Epilepsia

BackgroundUp to 35% of individuals diagnosed with epilepsy proceed to develop pharmacoresistant epilepsy, leading to persistent uncontrolled seizure activity that can directly, or indirectly, significantly degrade an individuals quality of life. The factors underlying pharmacoresistance are unclear, but it has been hypothesised that repeated ictogenic activity is conducive to the development of a more robust epileptogenic network. To ensure that the most effective treatment choices are made and ictogenic activity is minimised, accurate outcome modelling at the point of diagnosis is key. ObjectivesThis review therefore aims to identify demographic, clinical, physiological (e.g. EEG), and imaging (e.g. MRI) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). Data sources, study eligibility criteria, participants, and interventionsMEDLINE and EMBASE were searched for prediction models of treatment outcomes in patients with newly diagnosed epilepsy and any non-surgical treatment plan. Study appraisal and synthesis methodsStudy characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST tool. Prognostic factors associated with treatment outcomes are reported. ResultsAfter screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally high for susceptibility to bias. Outcomes reported were heterogenous, but fit broadly into four categories: pharmacoresistance, short-term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics (semiology), epilepsy history, and age at onset. ASM response was often included as a prognostic factor, potentially obscuring factor relationships at baseline. ConclusionsCurrently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD practices, and by avoiding including response to treatment as a prognostic factor. Implications of key findingsThis review identified semiology, epilepsy history, and age at onset as factors associated with treatment outcome prognosis, suggesting that future prediction model studies should focus on these factors in their models. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE. Key PointsO_LIThis paper presents a systematic literature search for treatment outcome prediction models in newly diagnosed epilepsy. C_LIO_LIThe risk of bias in the included models were evaluated using the PROBAST framework, finding a universally high risk level. C_LIO_LIThe relationship between semiology, epilepsy history, and age at onset with seizure remission should be examined in future prediction model studies. C_LIO_LITo improve the overall quality of prediction model development in NDE, prospective authors are advised to adhere to TRIPOD guidelines, and to avoid including response to treatment as a prognostic variable. C_LI

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

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 [≥]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.

Epilepsy, characterised by a predisposition towards unprovoked seizures, is one of the most common neurological disorders globally. Whilst 60-70% of individuals diagnosed with epilepsy will gain seizure control through anti-seizure medication, the mechanisms underlying seizure persistence are unclear. Intractability can significantly degrade a patients quality of life amongst other things; the use of predictive modelling of epilepsy outcomes in deciding on treatment therefore offers a tangible benefit to the patient. Early indicators of pharmacoresistance may discourage certain treatment options, and save time in what has been indicated to be a critical stage for newly-diagnosed epilepsy. Primarily, this paper aims to evaluate existing predictive models to identify demographic, clinical, physiological (e.g. EEG), and neuroimaging (e.g. MRI) factors that may be predictive of treatment outcomes in newly-diagnosed epilepsy. Two electronic databases, MEDLINE and EMBASE, will be searched with terms related to prognosis in newly-diagnosed epilepsy, and identified studies will be included for review if they have combined at least two demographic, clinical, neuroimaging, and/or physiological factors to predict treatment outcome in people with newly-diagnosed epilepsy. Papers will be screened by two independent reviewers via titles, abstracts and then full text against the inclusion criteria for eligibility. Data will be extracted by reviewers using standardised forms, assessed for risk of bias using the PROBAST tool and synthesised narratively. If considered appropriate the authors will carry out a meta-analysis on the available data. Prospero registration number- CRD42022329936

Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link is relevant for epilepsy, with implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy). We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase. Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles. Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

Key points1. Antiseizure medications may change how strongly seizures synchronise with seizure cycles estimated from seizure diaries. 2. One seizure rate can be produced by different seizure cycles, suggesting a one-to-many relationship between seizure rate and cycles. 3. Using seizure cycles to time medication and assess efficacy could prove challenging with paper-based monitoring due to the complexity of cycles and potential drug effects. A seizure cycle tracking algorithm combined with an electronic seizure diary might support this task. Evaluating effectiveness of anti-seizure medications in epilepsy often relies on seizure frequency, reported through seizure diaries before and after treatment initiation. Measuring efficacy with seizure frequency can be challenging and unreliable as seizures tend to occur in cyclical patterns-- seizure cycles--making it difficult to distinguish drug effects from natural fluctuations. Incorporating cycle information could aid treatment evaluation, but antiseizure medications (ASMs) may alter seizure cycles, warranting further study. We conducted an observational study using seizure and ASM tracking app data (Feb. 2023) from 86 individuals with epilepsy. Participants were grouped based on ASM regimen and [&ge;]50% seizure rate reduction at 4 months after a drug change (drug-switching-responders, n=7/45; drug-switching-non-responders, n=38/45) or random timepoint (drug-sustained-responders, n=8/41; drug-sustained-non-responders, n=33/41). We compared groups on three seizure cycle variables detected via diaries: 1. how strongly seizures synchronise with a cycle, measured by the Synchronisation Index (SI), 2. cycle period, and 3. number of detected cycles. Permutation tests (=0.05, p<0.004 with Bonferroni correction) assessed significance, and regression models examined correlations with seizure rate. Across an average 612-day study period, 22,976 seizures were reported. Following an ASM change, the SI of the seizure cycle was more likely to change (p<0.004). This was pronounced in drug-switching-responders (median absolute SI difference: 0.37 [IQR=0.26] vs. 0.11 [IQR=0.11] in the drug-sustained-responders, p<0.004, permutation test). Changes in cycle length and number of detected cycles were similar across groups, possibly due to a non-linear relationship between seizure rate and cycles, suggested by weak linear correlations and poorly fitting models. These findings suggest ASMs may influence how strongly seizures synchronise with diary-detected seizure cycles. However, this relationship is complex and not yet well understood, complicating clinical interpretation. Ongoing research into real-time seizure cycle tracking may support the use of seizure cycles in aiding treatment monitoring.

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

SCN1A variants cause a range of epilepsy syndromes, including Dravet syndrome, leading to early cognitive and functional impairment. Despite advances in medical management, drug-resistant epilepsy remains common. Vagal nerve stimulation (VNS) has been suggested reducing seizure frequency in these patients but there is a lack of long-term follow-up, quantitative analysis that corrected for confounding factors such as antiseizure medications (ASMs) and the impact of VNS settings on response. This two-center, retrospective cohort study analyzed 12-month and for the first time up to ten-year seizure outcomes in therapy-refractory epilepsy patients with loss-of-function SCN1A variants (93.75% Dravet Syndrome) who underwent VNS implantation. A [&ge;]50% seizure frequency reduction was observed in 93.75% (15/16) of patients in the 12-month and 87.5% (14/15) in the ten-year period. Median seizure frequency was significantly lower in both follow-up periods than in the pre-implantation period. Linear mixed-effects regression showed that the reduction in seizure burden was independent of ASM use, and the VNS duty cycle was significantly associated with seizure reduction. Three individuals (18.8%) experienced minor side effects. Our results highlight the benefits of genotype-driven therapeutic interventions such as VNS in patients with SCN1A-related epilepsy. This study emphasizes the need for further implementation of genotype-driven clinical decision-making.

ObjectiveSeizure dogs are service animals trained to respond supportively to seizures in people with epilepsy; some are also trained to detect seizure-specific scents, particularly ictal volatile organic compounds (VOCs). This survey study examines feasibility and safety of incorporating a seizure service dog (SSD) into an inpatient setting, as well as patient perceptions of having an SSD in the Epilepsy Monitoring Unit (EMU). MethodsOur SSD underwent specialized training for seizure response and seizure recognition based on seizure-specific VOCs, and accompanied his epileptologist owner in the EMU on rounds for over four years prior to the study. We administered surveys to patients hospitalized in the EMU before and after interactions with a trained seizure dog. The surveys assessed the patients comfort with the dog, perceived usefulness of service dogs, safety, and tolerability. Select case examples are also presented in which seizure dog spontaneously alerted prior to epileptic seizures; seizures later confirmed by independent EEG review. ResultsPatient responses underscored overall high enthusiasm for seizure dog therapy, with 93% of participants reporting feeling "very comfortable" or "extremely comfortable" with a seizure dog present. No adverse concerns or negative experiences were reported by participants. 91% reported personally experiencing benefits of working with the seizure dog, citing emotional and comfort benefits during their hospitalization. 94% of participants were comfortable with physical contact with the dog or had no proximity preference. ConclusionThese findings suggest that seizure service dogs can be safely integrated into the inpatient EMU setting and have potential to enhance patient care and emotional well-being during EMU monitoring. Summary PointsO_LITotal of 98 patients admitted to EMU were surveyed about opinions regarding seizure dogs and comfort with integration of seizure dog in EMU setting, with 35 patients completing post-test surveys after interacting with the seizure dog. C_LIO_LI93% of surveyed EMU patients completing post-test surveys felt very or extremely comfortable with the seizure dog; no negative experiences or safety concerns were reported. C_LIO_LI91% reported personally experiencing emotional benefits of working with the seizure dog. C_LIO_LISelect case examples demonstrate that the trained seizure dog in our study may be able to spontaneously identify epileptic seizures. C_LI

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

BackgroundDomoic acid (DA) is a naturally occurring neurotoxin harmful to marine animals and humans. California sea lions exposed to DA in prey during algal blooms along the Pacific coast exhibit significant neurological symptoms, including epilepsy with hippocampal atrophy. ObservationsHere we describe a xenotransplantation procedure to deliver interneuron progenitor cells into the damaged hippocampus of an epileptic sea lion with suspected DA toxicosis. The sea lion has had no evidence of seizures following the procedure, and clinical measures of well-being including weight and feeding habits have stabilized. LessonsThese preliminary results suggest xenotransplantation has improved the quality-of-life (QOL) for this animal and holds tremendous therapeutic promise.

ObjectiveThe transition from interictal discharges to ictal high-frequency activity (HFA) remains poorly understood. We investigated whether spike-associated high-frequency oscillations (Sp-HFOs) during interictal and preictal periods contribute to the emergence of ictal HFA. MethodsWe retrospectively analyzed the interictal to ictal transition in seizures from six patients with drug-resistant focal epilepsy who underwent stereo-EEG and subsequent surgical resection. Various interictal periods preceding seizure onset were selected for comparison. Time- frequency analysis (TFA) was used to characterize Sp-HFOs and ictal HFA. Frequency overlap was quantified using the I-Fusion metric, and linear regression assessed changes in I-Fusion values over time, with R{superscript 2} indicating correlation strength. ResultsVisual analysis of the time series revealed a preictal phase in all patients, during which brief high-frequency activity gradually emerged within spikes (Sp-HFOs), ultimately transitioning into sustained ictal HFA at the same frequency. TFA demonstrated increasing frequency similarity with time between Sp-HFOs and ictal HFA. I-Fusion values and R{superscript 2} coefficients rose consistently, indicating a progressive convergence in frequency content. Notably, Sp-HFOs and ictal HFA shared narrow-band frequency features within the same electrode contacts, especially in the epileptogenic zone (EZ). InterpretationOur findings support a dynamic, frequency-specific evolution from interictal Sp-HFOs to ictal HFA, suggesting that seizure onset is preceded by a gradual preparatory phase rather than an abrupt transformation. The progressive nature and spectral continuity of Sp-HFOs may reflect increasing neuronal synchrony, providing potential early biomarkers for seizure prediction and improved localization of the EZ.

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.

ObjectiveMore than one-third of the people with focal epilepsy do not achieve seizure freedom with medication, neuromodulation, or neurosurgery therapies. Palliative care with the goal of reducing epilepsy burden is an alternative for these patients. Minimizing severe seizures is essential for reducing morbidity. Existing seizure severity scales are qualitative and rely on patient reports, limiting our ability to rigorously track and intervene to curb severe seizures. The goal of this study is to develop and validate a quantitative metric for seizure severity. MethodsWe retrospectively analyzed preictal and ictal intracranial-EEG (iEEG) recordings from 54 people with drug-resistant epilepsy undergoing pre-surgical evaluation. We developed a new metric that objectively combines seizure duration, spread, and semiology to quantify seizure severity. We calculated preictal iEEG network features and fit a linear mixed-effects model to quantify patient-specific associations between preictal networks and seizure severity. ResultsWe evaluated 256 seizures from 54 patients using the quantitative seizure severity score. Seizure severity was consistent with clinical seizure type. Medication taper strategy was associated with seizure severity (p = 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical seizure severity was associated with better post-surgical seizure outcome (U = 465, p = 0.042). A linear mixed-effects model with preictal network features as regressors and seizure severity as response revealed a group-level positive trend. In 12 out of 14 patients with multiple types of seizures, more severe seizures were preceded by more abnormal preictal networks. SignificanceWe present a quantitative metric for seizure severity that correlates with clinical and electrographic features. We found that the seizure severity score was associated with abnormal preictal networks. We propose this measure to holistically capture patient condition and guide incremental changes in therapy to improve patient outcome over time.

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.

Closed-loop neuromodulation via responsive neurostimulation (RNS) of the thalamus has emerged as a promising therapy for drug-resistant epilepsy (DRE), particularly in patients with broad or multifocal onset. However, response to thalamic RNS is inconsistent, and there is a crucial need to identify factors that distinguish responders from non-responders. Given the heterogeneous composition of the thalamus, the specific contributions of individual thalamic nuclei during seizures may explain the variability in outcomes between patients and could potentially serve as biomarkers for guiding target selection. We analyzed 129 seizures from 28 patients with DRE who underwent stereo-EEG monitoring with recordings of the centromedian (CM: n = 15) or pulvinar (PLV: n = 13) thalamic nuclei and were subsequently treated with RNS targeting the corresponding nucleus (CM: 11/15 [73%] responders; PLV: 7/13 [54%] responders). Patients were classified as responders (Engel class I-III) or non-responders (Engel class IV) based on reduction in seizure frequency. For each seizure, we constructed functional connectivity networks spanning seizure onset to termination and quantified the role of the thalamic nucleus by computing its total node strength. We also used an automated detection algorithm to measure the time of seizure spread to each thalamic nucleus relative to seizure onset. Connectivity and spread timing were then compared between responders and non-responders within each nucleus group. The timing of thalamic recruitment following seizure onset did not differ significantly between responders and non-responders in either nucleus, although CM responders showed a non-significant trend toward earlier recruitment. Analysis of functional connectivity revealed nucleus-specific patterns. CM responders exhibited significantly higher thalamic node strength than non-responders during the late-seizure phase, with no significant difference at early- or middle-seizure phases. PLV responders showed significantly higher thalamic node strength during the middle-seizure phase, but there was no significant difference at early- or late-seizure phases. These findings suggest that the degree and timing of thalamic involvement during seizures may serve as biomarkers for predicting response to thalamic RNS in DRE. CM involvement in responders was characterized by stronger connectivity that persisted through seizure termination, whereas PLV involvement in responders was reflected primarily in connectivity during seizure propagation and progression. Incorporating these nucleus-specific ictal network features into pre-surgical evaluation could improve patient selection and guide nucleus-specific targeting for thalamic RNS.

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.

Objective Temporal lobe encephaloceles (ENC) are underdiagnosed causes of drug-resistant temporal lobe epilepsy (TLE), frequently associated with idiopathic intracranial hypertension (IIH). Emerging evidence suggests glymphatic system dysfunction in both IIH and TLE. We investigated glymphatic markers in TLE associated with ENC compared with seizure-free postoperative TLE controls of different aetiology. Methods Surgical specimens from 13 patients with TLE-ENC and 12 TLE-control patients were analyzed. Histological glymphatic markers included aquaporin-4 (AQP4), glial fibrillary acidic protein (GFAP), podoplanin (PDPN), perivascular space (PVS) enlargement, and vessel density. High resolution MRI was used to assess a global PVS score. Results Compared with TLE-controls, TLE-ENC specimens showed increased white matter AQP4 expression and AQP4/GFAP ratio, whereas the AQP4/GFAP ratio was reduced in grey matter. PDPN expression was significantly elevated in both grey and white matter in TLE-ENC cases. MRI demonstrated greater supratentorial PVS enlargement in in ENC patients. Radiological features suggestive of IIH were identified in 46.1% of TLE-ENC patients. Compared with controls, TLE-ENC patients had shorter disease duration and lacked association with previous febrile seizures. Surgical treatment achieved seizure freedom in 70% of ENC patients at a median follow-up of 32 months. Interpretation This study provides the first characterization of glymphatic alterations in TLE-ENC-related epilepsy. Dysregulation of AQP4 and PDPN together with increased PVS burden suggests a distinct glymphatic dysfunction pattern in TLE-ENC, supporting a potential pathophysiological link among ENC formation, IIH, and epileptogenesis mediated by altered cerebrospinal fluid dynamics.

ObjectiveThe incidence of Sudden Unexpected Death in Epilepsy (SUDEP) is especially high in those with Dravet syndrome (DS). Risk factors have been identified, but the mechanism(s) by which death occurs is not fully understood. Evidence supports ventilatory dysfunction in the pathophysiology of SUDEP. Understanding specific respiratory patterns present at baseline and after seizures at different ages, as well as the health of lung tissue, will allow us to better understand how sudden death occurs in this population. MethodsWhole body plethysmography (WBP) was used to monitor respiration before and after electrically induced seizure in the Scn1aA1783V/WT mouse model of DS weekly for a period of four weeks. Following the four-week WBP study, lungs from surviving animals were collected and stained with hematoxylin and eosin and Weigerts elastin and the density of tissue and elastin were analyzed. ResultsBreathing was diminished in the DS mouse at baseline and following evoked seizures in younger aged mice (P18-P24), consistent with prolonged post-ictal inspiratory time and low respiratory drive compared to the response seen in older animals. In older DS mice, consisting of those that have survived a critical period for mortality, the response to seizure was more robust and included higher respiratory drive, peak inspiratory and expiratory flow rates, tidal and expiratory volumes, and breathing frequency compared to wild-type and relative to baseline. Alveolar damage was also observed in P46-P52 DS mice. SignificanceDifferences in specific respiratory parameters in younger DS animals, during the time when mortality is greatest, compared to older DS animals (i.e. those that have survived the critical period) may allow us to better understand respiratory differences contributing to SUDEP. Lung tissue damage in DS may also contribute to respiratory dysfunction in SUDEP. KEY POINTSO_LIBaseline respiration is diminished in DS mice compared to wild type. C_LIO_LIElectrically induced seizure produced a different respiratory response in younger DS mouse compared to older DS animals. C_LIO_LIAlveolar septal damage is present in DS mice. C_LIO_LIBaseline and post-ictal breathing dysfunction and inefficient oxygenation and CO2 clearance likely potentiated by lung damage may serve as a potential mechanism by which SUDEP occurs in DS. C_LI

Background and ObjectivesThe proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography datasets can potentially allow comparing a new patient to past similar cases and make clinical decisions with the knowledge of how similar cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients in a large database impractical. We aim to develop an automated system that electrographically and anatomically matches seizures from a patient to those in a database. In addition, since we do not know what features define seizure similarity, particularly considering the various stereo-electroencephalography implantation schemes, we evaluate the agreement and features among experts in classifying seizure similarity. MethodsWe utilized SEEG seizures from consecutive patients who underwent stereo-electroencephalography for epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score through a graphical user interface. As our primary outcome, we developed and validated an automated seizure matching system by employing a leave-one-expert-out approach. Secondary outcomes included the inter-rater agreement and features for classifying seizure similarity. Results320 SEEG seizures from 95 patients were utilized. The seizure matching system achieved an area-under-the-curve of 0.82 (95% CI, 0.819-0.822), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearmans rho=0.75, p<0.001). Additionally, the moderate inter-rater agreement confirmed the practicality of our approach: for the four-level classification, median agreement was 73.9% (interquartile range, 7%), and beyond-chance Gwets kappa was 0.45 (0.16); for the binary classification of similar vs. not related, agreement stood at 71.9% (4.7%) with a kappa of 0.46 (0.13). DiscussionWe demonstrate the feasibility and validity of a stereo-electroencephalography seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the treatment and the results of treating similar patients in the past, potentially resulting in an improved surgery outcome.