Epilepsia

ObjectiveNeuropsychological assessment plays an important role in localizing epileptogenic regions during presurgical evaluation. However, its diagnostic potential is constrained by reliance on syndromic models of epilepsy. A network-grounded approach may provide higher resolution structure-function relationships, yet in vivo evidence linking epileptogenic networks to cognitive deficits remains limited. Here, we developed a network-based framework to test the hypothesis that patterns of sublobar epileptogenicity shape distinct cognitive profiles. MethodsRetrospective cohort study of 42 drug-resistant focal epilepsy patients undergoing stereo-EEG (SEEG) and pre-implantation neuropsychological assessment (16 indices). Epileptic networks were quantified using a composite SEEG-derived epileptogenicity metric ( EzPz score) providing a continuous measure of regional epileptogenicity. Sublobar EzPz values and neuropsychological z-scores were analyzed by Pearson correlations, PCA, and hierarchical clustering to derive network subtypes and domain-specific cognitive associations. ResultsMean age 34.9 years and 79% MRI-negative. Significant negative pairwise correlations were seen: dominant temporal with language, non-dominant mesiotemporal with visual memory; and non-dominant frontal with attention and visuospatial. Exploratory PCA/clustering identified nine network configurations with associated cognitive profiles. For example, dominant mesiolateral temporal configuration (86% MRI-negative): naming impairment with preserved verbal memory; non-dominant frontotemporal: severe executive and visual memory impairment; bimesiolateral temporal: severe language deficits with executive and visuoconstructional impairment. InterpretationEach of our nine network configurations were associated with a cognitive profile shaped by sublobar epileptogenic distribution, lateralisation, and network size. These findings support a shift from syndromic to network-based interpretation of neuropsychological data. Our framework may enhance the diagnostic potential of neuropsychological assessment in SEEG hypothesis generation and surgical planning.

Syntaxin-binding protein 1 (STXBP1) mutations lead to severe epilepsy, intellectual disability, developmental delay, and movement disorder. Effective treatments for these conditions do not exist. Recent studies in Munc18-1 (STXBP1) C. elegans models demonstrate that 4-phenylbutyrate (4-PBA) or related pharmacological chaperones stabilize Munc18-1 protein levels and rescue locomotion deficits. These studies suggest a novel treatment strategy for these patients. Here, we used a stxbp1a zebrafish model with a profound movement disorder to screen 4-PBA and alternative structural analogs identified using artificial intelligence (AI)-based screening. Automated locomotion assays conducted on larval stxbp1a mutant zebrafish at 5 days post-fertilization (dpf) confirm and extend the movement disorder endophenotype. Drug treatment (4-PBA or 16 identified candidates) failed to rescue the stxbp1a mutant zebrafish locomotion deficit. Electrophysiology studies in a stxbp1b zebrafish model characterized by spontaneous seizure activity (i.e., epilepsy) failed to detect a reduction in ictal-like events with 4-PBA treatment. Taken together, our results suggest caution in repurposing 4-PBA or related compounds for treatment of STXBP1 disorders.

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.

Objective: Interictal spikes have been proposed as a biomarker for both localizing seizure onset zones (SOZ) and tracking changes in seizure risk with neurostimulation in patients with drug-resistant epilepsy. Electrical stimulation can modulate spike rates acutely, and it has been proposed that measuring this modulation can help localize the SOZ. However, it is unclear whether stimulation-induced spike rate changes reflect epilepsy-specific pathology in the stimulated network or simply intrinsic regional excitability, which limits our understanding of their utility in epilepsy surgery planning. Methods: We analyzed low-frequency stimulation (LFS; 1 Hz) applied during a clinical seizure-induction protocol systematically targeting multiple brain regions in 43 patients with drug-resistant epilepsy undergoing intracranial EEG monitoring. A validated, automated spike detector was used to quantify pre-, during-, and post-stimulation spike rates. We tested whether the stimulation-evoked spike rate response (i) tracks the expected change in seizure risk from a seizure induction protocol, (ii) varies with anatomical stimulation site and epilepsy localization, (iii) localizes the SOZ beyond baseline spike rate, and (iv) is accompanied by changes in spike morphology. Results: Nearby LFS acutely increased spike rates in high-spiking channels (inter-stimulation median 2.25 vs. during-stimulation 4.25 spikes/min; p < 0.001), with effects attenuating with distance and resolving within approximately 30 seconds of stimulation offset. Mesial temporal lobe stimulation produced the largest increase in nearby spike rates relative to temporal neocortex and other cortex (Kruskal-Wallis p = 0.003), but this effect did not differ between patients with and without mesial temporal lobe epilepsy. A random forest classifier incorporating stimulation-evoked modulation features achieved an AUC of 0.787, comparable to a resting-state spike model (AUC 0.747; DeLong p = 0.81), indicating that stimulation-evoked spike changes do not add localizing information beyond resting-state spike rates. Stimulation produced a small but significant shift in spike morphology toward broader, higher-amplitude discharges (PERMANOVA p < 0.001), consistent with recruitment of a broader neuronal population. Significance: LFS-evoked increases in interictal spike rates reflect intrinsic regional excitability, greatest in the mesial temporal lobe, rather than epilepsy-specific pathology, and do not improve SOZ localization over resting-state spike rates. These results argue against using the change in spikes with stimulation to localize the SOZ. On the other hand, the transient spike rate increase induced by a pro-epileptic protocol supports the acute change in spike rate as a biomarker of the effect of stimulation on seizure risk, with potential to guide parameter selection for epilepsy neuromodulation.

Rationale: Reliable electroencephalography (EEG) biomarkers of cortical excitability could improve diagnosis and longitudinal monitoring in epilepsy, yet it remains unclear which metrics best balance sensitivity across individuals with intra-individual stability over time. Methods: We analyzed scalp EEG recordings from the open-access Temple University Hospital EEG Epilepsy Corpus, comprising 1,404 recordings from 96 individuals with neurologist-confirmed epilepsy and 85 healthy controls across multiple sessions. Eight global measures were computed: aperiodic exponent and offset, sample entropy, detrended fluctuation analysis exponent and derived index, spatial gamma-band phase consistency, and absolute and relative alpha power. Group differences were assessed by permutation tests with false discovery rate correction at recording, session, and subject levels. Associations with antiseizure medication burden, temporal stability, and cross-metric correlation structure were evaluated as secondary analyses. Results: Aperiodic parameters showed the most robust case-control separation, remaining significant after subject-level averaging (exponent: median difference = 0.20, q = 0.010; offset: median difference = 0.25, q = 0.011). Entropy and alpha power distinguished groups at the recording and session levels, while gamma-band phase consistency was significant at the session level only; none of these survived subject-level averaging, suggesting greater state-dependency. Higher medication burden was associated with reductions in alpha power and detrended fluctuation analysis, and adjusting for it substantially attenuated group differences, though residual effects in the aperiodic exponent persisted. Cross-metric correlation structure was preserved between groups but modestly reorganized by medication burden. Conclusions: Aperiodic spectral parameters are the most robust EEG markers of epilepsy, reflecting stable trait-like network properties. Complexity and synchrony measures capture complementary, state-sensitive dimensions. Medication burden substantially influences multiple metrics, underscoring the need to account for pharmacological effects when interpreting EEG biomarkers in epilepsy.

IntroductionBrain structural differences consistent with an older-appearing brain have been reported in people with epilepsy, but the extent to which these differences reflect clinical characteristics vs broader socioeconomic context is unclear. We investigated whether country-level socioeconomic factors are associated with neuroanatomical differences in adults with epilepsy using MRI-based age prediction, along with epilepsy subtype, sex, and clinical factors. MethodsStructural MRI and clinical data were collected from 26 epilepsy centres across 12 countries in the Americas, Australia, Europe, Asia and Africa. MRI-based age estimates were estimated using a previously developed prediction model trained on 29,175 healthy subjects. Brain predicted age difference (BrainPAD) was calculated as the difference between MRI-predicted brain age and chronological age. National gross domestic product (GDP) per capita and income inequality (Gini index) were obtained from the World Bank. Associations between BrainPAD and epilepsy subtype (temporal lobe epilepsy, extratemporal epilepsy, and genetic generalised epilepsy), national socioeconomic context (GDP per capita and Gini index), age and sex were assessed using regression models. ResultsWe analyzed 2,109 individuals with epilepsy and 1,041 healthy non-epilepsy controls (57% female; median age = 35; range 17-83). BrainPAD was higher in epilepsy than controls ({beta} 4.2 years, SE 0.4; t=10.6), with increases ranging from 2.5 to 6 years across subtypes. Male sex was associated with 1 year higher BrainPAD relative to females (SE 0.33, t=3.12). There were no main effects of GDP or Gini index; however, significant interactions between were observed. The effect of epilepsy on BrainPAD was greater in countries with lower GDP per capita (t=-2.74) and higher income inequality (t=2.72). ConclusionsClinical factors and socioeconomic context both influence brain structural ageing in epilepsy. These findings highlight the importance of geographic and economic diversity in neuroimaging research and underscore the relevance of global socioeconomic context when interpreting brain health measures.

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.

ImportanceImplantable sub-scalp EEG systems with a small number of channels have emerged as promising solutions for long-term seizure monitoring in patients with epilepsy. How seizure detection performance varies by montage configuration is unknown. ObjectiveTo quantify how automated seizure detection performance differs between full and reduced montages, and how these differences vary by epilepsy characteristics. DesignRetrospective cross-sectional study. SettingSingle-center at the Hospital of the University of Pennsylvania Epilepsy Monitoring Unit (EMU). ParticipantsEEG data from 2281 consecutive EMU admissions between January 2017 and December 2024 were screened. Admissions with at least one annotated seizure and one interictal clip [&ge;]20 minutes from any seizure were included. ExposureComputational simulation of published sub-scalp device montages using standard 10-20 EEG channels. Main Outcomes and MeasuresThe primary outcome was event-based F1 scores evaluated for three published seizure detectors--a one-class support vector machine (SVM), a convolutional neural network (SPaRCNet), and a long short-term memory autoregressive model (NDD)--across montages. ResultsA total of 466 admissions from 436 patients (mean [SD] age, 39.0 [14.4] years; 54.4% female) met inclusion criteria, comprising 1683 seizures and 1527 interictal clips. SPaRCNet achieved the highest performance (mean [SD] F1, 0.61 [0.30]), followed by NDD (0.56 [0.28]) and SVM (0.39 [0.25]). Performance decreased by at most 0.09 with reduced montages, depending on detectors. Patient factors accounted for the largest proportion of performance variance (29.2%), followed by detector choice (10.3%). Montage effects were minimal (0.4%), despite variation in optimal montage across detectors. Reduced-montage performance correlated moderately to highly with full-montage performance ({rho}=0.29-0.73), suggesting full-montage performance could help identify patients suitable for sub-scalp devices. Missed seizures were associated with lower amplitude and bandpowers than detected seizures, though they remained distinguishable from interictal data. Conclusions and RelevanceAutomated seizure detection achieved comparable accuracy, with only modest reductions, under simulated reduced montages. Performance differences were driven primarily by detector- and patient-level factors rather than montage. These findings support the feasibility of accurately detecting seizures with published sub-scalp devices and highlight the need for improved algorithms to optimize performance. Key FindingsO_ST_ABSQuestionC_ST_ABSHow do automated seizure detection algorithms perform with reduced-channel montages simulating published sub-scalp devices? FindingsIn this retrospective cross-sectional study, seizure detection performance decreased only modestly on reduced montages relative to the full montage (absolute F1 change -0.09 to 0.014), whereas patient- and algorithm-level factors accounted for most of performance variance (29.2% and 10.3%, respectively). Algorithm performance on full montage recordings was moderately correlated with performance on reduced channel montages ({rho}=0.29-0.73). MeaningReduced-montage sub-scalp devices are promising for ultra-long-term monitoring, but best performance requires selecting the right patients. Patient-specific seizure detectors will likely be required to optimize long-term performance.

Mutations in DOCK7 have been identified in individuals with epileptic encephalopathies. Given that epileptic encephalopathies are a set of disorders that result in seizure activity and associated cognitive and behavioral impairments, we investigated the role of Dock7 in seizure susceptibility and flurothyl kindling using the repeated flurothyl seizure model in mice. Male and female Dock7+/+ and Dock7{bigtriangleup}ex3-4/{bigtriangleup}ex3-4 mice were subjected to 8 daily flurothyl exposures (kindling, induction phase) followed by a 28-day incubation period and a subsequent flurothyl rechallenge (retest). No significant differences were observed in baseline myoclonic jerk or generalized seizure thresholds between genotypes or sexes. However, over the kindling period, male Dock7{bigtriangleup}ex3-4/{bigtriangleup}ex3-4 mice exhibited slightly higher myoclonic jerk and generalized seizure thresholds compared to Dock7+/+ males across trials. Female mice showed similar trends, but the differences were only significant for generalized seizure thresholds. Following the 28-day incubation period and flurothyl retest, male mice of both genotypes maintained their seizure thresholds upon retest. Dock7+/+ female mice showed increased myoclonic jerk and generalized seizure thresholds during retest, while Dock7{bigtriangleup}ex3-4/{bigtriangleup}ex3-4 females maintained their thresholds. A key finding was the emergence of more severe forebrain[-&gt;]brainstem seizures upon flurothyl retest in a significant percentage of mice across all groups. However, the proportion of mice developing these seizures did not differ significantly between genotypes. Although DOCK7 mutations have been linked to human epileptic encephalopathies and neurodevelopmental dysfunction, we find that Dock7{bigtriangleup}ex3-4/{bigtriangleup}ex3-4 male and female mice do not show heightened excitability or seizure susceptibilities using the repeated flurothyl seizure model. HighlightsO_LIDock7{bigtriangleup}ex3-4/{bigtriangleup}ex3-4 mice show slightly higher seizure thresholds during flurothyl kindling C_LIO_LIDock7{bigtriangleup}ex3-4/{bigtriangleup}ex3-4 mice do not exhibit heightened seizure susceptibility upon retest. C_LIO_LIForebrain-brainstem seizures emerged upon retest regardless of Dock7 genotype. C_LI

Developmental and epileptic encephalopathies (DEEs) are a group of neurological disorders primarily affecting young children and are characterized by severe seizures. DEEs are challenging to manage, with some patients experiencing severe side effects or not responding to frontline therapies. This is partly because of the many underlying mechanisms involved in DEE pathology and the relatively limited mechanism-specific action of current treatments. The CACNA1E gene, which encodes the voltage-gated calcium channel Cav2.3 (R-type), has recently been associated with DEEs. More than fifteen different mutations in CACNA1E have been identified in patients with DEEs; however, the mechanisms by which these mutations affect channel function and, thus, their relationship to DEEs, remain largely unknown. Previous research has begun to characterize the functional effects of R-type channel mutations on channel biophysics, but only a handful of mutations have been studied functionally to date. Here, we transiently expressed Cav2.3 channels and used whole-cell patch-clamp to examine the biophysics of one specific disease-associated R-type channel mutant in which leucine 228 is substituted with a proline (L228P). Compared to wild-type, the L228P mutant did not alter peak current density, inactivation kinetics, or recovery from inactivation, but showed a significant shift towards hyperpolarized voltages in both voltage-dependent activation and steady-state inactivation. This resulted in a broader window current shifted towards more hyperpolarized potentials, which predicts increased channel availability and activity at subthreshold voltages relative to wild-type channels. Our results contribute to the ongoing characterization of R-type mutants, with the long-term goal of informing mechanism-specific therapies for DEEs.

ObjectiveWater exchange across the blood-brain barrier (BBB) sustains tissue homeostasis; BBB dysfunction can raise neuronal excitability and promote seizures. Noninvasive imaging of water exchange probes BBB function. T2l1lrelaxation underl1lspinl1ltagging (TRUST) arterial spin labeling (ASL) separates intravascular and extravascular labeled water. We tested TRUSTl1lASL feasibility and scan-rescan reliability. MethodsTwelve participants (5 controls, 7 with epilepsy) underwent two 3T MRI sessions 2-4 weeks apart, acquiring T1, FLAIR, and TRUSTl1lASL. At each epilepsy visit we recorded seizure frequency, last seizure details, and medications. ASL images were processed in MATLAB to quantify CBF and water exchange time. Maps were registered to T1 and normalized to MNI space via FreeSurfer. Scan-rescan reliability across gray matter and regions (temporal cortex including amygdala/hippocampus, frontal, occipitoparietal, basal ganglia) used intraclass correlation coefficients. Individual agreement was evaluated with Bland-Altman plots and withinl1lsubject coefficient of variation. Statistical analyses used R. ResultsScan-rescan reliability was good for CBF (ICC = 0.79, 95% CI [0.28 - 0.94]) and excellent for water exchange time (ICC = 0.97, 95%CI [0.90-0.99]) across grey matter. Regional analyses revealed lower reliability in the basal ganglia compared to other subregions. Bland-Altman analysis demonstrated strong agreement between sessions, with mean differences near zero for CBF (limits of agreement: -20 to 20 ml/100g/min), and water exchange time (mean differences 0.17s; limits of agreement: -0.44 to 0.79 s). SignificanceOur findings show TRUSTl1lASL reliably measures BBB water exchange. Tex has high relative reliability (ICC) but substantial withinl1lsubject variability (wCV), limiting sensitivity for small individual longitudinal changes. Groupl1llevel reliability is strong, supporting use in longitudinal epilepsy studies. BBB water exchange is a promising noninvasive biomarker of BBB dysfunction in epilepsy. PLAIN LANGUAGE SUMMARYThe blood-brain barrier protects the brain from harmful substances while letting nutrients pass from the blood. In epilepsy, this barrier can become disrupted and may make seizures worse. We tested a noninvasive MRI method (no injections) to measure how well the blood and brain exchange substances. Our results show the technique can clearly visualize these exchanges and can be repeated reliably, offering a safe tool to study barrier function in people with epilepsy.

Mild malformation of cortical development with oligodendroglial hyperplasia and epilepsy (MOGHE) is a recently recognized cause of drug-resistant focal epilepsy. It is often MRI-negative or shows imaging features mimicking focal cortical dysplasias, which makes recognition difficult and limits presurgical counseling. We aimed to identify an intracranial EEG (iEEG) biomarker that distinguishes MOGHE from other developmental brain lesions encountered in epilepsy surgery. In a retrospective multicenter test cohort of 38 patients (18 MOGHE, 20 non-MOGHE), we analyzed long-term stereo-EEG and subdural recordings. Only MOGHE patients showed highly stereotyped clusters of very brief low-voltage fast activity (LVFA) events, organized into status-like 3 to 12-minute episodes that often lacked clear clinical symptoms. LVFA clusters were present in 16/18 MOGHE and 0/22 non-MOGHE patients. We then tested diagnostic performance in an independent, blinded single-center validation cohort of 22 patients (11 MOGHE, 11 non-MOGHE), in which visual identification of LVFA clusters correctly classified 10/11 MOGHE and 10/11 non-MOGHE cases (Cohens kappa=0.82). Penalized logistic regression further confirmed MOGHE histology as the strongest predictor of LVFA clusters, independent of age and lobe localization. Because LVFA clusters can be recognized visually on routine intracranial EEG recordings without specialized software, this biomarker is readily applicable in clinical practice and may improve presurgical identification of MOGHE. Future prospective studies should determine whether its recognition influences surgical planning, improves outcome prediction, or facilitates selection of patients for mechanism-based therapies.

Background and Purpose: Drug resistant epilepsy (DRE) affects approximately 15 million people worldwide, and surgery remains the only curative option. A key challenge in predicting outcomes is the lack of standardized, quantitative tools to help distinguish seizure driver regions from responder regions during stereoelectroencephalography (sEEG) recordings. We validated the CN Suite, a computational platform that uses causal network mapping and machine learning to assign criticality scores to sEEG contacts, testing whether higher scores correspond to surgically treated tissue in patients with favorable outcomes. Methods: We analyzed deidentified clinical data from 60 patients (aged 2 years and older) with focal or multifocal DRE who underwent sEEG monitoring and proceed to surgery at four U.S. Level 4 epilepsy centers. The algorithm was trained on an independent cohort (N=37) and locked prior to validation. The primary outcome was the standardized effect size (Cohens d) of the patient level surgical zone enrichment ratio between more favorable (Engel I or II) and less favorable (Engel III or IV) outcome groups. Contact level sensitivity, specificity, PPV, and NPV were evaluated at a prelocked threshold. Results: The findings support our hypothesis: the algorithm results showed significantly higher criticality values for surgically treated tissue in favorable outcome patients (d=0.74, 95% CI: 0.39 to 1.06, p=0.003). Three potentially clinically actionable findings emerged. First, high-criticality contacts formed spatially compact clusters (~9 mm nearest-neighbor distance vs. 17mm expected by chance), consistent with focal targets amenable to minimally invasive ablation. Second, sensitivity was highest in small focal procedures (80% at 10 or fewer treated contacts) and decreased with resection size. Third, in patients whose surgery failed, high-critical tissue remained outside the resection boundary, suggesting incomplete treatment coverage of the epileptogenic zone rather than mislocalization. Prediction specificity was 84% at the contact level. For adult and pediatric cases (n=28), 88% of contacts that were identified as seizure free were in fact seizure free. Conclusions: Causal network mapping of sEEG identifies compact epileptogenic targets that correspond to surgically treated tissue in patients with more favorable outcomes. CN-Suite performed best in focal procedures and may be best suited for LITT and other minimally invasive approaches. In addition, low-criticality regions were infrequently associated with seizure-generating tissue, particularly in the pediatric cohort although our sample size was small. When surgery failed, residual high-critical tissue outside the resection boundary offered both a mechanistic explanation for less favorable surgical outcome as well as a potential target for reoperation.

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.

Disease variants in GABR genes encoding {gamma}-Aminobutyric acid type A receptor (GABAAR) subunits are major causes of developmental and epileptic encephalopathies (DEEs). There is no effective treatment for these DEEs although the GABAAR is a major target for antiseizure drugs. We previously identified the therapeutic effect of 4-phenyl-butyrate (PBA) in Gabrg2+/Q390X knockin DEE mice and now test the effect of the drug in GABRA1 variants that encode the 1 subunit. We used a multidisciplinary approach including in silico structural modeling, flow cytometry, patch clamp recordings and bio-chemistry in conjunction with differential tagging of the wild-type and the mutant alleles to evaluate the effect of PBA on rescue of GABAAR subunit expression, surface trafficking, and function in vitro in heterologous HEK293T cell model and in vivo in Gabra1+/A322D mice. We found that both total and cell surface 1 expression was reduced when the variant 1 protein was present; suggesting reduced functional receptor on the cell membrane and synapse. Patch clamp recordings identified 1 variants reduced GABA-evoked current amplitude. In silico prediction indicated reduced protein stability for GABRA1 variants indicated by negative {Delta}{Delta}G values. PBA increased both total and surface expression of wildtype 1 and 1 variants; and improved expression of both wildtype and variant 1 alleles when these were co-expressed. Importantly, PBA also increased the GABAAR expression in the thalamus of the Gabra1+/A322D mice. This study indicates that PBA is a promising treatment option for DEEs associated with GABRA1 mutations. Our previous work has demonstrated that PBA improves proteostasis by enhancing expression of the wildtype allele, repairing the mutant allele, and reducing endoplasmic reticulum stress. Therefore, it can mitigate seizures and improve neurobehavioral phenotypes at behavioral levels. Based on this and our previous work on GABRG2 and SLC6A1 mutations, we propose that PBA holds promise as a common medicine for multiple genetic neurologic disorders that share the proteostasis pathology with a broad clinical application in DEEs.

Objective Memory impairment is a frequent comorbidity of focal epilepsy, incompletely explained by seizure frequency or structural pathology. Ictal and postictal hippocampal dysfunction disrupt memory processes, but their cumulative impact remains poorly quantified. This study introduces cumulative hippocampal seizure-related burden metrics and examines their association with long-term memory consolidation. Methods Twenty consecutive patients undergoing stereo-EEG in Marseille (2016-2018) were prospectively included. Continuous stereo-EEG recordings between two memory assessments (30 minutes and one week post-encoding) were analysed. Hippocampal ictal involvement and durations were assessed using epileptogenicity markers and visual stereo-EEG analysis. The postictal period was quantified using permutation entropy. Cumulative hippocampal seizure-related burden metrics (ictal, postictal and combined: c-HipSZB) were computed across hippocampus-involving ictal events. Verbal and visual memory were assessed using standardized recall and recognition tasks. Associations were examined using univariate and multivariate analyses. Results Higher dominant-hemisphere hippocampal burden was associated with poorer one-week verbal memory (performance and retention), independently of most covariates. Higher c-HipSZB was associated with lower total recall performance (RT; free + cued) and RT retention ({beta} = -25.04 and -23.88; R2 = 0.57 and 0.53; p < 0.05) and accounted for the greatest variance in both outcomes (adjusted R2= 0.59 and 0.53; {beta} = -25.45 and -24.27; p < 0.01), particularly when adjusting for epilepsy duration. No robust associations were observed between non-dominant-hemisphere hippocampal seizure-related burden metrics and visual memory. Effects predominantly involved recall. Interpretation Cumulative ictal-postictal hippocampal dysfunction is a major determinant of impaired long-term verbal memory consolidation in focal epilepsy.

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.

ObjectiveThe ketogenic diet (KD) is a high-fat, low-carbohydrate intervention widely used to treat drug-resistant epilepsy, thought to reduce seizures through a combination of metabolic, neuronal, and microbiota-dependent mechanisms. Additionally, recent studies suggest that the anticonvulsant effects of KD require the gut microbiota, with taxa such as Akkermansia and Parabacteroides contributing to seizure protection by modulating host neurotransmitter balance and neural excitability. While KD has been shown to be effective in reducing seizure burden across different epilepsies, its antiseizure effect on infection-driven seizures, which are often driven by acute neuroinflammation, has not been evaluated. Here, we evaluated the effects of KD on seizure burden, neuroimmune responses, and gut microbiota composition in the Theilers murine encephalomyelitis virus (TMEV) model of virus-induced epilepsy. MethodsMice were maintained on either a KD or a normal diet prior to intracerebral TMEV infection. Seizures were induced by handling and scored twice daily from day 3 to 7 post-infection. Neuroimmune responses were assessed by flow cytometry, and fecal microbial composition was analyzed using 16S rRNA gene sequencing. ResultsDespite achieving ketosis, KD did not reduce seizure incidence, seizure burden, or seizure severity during acute TMEV infection. KD also did not significantly alter overall immune cell infiltration into the central nervous system, indicating limited effects on global neuroinflammation. However, KD significantly reshaped the gut microbiota, reducing alpha diversity (richness, Shannon diversity, and evenness) and strongly altering community structure with clear separation between diet groups, including enrichment of taxa such as Akkermansia, Acetatifactor, Dorea, and Flintibacter, and depletion of fiber-associated taxa including Bifidobacterium and Roseburia. However, these microbial shifts were insufficient to mitigate inflammation-driven seizures. SignificanceThese results demonstrate that KDs anticonvulsant efficacy is highly context-dependent, and that KD-driven changes in microbiota- and metabolite-mediated mechanisms may be ineffective against infection-associated epilepsy, suggesting that inflammation-driven seizures require distinct therapeutic approaches. Key pointsO_LIThe ketogenic diet (KD) does not reduce acute seizure incidence and severity during TMEV infection despite achieving ketosis C_LIO_LIKD does not induce neuroinflammatory changes associated with seizure outcomes C_LIO_LIKD strongly reshapes gut microbiota, reducing diversity and altering community structure. C_LIO_LIMicrobiota changes are insufficient to protect against inflammation-driven seizures C_LIO_LIKD anticonvulsant effects are context-dependent and ineffective in infection-driven epilepsy C_LI

AIM: STXBP1-related disorder (STXBP1-RD) is a severe developmental and epileptic encephalopathy characterized by early-onset seizures and persistent cognitive and motor impairments. With disease-modifying trials emerging, a disorder-specific severity scale is needed. To address this, we adapted a validated clinician-reported measure from CDKL5 Deficiency Disorder to develop the STXBP1 Clinical Severity Assessment (S-CSA) and evaluated its psychometric properties. METHOD: The S-CSA was adapted from the CDKL5 Clinical Severity Assessment through expert consensus sessions with STXBP1 clinicians. Revisions addressed gaps in motor and vision domains, adding tremor and vision items. The measure was administered to 123 individuals with STXBP1-RD. Psychometric evaluation included confirmatory factor analysis, internal consistency, composite reliability, average variance extracted, and distinctiveness, compared with recommended thresholds. RESULTS: Analyses supported a three-domain structure (motor, communication, vision) with factor loadings >0.5 and strong internal consistency (Cronbachs alpha >0.7; composite reliability >0.88). Model fit and variance metrics met recommended standards, and domains demonstrated distinctiveness. No ceiling or floor effects were observed. Minimal skew was seen in motor (0.34) and communication (0.16) domains; positive skew in vision (2.2) was seen, identifying patients with and without cortical visual impairment. INTERPRETATION: The S-CSA demonstrates strong validity and reliability in STXBP1-RD and may show utility in clinical trials for STXBP1-RD and potentially other severe DEEs. Key Words: STXBP1-Related Disorder, Developmental and Epileptic Encephalopathies, Clinical Outcome Assessments