BACKGROUND:Chronic sleep deprivation (CSD), commonly observed in the elderly, is associated with an increased risk of neuroinflammation, depression, and cognitive impairment. Microglial efferocytosis plays a vital role in maintaining neural homeostasis and tissue repair. However, the mechanistic role of microglial efferocytosis in linking CSD to neurobehavioral impairments in aging remains largely undefined. METHODS:A 21-day CSD model was established in aged mice, and depression-like behaviors and cognitive function were evaluated. Hippocampal neuronal injury and neuroinflammation were evaluated using histological staining and immunohistochemistry/immunofluorescence. Microglial efferocytosis was assessed in both hippocampal tissues and an in vitro microglial model. Expression of inflammatory mediators and efferocytosis-related molecules was quantified by qRT-PCR and Western blotting, and TREM2 overexpression was applied to determine its functional contribution to efferocytic capacity and behavioral outcomes. RESULTS:CSD induced significant depression-like behaviors and cognitive impairment in aged mice, accompanied by hippocampal neuronal damage and enhanced neuroinflammation. Importantly, CSD markedly impaired microglial efferocytosis and was associated with selective downregulation of TREM2 expression. Pharmacological inhibition of efferocytosis with Annexin V further exacerbated neuroinflammation and behavioral deficits. In contrast, TREM2 overexpression restored microglial efferocytic activity, attenuated neuroinflammatory responses, and significantly improved depression-like behaviors and cognitive impairment in CSD-treated aged mice. CONCLUSIONS:Our findings reveal that TREM2-mediated impairment of microglial efferocytosis represents a key mechanism underlying CSD-related neuropathology in aging, highlighting TREM2 as a potential therapeutic target for sleep disturbance-related neuropsychiatric disorders.

BACKGROUND:Major depressive disorder (MDD) is a highly recurrent psychiatric illness, yet its neurobiological trajectory across acute, remission, and relapse phases remains unclear. Identifying stage-specific network alterations associated with long-term outcomes may help inform future personalized treatment strategies. METHODS:In a longitudinal diffusion MRI study, 95 first-episode MDD patients and 40 healthy controls underwent structural connectome analysis. Graph-theoretical methods were used to characterize rich-club organization and related network properties across disease stages. Associations with clinical symptoms, cognitive function, and relapse risk were systematically evaluated. RESULTS:Rich-club organization was preserved across all groups; however, distinct stage-specific alterations emerged. Acute and relapse phases showed pronounced reductions in rich-club connections strength and density, reflecting impaired hub-to-hub integration. By contrast, remission was characterized by a relative enhancement of rich-club connection strength, exceeding levels observed in healthy controls. Importantly, greater rich-club strength during remission was associated with a lower risk of subsequent relapse and positively correlated with cognitive performance. CONCLUSION:Rich-club connections in MDD follow a dynamic trajectory from disruption to relative reorganization. While acute and relapse phases are marked by network vulnerability, network reconfiguration during remission is associated with functional recovery and a lower risk of relapse. These findings suggest that rich-club connections may represent a neuroimaging-derived correlate of disease course and relapse vulnerability, and highlight hub connectivity as a potential target for future mechanistic and interventional investigations in MDD.

Speech production and perception are the main ways humans communicate daily. Prior brain-to-text decoding studies have largely focused on a single modality and alphabetic languages. Here, we present a unified brain-to-sentence decoding framework for both speech production and perception in Mandarin Chinese. The framework exhibits strong generalization ability, enabling sentence-level decoding when trained only on single-character data and supporting characters and syllables unseen during training. In addition, it allows direct and controlled comparison of neural dynamics across modalities. Mandarin speech is decoded by first classifying syllable components in Hanyu Pinyin, namely initials and finals, from neural signals, followed by a post-trained large language model (LLM) that maps sequences of toneless Pinyin syllables to Chinese sentences. To enhance LLM decoding, we designed a three-stage post-training and two-stage inference framework based on a 7-billion-parameter LLM, achieving overall performance that exceeds larger commercial LLMs with hundreds of billions of parameters or more. In addition, several characteristics were observed in Mandarin speech production and perception: speech production involved neural responses across broader cortical regions than auditory perception; channels responsive to both modalities exhibited similar activity patterns, with speech perception showing a temporal delay relative to production; and decoding performance was broadly comparable across hemispheres. Our work not only establishes the feasibility of a unified decoding framework but also provides insights into the neural characteristics of Mandarin speech production and perception. These advances contribute to brain-to-text decoding in logosyllabic languages and pave the way toward neural language decoding systems supporting multiple modalities.

Objective To evaluate whether quantitative subregional analysis of dual-tracer PET (18F-FDG and 18F-FMZ), integrated with ictal stereoelectroencephalography (SEEG), improves localization of the epileptogenic zone and prediction of postoperative outcomes in drug-resistant epilepsy. Methods We retrospectively included 43 patients with drug-resistant epilepsy who underwent resective surgery. Subject-specific quantitative analysis was used to identify PET abnormalities, which were divided into three mutually exclusive subregions: FDG-only, FMZ-only, and their intersection (AND region). Within the actual SEEG sampling coverage, we assessed the imaging characteristics of each subregion, their spatial correspondence with the ictal epileptogenicity map (EM), and the associations between resection coverage and postoperative Engel outcomes. Results The abnormal burden of the AND region was significantly higher in patients with Engel class I outcomes (P = 0.018). Although all PET subregions showed some spatial convergence with EM, only AND-EM concordance (P = 0.017) and its resection coverage (P = 0.016) were associated with favorable outcomes. EM intensity in the AND region was significantly higher than that in the FMZ-only region (P = 0.0039). The AND-EM fusion model achieved a cross-validated AUC of 0.902 for discriminating postoperative seizure freedom, with a cross-validated specificity of 0.874 at 80% sensitivity. In MRI-negative patients, it improved AUC by 0.22 over unimodal models. Conclusion Quantitative dual-tracer PET subregional analysis combined with ictal SEEG may help refine candidate epileptogenic regions and improve prediction of postoperative seizure freedom.

Chronic sleep deprivation (CSD) impairs hippocampal function and induces learning and memory deficits. Microglia-driven neuroinflammation and ferroptosis are implicated in CSD-associated hippocampal pathology, yet targeted pharmacological interventions remain limited. Here, we evaluated whether Sodium Houttuyfonate (SH) ameliorates CSD-related cognitive impairment and explored the underlying mechanisms. We used network pharmacology to predict SH targets and pathways in CSD, which were validated in CSD mice and in LPS- and Erastin-treated BV-2 microglia. Network pharmacology analysis identified multiple putative key targets shared between SH and CSD, with enrichment predominantly in inflammation-related signaling pathways. In CSD mice, SH improved cognitive performance and attenuated tissue damage in the hippocampal CA1, CA3, and DG regions. Concurrently, it suppressed hippocampal microglial activation, attenuated the inflammatory response, and alleviated CSD-induced ferroptosis-related alterations. In vitro, SH reversed LPS-induced inflammatory responses in BV-2 cells by modulating the SOCS3/STAT3 pathway, and si-SOCS3 treatment significantly diminished these anti-inflammatory effects of SH, confirming that SH's regulation of microglial inflammation is SOCS3-dependent. In an Erastin-induced ferroptosis model in BV-2 cells, SH restored the function of the classical ferroptosis regulators SLC7A11 and GPX4, as well as additional key ferroptosis-related proteins FTH1 and ACSL4, thereby ameliorating the ferroptosis phenotype. In summary, SH ameliorates CSD-associated cognitive impairment and mitigates hippocampal damage. Its mechanism may involve SOCS3/STAT3-mediated anti-inflammatory effects and regulation of multiple ferroptosis-associated proteins, including both classical regulators and additional key proteins, thereby suppressing microglia-mediated neuroinflammation and ferroptosis. This provides experimental support and new research directions for intervention strategies targeting CSD-related cognitive impairment.

To construct and validate an imaging-based predictive model grounded in multiscale progressive structural representation, and to systematically evaluate the incremental diagnostic value from whole-brain macroscopic gray matter volume to hippocampal subfield microstructural texture features in identifying mild cognitive impairment associated with type 2 diabetes mellitus (T2DM-MCI). A total of 280 patients with T2DM who met the diagnostic criteria of the American Diabetes Association were retrospectively enrolled, including 82 patients with T2DM-MCI and 198 cognitively normal individuals. All participants underwent 3.0T structural MRI scanning. Whole-brain gray matter volume (GMV) features were extracted based on the AAL atlas. Bilateral 24 hippocampal subfield volumetric features were segmented using FreeSurfer, and 2,232 hippocampal subfield radiomic features were extracted using PyRadiomics. A nested cross-validation framework was constructed, and stepwise dimensionality reduction was performed using the Mann–Whitney U test, mRMR, and LASSO to develop the GMV model, hippocampal subfield volume model (Hip-Volume), and hippocampal subfield radiomics model (Hip-Radscore), respectively. A combined model integrating clinical variables was further established. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV, with comparisons conducted using the DeLong test. Clinical utility was assessed using NRI, IDI, Brier score, and decision curve analysis (DCA). Model explainability was achieved through SHAP analysis, and Spearman correlation was applied to examine the association between Hip-Radscore and MMSE. The whole-brain GMV model demonstrated limited discriminative performance (AUC = 0.63 ± 0.04). When the analysis scale was focused on hippocampal subfield volumes, model performance improved significantly (AUC = 0.71 ± 0.03, P < 0.05). Under consistent anatomical regions, the hippocampal subfield radiomics model further increased to an AUC of 0.78 ± 0.03 (P = 0.004). The combined model integrating clinical variables and hippocampal subfield radiomic features achieved the best performance (AUC = 0.86 ± 0.02) and showed significantly superior reclassification ability compared with the “clinical + volume” model (NRI = 0.30, IDI = 0.08, both P < 0.001). SHAP analysis identified Hip-Radscore as the most important predictor. Hip-Radscore was significantly negatively correlated with MMSE scores (ρ = −0.58, P < 0.001). Multiscale progressive structural representation significantly improves the identification performance of T2DM-MCI, and hippocampal subfield radiomic features demonstrate greater early sensitivity than traditional volumetric indicators.

BACKGROUND:Clinical outcomes in postpartum depression (PPD) are highly heterogeneous, yet the neurobiological mechanisms underlying recovery remain unclear. Identifying network-based biomarkers that reflect the brain's capacity for adaptive reorganization may improve prognostic accuracy and support individualized treatment. METHODS:We conducted a longitudinal diffusion tensor imaging (DTI) study of 115 PPD patients and 40 healthy postpartum controls. All participants underwent DTI scans and clinical assessments at baseline (Time 1), 3 months (Time 2), and 6 months (Time 3). Based on 6-month outcomes, patients were classified into a recovery group (PPD-R, N = 72) and a non-recovery group (PPD-NR, N = 43). Graph-theoretical analyses assessed network topology across timepoints, focusing on rich-club connections, feeder connections, and local connections, as well as small-world properties. Psychosocial variables including perceived social support were also evaluated. RESULTS:At baseline, both PPD-R and PPD-NR groups showed reduced local connections (F = 13.034, P < 0.001) and small-world properties (F = 17.219, P < 0.001). During follow-up, only the PPD-R group exhibited significant recovery of rich-club connections (ttime1-time2 = -3.900, P < 0.001; ttime1-time3 = -4.325, P < 0.001), which correlated with initial small-world properties (βrich-club connections = 0.456, P < 0.001; β∆rich-club connections = 0.405, P < 0.001) and predicted final depression severity (OR = 0.713 [95%CI: 0.568-0.832], AUC = 0.819, P < 0.001). Perceived social support significantly moderated the relationship between small-world properties and rich-club recovery (βperceived social support-small-world properties = 0.223, P = 0.002). CONCLUSION:Recovery from PPD was associated with selective reorganization of rich-club connections. Efficient small-world topology was related to greater rich-club reorganization, and perceived social support moderated this relationship. These findings suggest that rich-club connections may serve as a potential prognostic marker of recovery in PPD.

Abstract Idiosyncratic brain functional organization shapes intricate cardiac dynamics through central-peripheral autonomic interactions, yet a comprehensive mapping between multi-scale heart rate dynamics and whole-brain functional architecture remains lacking. Here, combining highly comparative time-series analysis with multi-modal neuroimaging and intracranial electrophysiology, we establish heart rate dynamics as a physiological fingerprint that maps onto whole-brain functional architecture. Partial least squares analysis revealed a generalizable latent axis linking reduced heart rate temporal complexity and elevated micro-scale predictability to heightened resting-state functional connectivity across default mode, salience, and sensorimotor networks. This covariance aligns spatially with serotonergic, noradrenergic, and cholinergic neuromodulatory gradients, persists across physiological confound controls and cross-session validations, derives support from human intracranial electrophysiological recordings, and extends to active cognitive states. Furthermore, brain-covarying heart rate signatures underpin the predictive capacity of heart rate dynamics for individual fluid and crystallized intelligence, demonstrating a shared representational substrate. Our findings demonstrate a robust neurovisceral coupling architecture, establishing well-characterized heart rate dynamics as a scalable, neurobiologically anchored window into human brain functional organization and cognitive traits.

Chronic sleep deprivation (CSD) is closely associated with impairments in learning and memory, neuroinflammation, and ferroptosis, potentially increasing vulnerability to harmful environmental exposures. Polystyrene microplastics (PS-MPs) can induce oxidative stress and inflammation, yet their impact on hippocampal pathology and cognition under CSD remains unclear. Here, we established a mouse model combining oral PS-MPs exposure with CSD and assessed cognition using the novel object recognition (NOR) test, open-field test (OFT), Y-maze, and nest-building. CSD markedly promoted PS-MPs deposition in the hippocampus, and PS-MPs further aggravated CSD-induced cognitive deficits, accompanied by more severe neuronal structural damage and loss. PS-MPs also amplified CSD-induced neuroinflammation, increasing IL-6 and TNF-α, decreasing IL-4 and IL-10, and enhancing microglial activation. In BV2 cells, PS-MPs induced dose-dependent inflammatory responses with SOCS3 downregulation and increased p-STAT3. In addition, PS-MPs further elevated hippocampal ROS, MDA, and Fe²⁺ levels, reduced GSH, and aggravated mitochondrial shrinkage and membrane densification. In BV2 cells, PS-MPs also induced ferroptosis in a dose-dependent manner and suppressed SLC7A11/GPX4 expression. In summary, under CSD conditions, PS-MPs accumulate in the hippocampus and promote microglia-mediated neuroinflammation and ferroptosis through SOCS3/STAT3 and SLC7A11/GPX4 signaling, thereby worsening hippocampal injury and cognitive decline.

BACKGROUND:White matter repair after ischemic stroke is critical for long-term recovery. Edaravone dexborneol (EDB) has antioxidative and anti-inflammatory properties, but its role in white matter integrity remains unclear. METHODS:We enrolled 73 patients with first-ever left basal ganglia infarction, including the conventional treatment group (n = 33) and the EDB treatment group (n = 40). In addition, 32 healthy individuals were recruited as a control group. After 3 months, MRI (T1WI and DTI) was performed to assess FA values of 8 major tracts and rich-club network organization. In parallel, middle cerebral artery occlusion (MCAO) was induced in mice. Behavioral tests, histology, immunofluorescence, and Western blot were conducted to evaluate functional recovery, white matter injury, OPC proliferation/differentiation, and Akt/mTOR signaling. Primary OPC cultures were used to validate mechanisms in vitro. RESULTS:Clinically, the conventional group showed reduced FA values and weakened rich-club connectivity, whereas both metrics were significantly improved in the EDB group. In MCAO mice, EDB alleviated brain atrophy and white matter injury, promoted sensorimotor and cognitive recovery, and enhanced OPC proliferation and differentiation via Akt/mTOR activation. In vitro, EDB stimulated OPC proliferation/differentiation, while Akt/mTOR inhibition abolished these effects. CONCLUSION:EDB treatment was associated with improved local tract integrity and global network connectivity in stroke patients. In experimental models, EDB promoted OPC proliferation and differentiation via Akt/mTOR signaling, potentially contributing to remyelination and white matter repair, accompanied by improved long-term functional outcomes. These findings suggest that EDB may represent a promising strategy for post-stroke white matter repair.

High-grade glioma (HGG) exhibits substantial biological heterogeneity, which complicates prognosis and treatment, and clarifying the interplay between proliferating tumor cells and tumor-associated microvasculature may improve patient outcomes. We prospectively enrolled 221 patients with HGG from four institutions and integrated diffusion-weighted and dynamic contrast-enhanced MRI within a supervoxel-based framework to delineate tumor subregions termed density-enhancement compounded voxels (DECV). Four DECV subregions (DECV1-4) with distinct imaging characteristics were identified. DECV4, characterized by low apparent diffusion coefficient and gradual enhancement, was strongly associated with tumor aggressiveness and treatment resistance. Clustering analysis further revealed two DECV phenotypes that differed significantly in progression-free and overall survival; phenotype II showed a higher DECV4 proportion and a poorer prognosis. DECV phenotypes outperformed conventional imaging markers as independent predictors of survival and were validated in an independent cohort. These DECV-based imaging phenotypes provide a robust, non-invasive biomarker for characterizing HGG heterogeneity and show potential to enhance prognostic stratification and guide personalized therapy.

Aβ deposition in the brain does not necessarily lead to cognitive impairment, and that blood supply may have other unexplained regulatory effects on Aβ. Therefore, there appears to be a more complex relationship between blood supply, Aβ deposition, and cognitive impairment that warrants further exploration. This cohort study collected four longitudinal follow-up datasets, including a total of 281 subjects, followed for four years. Three-dimensional time-of-flight angiography and pseudo-continuous arterial spin labeling were used to assess hippocampal vascularization pattern (VP) and hippocampal cerebral blood flow (CBF). 11 C-Pittsburgh compound B (PiB)-PET/CT-based spatial measurements were used detect hippocampal PiB uptake as a reflection of hippocampal Aβ deposition. We explored the relationships between hippocampal blood supply (VP and CBF), hippocampal PiB uptake, and the occurrence of mild cognitive impairment (MCI) using a generalized nonlinear model. We demonstrated the synergistic effect of hippocampal VP and CBF on predicting the occurrence of MCI. We conducted confirmation and quantification of the relationship between hippocampal blood supply and hippocampal PiB uptake. Additionally, the predicted value of PiB uptake based on hippocampal blood supply not only exhibited strong predictive efficacy for the occurrence of MCI (AUC = 0.831, p < 0.001), but was also validated in cerebral small vessel disease cohorts (AUC = 0.792, p < 0.001) and well validated in an independent cohort (Kappa = 0.741, p < 0.001). Overall, we reveal that hippocampal blood supply at baseline can regulate hippocampal PiB uptake, which reflects hippocampal tolerable amount of Aβ deposition and serves as an effective predictor for the occurrence of MCI, providing an important extension on the relationship between hippocampal blood supply and Aβ deposition.

To develop a multimodal magnetic resonance imaging (MRI)-based spatial mapping framework for quantitatively characterizing intratumoral heterogeneity in recurrent glioblastoma (rGBM), identifying distinct imaging subregions, and classifying heterogeneity phenotypes predictive of treatment response and survival outcomes. A total of 140 rGBM patients were recruited and underwent standardized diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Pixel-wise colocalization of apparent diffusion coefficient (ADC) and DCE-MRI features identified four Multimodal Imaging Subregions (MIS). Entropy and Moran’s I quantified heterogeneity, and hierarchical clustering defined imaging phenotypes. Treatment response to 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea (CCNU), bevacizumab (Bev) + stereotactic radiotherapy (SRT), and Bev + CCNU was assessed by volumetric and component-level changes. Survival analyses were performed using Kaplan–Meier and multivariate Cox models. MIS4, defined by low ADC and slow-rising enhancement, was consistently treatment-resistant. Three imaging phenotypes with distinct heterogeneity patterns demonstrated significant prognostic stratification across regimens. Phenotype A showed the best outcomes under Bev-based regimens, while Phenotype B responded better to CCNU. Imaging phenotypes independently predicted progression-free survival (PFS) and overall survival (OS). This framework enables spatially resolved, phenotype-based analysis of rGBM heterogeneity using routine MRI. Imaging phenotypes serve as non-invasive biomarkers to guide personalized treatment planning and outcome prediction in recurrent glioblastoma. Not applicable.

Edaravone dextrose (EDB) is a commonly used clinical treatment for cerebral infarction due to its anti-inflammatory and free radical scavenging properties. However, its potential additional neuroprotective mechanisms need to be further investigated. In this study, we evaluated the effects of EDB on ferroptosis, cuproptosis, and blood-brain barrier (BBB) disruption after cerebral infarction in vivo and in vitro by constructing a mouse middle cerebral artery occlusion (MCAO) model, and an oxygen-glucose deprivation/reperfusion (OGD/R) model of neurons and brain microvascular endothelium. Our results showed that EDB treatment improved neurological impairment and brain histopathology in MCAO mice. EDB treatment significantly alleviated ferroptosis and cuproptosis in MCAO mice and OGD/R models of neuronal, in vitro and in vivo, the protective pathway of ferroptosis SLC7A11/GPX4 was detected to be activated and the cuproptosis-promoting protein SLC31A1 and FDX1 were down-regulated. We also found that EDB treatment ameliorated BBB damage in MCAO mice. The endothelial cell protective pathway PDGFRβ/PI3K/AKT activation was also detected in MCAO mice and OGD/R models of endothelial cells after EDB treatment. In conclusion, our study demonstrated that EDB has a good ameliorating effect on ferroptosis, cuproptosis, and BBB damage after cerebral infarction, which provides more evidence for the clinical application of EDB and provides new theories and directions for the study of the mechanism of EDB.

Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologic features of the lesions and patient prognosis. Materials and Methods This retrospective study included patients with AIS from multiple centers, who experienced stroke onset between September 2008 and October 2022 and underwent MRI as well as thrombolytic therapy and/or treatment with antiplatelets or anticoagulants. A SegResNet-based deep learning model was developed to segment core infarcts and white matter hyperintensity (WMH) burdens on diffusion-weighted and fluid-attenuated inversion recovery images. The model was trained, validated, and tested with manual labels (260, 60, and 40 patients in each dataset, respectively). Radiologic features extracted from the model, including regional infarct size and periventricular and deep WMH volumes and cluster numbers, combined with clinical variables, were used to predict favorable versus unfavorable patient outcomes at 7 days (modified Rankin Scale [mRS] score). Mediation analyses explored associations between radiologic features and AIS outcomes within different treatment groups. Results A total of 1008 patients (mean age, 67.0 years ± 11.8 [SD]; 686 male, 322 female) were included. The training and validation dataset comprised 702 patients with AIS, and the two external testing datasets included 206 and 100 patients, respectively. The prognostic model combining clinical and radiologic features achieved areas under the receiver operating characteristic curve of 0.81 (95% CI: 0.74, 0.88) and 0.77 (95% CI: 0.68, 0.86) for predicting 7-day outcomes in the two external testing datasets, respectively. Mediation analyses revealed that deep WMH in patients treated with thrombolysis had a significant direct effect (17.7%, P = .01) and indirect effect (10.7%, P = .01) on unfavorable outcomes, as indicated by higher mRS scores, which was not observed in patients treated with antiplatelets and/or anticoagulants. Conclusion The proposed deep learning model quantitatively analyzed radiologic features of acute and chronic brain lesions, and the extracted radiologic features combined with clinical variables predicted short-term AIS outcomes. WMH burden, particularly deep WMH, emerged as a risk factor for poor outcomes in patients treated with thrombolysis. Keywords: MR-Diffusion Weighted Imaging, Thrombolysis, Head/Neck, Brain/Brain Stem, Stroke, Outcomes Analysis, Segmentation, Prognosis, Supervised Learning, Convolutional Neural Network (CNN), Support Vector Machines Supplemental material is available for this article. © RSNA, 2025.

Epilepsy is a severe neurological disorder characterized by persistent seizures and, in some patients, associated neurobiological, cognitive, and psychosocial consequences. It is influenced by various genetic factors, including the Ephrin-B2 (EFNB2) gene. This study utilized bidirectional Mendelian randomization (MR) to explore the potential causal relationship between serum levels of EFNB2 and epilepsy using data from extensive genome-wide association studies (GWAS). We selected serum levels of EFNB2 and generalized epilepsy traits, applying strict criteria for instrumental variables to ensure validity and mitigate confounding influences. The analysis included sensitivity tests like the MR pleiotropy residuals and outliers test, as well as co-localization to evaluate shared genetic influences. Our results indicated a significant causal relationship between serum levels of EFNB2 and epilepsy, suggesting that EFNB2 could be involved in the pathogenesis of epilepsy through mechanisms that may not be directly linked to shared genetic pathways. These results suggest a potential association between EFNB2 and epilepsy, highlighting the need for further studies to clarify its role and explore its possible relevance as a therapeutic target.

Chronic cerebral hypoperfusion-induced white matter injury (WMI) is a significant cause of vascular cognitive impairment. Emerging evidence suggests that miR-218 may be involved in the pathogenesis of WMI. However, understanding of the relationship between miR-218 and chronic hypoperfusion-induced WMI remains insufficient. Our study investigated the relationship between miR-218 and chronic hypoperfusion-induced WMI at clinical, animal, and cellular levels. We found that serum miR-218 expression was elevated in clinical WMI patients, had a certain diagnostic efficacy for WMI, and was correlated with the degree of WMI, cognitive scores, and serum inflammatory factors. In addition, we constructed a mouse model with bilateral carotid artery stenosis (BCAS) to simulate chronic hypoperfusion-induced WMI and detected an increase in miR-218 expression in the white matter of BCAS mice. Following administration of Lv-sh-miR-218 to the white matter of BCAS mice, improvements were observed in both cognitive impairment and WMI. Furthermore, Lv-sh-miR-218 also reduced M1 polarization of microglia and neuroinflammation within the white matter. Subsequently, we confirmed that SOCS3 is the specific target of miR-218 through bioinformatics analysis and luciferase reporter gene assays. Injection of LV-SOCS3 into the white matter also led to improvements in cognitive impairment and WMI in BCAS mice, along with reduced M1 polarization of microglia and neuroinflammation. Moreover, in primary cultured microglia cells, we demonstrated that after chronic hypoxia, miR-218 regulates inflammatory factors through the SOCS3/STAT3 pathway. In summary, our current results indicate a strong correlation between elevated miR-218 levels and chronic hypoperfusion-induced WMI, and downregulation of miR-218 expression can improve neuroinflammation by upregulating SOCS3, thereby ameliorating WMI and cognitive impairment.

The primary method for assessing the depth of anesthesia during clinical surgeries currently relies on physiological and behavioral cues, such as heart rate, blood pressure, and reaction to external stimuli. These measures might not be dependable because their relationship with patients' levels of awareness is not firmly established. Here, we present a neuromorphic framework comprising a spiking neural network (SNN) with simulated neurons to access conscious states during general anesthesia, such as wakefulness and anesthesia, from intracranial electroencephalography (iEEG) and electroencephalogram (EEG) signals. This framework adjusts synapse weight of each neuron utilizing spiking‐timing‐dependent plasticity (STDP) rules. Our analysis revealed that the proposed neuromorphic approach can access states of consciousness during anesthesia and characterize the transition from wakefulness to anesthetic‐induced unconsciousness. We further implemented this framework on the field programmable gate array platform to enhance execution efficiency and address the clinical requirements for real‐time monitoring of anesthesia states. Importantly, this study signifies an initial viable investigation using neuromorphic computing techniques for evaluating patients' levels of anesthesia based on iEEG signals.

This study aims to examine whether neuro-metabolic lateralization of the cortico-striatal-cerebellar (CSC) circuit is associated with sex-specific mechanisms underlying episode types in bipolar I disorder (BD-I). A total of 109 patients in the acute phase of BD-I were included in the analysis. For comparisons of general clinical characteristics, CSC circuit structural volumes, metabolic features, and laterality indices (LI), analyses were conducted between the male group (n = 49) and the female group (n = 60). For predictive modeling, sex-stratified XGBoost models were built separately: the male cohort (n = 49) was subdivided into mania (n = 31) and depression (n = 18), and the female cohort (n = 60) was subdivided into mania (n = 17) and depression (n = 43). Structural MRI and MRS were utilized to obtain volumetric and metabolic features within the CSC circuit. Key sex-related features were identified through inter-group consistency analysis, Mann-Whitney U tests, and LASSO regression, followed by the calculation of laterality indices (LI). Sex-stratified XGBoost models were constructed to evaluate the predictive performance of CSC circuit features and LI for symptom onset, with SHAP analysis used to determine feature contributions. LI analysis demonstrated that males had significantly higher LI for ACC-sV, ACC-NAA, BG-Cho, and ACC-Glx (all P < 0.007), whereas females exhibited significantly higher LI for BG-sV (P < 0.007). In the sex-stratified XGBoost models, the LI-based models significantly outperformed the absolute-value-based models in predicting manic episodes in males (AUC = 0.803) and depressive episodes in females (AUC = 0.824) (Delong test: P < 0.05). However, the cross-sex prediction performance remained limited. Additionally, SHAP analysis indicated that ACC-Glx LI contributed most to manic episode prediction in males, while ACC-NAA LI and BG-Cho LI had the highest contributions to depressive episode prediction in females. In patients with BD-I, significant sex differences are evident in the structural and metabolic characteristics of the CSC circuit. Neuro-metabolic lateralization alterations in the ACC and BG regions may be involved in sex-specific mechanisms underlying different symptom onset types. Not applicable.

N6-methyladenosine (m6A) methylation is an essential epigenetic modification that regulates mRNA stability, splicing, and translation. Its role in neurological diseases, including epilepsy, ischemic stroke, and vascular dementia (VaD), remains poorly understood. We integrated multi-omics data, including GWAS, m6A quantitative trait loci (QTL), expression QTL (eQTL), and protein QTL (pQTL), and using FUSION to assess the association of m6A with these diseases. Transcriptome-wide association studies (TWAS) and Mendelian Randomization (MR) were performed to identify causal relationships between m6A sites, gene expression, and disease. Differentially expressed genes (DEGs) were analyzed via RNA sequencing and enriched for biological pathways. Protein-protein interaction (PPI) networks and m6A-related gene-disease associations were constructed to reveal regulatory mechanisms. We identified 218 m6A sites significantly associated with the three diseases, highlighting 3,430 associations between m6A sites and gene expression. Functional enrichment analysis revealed key pathways, including base excision repair and chemokine-mediated signaling. MR analysis identified causal relationships, such as NBL1 in epilepsy, TPGS2 in ischemic stroke, and SERINC2 in VaD. PPI analysis revealed interactions involving critical proteins like PARP1, MCL1, and CD40, underscoring their role in neuroinflammation and apoptosis. Our findings elucidate the genetic and epigenetic roles of m6A in epilepsy, ischemic stroke, and VaD, uncovering potential mechanisms by which m6A modulates gene and protein expression to influence disease outcomes. These insights highlight m6A as a promising biomarker and therapeutic target for neurological diseases.