Focal to bilateral tonic-clonic seizures (FBTCS) is a severe form of seizure associated with various adverse events. This study aimed to characterize abnormalities in the resting-state brain network related to FBTCS and use those findings to fit machine learning models for individual-level identification of patients with FBTCS. T1-weighted and resting-state functional magnetic resonance imaging (rfMRI) data were acquired from 84 patients with FBTCS (FBTCS+), 47 patients without FBTCS (FBTCS-), and 81 matched healthy controls (HCs). Amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated across whole brain and compared among 3 groups. Brain regions with significant differences between FBTCS+ and FBTCS- groups were seeded for resting-state functional connectivity (rs-FC) analysis. Four models were employed to classify FBTCS+ from FBTCS- patients at the individual level. Compared to HCs, both FBTCS+ and FBTCS- patients exhibited diffuse alterations in ALFF, ReHo, and DC, with similar patterns but more significant and widespread in FBTCS+ patients. Direct comparison demonstrated significant increase of DC in the ipsilateral temporal pole, with rs-FC increase to the ipsilateral lingual gyrus and the contralateral temporal pole and superior temporal gyrus, in the FBTCS+ patients relative to FBTCS- patients. Using significant differences as features, four classifiers performed well to distinguish FBTCS+ patient from FBTCS- patient, achieving an average AUC of 0.76. Ipsilateral temporal pole showed increased neural activity and hyper-connection to the temporo-occipital regions in FBTCS+ patients, which provide additional insights for FBTCS and carry individual-level information for sensitive identification of FBTCS+ patient.

OBJECTIVE:Epilepsy semiology is a major component of epilepsy diagnosis and plays a crucial role in its clinical management. This study aimed to develop the "Standard for Epilepsy Semiology and Semiological Description Dataset" to provide a reference for clinical practice and research, and to promote the standardization of relevant data. This work lays the foundation for the further development and construction of specialized epilepsy databases. A modified Delphi method was employed to reach a consensus on the framework and content of the dataset. METHODS:Two rounds of the Delphi process were conducted. Participants included experts from the fields of neurosurgery, neurology, pediatrics, and clinical neurophysiology, all specializing in epilepsy. The development was primarily based on the 2025 International League Against Epilepsy (ILAE) "Updated Classification of Seizures by the International League Against Epilepsy: Position Statement". The draft was also informed by previously published guidelines and consensus statements, case reports, and semiological reports extracted from clinical examination records of epilepsy patients. Data analysis included the expert positive coefficient, expert authority coefficient (Cr), expert coordination coefficient (Kendall's W), and coefficient of variation (CV) to reflect reliability and consensus. RESULTS:Consensus was reached on 285 key indicators for the dataset standard. To organize the data systematically, the standard was designed into two sections: the "Standard for Epilepsy Semiology and Semiological Description Dataset - Main Table"and the "Standard for Epilepsy Semiology and Semiological Description Dataset - Semiological Description Table". The first section includes 10 Level-1 indicators and 93 Level-2 indicators. The second section includes 6 Level-1 indicators, 88 Level-2 indicators, and 88 Level-3 indicators (encompassing 832 free-text fields). The expert authority coefficient was 0.83 for both rounds. Kendall's coefficient of concordance (W) was 0.222 (p < 0.001) in the first round and 0.175 (p < 0.001) in the second round, indicating a statistically significant degree of consistency among the experts. CONCLUSION:This study employed a modified Delphi method to develop the "Standard for Epilepsy Semiology and Semiological Description Dataset". By standardizing and integrating relevant terminology, this standard incorporates Chinese-specific synonym fields, aiming to provide a reliable data foundation for clinical practice and scientific research. The outcomes not only offer standardized references for specialists but also serve as practical tools for grassroots physicians in epilepsy semiological data collection, thereby effectively promoting the standardization of data in this field and laying a solid foundation for the in-depth utilization, sharing of epilepsy data, and the construction of specialized disease databases.

Cell transplantation-based regenerative medicine offers a promising strategy for repairing damaged neural pathways following spinal cord injury. Nonetheless, the integration of transplanted cells-particularly neurons-into host tissue remains insufficiently characterized. Notably, the molecular mechanisms underlying graft-host interaction are still poorly defined. In this study, we investigated how directly transplanted mature neurons contribute to the structural repair of fully transected spinal circuits in a rat xenotransplantation model and sought to identify candidate molecules involved in this process. To this end, we engineered human iPSC-derived neuronal tissueoids (Ntoids) in vitro using tissue engineering approaches. These Ntoids primarily consisted of mature, post-mitotic neurons interconnected into functional neural networks. Dependent on excitatory neurotransmission, they displayed electrophysiological signatures characteristic of excitatory neural networks. Importantly, their transplantable properties enabled them to fill tissue defects resulting from complete spinal cord injury. Histological analyses demonstrated that Ntoids survived for at least 8 weeks after spinal cord transplantation, with grafted cells retaining neuronal phenotypic characteristics. Furthermore, Ntoid transplantation significantly promoted reinnervation, synaptogenesis, and motor function recovery at the injury/graft site. Analysis of single-cell sequencing data from the developing rodent spinal cord suggested that the PTPσ-TrkC complex is involved in excitatory synaptogenesis. This was corroborated in human brain-spinal cord assembloid models, where PTPσ+ neurites extended from brain organoids into spinal cord regions and established connections with TrkC+ spinal neurons. Similarly, co-culture of rat organotypic brain slices with Ntoids showed that PTPσ and TrkC co-localized at developing synaptic junctions. Additionally, transplanted TrkC-expressing Ntoids established synaptic connections with host PTPσ+ supraspinal motor fibers (5-HT+) and sensory fibers (CGRP+). Thus, our findings revealed that the PTPσ-TrkC complex may participate in synaptic integration between transplanted neurons and host circuits, constituting a structural foundation for functional neural relay restoration in spinal cord injury repair.

OBJECTIVE:This study was undertaken to assess long-term efficacy, safety, and tolerability of adjunctive cenobamate in an open-label extension (OLE) of a randomized, double-blind, placebo-controlled, dose-response study (NCT04557085; YKP3089C035 [Study C035]) in Asian patients with uncontrolled focal seizures. METHODS:Patients 18-70 years old with uncontrolled focal seizures despite treatment with 1-3 antiseizure medications who completed the 24-week double-blind treatment period (n = 425) at cenobamate doses of 100, 200, or 400 mg/day or placebo could enter a 52-week OLE. All patients were converted to a cenobamate dose of 400 mg/day during a 20-week double-blind conversion phase and then entered a 32-week open-label maintenance phase at a starting dose of 300 mg/day. RESULTS:Among the 410 patients who entered the double-blind conversion phase, 357 (263 and 94 originally randomized to cenobamate and placebo, respectively) entered the OLE maintenance phase. The median percent focal seizure frequency reduction from double-blind baseline for patients who received at least one dose of cenobamate during the 32-week maintenance phase was 83.6%. The percent of patients achieving ≥50% and 100% responses during the maintenance phase was 75.1% and 25.5%, respectively. Among patients entering the OLE, 84.6% (347/410) completed the 52 weeks of treatment with cenobamate. The most commonly occurring treatment-emergent adverse events (TEAEs) during the entire OLE were dizziness, somnolence, γ-glutamyl transferase (GGT) increase, and COVID-19 infection. GGT increase was not associated with any clinically significant findings. Serious TEAEs occurred in 11.7% of patients (48/410). There were no deaths and no cases of DRESS (drug reaction with eosinophilia and systemic symptoms) syndrome reported. SIGNIFICANCE:The high long-term seizure frequency reductions, including a 100% responder rate of 25.5%, were consistent with previous long-term cenobamate efficacy. The OLE safety profile was also generally consistent with the known cenobamate safety profile, with no new safety signals identified. These results support the use of cenobamate in adult Asian patients with uncontrolled focal seizures.

Abstract Background Stroke remains a leading cause of mortality and disability worldwide, with nutritional status emerging as a crucial yet underexplored risk factor in elderly populations. The Geriatric Nutritional Risk Index (GNRI) represents a valuable nutritional assessment tool specifically developed for geriatric populations. This study examined the association between GNRI and stroke prevalence among elderly individuals using nationally representative data. Methods This cross-sectional analysis utilized National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018, including 16,092 participants aged ≥ 60 years. GNRI was calculated using serum albumin levels and body weight ratio, with participants categorized into quartiles. Stroke status was determined through self-reported physician diagnosis. Survey-weighted logistic regression models were constructed with progressive adjustments for demographic, lifestyle, and clinical factors. Results Among participants (mean age 70.0 years), 12.31% reported stroke history. GNRI demonstrated significant inverse association with stroke prevalence. Each one-standard-deviation increase in GNRI was associated with 12% lower stroke odds (odds ratio [OR]: 0.88; 95% CI: 0.83–0.93). Quartile analysis revealed progressively lower odds compared to the lowest quartile: Q2 (OR: 0.81; 95% CI: 0.69–0.95), Q3 (OR: 0.74; 95% CI: 0.63–0.87), and Q4 (OR: 0.77; 95% CI: 0.65–0.91) (P for trend < 0.05). Multiple analytical approaches consistently demonstrated a linear inverse association. Subgroup analyses revealed a stronger inverse association in females (OR: 0.96; 95% CI: 0.95–0.98) versus males (OR: 0.99; 95% CI: 0.97–1.00) and among current drinkers (OR: 0.96; 95% CI: 0.94–0.97). Conclusions Higher GNRI scores were significantly associated with lower stroke prevalence in elderly adults in a linear dose-response manner, with the association being particularly pronounced in females and current drinkers. These cross-sectional findings suggest that GNRI may be a useful nutritional risk screening tool in geriatric populations; however, prospective studies are needed to establish temporality and causality.

ObjectiveCatamenial epilepsy (CE) is a neuroendocrine disorder characterized by seizure exacerbation during specific phases of the menstrual cycle. The pathophysiology of CE remains elusive. This study investigates structural and functional brain alterations in women with Type-I CE, focusing on the interaction between hormonal fluctuations and epileptic networks.MethodsThirty-three CE women with Type-I CE and 27 healthy controls (HCs) underwent longitudinal multimodal MRI scans during the perimenstrual and midluteal phases. Voxel-based morphometry was used to detect abnormal gray matter volume (GMV), while amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) evaluated local signal changes. Functional connectivity was explored with brain regions showing abnormal GMV, fALFF, or ALFF as seed regions. Mediation analysis evaluated structural-functional relationships between hormonal changes and seizure frequency.ResultsReduced GMV was found in the CE group within the superior frontal gyrus, mediodorsal thalamus, insula, and limbic regions. Resting-state functional MRI demonstrated cycle-related fluctuations in the anterior cingulate cortex and the inferior temporal gyrus that were specific to women with CE relative to the HC group. In the seed-based FC analysis, the CE group exhibited hypoconnectivity within the temporo-limbic regions compared to HCs. In contrast to the temporal stability observed in HCs, women with CE displayed extensive hyperconnectivity involving the thalamo-striatal and fronto-parietal regions during the perimenstrual phase relative to the midluteal phase. Correlation analysis indicated that perimenstrual estradiol levels were negatively associated with ALFF value in the inferior temporal gyrus. Mediation analysis suggested mediodorsal thalamus GMV atrophy as a critical substrate linking estradiol fluctuations to perimenstrual seizure exacerbation.ConclusionsThese findings may implicate a hormone-sensitive network with widespread cortical alterations anchored in the thalamo-temporolimbic hub as central to CE pathophysiology. The structural vulnerability of the mediodorsal thalamus acts as a modulator where estradiol withdrawal might trigger functional network instability, suggesting a potential precision target for neuromodulation.

BACKGROUND:Obesity is common among people with epilepsy and is influenced by genetic susceptibility, lifestyle behaviours, and antiseizure medications (ASMs). How ASMs and lifestyle factors interact with genetic risk for obesity in epilepsy remains unclear. METHODS:This population-based cohort study analysed UK Biobank participants with epilepsy recruited between 2006 and 2010. Polygenic risk scores for body mass index (PRSBMI) classified individuals into low, medium, and high genetic risk groups. Associations between commonly used ASMs-including lamotrigine (LTG), valproate (VPA), carbamazepine (CBZ), and levetiracetam (LEV)-and overweight/obesity were examined using multivariable logistic regression, adjusting for demographic, socio-economic, and lifestyle factors. Gene-drug interactions were assessed, and Mendelian randomisation (MR) was used to explore potential links between LTG target gene expression and BMI. RESULTS:A total of 8451 individuals were included. In multivariable logistic regression analyses, LTG use was associated with lower odds of obesity (OR = 0.63, 95% CI: 0.47-0.85, P = 0.002) and overweight (OR = 0.72, 95% CI: 0.56-0.92, P = 0.014). VPA was associated with an increased obesity risk (OR = 1.31, 95% CI: 1.07-1.60, P = 0.010). Subgroup analysis suggested that LTG use was associated with a lower risk of obesity, particularly among individuals with low to moderate PRSBMI. As PRSBMI increased, the absolute difference in overweight risk between LTG users and non-users decreased. Sex-stratified analyses showed that LTG had a more substantial protective effect in males, while VPA was more strongly associated with obesity risk in females. Lifestyle factors were significantly associated with obesity and overweight risk, with higher physical activity levels and adherence to a healthy diet being associated with lower risk. MR analysis suggested a potential causal relationship between LTG target gene expression and BMI. CONCLUSIONS:Genetic predisposition, ASMs, and lifestyle behaviours were collectively associated with the risk of overweight and obesity in epilepsy. LTG use was associated with a lower risk of weight gain, particularly among individuals with lower genetic susceptibility, with this association attenuating as genetic risk for obesity increased. VPA was associated with an increased risk of obesity, especially in females. These findings support personalised metabolic risk management in epilepsy care.

OBJECTIVE:To assess the neurological prognosis of patients with autoimmune encephalitis (AE) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and construct risk prediction models for a worse prognosis. METHODS:This was a prospective, multicenter, observational cohort study. Patients with AE, with or without SARS-CoV-2, were followed up every 3 months. Multivariate logistic regression was used to identify factors influencing neurological prognosis. Prediction models were created using nine machine-learning strategies. RESULTS:SARS-CoV-2 infection was noted in 241 of 308 patients. Multivariate logistic regression showed inactivated immunizations plus recombinant or adenovirus vector vaccines protected against SARS-CoV-2 infection (odds ratio [OR] 0.05, 95% confidence interval [CI] 0.01-0.52, p = 0.01). Three months after infection, 12.4% of patients with stable AE had a worse neurological prognosis, 6.2% failed to recover their working conditions. Pre-infection modified Rankin Scale (mRS) = 1 (OR 4.06, 95% CI 1.11-14.91, p = 0.04) and mRS > 1 (OR 13.4, 95% CI 3.31-54.02, p < 0.01), immunotherapy during infection (OR 5.1, 95% CI 1.65-15.79, p = 0.01), and SARS-CoV-2-related drowsiness (OR 19.5, 95% CI 5.42-70.34, p < 0.01) and gastrointestinal symptoms (OR 4.4, 95% CI 1.58-11.96, p < 0.01) were identified as risk factors for worse prognosis. The ranger model(https://xingjieli1999.shinyapps.io/clinical_prediction_app/) using these four parameters showed a discrimination accuracy of 0.96 (95% CI 0.94-0.99). CONCLUSIONS:Patients with AE could experience exacerbated neurological symptoms following SARS-CoV-2 infection. Machine-learning algorithms showed feasibility of predicting prognoses based on clinical information in patients with AE.

Objective Antiseizure medications (ASMs) are the cornerstone of epilepsy treatment. However, evidence on direct comparison of ASMs is lacking. This network meta-analysis evaluated the comparative efficacy and safety of approved and investigational add-on third-generation ASMs for focal epilepsy in adolescents and adults. Methods Data were retrieved through an extensive literature search of PubMed, Embase, Cochrane Library, and ClinicalTrial.gov databases from inception through August 2025. Findings were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline (CRD420251180027). Primary efficacy outcomes were ≥50% and 100% responder rates at 12-weeks maintenance duration. Secondary outcomes were corresponding responder rates at 8-weeks maintenance duration. Tolerability was assessed as retention rate. Treatment-emergent adverse events (TEAEs) and TEAEs leading to treatment discontinuation were the safety outcomes. Results The literature search retrieved 345 studies, of which 35 studies were included. All ASMs showed significantly higher responder rates compared with placebo. Significantly higher 100% responder rate was observed with cenobamate (CNB; 400mg/d: Risk ratio [RR] 15; 95% CI, 7.0-39; 200mg/d: RR 8.7; 95% CI, 3.9-22) at a maintenance duration of 12 weeks and 8 weeks (400mg/d: RR 15; 95% CI, 7.0-41; 200mg/d: RR 8.6; 95% CI, 4.0-24). All ASMs showed a patient retention rate comparable with placebo. For overall TEAEs, brivaracetam (BRV; 50mg/d) and BRV ranked the lowest for individual and pooled doses, respectively; placebo ranked the highest in both cases. For TEAEs leading to treatment discontinuation, CNB ranked lower than the placebo. Significance All approved and investigational ASMs were effective add-on treatments for focal epilepsy, with CNB demonstrating the greatest likelihood of achieving seizure freedom.

The gut-brain axis is a complex bidirectional communication network linking the gastrointestinal tract and the central nervous system. Its dysregulation is closely associated with a wide range of gastrointestinal, metabolic, and neuropsychiatric disorders. Glycosylation, one of the most prevalent and structurally diverse post-translational modifications of proteins and lipids, is increasingly recognized as a regulator of multiple processes within the gut-brain axis. In this review, we summarize evidence that glycosylation influences several steps of gut-brain communication, including intestinal barrier function, microbial glycan metabolism, immune signaling, enteric and vagal pathways, blood-brain barrier integrity, and neural responses. We distinguish direct mechanistic findings from associative observations and discuss the more limited evidence for brain-to-gut regulation of intestinal glycosylation. In addition, we discuss the potential of glycosylation-targeted nutritional and microbiota-based interventions and highlight emerging opportunities to integrate glycomics with other omics approaches to dissect the complex regulatory networks underlying the gut-brain axis. In conclusion, elucidating how glycosylation shapes signaling along the gut-brain axis may open new avenues for understanding disease pathogenesis and for developing targeted therapeutic strategies.

Specialist nursing has become an integral part of the healthcare system, with neurology specialist nurses playing vital roles in providing high-quality specialized care to patients with neurological disorders. Role clarity and work engagement (WE) are associated with various aspects of nursing practice and patient outcomes. Our aim was to investigate the levels of role clarity and WE among neurology specialist nurses in China and to explore how role clarity relates to WE. A cross-sectional study was conducted between April 2025 and July 2025 in China. Through convenience sampling, a total of 343 neurology specialist nurses from 11 provinces were initially invited, of whom 279 completed an online survey. The survey included a questionnaire assessing sociodemographic characteristics, as well as the role clarity scale, the Perceived Organizational Support Scale (POSS), and the Utrecht Work Engagement Scale (UWES). A mediation model was employed to test direct, indirect, and total effects using the bootstrap method. The mediating effect of perceived organizational support (POS) was analyzed using the SPSS process macro (Model 4). A total of 266 valid questionnaires were included in the analysis. Among neurology specialist nurses in China, role clarity and WE were at moderate and high levels, respectively. There were significantly positive correlations among role clarity, POS, and WE (p < 0.001). Furthermore, mediation analyses revealed that the positive relationship between role clarity and WE was mediated by POS (B = 0.758, 95

AIM:The International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) has been validated for cross-cultural applicability in many populations, but data from Chinese-speaking cohorts remain lacking. This study aimed to examine the utility of IC-CoDE in Chinese people with epilepsy and characterize localized cognitive phenotypic patterns. METHOD:A total of 335 Chinese people with epilepsy (mean age 27.41 ± 8.03 years; 53.7% male) and 90 healthy local controls were enrolled. All participants underwent comprehensive neuropsychological assessments covering five cognitive domains, with at least two standardized. Test scores were converted to Z-scores using local control data for standardization. IC-CoDE phenotypes were classified at the -1.5 SD threshold (intact, single-domain impairment, bidomain impairment, generalized impairment). RESULTS:Application of IC-CoDE (-1.5 SD) revealed 44% (n = 149) of the Chinese sample had intact cognitive function, 27% (n = 90) had single-domain impairment, 16% (n = 53) had bidomain impairment, and 13% (n = 43) had generalized impairment. Memory was the most extensively impaired domain. Age, years of education, disease duration, number of antiseizure medications, and seizure side were associated with phenotypic classification (all ps < 0.05). CONCLUSION:This study demonstrates the cross-cultural adaptability of IC-CoDE in Chinese-speaking people with epilepsy, filling a gap in global IC-CoDE validation.

OBJECTIVE:Epilepsy is associated with increased risk of excess mortality compared to the general population. Identifying causes of death is critical for guiding prevention strategies. Earlier studies in resource-limited settings suggested that accidental deaths were the main causes, but whether the mortality pattern has changed over time remains unclear. This study aimed to update the knowledge on mortality and causes of death of people with epilepsy in rural China. METHODS:In this nationwide cohort study, we analyzed data from the Epilepsy Prevention and Management Project in rural China between January 1, 2018, and December 31, 2020. Mortality outcomes included all-cause mortality, causes of death, and standardized mortality ratios (SMRs). Kaplan-Meier, multivariable Cox, and restricted cubic spline analyses were used to assess mortality risk and associated factors. RESULTS:Among 17 515 participants followed for 25 922 person-years, 381 deaths were recorded, yielding an all-cause mortality rate of 14.7 per 1000 person-years. The leading causes of death were circulatory system diseases (47.8%), including cerebrovascular disease (28.9%), and heart disease (18.9%). The all-cause SMR was 3.51 (95% confidence interval = 3.33-3.70). The highest cause-specific excess mortality was observed for brain malignancies (SMR = 20.30). Male sex, older age at epilepsy onset, longer disease duration, and higher baseline seizure frequency were independently associated with mortality. SIGNIFICANCE:People with epilepsy in rural China continue to experience substantial and potentially preventable excess mortality. As mortality patterns shift, prevention strategies should extend beyond seizure control and injury prevention to include vascular risk management and timely neuroimaging for suspected structural lesions. Prospective studies are needed to determine whether these strategies improve survival.

OBJECTIVE:Heterogeneity of neural states leads to different responses to neurostimulation. Electroencephalography (EEG) microstate-locked transcranial magnetic stimulation (TMS) was applied to investigate the effect of microstates at the time of stimulation on the TMS responses in patients with genetic generalized epilepsy (GGE). METHODS:Resting-state EEG and TMS-EEG data were collected in 24 patients with GGE. Trials were classified based on four typical microstates at the time of stimulation. TMS-evoked potentials (TEPs), topographical distribution and natural frequency were analyzed to explore the differences in TMS-EEG characteristics across four microstates in GGE. Spearman correlation analysis was performed to examine associations between TEP components and clinical features. RESULTS:The P180 component of microstate D group (-4.794 μV) was significantly higher than of microstate A group (-1.668 μV, p = 0.027) and microstate C group (-2.079 μV, p = 0.027). Significant differences in the correlation values between each TEP component were detected, especially in microstate A, C and D. Compared with TEP amplitude during random stimulation, microstate A and microstate D showed slightly lower N100 and P180 amplitude, microstate C showed slightly higher P180 amplitude induced by stimulation. There was no difference in the TMS-induced natural frequencies among four microstates. No significant correlations were found between the amplitude of the TEPs and clinical variables in GGE. CONCLUSION:Microstate-specific TMS-EEG signatures in GGE may help reduce state-related variability and improve patient stratification and outcome assessment in trials and longitudinal monitoring. However, their robustness and clinical relevance require replication and prospective validation in larger, independent cohorts.

OBJECTIVE:Temporal lobe epilepsy (TLE) is the most common focal epilepsy but remains highly heterogeneous across hemispheric and structural etiology. This study aimed to characterize microstate-based network dynamics in TLE and evaluate their diagnostic value for seizure lateralization and structural etiology using machine learning. METHODS:Resting-state electroencephalography (EEG) recordings from 150 patients with unilateral TLE (71 right, 79 left) and 65 healthy controls (HCs) were analyzed. EEG signals were segmented into canonical microstates (A, B, C, D), and microstate-specific spatial, and temporal dynamic functional connectivity (dFC) variability metrics were extracted using phase lag index analysis. After two-step feature selection, the appropriate number of features were derived and input into Random Forest, XGBoost, and Support Vector Machine (SVM) classifiers to distinguish: TLE vs HCs, left vs right TLE, and magnetic resonance imaging (MRI)-negative (MRI-neg) vs hippocampal sclerosis (HS) TLE subtypes (TLE-HS). Model performance was evaluated on independent hold-out validation set using receiver operating characteristic analyses. RESULTS:Compared with HCs, patients with TLE exhibited increased duration and occurrence of microstate D and reduced expression microstate B, reflecting maladaptive attentional overactivation and visual suppression. Spatial variability was globally decreased, most prominently in left TLE. SVM achieved excellent performance for TLE detection (area under the curve [AUC] = .98) and lateralization (AUC = .97), whereas classification between MRI-neg TLE and TLE-HS was limited (AUC = .58). SIGNIFICANCE:EEG microstate-derived dFC metrics provide reliable, non-invasive biomarkers for identifying and lateralizing TLE using short duration resting-state EEG recordings. This framework advances understanding of TLE heterogeneity and supports the development of individualized electrophysiological tools for precision diagnosis.

Background: Glioblastoma (GBM) is characterized by pronounced transcriptional plasticity and a highly structured immune microenvironment, yet the molecular features associated with tumor-state transitions and immune remodeling remain incompletely understood. Methods: We used an integrative multi-omics framework to examine how secreted phosphoprotein 1 (SPP1) relates to tumor microenvironment organization in human gliomas. Results: Single-cell analyses associated SPP1 with myeloid populations, mesenchymal-like (MES-like) malignant states, inflammatory regulatory programs, and inferred ligand-receptor co-expression patterns involving SPP1-CD44 and SPP1-integrin pairs. Spatial transcriptomic analyses showed that SPP1-high regions were enriched for estimated myeloid abundance, MES-like tumor signal, and ECM/angiogenic programs, supporting an SPP1-associated spatial mesenchymal-myeloid program in GBM. Computational perturbation analyses provided network-level support for SPP1-CD44-associated stress-responsive programs. HPA immunohistochemistry provided tissue-level protein context for SPP1 and related mesenchymal/receptor-associated components. Ivy GAP analysis showed enrichment of SPP1-associated features in core-like anatomic compartments, and CODEX spatial protein imaging provided antibody-panel-based contextual support for mesenchymal-myeloid-associated features. In the TCGA-GBM cohort, elevated SPP1 expression and an SPP1-associated mesenchymal signature were associated with poorer overall survival. Conclusions: These findings support an inferential model in which SPP1 is associated with spatial mesenchymal-myeloid organization in GBM and nominate SPP1-associated programs as candidate readouts of tumor plasticity, inflammatory myeloid remodeling, and spatial tumor microenvironment organization.

BACKGROUND:The clinical understanding of limbic encephalitis associated with antibodies against adenylate kinase 5 (AK5) remains limited. Misinterpretation of antibody test results may lead to diagnostic errors and inappropriate management. We aim to assess the frequency of anti-AK5 encephalitis overdiagnosis and identify common diagnostic pitfalls. METHODS:Cases of confirmed and mimicking anti-AK5 limbic encephalitis from January 2021 to July 2024 using established criteria for autoimmune encephalitis (AE) were reviewed. AK5 mimics were defined as patients initially suspected of AE with a positive AK5 autoantibody result, but who ultimately received an alternative final diagnosis. RESULTS:A total of 21 patients were included (57.1% female; median age 34 years; range 14-82). Only 3 patients (14%) were diagnosed with definite anti-AK5 limbic encephalitis, while 18 patients (86%) were classified as AK5 mimics. Serum autoantibodies were predominantly of the IgG3 subclass, with titers ranging from 1:10 to 1:100. The mimics included primary psychiatric disorders (22%), central nervous system (CNS) infections (22%), other inflammatory disorders (28%), epilepsy (16%), neurodegenerative diseases (6%) and metabolic encephalopathy (6%). The most frequent confounding factor in misdiagnosis was the presence of prominent psychiatric and behavioral symptoms, seen in 50% (9 of 18) of AK5 mimics. The second most common confounder was the presence of low serum antibody titers or isolated serum positivity without corresponding cerebrospinal fluid (CSF) findings (< 1:100), observed in 94% (17 of 18) of mimics. CONCLUSION:Mimics of anti-AK5 encephalitis are common and that misdiagnosis is often driven by non-specific symptoms and clinically irrelevant antibody results.