe Praxis Analysis of Concordance (PAC) framework was implemented to assess the translational concordance between preclinical and clinical ASM response for 32 FDA-approved ASMs. Preclinical ASM responses in historically used seizure models were collected. Protective indices based on reported TD50 and ED50 values were calculated for each ASM in each preclinical model. A weighted scale representing relative anticonvulsant effect was used to grade preclinical ASM response for each seizure model. Data depth was further scored based on the number of evaluated ASMs with publicly available data. Established reports of clinical ASM use in patients with FOS were similarly evaluated and a weighted scale representing prescribing patterns and perceived efficacy used to grade clinical ASM response for each indication. To assess the predictive validity of preclinical models, a unified translational scoring matrix was developed to assign a concordance score spanning the spectrum of complete discordance (-1) to complete concordance (1) between preclinical and clinical ASM responses. Scores were summed and normalized to generate a global translational concordance score. ResultsThe preclinical models with the highest translational concordance and greatest data depth for FOS were rodent maximal electroshock seizure (MES), mouse audiogenic seizure, mouse 6 Hz (32mA) and rat amygdala kindling. SignificanceThe PAC-FOS framework highlights mouse MES, mouse audiogenic and mouse 6 Hz (32mA) as three acute seizure models consistently demonstrating high predictive validity for FOS. We provide a pragmatic decision tree approach to support efficient resource utilization for novel ASM discovery for FOS. KEY POINT BOXUsing a newly developed translational scoring matrix, we provide novel insights into the clinical validity of common preclinical seizure models for FOS. O_LIThe PAC-FOS Framework identifies mouse MES, audiogenic and 6-Hz 32 mA as three acute models with greatest predictive validity and versatility for FOS drug discovery. C_LIO_LIWe present a pragmatic approach and decision tree to support efficient use of drug discovery resources and in consideration of the 3Rs of animal ethics. C_LIO_LIThe work presented would allow for faster and more effective screening of ASMs, while potentially reducing future patient exposures to likely ineffective drugs. C_LI

Dravet syndrome (DS) is a rare childhood epilepsy disorder resulting in spontaneous, recurrent seizures (SRS) and behavioral co-morbidities. To facilitate the discovery and development of anti-seizure drugs for DS, the contract site of the NINDS Epilepsy Therapy Screening Program (ETSP) has continued to evaluate a mouse model of DS. Scn1aA1783V/WT mice exhibited increased hyperactivity, thigmotaxis, and deficits in nest-building behavior. Ex-vivo brain slice electrophysiology experiments revealed increased excitability of hippocampal CA1 neurons specifically due to increased action potential firing frequency in response to brief depolarizations and decreased frequency of spontaneous GABAergic synaptic events. A video-EEG study revealed mice had on average, 1 seizure per day, with males seizing significantly more frequently than females. Increased proportion of seizure activity occurred during the dark phase of the light/dark cycle in both sexes. While clobazam, a drug commonly prescribed to patients with DS, had no effect on SRS activity at the tested doses, the seizure history and frequency observed in this study aids in determining the sample sizes and experimental timeline needed for adequately powered preclinical drug studies. Overall, this study provides a broad description of the Scn1aA1783V/WT mouse and highlights the utility of this model in therapy discovery.

Approximately a third of patients with epilepsy continue to have uncontrolled seizures despite appropriate treatment with available antiseizure medications (i.e. drug-resistant epilepsy). There is emerging evidence that transcription factor (TF) activity is both dysregulated in epilepsy and modifiable by small molecule drugs, providing an opportunity for treatment innovation. Methods for identifying dysregulated TFs and their target genes are still in their nascent stage and the reproducibility of findings remains unclear. We aimed to determine concordance of findings, in terms of the TFs dysregulated, across different studies of drug-resistant epilepsy and to evaluate the performance of different methods for identifying dysregulated TFs. We used publicly available single nucleotide RNA-seq data to construct discovery and validation datasets comprising individuals with drug-resistant temporal lobe epilepsy and healthy controls. We found good concordance (83%, 105/126) between the pySCENIC (python implemented Single-cell Regulatory Network Inference and Clustering) and hdWGCNA (high-dimensional Weighted Gene Co-expression Network Analysis) methods. Cell-type specific concordance across the discovery and validation datasets was low (36% 137/377) and this could be attributed, in part, to differences in data quality. In contrast, we found strong concordance between TFs that met strict concordance criteria in the current study with those implicated in a tissue-level study in patients with drug-resistant epilepsies, with the overlap being higher for TLE-related modules relative to modules for other drug-resistant epilepsies [86% (32/37) vs. 21% (18/84), Fishers-exact test: 95% 7.40 to 85.5, p < 0.0001]. Most TFS identified had been reported as being associated with epilepsy in the overall literature (91%, 53/58). Our findings strengthen the hypothesis that TFs are key to the pathophysiology of drug-resistant epilepsy and could represent novel drug targets. We recommend that multiple methods be applied to optimise discovery.

ObjectiveDravet syndrome (Dravet) is a rare and severe form of developmental epileptic encephalopathy. First-line treatment for DS patients includes valproic acid (VA) or clobazam with or without stiripentol (CLB+STP), while sodium channel blockers like carbamazepine (CBZ) or lamotrigine (LTG) are contraindicated. As patients are rarely seizure-free, drug therapy focuses on reducing the seizure burden, as reported by caregivers. In addition to their effect on epileptic phenotypes, antiseizure medications (ASMs) were shown to modify the properties of background neuronal activity. Nevertheless, little is known about these background properties alternations in Dravet. MethodsUtilizing Dravet mice (DS, Scn1aA1783V/WT), we tested the acute effect of several ASMs on background electrocorticography (ECoG) activity and frequency of interictal spikes. ResultsCompared to wild-type mice, background ECoG activity in DS had lower power and reduced phase coherence, which was not corrected by any of the tested ASMs. However, acute administration of Dravet-recommended drugs, including VA or a combination of CLB+STP, caused, in most mice, a reduction of frequency of interictal spikes, alongside an increase in the relative contribution of the beta frequency band. Conversely, CBZ and LTG increased the frequency of interictal spikes with no effect on background spectral properties. Moreover, we uncovered a correlation between the reduction in interictal spike frequency, the drug-induced effect on the power of background activity, and a spectral shift toward higher frequency bands. SignificanceThese data provide a comprehensive analysis of the effect of selected ASMs on the properties of background neuronal oscillations and highlight a possible correlation between their effect on epilepsy and background activity. Thus, examining these properties, following an acute administration, may be used as an additional tool for rapid evaluation of the therapeutic potential of ASMs. Key PointsO_LIReduced background power and phase coherence in Dravet mice C_LIO_LIDS-recommended medicines (VA, CLB+STP) increase the relative beta power C_LIO_LIDS-contraindicated drugs (CBZ, LTG) do not cause spectral changes C_LIO_LICorrelation between reduction in background power and interictal spike frequency C_LIO_LICorrelation between theta to beta bands ratio and interictal spike frequency C_LI

ObjectiveKCNT1-related epilepsy is an ultra-rare pediatric onset epileptic encephalopathy with a broad clinical phenotype ranging from, most commonly, severe infantile-onset epilepsy and global developmental delay to, less commonly, milder phenotypes including nocturnal seizures, autism spectrum disorder, and learning disability. We initiated the first-ever prospective natural history study to comprehensively clinically phenotype individuals impacted by this disorder. MethodsThe primary study aim was to characterize seizures in individuals with KCNT1-related epilepsy. Secondary and exploratory aims included characterization of the full spectrum of disease symptoms, understanding caregiver burden, and collection of blood and urine samples for biomarker exploration. All study activities were conducted remotely (e.g., home-based assessments, telehealth visits). Results35 participants (n=20 males, 15 females) enrolled in this study. The average age at the baseline visit was 76.0 months old (s.d. = 75.5). This paper presents the study design and methods, presents several challenges that arose in its implementation, and discusses various solutions implemented in this medically complex population. SignificanceFuture work will apply the lessons from the current study in the planning and design of clinical trials for KCNT1-related epilepsy and possibly other developmental and epileptic encephalopathies. HighlightsO_LIKCNT1-related epilepsy is an ultra-rare pediatric onset epileptic encephalopathy with no disease-modifying therapy yet available. C_LIO_LIBecause of its rarity, little is known about the phenotypic range and natural history of symptoms in affected individuals. C_LIO_LITo inform clinical trial planning, we initiated the first ever prospective, longitudinal natural history study of KCNT1-related epilepsy. C_LIO_LIThe unique all-remote design of this study presented various challenges, opportunities, and learnings that will inform future studies. C_LI

ImportanceEpilepsy is the most common neurological disorder of childhood. Identifying genetic diagnoses underlying epilepsy is critical to developing effective therapies and improving outcomes. Most children with non-acquired (unexplained) epilepsy remain genetically unsolved, and the utility of genome sequencing after nondiagnostic exome sequencing is unknown. ObjectiveTo determine the diagnostic (primary) and clinical (secondary) utility of genome sequencing after nondiagnostic exome sequencing in individuals with unexplained pediatric epilepsy. DesignThis cohort study performed genome sequencing and comprehensive analyses for 125 participants and available biological parents enrolled from August 2018 to May 2023, with data analysis through April 2024 and clinical return of diagnostic and likely diagnostic genetic findings. Clinical utility was evaluated. SettingPediatric referral center ParticipantsParticipants with unexplained pediatric epilepsy and previous nondiagnostic exome sequencing; biological parents when available Exposure(s)Short-read genome sequencing and analysis Main Outcome(s) and Measure(s)Primary outcome measures were the diagnostic yield of genome sequencing, defined as the percentage of participants receiving a diagnostic or likely diagnostic genetic finding, and the unique diagnostic yield of genome sequencing, defined as the percentage of participants receiving a diagnostic or likely diagnostic genetic finding that required genome sequencing. The secondary outcome measure was clinical utility of genome sequencing, defined as impact on evaluation, treatment, or prognosis for the participant or their family. Results125 participants (58 [46%] female) were enrolled with median age at seizure onset 3 [IQR 1.25, 8] years, including 44 (35%) with developmental and epileptic encephalopathies. The diagnostic yield of genome sequencing was 7.2% (9/125), with diagnostic genetic findings in five cases and likely diagnostic genetic findings in four cases. Among the solved cases, 7/9 (78%) required genome sequencing for variant detection (small copy number variant, three noncoding variants, and three difficult to sequence small coding variants), for a unique diagnostic yield of genome sequencing of 5.6% (7/125). Clinical utility was documented for 4/9 solved cases (44%). Conclusions and RelevanceThese findings suggest that genome sequencing can have diagnostic and clinical utility after nondiagnostic exome sequencing and should be considered for patients with unexplained pediatric epilepsy. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is the utility of genome sequencing after nondiagnostic exome sequencing in individuals with unexplained pediatric epilepsy? FindingsIn this cohort study of 125 individuals with unexplained pediatric epilepsy and nondiagnostic exome sequencing, genome sequencing identified diagnostic genetic findings in five cases and likely diagnostic genetic findings in four cases. Of the nine solved cases, seven required genome sequencing to solve, and four had documented clinical utility. MeaningGenome sequencing can identify genetic diagnoses not detectable by exome sequencing and should be considered for participants with unexplained pediatric epilepsy, as first-line testing or after nondiagnostic exome sequencing.

Dravet syndrome (DS) is a severe genetic epilepsy primarily caused by de novo mutations in a voltage-activated sodium channel gene (SCN1A). Patients face life-threatening seizures that are largely resistant to available anti-seizure medications (ASM). Preclinical DS animal models are a valuable tool to identify candidate ASMs for these patients. Among these, scn1lab mutant zebrafish exhibiting spontaneous seizure-like activity are particularly amenable to large-scale drug screening. Prior screening in a scn1lab mutant zebrafish line generated using N-ethyl-N-nitrosourea (ENU) identified valproate, stiripentol, and fenfluramine e.g., Federal Drug Administration (FDA) approved drugs with clinical application in the DS population. Successful phenotypic screening in scn1lab mutant zebrafish consists of two stages: (i) a locomotion-based assay measuring high-velocity convulsive swim behavior and (ii) an electrophysiology-based assay, using in vivo local field potential (LFP) recordings, to quantify electrographic seizure-like events. Using this strategy more than 3000 drug candidates have been screened in scn1lab zebrafish mutants. Here, we curated a list of nine additional anti-seizure drug candidates recently identified in preclinical models: 1-EBIO, AA43279, chlorzoxazone, donepezil, lisuride, mifepristone, pargyline, soticlestat and vorinostat. First-stage locomotion-based assays in scn1lab mutant zebrafish identified only 1-EBIO, chlorzoxazone and lisuride. However, second-stage LFP recording assays did not show significant suppression of spontaneous electrographic seizure activity for any of the nine anti-seizure drug candidates. Surprisingly, soticlestat induced frank electrographic seizure-like discharges in wild-type control zebrafish. Taken together, our results failed to replicate clear anti-seizure efficacy for these drug candidates highlighting a necessity for strict scientific standards in preclinical identification of ASMs.

ObjectiveKCNQ5 encodes the voltage-gated potassium channel KV7.5, a member of the KV7 channel family, which conducts the M-current. This current was shown to be a potent regulator of neuronal excitability by mediating the medium and slow afterhyperpolarization. Recently, we have identified five loss-of-function variants in KCNQ5 in patients with genetic generalized epilepsy. Using the most severe dominant-negative variant p.(Arg359Cys) (R359C), we set out to investigate pharmacological therapeutic intervention by KV7 channel openers on channel function and neuronal firing. MethodsWhole-cell patch clamp recordings were conducted in human embryonic kidney cells to investigate the immediate effect of retigabine, gabapentin and intracellular application of zinc on the R359C variant in absence and presence of KV7.5-WT subunits. Transfected primary hippocampal cultures were used to examine the effect of R359C on neuronal firing and whether this effect could be reversed by drug application. ResultsRetigabine and gabapentin both increased R359C-derived K+ current density and M-current amplitudes in both homomeric and heteromeric mutant KV7.5 channels. Retigabine was most effective in restoring K+ currents. Ten {micro}M retigabine was sufficient to reach the level of WT currents without retigabine, whereas 100 {micro}M of gabapentin showed less than half of this effect and application of 50 {micro}M zinc only significantly increased M-current amplitude in heteromeric channels. Overexpression of KV7.5-WT potently inhibited neuronal firing by increasing the M-current, and medium afterhyperpolarization, whereas R359C overexpression had the opposite effect. All three aforementioned drugs reversed the effect of R359C reducing firing to nearly normal levels at high current injections. SignificanceOur study shows that a dominant-negative complete loss-of-function variant in KV7.5 leads to largely increased neuronal firing indicating a neuronal hyperexcitability. KV7 channel openers, such as retigabine or gabapentin, could be treatment options for otherwise pharmacoresistant epilepsy patients carrying loss-of-function variants in KCNQ5.

ObjectivePlanning invasive treatment for medication-resistant epilepsy relies on qualitatively interpreting seizure recordings from intracranial EEG (iEEG) recordings. Clinicians recommend treatment by mapping seizure onset patterns and locations, integrating multimodal data with their clinical experience and interpretation of the literature. Referencing a new patients seizures against past cases remains subjective, as implant strategies, electrode placement, and the electrodes relation to seizure onset vary across patients and centers. This study aims to rigorize this process by introducing a transformer model that embeds spatial and temporal information in iEEG recordings to categorize seizure networks and their relation to outcome across a large cohort of drug-resistant epilepsy patients. Our ultimate goal is to quantitatively compare multiple characteristics of new patients presenting for surgical intervention to thousands of prior patients to recommend best treatment. MethodsWe design and implement a custom spatiotemporal transformer that extracts features from iEEG seizure onset epochs. The model consists of convolutional layers that tokenize multi-channel iEEG, a spatiotemporal positional encoder that learns the relationship between sequences of tokens and the anatomical regions of the implantation to extract features across channels and time. Importantly, our model is flexible regarding to the number of iEEG contacts and the location of implants, being trained on both stereotactic EEG and electrocorticography implants. We validate seizure embeddings using unsupervised clustering to group seizure onset patterns using a cross-validated multi-class logistic regression model. ResultsThe spatiotemporal model is applied to 882 clinical seizures from 102 subjects with drug-resistant epilepsy. Unsupervised clustering reveals 74 clusters of seizures that span multiple subjects, and a multi-class logistic regression model with 10-fold cross-validation reveals significant clustering of onset patterns in embedding space (validation accuracy = 0.8159). At the group level, seizures occurring closer in time exhibit more similar embeddings (p < 0.05), modeled with subject-specific random slopes and intercepts. Seizure clusters did not differentiate patients by therapy or postsurgical outcome, but they showed significant associations with the anatomical region of onset and seizure classification. ConclusionsWe propose a method for representing iEEG recordings of seizures with embeddings that contain spatial and temporal information. These embeddings can be characterized and compared across subjects to reveal common patterns in seizure onset. While this clustering did not separate patients by therapy and postsurgical outcome, there were significant associations with the anatomical region of onset and seizure classification. Future work will refine these methods to build a framework for characterizing seizures with deep learning incorporating multimodal data, including structural and functional imaging, semiology, patient history and demographics. We present this work as a first step toward quantitative, evidence-based decision making for patients with drug resistant epilepsy.

Objectives: Our understanding of the pathomechanisms of epilepsy has improved through techniques that access the living human brain. We recently reported that explanted stereo-electroencephalography (SEEG) electrodes from patients with epilepsy carry residual biomolecules and cells which may be utilised for transcriptome and DNA methylation profiling. Methods: Here, we applied bioinformatic and other analyses to explore the transcriptomes (RNA sequencing-based) of those SEEG cases to better understand the types of recovered transcripts in terms of representation of genes expressed by different cell types, brain structures, and the extent to which the signal may reflect local epileptiform activity. Results: Electrodes from all clinical cases retained protein-coding transcripts which reflected the local molecular microenvironment as well as epileptiform activity. Expression of genes involved in housekeeping functions as well as markers of neuronal activity were consistent between patients and between the electrode locations within the brain. We detected transcripts representing various cell types and subtypes including excitatory and inhibitory neurons, all major classes of glia, and endothelial cells, as well as transcripts enriched in specific brain regions. Several genes showed a gradient of expression depending on the electrode position within the brain. We found examples of gene expression that correlated with epileptiform activity as recorded by SEEG. Interpretation: These findings extend the evidence that SEEG electrodes reflect the molecular microenvironments of brain activity in patients with epilepsy, both at sites of seizure onset and within the wider seizure network. The approach has potential applications in intraoperative surgical decision-making as well as to identify molecular biomarkers or therapeutic targets for the drug-resistant epilepsies.

SLC13A5 citrate transporter disorder is a rare epileptic encephalopathy caused by loss of function pathogenic variants in the SLC13A5 gene. Loss of sodium/citrate cotransporter (NaCT) function causes a severe early life epilepsy resulting in life-long developmental disabilities and increased extracellular citrate. Current antiseizure medications may reduce seizure frequency, yet more targeted treatments are needed to address the epileptic and neurodevelopmental SLC13A5 phenotype. We performed preclinical studies in SLC13A5 deficient mice evaluating phenotype rescue with adeno-associated virus (AAV) vector carrying a functional copy of the human SLC13A5 gene (AAV9/SLC13A5). Cerebrospinal fluid-delivery of AAV9/SLC13A5 decreased extracellular citrate levels, normalized electrophysiologic and sleep architecture abnormalities, and restored resistance to chemically induced seizures and death. Treatment benefits were achieved with administration during early brain development and in young adult mice, supporting a broad therapeutic window for this disorder. Comparison of delivery routes in young adult KO mice showed that higher brain targeting achieved with intra-cisterna magna delivery resulted in greater treatment benefit as compared to intrathecal lumbar puncture delivery. Together, these results support further development of AAV9/SLC13A5 for treating SLC13A5 citrate transporter disorder. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/663044v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@ddc591org.highwire.dtl.DTLVardef@1d5e967org.highwire.dtl.DTLVardef@ce6c2forg.highwire.dtl.DTLVardef@209176_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundA retrospective, multi-site case control study was carried out to validate a set of candidate biomarkers of seizure susceptibility. The objective was to determine the robustness of these biomarkers derived from routinely collected EEG within a large cohort (both epilepsy and common alternative conditions which may present with a possible seizure, such as NEAD). MethodsThe database consisted of 814 EEG recordings from 648 subjects, collected from 8 NHS sites across the UK. Clinically non-contributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [N = 2], network-based [N = 4] and model-based [N = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods in order to identify whether potential confounding variables (e.g. age, gender, treatment-status, comorbidity) impacted model performance. FindingsWe found levels of balanced accuracy of 68% across the cohort with clinically non-contributory normal EEGs (sensitivity: 61%, specificity: 75%, positive predictive value: 55%, negative predictive value: 79%, diagnostic odds ratio: 4.64). Group-level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance. InterpretationThese results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilising these biomarkers in carefully designed prospective studies. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched Google Scholar and Pubmed (March 21, 2022) for the following phrases (("EEG" OR "electroencephalogram" OR "electroencephalography") AND ("biomarker") AND ("epilepsy" OR "seizure") AND ("resting state" OR "resting-state") OR ("normal")). Several of the existing studies developed deep learning approaches for identifying the presence of interictal epileptiform discharges (IED), with the overarching aim to develop an automated stand-alone diagnostic tool. These approaches are particularly sensitive to the potential presence of artefacts in the EEG recordings and typically include spectral rather than network- or model-based features. We found no studies of more than 100 participants that assessed the cross-validated performance of candidate biomarkers on routine EEG recordings that were clinically non-contributory. One study found near-chance performance of a deep-learning based method using spectral features on a smaller cohort of people suspected of epilepsy (N=33 epilepsy; N=30 alternative conditions) with clinically non-contributory EEGs. Another study found overall accuracy of 69% (N=74 epilepsy; N=74 alternative conditions) but this framework did not use any independent cross-validation methods. Estimates of sensitivity of clinical markers of seizure susceptibility in routine EEG recordings vary between 17-56%. To the best of our knowledge no studies have assessed whether computational biomarkers offer sufficient discrimination between people with epilepsy and an alternative diagnosis to provide potential decision support for people with suspected epilepsy. Added value of this studyWe show that data-driven analysis of routinely collected EEGs that are currently considered clinically non-informative (i.e. absence of apparent epileptiform activity) can be used to distinguish EEGs from people with epilepsy from people with an alternative diagnosis with better-than-chance performance. To the best of our knowledge, this is the largest retrospective study assessing the performance of computational biomarkers derived from clinically non-contributory EEG recordings. The resulting statistical model is interpretable and relies on both spectral and computational (network- and model-based) features. We perform a series of validity and sensitivity analysis to assess the overall robustness of the final statistical model used for classification. We also conduct several statistical tests to analyse any shared characteristics (e.g. site, comorbidity) amongst the primary classes (FP, FN, TP, TN). These findings validate previous biomarker discovery- or development-studies, and provide evidence that they offer better-than-chance performance in a clinically relevant context. Future large-scale studies could consider combining these methods with interictal features for non-specialist settings. Implications of all the available evidenceOur study presents evidence that computational analysis of clinically non-contributory EEGs could provide additional decision support for both epilepsy and alternative conditions. Since the statistical model and underlying features are interpretable, they could provide the starting point for further exploring the mechanisms that drive overall seizure-likelihood. Future work should focus on prospective testing and validation (e.g. identification of specific situations or cases in which these methods could be of added value) as well as assessing heterogeneity across different syndromes and diagnoses (e.g. NEAD, focal vs generalised epilepsy).

BackgroundDistinguishing epilepsy from functional/dissociative seizures (FDS) is an ongoing diagnostic challenge. Misdiagnosis delays appropriate treatment and puts patients at significant risk. Quantitative analyses of clinical EEG offer a potential avenue for developing decision-support tools in the diagnosis of seizure disorders. Recent work using univariate features demonstrated that reliably identifying diagnostic traits in the presence of confounding factors remains challenging. However, diagnostic information might be available in multivariate features such as network-based measures. Using a well-controlled dataset, we run the first diagnostic accuracy study assessing the potential of multivariate resting-state EEG markers to directly discriminate between a diagnosis of epilepsy and one of FDS at the time when a diagnosis is suspected and prior to treatment initiation. MethodsThe dataset, previously examined in a published study, includes 148 age- and sex-matched individuals with suspected seizure disorders who were later diagnosed with non-lesional epilepsy (n=75) or FDS (n=73). Eyes-closed, resting-state EEG data used for the analyses were normal on visual inspection, and acquired while participants were medication-free. Functional network measures in the 6-9 Hz range were extracted and machine learning implemented to assess their predictive potential; different model configurations (including varying model types, dimensionality reduction methods, and approaches to enhance feature stability) were tested to identify the most promising approach for future translational implementations. ResultsNetwork measures derived from resting-state EEG discriminate between conditions at levels significantly above chance (maximum balanced accuracy: 67.5%). Their sensitivity to epilepsy (81.8%) is consistently higher than their sensitivity to FDS (53.3%). A systematic assessment of model choices indicates that improving the temporal stability of network features through epoch-wise averaging improves classification accuracy (62.6% to 67.5%). Multiple nonlinear model types succeed on the classification problem, with the three-best performing assigning a consistent diagnostic label to 77.5% of the individuals; however, model choice remains a strong determinant of overall classification accuracy. Dimensionality reduction did not provide a significant advantage in our models. ConclusionWe establish evidence for the clinical validity of selected network-based markers to discriminate between a diagnosis of non-lesional epilepsy and FDS prior to treatment initiation, highlighting the measures potential to support post-test probability estimation in the clinic. Our models, configured to optimise balanced accuracy, classified people with epilepsy more accurately than people with FDS, indicating that these measures are specific to epilepsy and should not be interpreted as markers of a positive diagnosis of FDS.

ObjectiveTo characterize the type and distribution of epileptiform activity across anatomical regions and functional networks in a large cohort of patients with drug-resistant epilepsy (DRE) undergoing intracranial electroencephalography (iEEG). MethodsWe retrospectively reviewed iEEG recordings from 93 patients with DRE who underwent stereoelectroencephalography at our institution between 2019 and 2025. Epileptiform activity was classified as "ictal", "interictal", or "ictal spread" based on visual inspection and clinical correlation. Electrode coordinates were localized to anatomical regions using the Brainnetome Atlas and to functional networks using the DU15NET-Consensus Atlas. The distribution of epileptiform activity across regions and networks was compared with global baselines using Chi-square tests with false discovery rate correction. ResultsInterictal discharges were the most prevalent type of epileptiform activity (median 9.5% of contacts per patient), followed by ictal (6.0%) and ictal spread (2.1%). Temporal regions exhibited an increased prevalence of all types of epileptiform activity compared to the global baseline, whereas frontal regions showed marked reductions, despite dense sampling. At the network level, epileptiform activity was overrepresented in Default Network-A and underrepresented in the Salience/Parietal Memory Network. SignificanceEpileptiform activity shows consistent, non-uniform patterns across both anatomical regions and functional networks. These findings reinforce the central role of the temporal lobe and associative networks in epileptogenesis and support the view of epilepsy as a disorder of distributed brain networks. Mapping epileptiform activity in a network framework may enhance biomarker development and inform circuit-based surgical and neuromodulatory treatment strategies.

ObjectiveNeuroimaging research is providing insights into epilepsy as a disorder of brain connectivity linked to functional impairments which may have an identifiable genetic component. This case-control study aims to identify imbalances in a functional connectome dimension spanning from unimodal to transmodal networks and explore the potential genetic basis of such alterations in patients with newly diagnosed focal epilepsy (NDfE). MethodsWe used gradient-based analysis of resting-sate fMRI data comparing cortical gradient maps in patients with NDfE (n = 27) to age and sex-matched controls (n = 36). Using a brain-wide gene expression dataset, gene combinations associated with altered brain regions were then entered into an enrichment analysis. ResultsWe found an increased differentiation of connectivity profiles between unimodal and transmodal networks in NDfE, which was particularly pronounced in the patients with persistent seizures at 12-months follow-up (n=10). Differences corresponded to gradient score reductions in a visual network and increases in limbic and default mode systems which subserve higher-level cognition. Cortical difference maps were spatially correlated with regional expression of a weighted gene combination. These genes were enriched for disease and ontology terms and pathways previously associated with epilepsy and seizure susceptibility. InterpretationsLarge-scale functional hierarchy may be altered from in focal epilepsy from diagnosis and correlate with response to treatment. Combining functional neuroimaging and transcriptional data analysis may provide a framework for understanding the wide-ranging impairments associated with the disorder and mechanistic insight into how gene processes may drive alterations in brain function mediating the genetic risk of epilepsy.

Organophosphate (OP) compounds are highly toxic and include household, industrial, agricultural, and chemical warfare nerve agents (CWNA). OP exposure inhibits acetylcholinesterase enzyme, causing cholinergic overstimulation that can evolve into status epilepticus (SE) and produce lethality. Furthermore, OP-SE survival is associated with mood and memory dysfunction and spontaneous recurrent seizures (SRS). Here we assessed hippocampal pathology and chronic SRS following SE induced by OP agents in rats. Male Sprague-Dawley rats were injected with 1.5x LD50 of various OP agents, followed by atropine and 2-PAM. At 1-h post-OP-SE onset, midazolam was administered to control SE. Approximately 6 months following OP-SE, SRS were evaluated using continuous video-EEG monitoring. Histopathology was conducted using Hematoxylin and Eosin (H&E), while silver sulfide (Timm) staining was utilized to assess Mossy Fiber Sprouting (MFS). Over 60% of OP-SE surviving rats developed SRS with varying seizure frequencies, durations, and Racine severity scores. H&E staining revealed a significant hippocampal neuronal loss, while Timm staining revealed extensive MFS within the inner molecular region of the dentate gyrus of SRS-expressing OP-SE rats. This study demonstrates that OP-SE is associated with hippocampal neuronal loss, extensive MFS, and SRS, all hallmarks of chronic epilepsy.