Epilepsy & Behavior

Background Psychiatric comorbidities commonly have a negative impact on epilepsy outcomes. However, they are continuously ignored in routine epilepsy care, with focus directed more towards seizure control. There is paucity of data on the burden of psychiatric morbidity among those living with epilepsy in Kenya. This study sought to determine the prevalence and associated factors of psychiatric morbidity among patients living with epilepsy at a tertiary referral hospital in Western Kenya. Methods This was a descriptive cross-sectional study. Consecutive sampling was used to recruit participants, with a sample size of 278. Data were collected using a structured pretested sociodemographic and clinical characteristics questionnaire, and the Mini International Neuropsychiatric Interview (MINI), and analyzed using STATA version 16. Pearson Chi-square test/Fishers Exact test and logistic regression were used to assess relationships at bivariate and multivariate levels respectively. Results The prevalence of psychiatric morbidity was 52.2%. Major depressive disorder was the most prevalent (36%), followed by anxiety disorders (26.2%), psychotic disorders (16.9%), and suicidality (15.1%). Casual/self-employment (aOR=2.590, p=0.020), seizure-related physical trauma (aOR=4.032, p=0.004), antiepileptic polytherapy (aOR=4.280, p=0.001), frequent seizures (aOR=3.801, p<0.001), and comorbid medical conditions (aOR=5.478, p=0.047) were independent predictors of psychiatric morbidity. Having attained a tertiary level of education was protective against psychiatric morbidity (aOR=0.221, p=0.036). Conclusion More than half of the patients living with epilepsy had at least one psychiatric comorbidity. Routine psychiatric screening and integration of mental health services in epilepsy care is essential to improve clinical outcomes.

Many patients with epilepsy have inadequate seizure control using current anti-seizure medications (ASMs), illustrating the need for new treatments. Genetic epilepsy syndromes like pathogenic variants in voltage gated sodium channel SCN2A and SCN8A are often poorly controlled by current medications, highlighting the need for better models. Voltage gated sodium channel pathogenic variants that induce epilepsy are often gain-of-function, producing hyperexcitability. We established a fast and precise zebrafish seizure assay using mRNA overexpression of SCN2A and SCN8A variants, which allows rapid screening of both variants and ASMs. These short-term genetic seizure models are assayed in 3 days postfertilization (dpf) larvae. Pathogenic variants of SCN2A and SCN8A produced sporadic seizure behavior. We tested human SCN2A R1882Q, SCN2A R853Q and SCN8A R1872Q pathogenic variants that were identified in epilepsy syndrome patients. These models were used to evaluate the efficacy of 3 ASMs: Topiramate, GS967 and PF-04856264. All 3 epilepsy-associated variants increased seizure activity, and the ASMs significantly decreased this seizure activity. This mRNA overexpression assay successfully evaluates seizure activity induced by variants in voltage gated sodium channel genes and examines ASM efficacy in patient specific pathogenic variants.

Early acquired brain injury is a major risk factor for infantile epileptic spasms syndrome (IESS), which may impair cognitive development, especially if diagnosis and treatment are delayed. However, individual-level prediction of which infants will develop IESS is currently not possible. We assessed whether high-frequency oscillations (HFOs) in scalp EEG or recurrent interictal epileptiform discharges (IED) during the first months of life could predict forthcoming IESS. Our population-based cohort included 36 infants with cortical injury due to infarction, haemorrhage, infection or trauma involving a large cortical area ([&ge;] anterior/posterior cerebral artery territory or [&ge;] half of the middle cerebral artery territory), or hypoxic-ischaemic encephalopathy with cortical and deep grey matter involvement. The infants underwent repeated EEGs during the first year of life until 12 months of age or until IESS diagnosis. HFOs during sleep were scored both visually and automatically, whereas IEDs were assessed visually only. We tested whether HFO rate increased during the first year of life using a mixed-effects model with within- and between-subject random effects. Using only EEGs recorded prior to IESS diagnosis, we evaluated whether HFO rate or recurrent IEDs could predict IESS development by training a ridge-regularized logistic regression model with exhaustive leave-2-subjects-out cross-validation. Eleven infants (31%) developed IESS. HFO rate increased with age in both groups but more steeply in the IESS group [within person slope {beta} = 2.43 (IESS) vs. 0.06 (no-IESS) units/month, P < 0.001]. The logistic regression model showed that both HFO rate [AUC 0.801 (95% CI 0.668, 0.936)] and recurrent IEDs [AUC 0.826 (95% CI 0.720, 0.932)] were able to predict forthcoming IESS. However, in a multivariable model, only recurrent IEDs remained independently associated with IESS, and HFO rate did not add predictive value. The marked increase in HFO rate toward IESS diagnosis supports their role as a biomarker of epileptogenesis. During the first months of life, HFOs and recurrent IEDs performed equally well in predicting subsequent IESS. However, IEDs are easier to apply to clinical practice.

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.

The aim of this work is to validate patient-specific EEG baseline establishment using the e-norms method as a screening and retrospective-review tool for seizure detection in pediatric epilepsy. The method was applied to 247 seizure-free EEG recordings (263.92 hours) from 10 patients in the CHB-MIT Scalp EEG Database (ages 3-18). A composite stability metric combining first-derivative dynamics, spectral entropy, variance, and line length was computed per 2-second epoch across 23 channels. Patient-specific detection thresholds were derived from each patient's seizure-free baseline using a weighted statistical procedure. Performance was validated against 72 expert-annotated seizures (2,705 epochs) across 62 seizure files, with durations spanning 6 to 264 seconds (44-fold range). The results show that detection achieved 94.4% event-level sensitivity (68 of 72 seizures; 95% CI 86.6-97.8%) and 81.5% epoch-level sensitivity (2,204 of 2,705 epochs; 95% CI 80.0-82.9%). Eight of ten patients achieved 100% event-level sensitivity with epoch-level sensitivity ranging from 58.7% to 100.0%. Two patients showed partial event-level failures (CHB-15: 17 of 20; CHB-18: 5 of 6), with the four missed events attributable to two characterizable failure modes. Patient-specific thresholds ranged from 4.06 to 4.81 (mean 4.51 +/- 0.25); threshold variation did not correlate reliably with age or sex, confirming that no universal threshold could achieve comparable performance. Detection margins ranged from 0.88 to 1.24 times. Patient-specific e-norms achieves 94.4% event-level sensitivity for pediatric EEG seizure detection without requiring labeled seizure training data, exceeding published human expert inter-rater agreement (50-76%) and recent automated approaches in adult cohorts using behind-the-ear EEG and wearable ECG. Two characterizable failure modes account for the four missed events and inform appropriate clinical use. As a high-sensitivity screening tool complementary to real-time alarm systems, the method is ready for adult validation, prospective deployment, and head-to-head benchmarking.

Background and Objectives: Stereoelectroencephalography (SEEG) involves the implantation of intracerebral electrodes to investigate drug-resistant epilepsy. SEEG requires millimetric accuracy to ensure safety and optimal mapping. Although studies have evaluated SEEG accuracy, there is substantial variability in reporting. Here we report on implantation accuracy in a large series using the most common accuracy metrics described in the literature and perform a detailed analysis of contributing factors. Methods: SEEG implantations between 2013 and 2025 were included. Application accuracy was computed for each implanted electrode. Specifically, Euclidean, radial, depth, and angle error were calculated at both target and entry points. Correlative and multivariable analyses were conducted between each variable and error metric. Trajectories were also grouped by atlas-derived lobar target. Results: No metrics met assumptions of normality and thus we report accuracy using median with interquartile range (IQR). In a series of 3176 trajectories, median Euclidean target and entry errors were lower for robot-assisted electrodes (n=2858) at 2.19 (IQR: 1.54-2.98) mm and 1.38 (IQR: 0.89-2.01) mm respectively, compared to frame-based (n=318, p<.001) at 2.76 (IQR:1.79-3.76) mm and 2.21 (IQR: 1.42-3.32) mm. Correlation and multivariable regression analysis showed target error was positively correlated with implantation angle, scalp thickness, skull thickness, and trajectory length. Target error was also higher in obese patients. On lobar analysis, parietal lobe trajectories were the most accurate and frontal lobe trajectories were the least accurate. On temporal lobe trajectory analysis, posterior hippocampus trajectories were the most accurate and temporal pole trajectories were the least accurate. Presence of mesial temporal sclerosis also impacted accuracy. Conclusions: We present a detailed description of SEEG implantation accuracy, demonstrating the superior accuracy and speed of robot-assisted to frame-based methods. Furthermore, we analyzed how accuracy varies with specific factors from a global to trajectory level, which can be accounted for when planning SEEG implantations.

Objective: Pediatric epilepsy patients undergoing stereo-electroencephalography (sEEG) for ictal onset evaluation provide a rare window to study the developing brain. While methodological frameworks for task-based sEEG research are well-established in adults, pediatric-specific guidance remains underdeveloped. Furthermore, many pediatric epilepsy patients have comorbidities that might typically exclude them from participating in research. We examine factors that influence research participation and discuss considerations for conducting sEEG research in children. Methods: Here, we present a retrospective analysis of task-based research participation patterns from an NIH-funded study of speech and language representations (1R01DC018579) in 66 patients (ages 4-24) undergoing sEEG monitoring at Texas Children's Hospital to determine whether specific comorbidities influenced research participation. Results: Eighty-nine percent (n=66) of patients approached for consent agreed to participate in the study. Despite high rates of comorbidities including neurocognitive disorder (66.67%), language delay (31.75%), global developmental delay (23.81%), mood disorders (33.33%), ADHD (46.03%), autism spectrum disorder (14.29%) or other cognitive/intellectual disabilities (36.51%), all participants engaged in at least one task. While the majority of these diagnoses did not appear to influence subject participation, global developmental delay was associated with a significant reduction in time spent on active tasks. Discussion: Despite high prevalence of neuropsychological comorbidities among participants, our evidence suggests that these participants contribute meaningfully to studies investigating important developmental questions. We suggest strategies for tailoring task-based research to accommodate the unique needs of individuals in this population. Such practices are important for ensuring that research studies reflect the true diversity of the population.

Abstract Importance: Drug-resistant focal epilepsy affects approximately 30% of children with epilepsy and carries excess mortality, impaired neurodevelopment, and substantial costs. Epilepsy surgery is underutilized despite proven superiority over medical management. MRI-guided laser interstitial thermal therapy (MRgLITT) is a minimally invasive alternative to open resection, but comparative evidence to guide procedure selection is limited. Objective: To estimate lifetime outcomes and costs of epilepsy surgery versus medical management for pediatric drug-resistant focal epilepsy, and to provide etiology-informed guidance for choosing between open resection and MRgLITT. Design: Markov decision analytic model with a lifetime horizon, parameterized from published systematic reviews, meta-analyses, and cohort studies. Setting: United States, healthcare payer perspective. Participants: Hypothetical cohort of 10-year-old children with drug-resistant focal epilepsy and a seizure focus <3 cm3. Interventions: Best medical management, open resective surgery, or MRgLITT. Main Outcomes and Measures: Quality-adjusted life years (QALYs), lifetime direct medical costs, incremental cost-effectiveness ratios, and lifetime survival. Seizure outcomes were classified as seizure freedom or disabling seizures. Cost-effectiveness was assessed at $100,000/QALY. Results: Both surgical strategies were associated with a 4.6-year survival advantage, 3.6 additional lifetime QALYs, and lower costs than medical management. MRgLITT yielded 22.64 QALYs at $120,943; open resection yielded 22.62 QALYs at $121,650; medical management yielded 19.00 QALYs at $127,471. The difference between MRgLITT and open resection was 0.015 QALYs, reflecting near-equivalent effectiveness; in probabilistic sensitivity analysis, MRgLITT was optimal in 50.3% of iterations and open resection in 38.3%, with neither showing clear superiority. Etiology-specific analyses favored MRgLITT for focal cortical dysplasia and mesial temporal sclerosis, and open resection for tumor-related and cavernoma-related epilepsy. Conclusions and Relevance: Both open resection and MRgLITT were associated with substantially better lifetime outcomes and lower costs than medical management, supporting early surgical referral. Overall effectiveness between surgical approaches was clinically similar, with neither demonstrating clear superiority; the model suggests epilepsy etiology, rather than expected effectiveness alone, should guide procedure selection between MRgLITT and open resection.

Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compressed spectral array (CSA). Methods Using a public dataset of annotated neonatal EEGs, features of the aEEG and CSA were extracted from the left and right centroparietal electrodes. These features were then used to train and test three machine learning classifiers, Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Results The trained RF, SVM, and ANN classifiers had areas under the curve (AUC) of 0.80, 0.69, and 0.79 for capturing seizure time periods and an average accuracy of 0.91, 0.90, and 0.92 respectively for capturing seizure and non-seizure time periods. Median accuracy scores were higher among patients without hypoxic-ischemic encephalopathy (HIE; median = 1 for all three classifiers) than HIE patients (median = 0.92, 0.93, 0.93). Conclusion A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.

Abstract Objective To validate a neonatal seizure detection algorithm that is based on extracted clinical features of the aEEG and CSA on a cohort of cooled neonatal patients with HIE. Methods A seizure detection algorithm was designed using aEEG margin features, CSA features, trained on a public dataset of 79 neonatal EEGs with three supervised machine learning classifiers. It was subsequently tested on an inhouse cohort of 23 neonates with asphyxia whose EEGs were collected during hypothermia therapy. Results The trained Random Forest Classifier, Support Vector Machines and Artificial Neural Network classifiers had an AUC of 0.76, 0.77, and 0.77 and an average accuracy of 0.85, 0.86, and 0.85 respectively. Finally, the average AUC across the 10 seizure patients included was 0.85. Conclusion A neonatal seizure detection algorithm that uses a combination of aEEG and CSA clinical features can capture seizures in HIE patients. Performance across seizure patients is not correlated with seizure duration.

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.

Background: Free-text EEG reports typically lack structure, hindering scalable analysis. We evaluate a large language model (LLM) pipeline to extract structured diagnostic labels and confidence levels from these reports. Methods: We developed a hierarchical annotation schema to classify EEG reports for four specific abnormality types using a four-point confidence scale. To establish ground truth, two certified EEG technicians annotated a diverse dataset of reports authored by neurologists with distinct writing styles. We then implemented a grammar-constrained Mistral-7B pipeline, iteratively prompt-tuned on a development set to mirror these expert annotations. The pipeline's effectiveness was evaluated against the human expert benchmark using core agreement (diagnostic accuracy) and certainty-adjusted agreement (confidence alignment), with classical NLP models serving as a secondary baseline. Results: Mistral-7B significantly outperformed baselines, achieving 96% accuracy for overall abnormality detection, approaching the human benchmark of 98%. Crucially, the model successfully identified rare epileptiform abnormalities where traditional models failed and generalized robustly across distinct reporting styles. While diagnostic accuracy was high, a performance gap persisted in certainty-adjusted agreement, indicating that accurately modeling nuanced clinical confidence remains a challenge. Conclusion: LLMs can effectively automate the extraction of structured diagnostic information from EEG reports with near-human accuracy and strong generalization. While confidence calibration requires further refinement, the combination of accurate classification and explainability makes this pipeline a promising tool for standardizing clinical data at scale. Keywords: Routine Clinical Electroencephalography; Large Language Models; Clinical NLP; Confidence Assessment; Explainable AI; Neurophysiological Evaluation

Heterozygous de novo missense mutations in the EEF1A2 gene encoding translation elongation factor eEF1A2 result in neurodevelopmental disorders, typically characterised by early onset epilepsy and intellectual disability (ID). The E122K mutation is the most commonly reported missense mutation and is amongst the more severe in terms of epilepsy and ID. Here we made use of a recently developed mouse model which recapitulates the E122K mutation to examine how mutations in EEF1A2 might disrupt neuronal gene expression. Primary neurons from mutant mice and transfected HEK293T cells were used to examine effects on global protein synthesis. In contrast to previous reports, we were unable to detect a change in global protein synthesis using either of two different assay systems. TRAP-seq and mass spectrometry were then employed to study the effects of the mutation on the translatome and proteome respectively. These analyses revealed perturbation of expression of a subset of genes, with a slight skew towards downregulation, particularly for longer transcripts. Further analysis indicated a down regulation of proteins involved in synaptic function in both the translatomic and proteomic datasets.

Background The field of implantable Brain-Computer Interfaces (iBCIs) is rapidly advancing, with individuals with amyotrophic lateral sclerosis (ALS) as key beneficiaries. However, ALS-related cortical degeneration may impair iBCI effectiveness. This study investigated whether structural magnetic resonance imaging (MRI) and functional MRI (fMRI) metrics are associated with the quality of electrocorticography (ECoG) signals critical for iBCI use. Methods Six late-stage ALS participants and 76 controls underwent T1-weighted structural MRI and task-based fMRI during right-hand movement or attempts thereof. ECoG data of ALS participants was benchmarked using ECoG data acquired in epilepsy patients. Grey matter thickness in the sensorimotor cortex and fMRI activation in the motor-hand area were measured. Results Four ALS participants showed >0.4 mm thinning in the precentral gyrus, while the postcentral gyrus was spared. ECoG signal quality was significantly associated with precentral grey matter thickness, but not with fMRI activity. Conclusions These findings suggest that presurgical assessment of precentral grey matter thickness could potentially prove useful for iBCI candidate selection in advanced ALS.

Monogenic epilepsies are 1.6 times more likely to be treatment-resistant compared to other epilepsies, emphasizing the need for additional therapeutic strategies. Sleep dysfunction beyond sleep-related breathing disorders is common yet insufficiently characterized and treated in monogenic epilepsies. We therefore sought to study sleep phenotypes across these epilepsies, examine associations with seizure severity, and assess the diagnostic rate of sleep disorders. From 2,519 individuals enrolled in the Epilepsy Genetics Research Project at Children's Hospital of Philadelphia, we identified the monogenic epilepsies most frequently associated with sleep-related diagnoses, yielding 252 individuals across nine genetic diagnoses (STXBP1, n = 79; SCN1A, n = 57; SCN2A, n = 34; KCNQ2, n = 21; SLC6A1, n = 14; SYNGAP1, n = 13; WDR45, n = 13; KCNT1, n = 11; PCDH19, n = 10). Monogenic epilepsies exhibited distinct sleep endophenotypes, including insomnia, parasomnia, and sleep-related movement disorders in SCN1A-related disorders; frequent epileptiform discharges in sleep with insomnia symptoms in SCN2A-related disorders; sleep dysfunction restricted to the developmental and epileptic encephalopathy subtype in KCNQ2-related disorders; and insomnia without nocturnal seizure involvement in SYNGAP1-related disorders. Formal sleep diagnoses were present in only 25% of individuals (63/252), yet 58% (145/252) reported sleep difficulties, suggesting substantial underdiagnosis. Persistent seizures were associated with higher odds of sleep disorder diagnoses (OR 2.87, 95% CrI 1.57-5.36), disrupted sleep architecture (OR 2.06, 95% CrI 1.08-4.16), nocturnal seizures (OR 4.47, 95% CrI 2.50-8.28), hypersomnolence (OR 2.38, 95% CrI 1.27-4.58) and insomnia (OR 1.80, 95% CrI 1.06-3.05). Neuropsychiatric comorbidities were independently associated with sleep burden after adjustment for seizure severity (OR 2.49, 95% CrI 1.40-4.49). We find that monogenic epilepsies exhibit distinct, gene-specific sleep endophenotypes that are underdiagnosed. Treating sleep difficulties beyond obstructive sleep apnoea may improve seizure control and developmental outcomes, highlighting the need for timely diagnosis of co-occurring sleep disorders.

Traumatic brain injury (TBI) has long-term consequences that include chronic traumatic encephalopathy (CTE) and an elevated risk for Alzheimer Disease (AD). These dementias ultimately manifest as tauopathies but may begin with acute neuronal dysfunction including post-traumatic seizures. Provocative evidence suggests that these prodromal seizures are a viable target to mitigate the later onset of dementias, and anti-epileptic drugs (AED) that increase the threshold of action potentials have indeed been shown to mitigate later tauopathies[1, 2]. Here, we test whether AEDs and other compounds that modulate synaptic transmission, applied immediately after TBI, can also act as prophylactics that block subsequent CTE-like tau aggregation and neurodegeneration in a larval zebrafish model. Levetiracetam (LEV) is an AED that modulates synaptic vesicle release. Application of LEV immediately following TBI abrogated TBI-induced tau tau aggregation (IC50 = 3.168 x10-3 mM) and cell death in the larval zebrafish TBI model. We next considered a polypharmacy approach involving mGluR2, because mGluR2 positively allosteric modulators (PAMs) such as JNJ-42153605 have previously been able to improve LEVs action in reducing some recalcitrant forms of seizure in a mouse model. We found that JNJ-42153605 was itself effective at blocking TBI-induced tau aggregation (IC50 = 8.691 x10-5 mM). Moreover, a subeffective dose of JNJ-42153605 (10-5 mM) was able to substantially improve the efficacy of LEV (~16-fold) in its prophylactic actions. Thus, LEV and JNJ-42153605 applied briefly after TBI offer a potent polypharmacy approach, at least in our preclinical animal model, to tackle the later tau aggregation and neurodegeneration that follows from TBI neurotrauma. These results warrant further investigation, including testing into mammalian TBI models (with longer disease course).

Background: Children with structural heart disease (SHD), particularly congenital heart disease (CHD), are increasingly recognised as being at risk of adverse neurological outcomes. Although advances in cardiac surgery and perioperative care have markedly improved survival, epilepsy has emerged as an important long-term complication. Reported prevalence estimates vary considerably across studies, and the overall burden remains uncertain. This systematic review and meta-analysis aimed to estimate the pooled prevalence of epilepsy among children with SHD and explore differences according to geographic region, lesion characteristics, and surgical exposure. Methods: This systematic review and meta-analysis was conducted in accordance with PRISMA 2020 and MOOSE guidelines and registered in PROSPERO (CRD420261378572). PubMed/MEDLINE, Scopus, and ProQuest were searched for observational studies published between January 2000 and December 2025. Eligible studies included children aged 0-18 years with SHD or CHD reporting epilepsy prevalence or incidence. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. A random-effects meta-analysis was performed to estimate pooled prevalence with 95% confidence intervals (CI). Results: Eight cohort studies comprising 21,731 children were included. Studies were conducted across North America, Europe, and Asia and predominantly involved surgically managed CHD populations. The pooled prevalence of epilepsy was 3.0% (95% CI 1.3%-4.8%), substantially higher than estimates reported in the general paediatric population. Heterogeneity was considerable (I{superscript 2} = 98.0%; p < 0.001). The 95% prediction interval ranged from 0% to 8.1%, indicating substantial variability across populations. Narrative subgroup synthesis suggested higher epilepsy prevalence among children with cyanotic and complex lesions and among surgically managed cohorts, particularly those exposed to cardiopulmonary bypass and perioperative neurological complications. Most studies were rated as having low risk of bias, and sensitivity analyses demonstrated stable findings. Conclusions: Children with SHD have a substantially increased burden of epilepsy compared with the general paediatric population. Complex lesions, perioperative neurological injury, and cardiac surgical exposure may contribute to epileptogenesis. Long-term neurological surveillance and multidisciplinary neurodevelopmental follow-up should be integrated into routine care for children with SHD.

Dravet syndrome (DS) is a severe developmental and epileptic encephalopathy whose clinical and research representation requires integration of heterogeneous knowledge spanning seizures, development, behavior, SUDEP/autonomic risk, genetics, comorbidities, electrophysiology, pharmacology, and drug responsiveness. We report the development of a DS-focused ontology created by expert-guided specialization of a previously published epilepsy ontology. Scope expansion was defined through a scientific advisory board, structured review meetings, and iterative ontology curation in OWL. The resulting resource reorganized DS content across nine major domains and expanded the publicly released ontology from the pre-extension baseline to the current BioPortal version. Beyond structural growth, the ontology was assessed through expert-guided curation and downstream task-based reuse, including two published ontology-enabled LLM studies and an ongoing ontology-derived DS knowledge graph and AI assistant platform. These results suggest that disease-focused ontology specialization can provide durable infrastructure for DS data harmonization, knowledge representation, and AI-enabled translational informatics.

Introduction: Autism spectrum disorder, or autism, is a common neurodevelopmental condition characterized by socio-communicative problems together with restrictive and repetitive behaviors. Typically, the latter is manifested as deficits in behavioral flexibility, i.e. changing routine behaviors to adapt to environmental changes. Despite noticeable difficulties with flexible behavior in autism, there is to date not adequate knowledge about the intricacies of such challenges and neurobiological processes that may subserve them. This study aims to investigate both cognitive and motor flexibility in autistic compared with neurotypical adults using a novel combination of detailed methods for brain imaging and behavioral investigations in relation to probabilistic reversal learning (PRL) paradigms. In addition, the experiences of autistic adults on flexible behavior in education and everyday activities will be explored. Methods and analysis: Differences in cognitive flexibility between autistic (n[&ge;]20) and neurotypical (n[&ge;]20) adults (18-35 years) will be investigated in terms of brain activations, measured by functional magnetic resonance imaging (fMRI), during two-choice PRL performance (cognitive task). In addition, group differences in microcirculation as measured by arterial spin labelling (ASL) will be evaluated. Group differences in motor flexibility will be investigated as expressed in movement planning and execution (spatio-temporal parameters), measured by a robotic manipulandum platform (KinArm End-Point Robot), during two-choice PRL performance (motor task). Semi-structured interviews will be conducted individually with autistic participants (n=15). Questions concern own experiences of cognitive and motor behavior, and strategies used to support flexibility in these behaviors. Data from this qualitative approach will be analyzed by thematic analysis. Ethics and dissemination: Ethical approval has been obtained from the Swedish Ethical Review Authority (ref:2025-07939-01) and the study will be conducted in accordance with the Declaration of Helsinki, the European Union General Data Protection Regulation (GDPR) and national guidelines for the storing of personal data. The different investigations included are well-established, non-invasive and safe. Study outcomes will be published in peer-reviewed international scientific journals (open access), presented at national and international conferences, and to any interested audience/stakeholders.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSIn temporal lobe epilepsy (TLE), the thalamus acts as a nexus in a pathophysiological network that implicates mesiotemporal, subcortical, and neocortical regions. Studying a large multimodal and multicentre dataset, we profiled thalamic, hippocampal, and neocortical functional connectivity (FC), assessed structural mediators, and examined clinical associations. MethodsWe studied resting-state FC alongside structural and diffusion MRI data in 250 unilateral TLE patients and 259 healthy controls, with measures aggregated across four independent datasets. Data were processed using open-access neuroinformatics workflows and analyzed at a subregional level to maximize anatomical precision. Statistical analysis and mediation models assessed between-group FC changes, structural contributors, and clinical correlations. ResultsCompared to controls, TLE patients presented with reduced thalamo-cortical FC, which was most marked in mesiotemporal, fronto-central, and occipital regions. Thalamo-hippocampal FC was also reduced, with effects seen in all CA subfields. In the thalamus, FC reductions peaked in the ventral posterior nucleus when considering neocortical target regions and in the mediodorsal nucleus when considering hippocampal target regions. While ipsilateral hippocampal volume and diffusion changes mediated thalamo-hippocampal FC, thalamo-cortical FC appeared decoupled from structural alterations. Findings were consistent in left and right TLE patients, in patients with short and long disease duration, and across imaging sites, suggesting that thalamo-cortical FC imbalances are a consistent signature of TLE. Conversely, thalamo-hippocampal FC was elevated in patients with focal-to-bilateral-tonic-clonic seizures and FC alterations were more marked in the subgroup of operated patients that became seizure-free after surgery. ConclusionOur multi-site findings demonstrate marked thalamic circuit fragmentation in TLE. Ipsilateral findings robustly showed subdivision-specific effects, which point to both mesiotemporal co-lateralization as well as broader system-level involvement. Mediation analyses furthermore confirmed a key role of hippocampal pathology in disrupted thalamo-hippocampal connectivity in TLE, while broader thalamo-cortical fragmentation becomes increasingly independent of mesiotemporal compromise. Critically, thalamic FC represents a network substrate for seizure generalization and can serve as a prognostic indicator for surgical outcome. These results underscore the contribution of the thalamus as a hub in macroscale dysfunction in TLE.