Epilepsia

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.

Background: The detection of subtle epileptogenic lesions such as focal cortical dysplasias (FCDs) is a clinical challenge in the management of drug-resistant focal epilepsy (DRFE). Ultra-high field (UHF) MRI offers increased signal-to-noise ratios and spatial resolution compared to 3Tesla (T) MRI and may improve diagnostic yield. Here, we present a 9.4T MRI cohort study of patients with DRFE. Methods: We recruited n=21 DRFE patients (with 3T-MRI findings: 2 positive, 3 equivocal, 16 negative) undergoing presurgical workup, and n=20 healthy controls for 9.4T MRI (0.8 mm isotropic MP2RAGE, slabs of 0.375 x 0.375 x 0.8 mm T2*-weighted GRE) and 3T MRI (MP2RAGE, FLAIR) acquisitions. Visual review for possible epileptogenic lesions was performed by clinical experts. For histopathologically confirmed FCD lesions, we extracted surface-based quantitative features (cortical thickness, qT1, FLAIR, T2*, and QSM values) across cortical depths and distances from the lesion centre and performed high-resolution cortical profiling of 9.4T T2* values. Results: No new epileptogenic lesions were visually identified at 9.4T in 3T MRI negative patients. In the two patients with histopathologically confirmed lesions, the FCD IIb lesions were visible with distinct qualitative and quantitative features at both field strengths. One of these FCD IIb showed a focal cortical T2* reduction at 9.4T that could here be quantified via automated cortical profiling, consistent with the previously described "black line sign". Conclusion: 9.4T MRI findings in epileptogenic lesions underlying DRFE are consistent with those on 3T MRI. While additional lesions were not identified in patients with negative 3T MRI, higher resolution T2*-weighted sequences can reveal a feature not seen at 3T: Cortical profiling of FCDs highlights the black line sign and can possibly help refine surgical or ablation targeting for some FCDs. Further optimization of UHF protocols and analysis methods on larger cohorts may reveal clinically applicable diagnostic benefits.

ImportanceTracking and predicting seizure frequency in patients with epilepsy is important for prognostication and therapy management. Interictal spikes have been proposed as a biomarker of seizure burden, but their association with seizure frequency has not been well quantified across epilepsy subtypes. ObjectiveTo measure the association between spike rate and seizure frequency and how this varies by epilepsy subtype. Design, Setting and ParticipantsWe studied 3,614 consecutive routine outpatient EEGs from 3,245 patients with epilepsy. A validated automated detector (SpikeNet2) estimated spike frequency. Validated large language models performed natural language processing on outpatient clinic notes to extract seizure frequency and epilepsy subtype. Main Outcomes and MeasuresSpearman correlation between spike frequency (spikes/hour) and seizure frequency (seizures/month) for all patients with epilepsy and for patients with generalized epilepsy, temporal lobe epilepsy, and frontal lobe epilepsy. ResultsOverall, spike frequency was modestly associated with seizure frequency (N = 3,245, {rho} = 0.11, p < 0.001). Significant positive associations were observed in generalized epilepsy (N = 625, {rho} = 0.23, Bonferroni-adjusted p < 0.001) and temporal lobe epilepsy (N = 834, {rho} = 0.12, p = 0.0013), but not in frontal lobe epilepsy (N = 263, {rho} = 0.11, p = 0.22). Conclusions and RelevanceIn this large outpatient cohort, higher interictal spike rates on routine EEG were associated with higher seizure frequencies, with the strongest relationship observed in generalized epilepsy. These associations support interictal spike rate as a quantitative EEG marker of seizure burden. Spike rate may have clinical utility for risk stratification at diagnosis and for monitoring longitudinal changes in seizure burden in response to therapy.

The Brahma-related gene 1 (BRG1) encodes the catalytic subunit of the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex and plays an important role in brain development. Variants in SWI/SNF components are often found in patients with epilepsy, but it is still unclear how loss of BRG1 function contributes to seizure development. In this study, we analyzed the role of Brg1 in seizure susceptibility using zebrafish models with both pharmacological inhibition or genetic reduction of Brg1. Reduced Brg1 function caused seizure-like behavior and increased neuronal activity in larvae, while basic locomotor activity was preserved. Further analyses showed reduced expression of several GABAergic system markers. In contrast, glutamatergic markers did not show major changes. These results point to a selective impairment of inhibitory signaling. When GABA levels were increased pharmacologically, seizure-like behavior was reduced. This suggests that loss of inhibitory transmission plays an important role in the observed hyperexcitability. Unbiased omics analyses also identified changes in proteins associated with vitamin B6 binding. Treatment with active vitamin B6 reduced seizure-like behavior in larvae with reduced Brg1 function. Taken together, these results indicate that Brg1 is required for proper inhibitory neurotransmission and that partial loss of Brg1 function increases seizure susceptibility. These findings may help to better understand why mutations in chromatin remodeling genes are often associated with epilepsy and could support future studies on targeted modulation of inhibitory signaling in these conditions. Significance StatementChromatin remodeling genes are often mutated in patients with epilepsy, but it is still unclear how these mutations lead to seizures. In this study, we show that reduced function of the chromatin remodeler Brg1 affects inhibitory neurotransmission by impairing the GABAergic system. This leads to increased neuronal activity and seizure-like behavior. Our results identify the chromatin remodeler Brg1 as an important regulator of inhibitory neurotransmission and seizure susceptibility, which may be important for understanding epilepsy associated with neurodevelopmental disorders.

1.PurposeSYNGAP1-related developmental and epileptic encephalopathy (SYNGAP1-DEE) is characterized by high rates of both epilepsy and autism spectrum disorder (ASD). While the clinical spectrum is well-documented, the link between specific seizure semiologies and caregiver-reported autistic behaviors is not well understood. This study analyzed the correlation between ten distinct seizure types, their frequencies, and a caregiver-reported autistic behavior score. MethodClinical data were extracted from the PATRE (PATient-based phenotyping and evaluation of therapy for Rare Epilepsies) Registry for SYNGAP1, in the framework of the EURAS project (Grant No. 101080580, Horizon Europe). This study employed a retrospective cross-sectional analysis of caregiver-reported registry data. Analysis was restricted to an analytic cohort of N=337 participants with complete data for both the epilepsy questionnaire (including epilepsy status, seizure semiology, and peak seizure frequency items) and the behavior questionnaire (from a total N=522 registry participants). Caregiver-reported autistic behaviors were quantified using a standardized caregiver-reported scale (Likert 1-5). Statistical associations were evaluated using the Wilcoxon rank-sum test to compare caregiver-reported autistic behavior scores across different seizure semiologies and Spearmans rank correlation to assess the impact of seizure frequency (9-point scale). ResultsWithin the analytic cohort (N=337), epilepsy was reported in 259 patients. Eyelid myoclonia was the most prevalent semiology, affecting 64.9% (n=168) of the epilepsy-positive group. Atypical absences (n=77) demonstrated the most profound and statistically robust association with higher caregiver-reported autistic behavior scores (FDR-adjusted p = 0.001). Significant associations were also observed for typical absences (n=70, FDR-adjusted p = 0.018), eyelid myoclonia (FDR-adjusted p = 0.018), myoclonic-atonic seizures (n=40, FDR-adjusted p = 0.019), and atonic seizures (n=72, FDR-adjusted p = 0.025). Focal and tonic-clonic seizures showed weaker associations (FDR-adjusted p = 0.026 and p = 0.047, respectively). Crucially, quantitative analysis revealed no significant correlation between ordinal caregiver-reported peak seizure frequency ratings and caregiver-reported autistic behavior scores across all semiologies (e.g., Eyelid Myoclonia: p=0.096; Atypical Absences: p=0.744), indicating no detectable association between peak-frequency ratings and caregiver-reported autistic behavior scores. ConclusionHigher caregiver-reported autistic behavior scores in SYNGAP1-DEE were most strongly associated with the presence of atypical absences, representing a generalized, thalamocortical seizure network dysfunction. In contrast, no detectable association was observed between caregiver-reported autistic behavior scores and the ordinal caregiver-reported peak seizure frequency metric. Atypical absences and related semiologies may serve as clinical "red flags" for increased neurodevelopmental comorbidity severity, regardless of reported peak seizure frequency. Abstract SummaryThis study investigates the relationship between ten seizure semiologies, seizure frequency, and severity of caregiver-reported autistic behaviors in a large-scale international cohort of N=337 patients with SYNGAP1-DEE. We identify a robust association between elevated caregiverreported autistic behavior scores and specific thalamocortical seizure patterns, most prominently atypical absences. Notably, our analysis reveals that this association is independent of seizure frequency, demonstrating no detectable association between this ordinal, caregiver-reported seizure frequency metric and caregiver-reported autistic behaviors.

Objective: Electrical brain stimulations (EBS) are central to epileptic network identification and functional mapping during stereo-electroencephalography (SEEG), yet stimulation frequencies remain empirical, and standardized across patients and brain regions, producing false negatives and false positives, and potentially compromising surgical outcome. We investigated theta-range EBS (7 Hz) in the temporal lobe, a prominent physiological frequency band in this region, and compared it with conventional 1-Hz and 50-Hz protocols. Methods: We analyzed 1,408 temporal EBS in 25 patients with drug-resistant epilepsy. Epileptic responses (afterdischarges, seizures) and clinical signs were assessed across the epileptic network and temporal structures (amygdala, hippocampus, neocortex, parahippocampal gyrus, white matter), and analyzed according to stimulation parameters (frequency, intensity, duration, total charge). Results: At matched intensity and duration, 7-Hz EBS were associated with a higher occurrence of afterdischarges and clinical signs than 1-Hz EBS in several temporal structures (e.g., parahippocampal epileptogenic zone: p=0.014). Effects on usual seizure induction were less consistent. Comparisons with 50 Hz showed no systematic significant differences, with responses observed at one or both frequencies depending on structure and outcome. When controlling for total charge, frequency-related differences were attenuated. Some effects were sporadically observed at both intermediate frequency and charge quantity. No adverse events occured. Significance: Theta-range stimulation modulates electrophysiological and clinical responses during SEEG mapping and may provide complementary information to conventional frequencies. These findings support exploring a broader range of stimulation frequencies, rather than relying solely on standard protocols.

BackgroundPathogenic KCNQ2 variants are the most common genetic cause of neonatal-onset epilepsies, with phenotypes ranging from self-limited (familial) neonatal epilepsy (SeL(F)NE) to severe developmental and epileptic encephalopathy (KCNQ2-DEE). Sodium channel blockers (SCBs) have shown promise for seizure control in these disorders, but their impact on neurodevelopmental outcomes and possible relationship with timing of initiation remain incompletely understood. MethodsWe leveraged a large, multicentre international cohort comprising 282 individuals with KCNQ2 pathogenic variants to retrospectively assess the effectiveness of antiseizure medications (ASMs), particularly SCBs, on seizure control and neurodevelopment. Individuals were grouped according to the predicted variant-specific functional effects: loss-of-function (LOF) variants known to be associated with SeL(F)NE or DEE respectively, and gain-of-function (GOF) variants. Epilepsy course, ASM effectiveness, and neurodevelopmental milestones were systematically collected and analysed. ResultsSCBs, especially carbamazepine (CBZ) and oxcarbazepine (OXC), emerged as the most effective ASMs in both LOF groups. In LOF KCNQ2-DEE, early SCB initiation within the first month of life was associated with significantly more favourable neurodevelopmental trajectories, including higher rates of achievement of major motor milestones. Early seizure freedom itself was a strong predictor of improved neurodevelopment, with the positive effect of SCBs likely mediated by their ability to control seizures. However, considerable phenotypic variability persisted, with some individuals experiencing severe impairment despite early seizure control and SCB initiation. Variant severity and possible genetic modifiers likely contribute to this heterogeneity, underscoring the need for precision therapies beyond nonspecific ASM approaches. ConclusionOur results strongly support the use of SCBs as first-line therapy in (LOF) KCNQ2-DEE and SeL(F)NE due to their high effectiveness. Moreover, SCBs appear most beneficial when initiated during the neonatal period, with earlier treatment linked to earlier seizure offset and better developmental outcomes. These results highlight the importance of early genetic diagnosis and timely SCB therapy, and support CBZ or OXC as first-line agents. We however emphasise that early treatment is not universally transformative, and further work, including exploration of targeted therapies but also standardised neurodevelopmental assessments, is needed to optimise long-term outcomes in this heterogeneous population.

BackgroundGenetic testing is increasingly used in presurgical evaluation, but the yield of resection across germline genetic epilepsies remains uncertain. MethodsWe conducted a systematic review of MEDLINE (PubMed) and Scopus and added cases from three institutional cohorts and the Pediatric Epilepsy Research Consortium (PERC) databases, including individuals with pathogenic/likely pathogenic germline variants besides tuberous sclerosis and neurofibromatosis who underwent resection or laser ablation. Etiologies were grouped into biologically informed categories (GATORopathies, vascular, overgrowth, CNVs, channelopathies, synaptopathies, other). Primary outcome was seizure freedom (Engel I) at last follow-up. Group comparisons used Fishers exact and Kruskal-Wallis tests (=0.05; Bonferroni when applicable). Prespecified sensitivity analyses stratified by lesional status, excluded GATORopathies, and restricted to literature-only cases. ResultsWe included 223 literature cases (64 studies), 35 institutional cases, and 11 PERC cases (n=269). Median follow-up was 24 months (IQR 12-48.0). Seizure freedom was achieved in: vascular disorders 14/19 (73.6%), GATORopathies 79/120 (67.5%), CNVs 18/31 (66.7%), overgrowth 7/13 (53.8%), other 16/25 (41.7%), channelopathies 13/43 (33.3%), and synaptopathies 4/18 (22.2%) (overall p<0.001). Among cases with known imaging, 208/253 (82.2%) had epileptogenic MRI lesions, including 64% of channelopathies and 80% of the synaptopathies. In univariate contrasts (each category vs all others), odds of seizure freedom were higher for vascular disorders (2.66-fold, 95% CI 0.87-9.72) and GATORopathies (2.46-fold, 95% CI 1.46-4.18), and lower for synaptopathies ([~]4.2-fold lower, OR 0.24, 95% CI 0.05- 0.78) and channelopathies ([~]4.5-fold lower, OR 0.22, 95% CI 0.09-0.48). Direction and magnitude were consistent across prespecified sensitivity analyses (lesional-only, literature-only, exclusion of GATORopathies). ConclusionsResective surgeries can be effective in germline genetic epilepsies, but outcomes vary by pathway. Disorders with discrete, lesional substrates (GATORopathies, vascular) show the highest likelihood of seizure freedom, whereas channelopathies and synaptopathies, despite the presence of MRI lesions, have substantially lower yields even in lesional cases. Prospective, genotype-aware surgical registries with standardized reporting (EEG, imaging, pathology) and time-to-event outcomes are needed to refine the selection of surgical candidates and quantify seizure and non-seizure-related outcomes.

ObjectiveMany patients develop post-traumatic epilepsy (PTE). Post-traumatic epileptogenesis has been carefully studied in rodents, but the time course of epileptogenesis is correlated to brain size, raising the possibility that large animal models will provide more translatable data regarding epileptogenesis. Here, we use our large-brained model to describe the development, rate, and seizure semiology of PTE. MethodsAdult male and female swine received bilateral cortical impact (N=16) or sham surgery (N=9) and were screened for convulsions via video EEG for up to one year. PTE was defined as 2 seizures after 1 week post-injury. ResultsNine out of sixteen pig (56%) receiving bilateral cortical impact developed PTE, with an average latent period of 6.6 months ({+/-} 3.9, SD). Seizure began focally, sometimes with motor onset including automatisms (lip smacking, yawning) and sometimes nonmotor (freezing) before becoming generalized, with tonic-clonic or tonic convulsions. Most pigs had a period of post-ictal stillness (nonmotor) after the convulsions. Temporary incoordination occurred both pre- and post-ictal. We defined a library differentiating peri-ictal behaviors (N = 27) from rhythmic/odd behaviors typical in healthy pigs (N = 11). Pigs with PTE had an average of 5.6 behaviors per seizure, with a max of 22 behaviors in a seizure. The longest seizure was 7.9 minutes. For seizures comprised of multiple convulsive episodes, the first convulsion had a greater number of peri-ictal behaviors than subsequent convulsions (P < 0.02). The array of peri-ictal behaviors displayed was pig-specific, with many behaviors consistently observed across seizures. The overall seizure frequency was 0.43/day. SignificanceThis large-brain model of PTE exhibits a prolonged period of epileptogenesis, a substantial rate of PTE, and an expansive repertoire of ictal behaviors. This first description of semiology in this species will serve as a guide for other porcine epilepsy models. Biofidelic models of PTE are expected to increase our understanding of the pathophysiology of post-traumatic epileptogenesis and to identify and test therapeutics that translate into human patients. Key PointsO_LIThe average time from bilateral cortical impact to post-traumatic epilepsy is 6 months. C_LIO_LISwine with post-traumatic epilepsy display an array of specific behaviors distinct from pigs without post-traumatic epilepsy. C_LIO_LIPigs have individualized stereotypical behaviors around convulsions and can have many convulsions within a seizure. C_LIO_LIThough convulsions last a few seconds, the entire seizure, with the associated peri-ictal behaviors, lasts up to 7.9 minutes. C_LI

Maintaining electrical signaling homeostasis in the human neocortex relies on cell-type specific gene expression programs. However, when these programs are disrupted, the resulting imbalances can contribute to the pathogenesis of neurological disorders like epilepsy. Genetic factors are particularly implicated in a specific subtype of epilepsy known as refractory epilepsy or drug-resistant epilepsy (RE/DRE). This study shows the main results of the analysis of single cell transcriptomics for five pediatric RE patients in Colombia. A total of six samples obtained through surgical resection were analyzed by single-nuclei RNA sequencing (snRNA-seq). The genome of one patient was sequenced using high fidelity long-read sequencing. Functional enrichment of differentially expressed genes (DEGs) revealed glia-driven dysregulation of synaptic signaling, impaired glial-neuronal communication, and altered expression of genes related to neurotransmitter transport and calcium signaling. Activation of taste receptors in neurons was associated with neuroinflammatory processes. Structural variants were detected in genes associated with alterations of expression in specific cell types. This new data resource increases the diversity of information needed to develop new strategies for diagnosis of refractory epilepsy.

Epilepsy is one of the most prevalent neurological diseases, with 25-33% of patients developing drug-resistant epilepsy (DRE). The precise etiology of DRE remains unidentified. Recent studies have revealed an increase in tetraploid astrocytes in drug-resistant temporal lobe epilepsy (DR-TLE), a common subtype of DRE. This study aims to characterize the function of tetraploid astrocytes in the brain of subjects without central nervous system diseases and in DR-TLE. Cortical samples adjacent to the epileptogenic zone were obtained from DR-TLE patients undergoing resective neurosurgery and from postmortem donors without neurodegenerative, neurological, or psychiatric disorders. Tetraploid astrocytes were identified using the astrocytic marker NDRG2, and their functional characterization was assessed by evaluating markers of metabolism (ALDH1L1), transport (SOX9), electric function (NF1A), or reactive astrocytes (NF{kappa}B p65 and pSTAT3), via immunostaining followed by flow cytometry. Tetraploid astrocytes expressed all functional markers tested. The percentage of tetraploid astrocytes expressing ALDH1L1 or SOX9 was significantly increased in DR-TLE with respect to controls, whereas NF1A remained unchanged. Inflammatory markers pSTAT3 and NF{kappa}B p65 showed an upward trend in 4C astrocytes. In contrast, diploid (2C) astrocytes expressing these markers were reduced in DR-TLE, suggesting a functional shift toward polyploid cells in the DR-TLE cortex. Our findings suggest the preservation of markers of metabolism, transport and electric function in tetraploid astrocytes in physiological conditions and in DR-TLE patients. Moreover, the astrocytes with metabolic and transporter markers were significantly increased in DR-TLE. These findings point to tetraploid astrocytes as potential contributors to DR-TLE mechanisms.

ObjectiveQuantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative MRI. Current methods for resection cavity masking are time-consuming and labour-intensive, while existing automated approaches exhibit variable segmentation accuracy, particularly on extra-temporal resections. We developed MELD-PostOp, a deep learning tool trained and evaluated on a large, heterogeneous cohort to automatically segment resection cavities. MethodsThe study included 1.5 and 3T postoperative 3D T1-weighted MRI images from the Multicentre Epilepsy Lesion Detection (MELD) project (nsubjects=969, 27 centres) and from the EPISURG dataset (n=133). The cohort included children and adults, alongside a range of resection locations, pathologies, and MRI characteristics. Resection cavities were individually segmented in 285 subjects and used to train an nnU-Net prototype model. The prototype model was used to generate an additional 680 resection masks, which were subsequently quality-controlled, edited and combined with the original 285 to train the final MELD-PostOp model (n=965). A Stratified (STC; n=50) and Independent Test Cohort (ITC; n=87) were withheld for model evaluation. Performance was evaluated using Dice Similarity Coefficient (DSC), 95th percentile Hausdorff distance (HD95), number of predicted clusters and inference runtime; and compared against established tools (Epic-CHOP and ResectVol). ResultsMELD-PostOp achieved a median DSC of 0.85 and HD95 of 3.61 on the combined test cohort, outperforming Epic-CHOP (DSC 0.68, HD95 9.54) and ResectVol (DSC 0.66, HD95 12.07), with significant improvements seen in both temporal and especially extra-temporal resections. The model detected 99% (135/137) of resection cavities. MELD-PostOp runtime was 17s per MRI, compared to 612s (ResectVol) and 3205s (Epic-CHOP). MELD-PostOp performance remained high across clinical and imaging subgroups (median DSC > 0.8). SignificanceMELD-PostOp provides an accurate, efficient and generalisable solution for postoperative resection cavity segmentation using only postoperative MRI scans. This open-source tool facilitates large-scale quantitative analysis to define what tissue is essential to resect for optimal epilepsy surgical outcomes. Key points- MELD-PostOp automatically segments resection cavities from a postoperative MRI scan in 17 seconds, a 100-200x faster runtime than existing tools - The model was trained on 965 annotated T1-weighted postoperative scans from sites worldwide, supporting its generalisation across diverse clinical and imaging settings. - The model achieves high segmentation accuracy (DSC > 0.8), with consistently high-quality segmentations across age groups, sex, image resolutions, and resection locations.

Background and PurposeWhile the idiopathic generalized epilepsy (IGE) comprise around one fifth of all epilepsy, the pathogenesis of it is largely unknown. Previous studies identified cognitive deficits in IGE patients, nevertheless, whether (and how) the brain structure and functional connectivity (FC) reflect these deficits remains underexplored. Here, we aim to find structural and FC differences in cognitively impaired IGE patients. Materials and MethodsWe recruited 36 IGE patients and 49 matched healthy controls (HC) in this cross-sectional study. All participants underwent structural and resting-state fMRI (rs-fMRI) scanning with a 3 Tesla MRI. Voxel-based morphometric analysis (VBM) was used to assessed structure differences, and seed-based analysis of rs-fMRI was used to examine FC. We examined the cognitive performance of patient with MoCA (Montreal Cognitive Assessment), grouped them into high (HMoCA, >25) and low (LMoCA, [&le;]25) group, and further examined the brain structural changes functional changes in each group. ResultsIGE patients showed right significant decrease in cerebellar gray matter volume (GMV), negatively correlating with the disease duration (r=-0.542, p=0.001), and increase in the left dorsolateral superior frontal gyrus GMV. Right cerebellum showed increased connectivity to the precuneus and angular gyrus, decreased connectivity to the postcentral gyrus and Rolandic operculum. Surprisingly, we found that LMoCA IGE patients (with more cognitive deficits) had increased right nucleus accumbens (NAc) GMV (t = -4.413, p < 0.001) and FC and a stronger NAc - prefrontal cortex FC (t = -2.683, p = 0.013), in comparison with the patients with high MoCA. ConclusionsCognitive impairment in IGE patients is linked to the NAc structural changes and NAc-prefrontal circuit alterations. These results provide novel circuit-level insights into understanding the cognitive impairment in IGE patients, contributing to revealing the pathophysiological mechanisms of IGE.

About half of patients who undergo epilepsy surgery for drug-resistant epilepsy have seizure recurrence, supporting the need for approaches that more accurately identify the epileptogenic zone, defined as the brain areas whose removal causes cessation of seizures. Altered network connectivity has emerged as a candidate biomarker of the epileptogenic zone, but how connectivity is altered in the epileptogenic zone remains uncertain, with prior studies reporting inconsistent results. We hypothesized that a difference in intrinsic versus extrinsic connectivity of the epileptogenic zone may explain prior discrepant findings. We studied a multicenter cohort of adult and pediatric patients who underwent intracranial EEG recording and brain stimulation as part of epilepsy surgery planning. We measured spontaneous connectivity using Pearson correlation and perturbational connectivity using stimulation evoked potentials, modeling the connectivity according to the location of contacts in relation to the seizure onset zone (SOZ) while controlling for inter-electrode distance. We analyzed 79 patients (37 adults, 42 children). For both adult and pediatric patients, resting connectivity was higher within compared to outside the SOZ, but resting connectivity between SOZ and non-SOZ contacts was reduced. Stimulation connectivity followed a similar pattern, with elevated within-SOZ connectivity but reduced connectivity between SOZ and non-SOZ. The results support the hypothesis that the epileptogenic zone is disconnected from the rest of the brain but intrinsically hyperconnected. This result helps reconcile prior inconsistencies across studies, aligns with the results of basic science studies, and suggests that future translational work should model this heterogeneous pattern to increase the yield of using connectivity to localize the epileptogenic zone.

ObjectiveBrain infection is an underrecognized global cause of epilepsy due to the ensuing neuroinflammation and neurological damage. Immune system response, including underlying neuroinflammation, is dynamically shaped by the intestinal microbiome. In experimental rodent epilepsy models, seizure burden and antiseizure medication (ASM) activity can be dramatically influenced by gut dysbiosis, including in the Theilers murine encephalomyelitis virus (TMEV) infection model of acute symptomatic seizures and long-term epilepsy. We previously demonstrated that experimentally induced gut dysbiosis via repeated antibiotic administration alters seizure burden and carbamazepine (CBZ) anticonvulsant activity in this model (1). However, whether dysbiosis and CBZ differentially shape neuropathological damage and neuroinflammation following TMEV infection was not reported. MethodsHere, we extended our earlier study to quantify the extent to which antibiotic-induced gut dysbiosis and repeated CBZ administration during TMEV infection altered the severity of acute neuropathology. Hippocampal tissue was analyzed 7 days post-infection using quantitative immunofluorescence to assess neuronal death, microglial and astroglial reactivity, and neuronal proliferation across CA1, CA3, and dentate gyrus (DG) subregions. ResultsDysbiosis markedly exacerbated hippocampal neurodegeneration and gliosis, accompanied by increased glial proliferation, whereas CBZ administration reversed these effects in a hippocampal region-dependent manner. Collectively, these findings demonstrate that the gut microbiome primes hippocampal neuroimmune responses to viral infection-induced acute seizures and modifies associated neuropathology in a hippocampal region-specific manner. SignificanceThis work identifies the gut-brain axis as a critical determinant of neuroinflammatory damage after infection-induced symptomatic seizures, highlighting the gut microbiome as a potential therapeutic target to alleviate the worldwide epilepsy burden. HighlightsO_LIExperimentally-evoked gut dysbiosis exacerbates hippocampal neurodegeneration after brain viral infection. C_LIO_LIExperimentally-evoked gut dysbiosis increases microgliosis and glial proliferation after brain viral infection. C_LIO_LICarbamazepine reversed dysbiosis-induced neuroinflammation and neurodegeneration. C_LIO_LIExperimentally-evoked gut dysbiosis differentially modulates glial response in the dentate gyrus. C_LI

ObjectiveIn individuals with drug-resistant epilepsy, accurately identifying the brain regions where seizures originate is a critical prerequisite to guide surgical treatment and achieve seizure freedom. To accomplish this, intracranial EEG is considered the gold standard, providing the spatiotemporal high-resolution data necessary to pinpoint epileptogenic activity. However, this precision is achieved through an invasive procedure with significant patient burden, which is fundamentally limited by the electrode placement and spatial coverage. MethodsIn this study, we investigated the potential utility of preoperative resting-state fMRI to non-invasively map alterations in brain dynamics at the whole brain level. Region-wise brain dynamics were quantified with complementary measures of local autocorrelation decay rates. We assessed the capacity of these derived features to effectively identify intracranial EEG confirmed seizure onset zones in 18 individuals with drug-resistant medial temporal lobe epilepsy. Overall, the study cohort contained 3867 implanted electrodes of which 159 classified as seizure onset zones by two independent board-certified epileptologists. ResultsOverall, our findings reveal more constrained temporal dynamics for brain regions associated with seizure onsets compared to non-seizure onset zones. Individual-level prediction showed a performance better than chance in 15 of the 18 patients. The overall predictive performance across all patients yielded a median AUC of 0.81, a median true positive rate of 0.75, and a median true negative rate of 0.83. Furthermore, in a subset of 13 patients, those with negative seizure outcomes showed higher probabilities of seizure onset zone predictions outside the resection area compared to those with good outcomes. SignificanceOverall, our findings suggest that altered temporal dynamics derived from preoperative resting-state fMRI represent a promising non-invasive approach for delineating epileptogenic tissue, potentially informing intervention strategies and guiding electrode placement.

BackgroundEpilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures. Epilepsy manifests as different seizure types and epilepsy types, which have important implications for treatment and prognosis. Electronic health record systems containing longitudinal data on large epilepsy cohorts can be valuable resources for clinical research. However, detailed epilepsy phenotypes are poorly captured by structured data such as diagnostic codes and are instead buried in unstructured clinical notes. MethodsWe evaluated two transformer-based language models for automated epilepsy and seizure type phenotyping from clinical notes: a fine-tuned BERT model and a large language model, DeepSeek-R1. A subset of notes was annotated by epileptologists, and model performance was benchmarked against expert agreement. The best-performing model was then deployed across all epilepsy progress notes at a large academic medical center to generate patient-level longitudinal epilepsy and seizure phenotypes. ResultsBoth models achieved performance comparable to expert agreement for classifying epilepsy type as focal, generalized, or unspecified (Matthews correlation coefficient [95% CI]: DeepSeek = 0.85 [0.80-0.90], BERT = 0.73 [0.67-0.80], human = 0.77 [0.70-0.83]) and classifying seizure type as convulsive or non-convulsive (DeepSeek = 0.74 [0.66-0.81], BERT = 0.60 [0.49-0.69], human = 0.49 [0.39-0.59]). For more granular classification tasks, DeepSeek maintained performance comparable to expert agreement, whereas BERT performance declined. Deploying DeepSeek-R1 on 77,049 clinical notes from 18,566 patients revealed system-level clinical patterns, including diagnostic stabilization over time, frequent co-occurrence of seizure types, and variation in seizure outcomes by epilepsy type. ConclusionsBy extracting expert-level epilepsy phenotypes from routine clinical text at scale, this approach transforms unstructured EHR data into a resource for longitudinal, population-informed epilepsy care. Automated phenotyping enables analyses of epilepsy trajectories and treatment outcomes that are not feasible with structured data alone, supporting future clinical and translational research applications.

Objective: The postictal state is a major yet underrecognised component of epilepsy burden. We aimed to develop a structured patient-reported instrument to quantify postictal recovery, characterise its multidimensional burden and identify demographic, clinical, psychiatric and treatment-related factors associated with postictal severity and duration. Methods: We conducted a prospective, single-centre observational cohort study (Timone Hospital, Marseille, February 2025 - March 2026). Consecutive patients aged >=15 years admitted for scalp or stereo-EEG video-monitoring were included. Patients completed the Postictal Recovery Scale (PRS), an 11-domain questionnaire assessing fatigue, mood, sensory, motor, language, orientation, time perception and postictal amnesia. Items were rated from 0 (severe impairment) to 3 (no symptoms), yielding a total score of 0-33. Internal consistency was assessed using Cronbach alpha. Associations between PRS scores, subjective postictal duration and covariates were analysed using group comparisons, correlations and regression models. Results: Of 107 enrolled patients, 96 were included. PRS showed good internal consistency (Cronbach alpha; = 0.79). 96% of patients reported experiencing postictal symptoms, with fatigue (80%) and postictal amnesia (79%) being the most frequent and severe manifestations. Recovery exceeded one hour in 21% of patients. Greater postictal impairment was associated with higher interictal anxiety (Spearman {rho} = -0.32, p = 0.0018) and depressive symptoms (Spearman {rho} = -0.40, p = 0.0001), whereas demographic, epilepsy-related and treatment variables showed no significant associations. Altered postictal time perception was reported by 40% of patients and was associated with disorientation, but not psychiatric symptoms. Subjective postictal duration was longer than subjective ictal duration (Wilcoxon test, p < 0.0001). Significance: The postictal state is a frequent and multidimensional patient-reported experience. Greater postictal severity, particularly concerning anxiety and depression, is associated with interictal psychiatric comorbidity, while altered temporal experience emerges as a distinct dimension of postictal dysfunction. These findings support integrating postictal measures into clinical practice and trials.

Dravet syndrome is an epileptic encephalopathy most often caused by loss-of-function mutations in the SCN1A gene, leading to haploinsufficiency of the voltage-gated sodium channel NaV1.1. Seizures begin during infancy and generally wane throughout childhood, but behavioral symptoms, such as intellectual disability, motor impairments, and autistic features, remain through adulthood. Seizures primarily stem from inhibitory neuron hypo-excitability in the cortex, hippocampus, and thalamus, but circuit abnormalities underlying persistent behavioral symptoms are poorly understood. Prior work showed synapse dysfunction in thalamocortical neurons in four-week-old DS mice. To understand when synaptic deficits develop and whether they could contribute to persistent thalamic dysfunction, we investigated synapse function in the ventral posterolateral (VPL) and ventral posteromedial (VPM) thalamus prior to seizure onset (P13-P17), after the period of highest seizure burden (P28-P32), and in adulthood (P58-P63). Recordings of VPL and VPM synaptic activity showed excitatory input to the VPL was significantly reduced after seizure onset and this reduction persisted through adulthood, while VPM excitatory input was unaffected. We further showed a selective reduction in the function and number of excitatory sensory synapses in the VPL, with no change to cortical synapses. VPL and VPM neurons both showed inhibitory synapse dysfunction at four weeks, which persisted into adult DS mice only in VPL neurons. These results revealed persistent input- and cell-type-specific alterations to thalamic synapses that develop after seizure onset and are maintained into adulthood, suggesting that synaptic deficits could contribute to ongoing circuit dysfunction in DS.

BACKGROUND AND OBJECTIVESPatients with epilepsy (PWE), especially temporal lobe epilepsy (TLE), experience impaired memory for personally experienced events. However, current assessments of episodic memory are limited in their ecological validity with a potential to miss detection of subtle cognitive decline. We conducted an exploratory study to determine whether a naturalistic film-viewing task with open-ended spoken recall could detect memory differences between TLE patients and healthy controls (HCs). METHODSTLE patients (ages 18-60, fluent in English, not legally blind) were recruited from a Level 4 Epilepsy Center (2018-2024). TLE diagnosis was based on seizure semiology, MRI Brain, and EEG. TLE patients scored [&ge;]22/30 on the Montreal Cognitive Assessment (MOCA); HCs scored [&ge;]26/30. Subjects watched 6 short films and then freely recalled film details. Spoken responses were recorded, transcribed, segmented, and scored for film- and event-level recall. Recall order was assessed using the Damerau-Levenshtein distance. Semantic and causal centrality were quantified using sentence embeddings and rater-identified causal links, respectively. Beta regression with cluster-robust standard errors assessed group and centrality effects on recall probability. Beta regression evaluated the influence of age, MOCA, and testing platform on sequence recall error. RESULTSWe recruited 51 subjects (27 TLEs; 24 HCs, 70.1% F, mean 29.9 {+/-}8.3 years). TLE patients and HCs showed similar recall of films (HC 89% {+/-}11% vs TLE 88% {+/-}18%, p = 0.54), coarse events (HC 50% {+/-}16% vs TLE 44% {+/-}18%, p = 0.19) and fine events (HC 25%{+/-}10% vs. TLE 22%{+/-}12%, p=0.17). Both groups recalled high causal centrality events better. For coarse event sequence recall, TLE patients showed a numerical trend toward greater sequence errors compared to HCs (HC 10.8% {+/-} 10.5% vs. TLE 19.5% {+/-} 18%, p = 0.06), although this difference did not reach statistical significance. However, TLE patients showed significantly greater fine event sequence errors at recall than HCs (HC 15% {+/-}13% vs 23% {+/-}18%, p = 0.02, Hedges g = 0.85, Cliffs {delta} = 0.51), with RTLE demonstrating more sequence errors than HCs (15%{+/-}13 vs. 29%{+/-}21% p = 0.021) Age, education, MOCA, and performance on standard verbal and visual memory tasks were unrelated to film, event, and sequence recall performance. DISCUSSIONWe demonstrate that a short film task with spontaneous spoken recall can identify sequence memory impairment in TLE patients despite intact film- and event-level recall. Sequence memory may represent a subtle manifestation of memory impairment that is not detected by standard cognitive testing. Key PointsO_LIWe asked whether a naturalistic film recall task could detect episodic memory impairment in a temporal lobe epilepsy cohort. C_LIO_LIPatients with temporal lobe epilepsy showed comparable film and event recall compared to healthy controls but were found to have impaired sequence memory. C_LIO_LISequential memory for temporal order is an overlooked aspect of episodic memory that may detect subtle memory decline. C_LI