OBJECTIVES:This study aimed to evaluate the clinical utility of positron emission tomography (PET)-based Z-score distribution mapping (Z-map) in the noninvasive presurgical localization of seizure onset zones (SOZs), with a particular focus on regional differences in performance and methodological robustness. METHODS:We analyzed a cohort of 120 patients with drug-resistant epilepsy who underwent stereoelectroencephalography (SEEG) implantation between 2021 and 2024. Multimodal imaging data, including PET and structural MRI, were processed using FreeSurfer and 3D Slicer to reconstruct electrodes, segment gray matter, and generate Z-maps. Hypometabolic regions were defined as the bottom 0.5% of Z-scores. The classification performance of the Z-map was validated against SEEG-defined SOZs. RESULTS:The Z-map showed high classification performance in the frontal and parietal lobes (sensitivity: 0.74/0.72; κ: 0.68/0.65), but reduced effectiveness in the temporal and insular lobes (sensitivity: 0.41/0.65; κ: 0.44/0.47). The overall specificity (0.94) and negative predictive value (0.91) indicated strong exclusionary capability. Regional disparities were primarily attributed to anatomical complexity and technical limitations. CONCLUSION:Z-map offers clinically valuable support for localizing SOZs in the frontal and parietal cortex and optimizing SEEG implantation, particularly in patients with inconclusive noninvasive findings. However, its limited sensitivity in temporal and insular regions and high dependency on preprocessing quality underscore the need for standardized pipelines and multimodal integration. Future research should focus on improving robustness and reproducibility to facilitate clinical translation.

Focal motor seizures (FMS) are often unresectable because of motor risk. We aimed to evaluate the long-term effectiveness and safety of subthalamic nucleus deep brain stimulation (STN-DBS) in patients with FMS and to identify suitable candidate patients. We analyzed long-term outcomes in 22 patients with FMS treated with STN-DBS, classifying patients as responders (≥50% seizure reduction) or non-responders and relating outcomes to seizure-focus topography using focus-frequency maps and region-of-interest–based volumetrics. In a SEEG cohort of 13 patients with electrodes in the sensorimotor cortex and the STN, including 1 from the DBS cohort, we quantified changes in interictal spike (IIS) rates and broadband (0.5–90 Hz) power spectral density (PSD) across distinct sensorimotor subregions during 100-Hz STN stimulation. At final follow-up (mean 45 months), median seizure reduction was 55%, with 14/22 responders and six patients achieved >90% reduction, including one seizure-free. STN-DBS was well tolerated, with no surgical complications and one explant for infection. Responders’ seizure foci clustered in a medial sensorimotor strip comprising the paracentral lobule (PCL), supplementary motor area (SMA) and trunk representations of the precentral and postcentral gyri, and greater involvement of the PCL and trunk areas correlated with better outcome. SEEG analyses showed a global reduction in broadband power but regionally selective suppression of epileptiform activity, confined to the PCL and SMA. Together, STN-DBS appears to be a safe, effective option for FMS, and patients whose seizure foci involve the medial sensorimotor strip may be potential candidates.

Alzheimer’s disease (AD) is conventionally framed as a consequence of progressive amyloid-β and tau pathology, yet substantial heterogeneity in cognitive outcome at any given level of pathological burden indicates that cognitive resilience constitutes a parallel determinant of disease risk. Here we test whether pathology and resilience function as independent and synergistic dimensions of incident AD dementia. In 3,119 older adults from the China Cognition and Aging Study, followed for a median of 13.7 years, we derived two longitudinal indices from repeated measurements: a pathology score indexed by tau phosphorylated at threonine 181/amyloid-β42, and a cognitive resilience score defined as the residual of the cognitive slope after adjustment for pathology, age and sex. Both indices independently predicted incident AD dementia (pathology: hazard ratio 2.50 per s.d., 95% confidence interval 2.30–2.72; resilience: hazard ratio 0.51 per s.d., 95% confidence interval 0.48–0.55). The two dimensions contributed comparable, complementary shares of 10-year AD risk and together captured substantially more of the explainable risk than either dimension alone. They also interacted multiplicatively: the lowest risk of incident AD was observed in individuals with both high resilience and low pathology, whereas the highest risk occurred in those with both high pathology and low resilience. Findings were replicated in an independent cohort and were robust in reverse-causation sensitivity analyses. These findings broaden the understanding that AD dementia is shaped jointly by pathological burden and cognitive resilience, and argue for therapeutic trial and prevention strategies that strengthen resilience alongside reducing pathology. ClinicalTrials.gov registration: NCT03653156 . A 15-year cohort study shows that Alzheimer’s dementia risk is jointly shaped by Alzheimer’s pathology and cognitive resilience, with high resilience linked to lower risk, even under greater pathology.

Aging is a primary risk factor for chronic diseases, yet its progression varies among individuals and between sexes. Here, under the X-Age Project, we profiled the clinical aging phenome of the Multicentric Chinese Aging Study (mCAS) through a cross-sectional analysis of 172 clinical measures from more than 100,000 participants aged 18-98 years across three centers. These profiles enabled sex-specific clinical aging clocks that revealed divergent aging trajectories between women and men during midlife that converged in later life. Phenome-wide analyses revealed age-related accumulation of metabolic factors, including low-density lipoprotein, triglycerides, glucose and uric acid, and tumor markers, such as carcinoembryonic antigen and human epithelial protein 4. These age-accumulating factors induced senescence-related phenotypes in human endothelial cells. Furthermore, a high-fat diet mouse model with dietary reversal supported the modifiability of metabolic burden-induced aging. Together, this work establishes metabolic and tumor marker accumulation as actionable drivers of human aging, paving the way for personalized, sex-stratified geroprotective interventions.

OBJECTIVE:The aim of this study was to evaluate the diagnostic yield, safety, and workflow advantages of the Remebot system in intracranial stereotactic biopsies. METHODS:The authors retrospectively analyzed 496 consecutive patients who underwent Remebot-assisted stereotactic brain biopsy at a single institution between 2023 and 2024. Demographic, clinical, radiological, and pathological data were collected. Logistic regression analysis was used to identify predictors of nondiagnostic biopsy. Model performance was evaluated using McFadden's pseudo-R2 and receiver operating characteristic analysis. Perioperative complications were defined and graded according to the Common Terminology Criteria for Adverse Events (version 5.0). RESULTS:The overall diagnostic yield was 85.9% (426/496), with high-grade glioma (32.7%) and hematological malignancies (20.4%) as the most common pathologies. Multivariate analysis identified deep lesion sampling (OR 0.51, 95% CI 0.27-0.94; p = 0.034) and younger age (OR 0.98, 95% CI 0.97-0.99; p = 0.006) as independent predictors of nondiagnostic results. Sex, lesion location (supratentorial vs infratentorial), and brainstem involvement were not associated with yield. The perioperative complication rate was 1.8% (9/496), with hemorrhage accounting for 77.8% of events. All complications occurred in male patients, predominantly in those with high-grade glioma. The Remebot system demonstrated notable workflow advantages, including integrated planning, automated registration, and compact modular design. CONCLUSIONS:Remebot-assisted stereotactic biopsy achieved a high diagnostic yield and low complication rate, comparable with that of international robotic platforms. Deep lesion sampling and younger age were found to increase the risk of nondiagnostic results, whereas sex and lesion location did not. The system's integrated workflow, automated registration, and cost-efficient accessibility broaden its clinical applicability and support its role as a reliable tool for minimally invasive neurosurgical diagnosis.

OBJECTIVE:Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) is a minimally invasive technique that allows for real-time magnetic resonance imaging (MRI) monitoring and precise ablation of epileptogenic lesions. This study reports our initial clinical experience with a domestically developed MRgLITT system in patients with drug-resistant epilepsy (DRE) and evaluates its efficacy, safety, and learning curve. METHODS:We retrospectively reviewed 36 patients with focal DRE who underwent MRgLITT between October 2020 and May 2021. Clinical characteristics, operative variables, ablation rate, and length of hospital stay were analyzed. Prognostic factors were examined using univariate and Kaplan-Meier survival analyses. The surgical learning curve was evaluated using cumulative sum (CUSUM) analysis of the operative time. RESULTS:The mean follow-up duration was 40.86 months. At the last follow-up, 66.7% patients (24/36) achieved seizure freedom (ILAE I-II), and the overall response rate (ILAE I-IV) was 94.4% (34/36). Single lesion (p = 0.002) and ablation rate of ≥90% (p = 0.009) were significant predictors of seizure freedom. CUSUM analysis identified a turning point in the 19th case, after which the operative time and total hospitalization, particularly the preoperative evaluation time, were significantly reduced. However, the ablation rate and seizure outcomes remained stable across phases. No long-term postoperative complications were observed. SIGNIFICANCE:MRgLITT is safe and effective in patients with DRE, and adequate ablation and well-localized single lesions predict a higher likelihood of favorable outcomes. We present the first evaluation of the MRgLITT learning curve in China and confirm that the technique can be readily adopted with consistent clinical outcomes. PLAIN LANGUAGE SUMMARY:This study assessed the use of a domestic MRgLITT system of China in drug-resistant epilepsy with more than 3 years of follow-up. Seizure freedom was achieved in 66.7% of patients, and over 90% experienced significant improvement without long-term complications. Single lesions and ablation rate above 90% predicted favorable outcomes. Surgical proficiency was reached after 19 cases, reflecting improved efficiency while maintaining stable efficacy and safety. MRgLITT is a safe, effective, and easily adoptable minimally invasive option for selected patients with drug-resistant epilepsy.

Background : The dominant model of epilepsy at the systems level - the tripartite epileptic network, comprising seizure onset zone (SOZ), propagation zone, and uninvolved cortex - has guided clinical practice for decades. This model rests on a largely untested assumption: that epileptic seizures propagate within a disease-specific pathological circuit that is distinct from the normal functional architecture of the brain. We asked a more fundamental question: is this assumption correct, or do seizures instead disrupt and propagate within normal functional brain networks, with the SOZ representing nothing more than a focal point of pathological vulnerability embedded in an otherwise intact network? Methods : We trained a 6-layer Transformer encoder via masked autoencoding and subject-contrastive learning on 2,656 hours of continuous intracranial EEG (iEEG) from 18 patients (SWEC-ETHZ long-term dataset) - entirely without human-defined labels - and applied it in zero-shot fashion to two additional independent datasets (HUP dataset: 54 patients; SWEC-ETHZ short-term: 15 patients), yielding 87 patients and 537 seizures in total. We characterised the temporal dynamics, spatial organisation, and functional network affiliation of seizure electrophysiological fingerprints using the Schaefer 400-region/Yeo-7 network parcellation. Results : Four convergent findings emerged, all inconsistent with the disease-specific circuit model. First, seizures are abrupt phase transitions in AI feature space, with seizure termination producing a significantly larger feature-space displacement than initiation across all three datasets (SWEC long-term: offset jump 0.541 vs. onset jump 0.304, p=0.0003; HUP: 1.423 vs. 0.982, p<0.0001; SWEC short-term: 0.566 vs. 0.291, p<0.0001). Second, SOZ anomalies are seizure-specific: ictal AI–SOZ overlap (0.469) vastly exceeded interictal overlap (0.157; p=0.0002), and AI-identified peri-SOZ channels were significantly closer to the SOZ than chance (37.2 mm vs. 46.5 mm; p<0.0001). Third, seizure propagation exhibited robust within-network synchrony in 100% of 47 patients (sync ratio median=0.79; t=−20.2 vs. random baseline, p<0.0001), following a stereotyped network activation sequence (Default Mode → Salience/Ventral Attention → Limbic → Dorsal Attention → Frontoparietal Control → Somatomotor → Visual; Kruskal-Wallis p<0.0001) that precisely recapitulates established ictal semiology. Fourth, age of epilepsy onset predicted the interictal persistence of SOZ anomalies (Spearman r=0.48, p=0.005). Conclusions : These results directly challenge the epileptic network model. We propose the Network Disruption Theory of Epilepsy: epileptic seizures do not travel through a disease-specific circuit but instead originate at a focal disruption point (the SOZ) within a normal functional network and propagate along its pre-existing connections. This reconceptualisation shifts surgical focus from "resect the lesion" to "restore network integrity".

Perivascular macrophages (PVMs) are increasingly recognized as key players in maintaining brain homeostasis, yet their role in maintaining neurovascular-metabolic homeostasis has not been fully explored. We hypothesized that PVM depletion compromises cerebrospinal fluid-interstitial fluid exchange through glymphatic system (GS) dysfunction, thereby exacerbating cortical hyperexcitability manifested as increased epilepsy susceptibility and seizure intensity. Using clodronate liposomes (CLOs), we achieved >85% PVM depletion in mice. Following pentylenetetrazole (PTZ) challenge, PVM-depleted mice exhibited anxiety-like behaviors (reduced center time, p < 0.05), impaired working memory (decreased spontaneous alternation, p < 0.05), and increased cortical hyperexcitability, including shorter seizure latency and elevated EEG total power (p < 0.05). Mechanistically, PVM loss led to dysregulation of extracellular matrix components (increased laminin and collagen IV), impairing perivascular space integrity and GS function (reduced CSF tracer clearance, p < 0.05). AQP4 inhibition with TGN-020 further exacerbated PTZ-induced EEG abnormalities (increased total power, p < 0.05). Analysis of human epileptic tissue confirmed elevated collagen IV deposition in the seizure focus (p < 0.05) and a trend toward increased PVM density (p = 0.0638). These results highlight PVMs as essential modulators of the glymphatic-metabolic axis, linking vascular health to brain excitability. Targeting the PVM-GS interface offers therapeutic potential for disorders involving vascular dysfunction and neuronal hyperexcitability.

Human aging is characterized by complex structural and functional decline, but quantifying its heterogeneity and assessing biological age remain challenges. We present the mCAS (multicentric Chinese aging standardized cohort) developed from 2,019 Chinese individuals aged 18-91 years. Integrating high-dimensional clinical, physiological, and molecular-level data, we constructed a three-tiered aging framework: the core capacity clock (CC-clock) to quantify clinical physiological decline, the multimodal clock (MM-clock) with extensive parameter coverage and enhanced predictive precision, and organ-associated aging clocks. Cross-layer analysis demonstrates that plasma protein clocks not only capture chronological age but also serve as efficient proxies for systemic physiological capacity. Leveraging this framework for discovery, we identified the age-dependent accumulation of coagulation factors as a driver of multi-organ senescence and systemic inflammatory activation. This study provides a foundational framework that bridges molecular signatures with functional decline, identifies new biomarkers for aging assessment, and reveals a novel translational driver of aging.

OBJECTIVE:To establish a high-resolution atlas of the corpus callosum (CC) using diffusion spectrum imaging (DSI), aiming to detail the subregional connectivity and improve the understanding of interhemispheric communication for clinical applications. METHODS:This research employed DSI in conjunction with quantitative anisotropy (QA)-based deterministic fiber tracking on 44 healthy individuals to map the connectivity patterns of the CC, correlating these with cortical subregions defined in the automated anatomical labeling (AAL) and human brainnetome atlas (BNA). RESULTS:The study identified 41 regions corresponding to the AAL atlas and 101 regions related to the BNA atlas at the midsagittal plane of the CC. Specifically, it included 34 frontal subregions associated with higher brain functions located predominantly in the anterior part of the CC. The midbody of the CC harbored subregions related to primary motor and sensory functions, while the splenium was characterized by subregions containing temporal projections. This comprehensive mapping revealed a complex and nuanced connectivity pattern within the CC, highlighting significant heterogeneity across regions that reflects its diverse structural and functional roles in brain functionality. CONCLUSION:The developed atlas represents the first extensive mapping of the CC integrating both anatomical and functional connectivity paradigms, using QA-based DSI deterministic tractography. This atlas, which will be freely available, provides a valuable resource for neuroscientific research and clinical practice, offering detailed insights into the structural and functional organization of the CC.

Temporal lobe epilepsy (TLE) is the most common and severe form of drug-resistant epilepsy. Glial cell-induced neuroinflammation is recognized as a key contributor to neuronal hyperexcitability in TLE. Previous studies utilizing single-cell sequencing have identified inflammatory responses in glial cells. However, no research has yet provided computational evidence to identify the major regulatory cell types and effector molecules involved in TLE. Our study constructed activated regulatory networks of TLE using single-nucleus RNA sequencing and identified interferon regulatory factor 7 (IRF7) in microglia as a key regulator of the TLE microenvironment, which leads to pro-inflammatory activation. By integrating microglia-neuron co-culture models and RNA sequencing, we revealed that IRF7 translocation and microglial IFN-β release contributed to neuronal injury. Notably, we highlight that microglia primarily interact with retinoic acid-related orphan receptor beta (RORB)-positive neurons and disrupt neuronal homeostasis by reducing RORB-mediated transcriptional activation of the stromal interaction molecule 1 (STIM1) through neuroinflammatory processes, thereby contributing to the epileptic phenotype. Our study positioned IRF7 as a central regulator of the TLE neuronal inflammation. It integrated IFN-related inflammation with neuronal RORB–STIM1 signaling to induce neuronal damage, suggesting that targeting IRF7 could be a potential neuroprotective therapy for TLE.

Most existing ictal stereoelectroencephalography (SEEG)-based seizure onset zone (SOZ) localization methods rely on patient-specific training, limiting their clinical applicability due to the scarcity of seizure recordings and substantial inter-patient variability. Consequently, robust patient-independent SOZ localization remains a major challenge. In this work, we propose a deep learning approach for patient-independent SOZ localization using ictal SEEG recordings, aiming to improve cross-patient generalization while preserving seizure-related temporal characteristics. To mitigate domain shifts across subjects, we introduce a clinically guided feature learning strategy that combines a cross-frequency coupling (CFC) mechanism to capture SOZ-related abnormal interactions across frequency bands with a self-comparison (SC) mechanism to emphasize seizure-onset evolution patterns within SEEG channels. We further incorporate seizure detection as an auxiliary task within a multi-task learning framework to provide seizure-onset-related temporal supervision, thereby improving the temporal awareness and generalizability of SOZ localization. Experiments on the public OpenNeuro HUP dataset demonstrate substantial improvements over existing methods, while additional evaluations on a private clinical dataset further validate the robustness and cross-patient generalization capability of the proposed method. Moreover, comparisons between the learned CFC representations and clinically established phase-amplitude coupling (PAC) metrics reveal consistent physiological patterns, supporting the interpretability of the learned representations.

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) poses significant challenges in therapeutic management. While studies have demonstrated seizure-induced alterations in peripheral immune molecules, the complement system, a central component of immune function, remains insufficiently characterized at single-cell resolution and spatial distribution in TLE-HS. This study aimed to comprehensively investigate the spatiotemporal dynamics of complement system components and their clinical implications in patients with TLE-HS. We first identified patterns of complement activity changes in epilepsy using bulk RNA sequencing. Then, we employed a TLE-HS mouse model for single-cell RNA sequencing and S1000 high-resolution spatial transcriptomics. We performed integrative bioinformatic analyses on single-cell data to quantify complement-system activity and define microglial heterogeneity, leading to the identification of complement-associated microglial subpopulations. Spatial transcriptomic data then validated the anatomical localization of these identified subpopulations. Finally, we developed and evaluated two machine-learning models based on complement-related gene signatures. Complement activity was elevated in epilepsy. The levels were higher in hippocampal sclerosis (HS) tissue than in normal hippocampus. They were also increased in mesial temporal lobe epilepsy with hippocampal sclerosis compared with mesial temporal lobe epilepsy without hippocampal sclerosis, and in patients with high seizure frequency (HSF) compared with those with low seizure frequency (LSF). Complement-related signatures were further associated with antiseizure medication response. Candidate biomarkers including IRF2, GNB2, EHD1, CTSB, and CFH were identified using statistical modeling and machine learning. Among multiple classifiers, the support vector machine model showed the best predictive performance, and SHAP analyses indicated distinct contribution directions for these candidate genes. In the kainic acid (KA) mouse model, single-cell analyses showed that complement activity was upregulated across cell types in HS. Microglia exhibited the highest complement activity. Re-clustering and trajectory inference defined HS-associated microglial subpopulations that were enriched in terminal differentiation states. hdWGCNA together with differential expression highlighted Ctsb, C1qa, and Fcer1g as core complement-linked genes. Using Ctsb-defined microglial states, eight diagnostic biomarkers were selected, and a multi-layer perceptron model achieved superior classification accuracy in epilepsy diagnosis. Finally, cell-cell communication and spatial transcriptomics consistently implicated an Spp1-related signaling axis associated with Ctsbhigh microglia in hippocampal sclerosis regions. The resulting diagnostic and response-prediction models were deployed as exploratory web-based research tools pending independent validation. Our study systematically characterized the complement system in TLE-HS by integrating multi-level omics data, including bulk RNA sequencing, single-cell sequencing, and spatial transcriptomics. We revealed the potential value of complement system gene signatures in clinical diagnosis and personalized treatment of epilepsy.

Diffuse glioma-related epilepsy (dGRE) frequently presents with epilepsy as the initial symptom and is closely associated with tumor progression or recurrence, imposing significant social and psychological burdens on patients. The pathogenesis of dGRE is highly complex, involving both peritumoral microenvironmental mechanisms and tumor-intrinsic factors. Diagnosis requires a comprehensive approach integrating neuroimaging, EEG, molecular biomarkers, and spatial correlation between the tumor and the epileptogenic zone. Management aims to control seizures and improve prognosis. Non-enzyme-inducing anti-seizure medications (ASMs), such as levetiracetam and lacosamide, are recommended as first-line therapy, while valproic acid serves mainly as a second-line agent. Surgical resection, particularly maximal safe and supratotal removal guided by electrophysiological monitoring, significantly improves seizure outcomes. Radiotherapy, chemotherapy, and targeted agents further contribute to seizure control. The updated 2025 Chinese clinical practice guidelines incorporate recent advances in ASM use, postoperative withdrawal strategies, and multidisciplinary treatment algorithms. These updates provide an evidence-based reference for standardized diagnosis and management of dGRE.

OBJECTIVE:This study aimed to investigate the regulatory roles of distinct neuronal subtypes within the anterior cingulate cortex (ACC) in acute seizures and to identify cell type-specific mechanisms underlying seizure modulation in this region. METHODS:Acute seizure models were established in mice via pentylenetetrazol injection. In vivo fiber photometry and miniscope calcium imaging were employed to monitor neuronal calcium activity, and multichannel electroencephalography was used to record brain electrical signals simultaneously. Subsequently, bidirectional chemogenetic and optogenetic manipulations were performed on calcium/calmodulin-dependent protein kinase II (CaMKII) excitatory neurons and vesicular γ-aminobutyric acid (GABA) transporter (vGAT)-expressing GABAergic interneurons, as well as parvalbumin (PV) and somatostatin (SST) interneuron subpopulations. RESULTS:Calcium recordings demonstrated that both excitatory and inhibitory neurons in the ACC exhibited significant, temporally coordinated hyperactivity during acute seizures. Inhibition of CaMKII neurons significantly reduced seizure severity, whereas their activation induced spontaneous seizurelike activity. Enhancing GABAergic interneuron activity significantly decreased seizure frequency and severity. Notably, inhibition of GABAergic interneurons resulted in markedly more severe seizures. Further examination of interneuron subtypes revealed functional heterogeneity; activation of SST interneurons effectively suppressed seizures, whereas PV activation did not produce significant antiseizure effects. Critically, inhibition of either PV or SST interneurons triggered spontaneous seizurelike activity, indicating that both subtypes are necessary for maintaining network stability. SIGNIFICANCE:This study elucidates the differential roles of ACC neuronal populations in acute seizure dynamics. Activating GABAergic interneurons or inhibiting CaMKII-positive neurons effectively suppresses seizure activity. Among interneuron subtypes, SST activation reduces seizures, whereas PV activation does not. However, inhibiting either subtype triggers spontaneous epileptiform activity, indicating both are indispensable and play complementary roles in maintaining network homeostasis. These findings reveal the microcircuit mechanisms by which the ACC modulates acute seizures, highlighting that cortical stability relies on both the classical excitation-inhibition balance and the cooperative interplay of multiple interneuron subtypes.

As a critical hub in the thalamo-cortical circuit, the human thalamus engages in a spectrum of fundamental and advanced brain functions through widely distributed circuits. Clinically, dysfunction of thalamo-cortical circuits is shown to be profoundly implicated in a wide range of neurological and psychiatric diseases. Nevertheless, the neuroanatomical substrates governing these functions of the thalamus have rarely been directly mapped in humans. Here, we overviewed the acute responses of direct electrical stimulation (DES) delivered to the distributed thalamus sites in 52 epilepsy patients admitted for presurgical stereoelectroencephalography. Specifically, DES of the thalamus evoked a broad spectrum of in-situ responses spanning fundamental functions such as sensory and motor processing, extending to complex neural operations encompassing neurovegetative regulation, cognitive processing, emotional modulation, and multimodal responsiveness. Moreover, through the integration of DES with functional human connectome (n = 1000), we found an intra-thalamic intrinsic functional network associated with each specific clinical response. Our data provide direct substantiation for the complex functional architecture of the human thalamus, advancing current understanding of its role and potentially culminating in targeted therapeutic strategies tailored to ameliorate symptom-specific neural circuit disorders.

White matter dysfunction is increasingly implicated in working memory impairment across neurological and psychiatric disorders, yet the electrophysiological basis of white matter involvement in working memory remains poorly understood. Although recent functional MRI studies suggest that white matter tracts exhibit task-related BOLD modulation, these methods cannot resolve the fast, frequency-specific electrophysiological interactions associated with working memory. Here, we recorded intracranial stereotactic EEG (sEEG) from 20 patients with drug-resistant epilepsy performing an N-back working memory task to characterize neural activity and connectivity within human white matter. Working memory load evoked robust, tract- and frequency-specific modulations of local field potentials: gamma and high-gamma power increased in the frontal blade tract but decreased in the splenium of the corpus callosum/superior parietal blade tract, whereas the temporal blade exhibited enhanced theta power with relatively stable high-frequency responses. Although within-frequency functional connectivity among white matter regions remained highly stable across task conditions, cross-frequency connectivity, particularly theta-high-gamma coupling, was selectively enhanced with increasing working memory load. These findings provide intracranial electrophysiological evidence that human white matter exhibits load-dependent, tract-specific, and frequency-resolved functional dynamics during working memory.

AIMS:While various animal models of mesial temporal lobe epilepsy (MTLE) exist, a validated model based solely on hippocampal GABAergic interneuron loss is lacking. We aimed to establish and characterize a novel MTLE with hippocampal sclerosis (HS) model by selective ablation of these neurons. METHODS:Using AAV vectors (flex-DTA or DIO-taCasp3-TEVp), we performed unilateral, partial ablation of GABAergic neurons in the dentate gyrus (DG) or CA1 subregions of VGAT-Cre mice. Spontaneous recurrent seizures (SRS) were monitored by video-EEG. Behavioral tests and histopathological analyses were conducted to assess comorbidities and HS features. RESULTS:DG-ablated mice all developed SRS, whereas 50%-70% of CA1-ablated mice did. Lethal SRS occurred in around 50% of DG-ablated mice. Both ablation models exhibited sustained SRS from Days 8-28, distinct from kainic acid model severity in some parameters. The model recapitulated anxiety-like behaviors, cognitive deficits, and hallmark HS pathology including gliosis, granule cell dispersion, and mossy fiber sprouting. CONCLUSION:Selective unilateral hippocampal GABAergic interneuron loss is sufficient to drive chronic epilepsy with HS. This model provides direct causal evidence and a precise platform for investigating MTLE-HS mechanisms and therapies.

Evidence on the association between ambient air pollution, especially PM2.5 constituents, and epilepsy remains limited, and the underlying biological mechanisms remain poorly understood. We aimed to investigate the short-term effects of particulate and gaseous pollutants on epilepsy hospitalization and explore biologically plausible pathways using an adverse outcome pathway (AOP) framework. We conducted an individual-level time-stratified case-crossover study including 4,350 epilepsy hospitalizations. Daily exposures to ambient air pollutants and PM2.5 constituents were assigned at the residential level. Conditional logistic regression was used to estimate the percentage change in epilepsy hospitalization associated with an interquartile range (IQR) increase. To explore potential mechanisms, shared targets were identified using the CTD, GeneCards, and GTEx databases, followed by protein-protein interaction, hub gene, GO/KEGG enrichment. PM2.5 and its constituents were positively associated with epilepsy hospitalization at lag 6. For an IQR increase in exposure, the percentage changes in hospitalization were 7.09%-7.94% for PM2.5 and its constituents. SO2 showed a significant positive association at lag 6, with a 5.52% increase in hospitalization. Stratified analyses showed that these associations statistically significant among individuals aged ≥18 years, female patients, during the cool season, and in eastern China. The AOP-informed analysis provided exploratory, hypothesis-generating biological context that identified candidate pathways linking PM exposure to epilepsy-related abnormalities, including glutamate metabolism, Ca2+ dynamics, and maladaptive synaptic plasticity; however, these findings do not establish causal mechanisms. Short-term exposure to PM2.5 was significantly associated with increased epilepsy hospitalization. These findings suggest that fine particulate pollution may be important environmental triggers for epilepsy exacerbation.