Purpose:Nontraumatic subarachnoid hemorrhage (ntSAH) is associated with high in-hospital mortality, and current prognostic models often overlook systemic hemostatic disturbances. We aimed to develop a coagulation-platelet index (INR_PLT) for mortality risk, quantify its nonlinear threshold, and construct an externally validated risk model using stacking ensemble learning. Patients and Methods:This retrospective study included ntSAH patients from Huizhou Central People's Hospital (n = 287, training cohort) and the MIMIC-IV (n = 621, external validation). INR_PLT was derived by logistic regression: -1.596 + 1.364×INR - 0.008×PLT. Nonlinear relationships and thresholds were identified using restricted cubic splines and segmented regression. Nomograms and machine learning models (LR, SVM, DT, and LGBM) were developed, with a stacking ensemble as the final model. The performance of the models was evaluated by receiver operating characteristic (ROC) curves, calibration plots, and SHAP. Results:Of the 908 patients, 163 (17.95%) died. The independent predictors included age, GCS score, nimodipine use, and the INR_PLT (OR 2.425). INR_PLT was significantly correlated with antiplatelet therapy (P = 0.006) and mediated 19.8% of diabetes-associated mortality risk. A threshold at INR_PLT = -2.457 markedly increased mortality risk (OR 2.527, P = 0.002). The nomogram (C-index 0.847) and stacking model (AUC 0.80, F1 = 0.763) demonstrated strong performance. Conclusion:The INR_PLT is a bedside index for identifying a significant mortality threshold in patients with ntSAH, supporting precise risk stratification with external validation.

Mild cognitive impairment (MCI) is a clinically heterogeneous prodromal stage of Alzheimer's disease (AD) in which accurate risk stratification could support earlier intervention and more efficient trial design. Using baseline T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative and the National Alzheimer's Coordinating Center cohorts, we developed a dual-space anatomical radiomic-network signature that integrates regional radiomic features and radiomics similarity network metrics extracted in standard and native spaces. The signature improved prediction of MCI-to-AD conversion relative to regional volumetric and clinical models, achieved consistent external validation performance, and separated participants into high- and low-risk groups. The same feature set identified five imaging-defined MCI subtypes with distinct anatomical patterns, conversion risks, and clinical, APOE ε4, and cerebrospinal fluid biomarker profiles; fronto-parietal and parahippocampal-temporal subtypes showed the highest risk. These findings support structural MRI radiomic-network profiling as a complementary framework for individualized prognosis and cohort stratification in AD research.

Genes impacting the bioaccumulation of perfluoroalkyl and polyfluoroalkyl substances (PFASs)and their neurotoxic effects on the brain and behavior remain unclear. Here,we examined genome-wide associations with serum accumulation of 13 PFASs in 6,823 Chinese adults. We revealed that perfluoroheptanoic acid (PFHpA) accumulation was associated with genetic variants at two loci (3q29: P = 5.20 ×10-19; 6p22.2: P = 3.69 ×10-23), mapping to 56 genes.Blood expression of 27 of these genes was associated with PFHpA accumulation in 573 subsamples. Eight genes showed potential causal effects on PFHpA accumulation,functionally linked to innate immunity (TRIM38, ZDHHC19, MUC20)and organic solute transport (SLC51A and SLC17A3). We assessed the impact of PFASs on cortical thickness and surface area, white matter fractional anisotropy and mean diffusivity,along with 25 behavioral phenotypes. We identified that seven PFASs were correlated with reduced cortical morphology, primarily in the prefrontal cortex. We also found a statistical causal effect of PFHpA accumulation on the surface area in the right frontomarginal cortex, which mediated the effect of PFHpA on anxiety. These findings indicate that serum PFHpA accumulation may be regulated by genes related to innate immunity and solute transport, heightening anxiety by impairing the prefrontal cortex.

Maternal smoking during pregnancy (MSDP) has been linked to adverse effects on offspring brain health. However, the underlying mechanisms remain unclear. Given that offspring exposed to MSDP exhibit shortened leukocyte telomere length (LTL)—a potential biomarker of accelerated biological ageing—we hypothesized that LTL may mediate the association between MSDP and long-term brain health consequences in adult offspring. Using data from the UK Biobank cohort, we performed association analyses to assess the effects of MSDP (almost 100 000 patients) on LTL, 363 imaging derived phenotypes (IDPs) from structural MRI, 11 kinds of cognitive performances, and 4 mental health outcomes in adult offspring. Mediation analysis was further conducted to evaluate whether LTL mediates the relationship between MSDP and these brain health outcomes. Offspring exposed to MSDP exhibited significantly shorter LTL, greater atrophy in 30 IDPs, poorer performance on 3 cognitive tests, and higher scores on 4 mental health questionnaires compared to unexposed offspring. Mediation analysis revealed that shortened LTL partially mediated the association between MSDP and atrophy in 11 IDPs—particularly in the hippocampus and its subregions—as well as reduced fluid intelligence performance and increased depressive symptoms. In conclusion, this study demonstrates that MSDP may contribute to long-term adverse brain health outcomes in adult offspring. Crucially, we identify accelerated telomere shortening as a partial mediator reflecting cumulative aging that partially explains the link between MSDP and specific adverse outcomes in adult offspring, particularly hippocampal atrophy, declines in fluid intelligence, and increased depressive symptoms. These results underscore the importance of reducing prenatal tobacco exposure to improve population-level brain health.

Meningeal lymphatic vessels (mLVs) are critical for central nervous system waste clearance and immunomodulation， implicated in multiple sclerosis pathogenesis. Although ofatumumab is effective in treating relapsing-remitting MS (RRMS), its effects on mLVs function remain unclear. This study aimed to investigate its impact on mLVs drainage in RRMS patients. Fifteen RRMS patients were enrolled in this prospective cohort study with ofatumumab, and nine healthy controls were recruited. Drainage function of mLVs was quantified via dynamic contrast-enhanced magnetic resonance imaging at baseline, month 6 and month 12. Correlations between changes in drainage function and immune cell subsets were analyzed. Following ofatumumab treatment, significant reductions were observed in mean time-to-peak (0.75 to 0.52, p = 0.007) and area under the curve (AUC, 30.70 to 12.67, p = 0.001) within mLVs. Baseline mLVs parameters did not differ between RRMS patients and healthy controls. Change of AUC (ΔAUC) was positively associated with regulatory T cell frequency (ΔTreg, r = 0.539, p = 0.038) and negatively associated with central memory CD4+ T cells (ΔTCM, r = -0.535, p = 0.040). Symbol Digit Modalities Test scores were negatively associated with TTP at baseline (r = -0.535, p = 0.040), whereas no correlations were observed between changes in mLVs parameters and clinical outcomes (all p > 0.05). Ofatumumab improved mLVs drainage in RRMS and was associated with a shift toward regulatory T cell phenotype. These findings suggest that modulation of meningeal lymphatics may contribute to the immunomodulatory effects of anti-CD20 therapy.

Structural MRI analysis for Alzheimer’s disease (AD) is limited by balancing group-level comparability in standard space with anatomical fidelity in native space. We therefore propose a multi-space, hybrid-feature framework, integrating radiomics and network metrics from both spaces to classify AD and predict mild cognitive impairment (MCI) progression. An integrated dual-space analytical framework was applied to T1-weighted MRI data. Models were developed on 1,477 participants from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and externally tested on an independent cohort of 1,349 participants from National Alzheimer’s Coordinating Center (NACC). The framework extracts parallel radiomic and graph-based network features from both Montreal Neurological Institute (MNI) standard space and native space. These features were used to build machine learning models for three-class diagnosis (NC vs. MCI vs. AD) and 6-year prognostic prediction of MCI-to-AD conversion. For each task, the models using standard-space, native-space, and combined-space features were systematically compared. Model interpretation was performed using Shapley Additive Explanations (SHAP), and the features were validated against established AD biomarkers. The combined-space model demonstrated superior performance in both diagnostic classification (Macro-Averaged AUC: 0.96 in ADNI cohort, 0.94 in NACC cohort) and prognostic prediction of MCI-to-AD conversion (C-index: 0.83; HRs: 7.60, 95

Background Non-traumatic subarachnoid hemorrhage (ntSAH) has high mortality, but coagulopathy’s role in non-aneurysmal subtypes is poorly understood. Current models lack coagulation-platelet dynamics, nonlinear threshold effects, and multicenter validation. We aimed to establish a composite coagulation-platelet index (INR_PLT), identify its nonlinear threshold with mortality, and improve risk stratification using a Stacking machine learning model. Methods This retrospective multicenter study analyzed ntSAH patients from Huizhou Central Hospital (n = 287, training) and MIMIC-IV (n = 621, validation). After multiple imputation, LASSO regression selected predictors. INR_PLT was defined via multivariable logistic regression: -1.596 + 1.364×INR − 0.008×PLT. Restricted cubic splines (RCS) explored nonlinearity and thresholds. A nomogram was constructed. Machine learning models (LR, SVM, DT, LGBM) and a Stacking ensemble were developed, evaluated by ROC, calibration, decision curve analyses, and SHAP. Results Among 908 patients, 163 (17.95%) died. Independent predictors included age (OR 1.056), male sex (OR 0.386), GCS (OR 0.742), nimodipine (OR 0.301), and INR_PLT (OR 2.425). INR_PLT’s effect was stronger without antiplatelet therapy (interaction P = 0.006). INR_PLT mediated 19.8% of diabetes’ mortality risk (β = 0.224, P < 0.001). RCS revealed a nonlinear INR_PLT threshold (-2.457); mortality risk increased significantly above this point (OR = 1.527, P = 0.002). The nomogram (C-index 0.847/0.860) and Stacking model (AUC = 0.80, F1 = 0.763) showed strong performance. Conclusion INR_PLT is an independent predictor of ntSAH mortality with a nonlinear threshold effect and mediates diabetes-related risk. The Stacking model and nomogram provide robust risk stratification, highlighting INR_PLT and nimodipine as key decision features.

Time-dependent diffusion MRI (td-dMRI) has potential in characterizing microstructural features; however, its value in imaging endometrioid endometrial adenocarcinoma (EEA) remains uncertain. Patients surgically confirmed with EEA were finally enrolled in our study. The td-dMRI data were acquired using pulsed gradient spin echo sequence and oscillating gradient spin echo sequences. The microstructural markers, including cell diameter, intracellular volume fraction (Vin), cellularity, and extracellular diffusivity (Dex), were fitted with the imaging microstructural parameters using a limited spectrally edited diffusion (IMPULSED) model. The parameters were compared between low- and high-risk groups and between low- and high-proliferation groups. The diagnostic performance was evaluated using receiver-operating characteristic curve and logistic regression analysis. Diameter, Dex, ADCPGSE, ADCN1, and ADCN2 were significantly low, whereas cellularity, ΔADC1 and ΔADC2 were significantly high in the high-risk and high-proliferation groups. Cellularity, ΔADC1, and ΔADC2 demonstrated excellent diagnostic efficacy in predicting both risk stratification and proliferation status. Cellularity was the only independent predictor for risk stratification, which exhibited a satisfactory positive correlation with cell density in histopathologic examination. The diagnostic potential of td-dMRI-based microstructural mapping was demonstrated to noninvasively probe the pathologic characteristics of patients with EEA in a clinical setting, which provided a valuable contribution to surgical guidance.

BACKGROUND:The glymphatic system (GS) plays a central role in eliminating metabolic waste from the human brain. Diffusion tensor image analysis along the perivascular space (ALPS) has emerged as a noninvasive biomarker for evaluating GS function. While decreased ALPS is consistently linked to impaired GS in various central nervous system pathologies, the genetic architectures and neural mechanisms underlying ALPS and its role in maintaining brain health remain unknown. METHODS:A genome-wide association study (GWAS) of ALPS was conducted in 31,579 participants from the UK Biobank. Genetic associations were identified using positional, expression quantitative trait loci, and chromatin mapping strategies. Gene-set enrichment analysis and Mendelian randomization (MR) were performed to characterize biological pathways and causal relationships between ALPS, brain phenotypes, and neurological disorders. RESULTS:The GWAS identified 6 unique loci and 175 genes associated with ALPS. Gene enrichment analyses identified significant associations with brain morphogenesis, along with implications for GS function and neurodegenerative pathways. Genetic and individual-level correlations linked ALPS to brain volume, cerebrospinal fluid-related imaging phenotypes, and cognitive metrics. MR demonstrated that genetically predicted lower ALPS increased the risk of multiple sclerosis and Alzheimer's disease. CONCLUSIONS:This study elucidates the genetic architecture of ALPS, a biomarker that reflects GS function, and its association with brain health. The findings highlight decreased ALPS as a potential risk factor for neuroinflammatory and neurodegenerative disorders, emphasizing the importance of GS integrity in maintaining neurological health.

BACKGROUND:Neurodegenerative diseases (NDs) lead to a progressive loss of neuronal cells and link to atrophy of subcortical brain structures, but the causal intermediates are not known. To test whether major NDs (Alzheimer's disease (AD), Parkinson's disease, multiple sclerosis, and amyotrophic lateral sclerosis) causally affects subcortical atrophy, and whether serum vitamin level play a mediating role in this process. METHODS:Using large-scale genome-wide association study (GWAS) summary data, we performed two-sample Mendelian randomization (MR) to assess the causal effect of NDs on the volume of seven subcortical structures, and then adopted two-step multivariable MR approach to quantify the proportion of the effect of NDs on the volume of subcortical regions mediated by serum vitamin level. Finally, we utilized animal experiments to validate results and explored the potential molecular mechanisms. RESULTS:Genetically predicted AD was associated with atrophy of the nucleus accumbens (NAc) (β = -0.09; p = 5.13 × 10-5), amygdala (β = -0.07; p = 8.44 × 10-4), and hippocampus (β = -0.07; p = 0.001), as well as with low serum vitamin D level (β = -0.02; p = 6.84 × 10-6). Specifically, decreased serum vitamin D level mediated 3.99 % (95 % CI: -0.006 to -5.82 × 10-5) and 3.97 % (95 % CI: -0.007 to -2.94 × 10-4) of the total effect of AD on hippocampal and NAc atrophy, respectively. Animal experiments further confirmed significant delays in hippocampal and NAc atrophy, a significant reduction of β-amyloid deposits and an increase of vitamin D receptor expression in hippocampus in AD mice with high-dose vitamin D diet. CONCLUSIONS:These findings provide important insights into the effect sizes of vitamin D-mediated roles in AD and atrophy of subcortical structures. Interventions to increase serum vitamin D levels at a population level might attenuate damage to hippocampus in patients with AD.

Kidney disease is currently prevalent worldwide but only shows insidious symptoms in the early stages. The second near-infrared window (NIR-II) fluorescence imaging has become a widely used preclinical technology for evaluating renal dysfunction due to its high resolution and sensitivity. However, bright renal clearable NIR-II fluorescence nanoprobes with a simple synthesis process are still lacking. Herein, we develop a lactoglobulin (LG)@dye nanoprobe for NIR-II fluorescence imaging of kidney dysfunction in vivo based on a purification-free method. The nanoprobe was synthesized by simply mixing LG and IR820 in aqueous solutions at 70 °C for 2 h based on the covalent interaction between the meso-Cl in IR820 and LG. The synthesized LG@IR820 nanoprobe has bright and stable NIR-II fluorescence, ultra-small size (<5 nm), low toxicity, and renal-clearable ability. The high reaction efficiency and pure aqueous reaction media make the synthesis method purification-free. In a unilateral ureteral obstruction mouse model, incipient renal dysfunction assessment was achieved by LG@IR820 nanoprobe, which couldn’t be diagnosed with conventional kidney function indicators. This study provides a bright and purification-free NIR-II LG@IR820 nanoprobe to visualize kidney dysfunction at the early stage.

Previous observational investigations suggest that structural and diffusion imaging-derived phenotypes (IDPs) are associated with major neurodegenerative diseases; however, whether these associations are causal remains largely uncertain. Herein we conducted bidirectional two-sample Mendelian randomization analyses to infer the causal relationships between structural and diffusion IDPs and major neurodegenerative diseases using common genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables. Summary statistics of genome-wide association study (GWAS) for structural and diffusion IDPs were obtained from 33,224 individuals in the UK Biobank cohort. Summary statistics of GWAS for seven major neurodegenerative diseases were obtained from the largest GWAS for each disease to date. The forward MR analyses identified significant or suggestively statistical causal effects of genetically predicted three structural IDPs on Alzheimer’s disease (AD), frontotemporal dementia (FTD), and multiple sclerosis. For example, the reduction in the surface area of the left superior temporal gyrus was associated with a higher risk of AD. The reverse MR analyses identified significantly or suggestively statistical causal effects of genetically predicted AD, Lewy body dementia (LBD), and FTD on nine structural and diffusion IDPs. For example, LBD was associated with increased mean diffusivity in the right superior longitudinal fasciculus and AD was associated with decreased gray matter volume in the right ventral striatum. Our findings might contribute to shedding light on the prediction and therapeutic intervention for the major neurodegenerative diseases at the neuroimaging level.

Prevention of Alzheimer's disease (AD) is a global imperative, but reliable early interventions are currently lacking. Microglia-mediated chronic neuroinflammation is thought to occur in the early stage of AD and plays a critical role in AD pathogenesis. Here, oxytocin (OT)-loaded angiopep-2-modified chitosan nanogels (AOC NGs) were designed for early treatment of AD via inhibiting innate inflammatory response. Through the effective transcytosis of angiopep-2, AOC NGs were driven intravenously to cross the blood-brain barrier, enter the brain, and enrich in brain areas affected by AD. A large amount of OT was then released and specifically bound to the pathological upregulated OT receptor, thus effectively inhibiting microglial activation and reducing inflammatory cytokine levels through blocking the ERK/p38 MAPK and COX-2/iNOS NF-κB signaling pathways. Consecutive weekly intravenous administration of AOC NGs into 12-week-old young APP/PS1 mice, representing the early stage of AD, remarkably slowed the progression of Aβ deposition and neuronal apoptosis in the APP/PS1 mice as they aged and ultimately prevented cognitive impairment and delayed hippocampal atrophy. Together, the findings suggest that AOC NGs, which show good biosafety, can serve as a promising therapeutic candidate to combat neuroinflammation for early prevention of AD.

A few observational neuroimaging investigations have reported subcortical structural changes in the individuals who recovered from the coronavirus disease-2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the causal relationships between COVID-19 and longitudinal changes of subcortical structures remain unclear. We performed two-sample Mendelian randomization (MR) analyses to estimate putative causal relationships between three COVID-19 phenotypes (susceptibility, hospitalization, and severity) and longitudinal volumetric changes of seven subcortical structures derived from MRI. Our findings demonstrated that genetic liability to SARS-CoV-2 infection had a great long-term impact on the volumetric reduction of subcortical structures, especially caudate. Our investigation may contribute in part to the understanding of the neural mechanisms underlying COVID-19-related neurological and neuropsychiatric sequelae.

Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.

The UK Biobank (UKB) has the largest adult brain imaging dataset, which encompasses over 40,000 participants. A significant number of Mendelian randomization (MR) studies based on UKB neuroimaging data have been published to validate potential causal relationships identified in observational studies. Relevant articles published before December 2023 were identified following the PRISMA protocol. Included studies (n = 34) revealed that there were causal relationships between various lifestyles, diseases, biomarkers, and brain image-derived phenotypes (BIDPs). In terms of lifestyle habits and environmental factors, there were causal relationships between alcohol consumption, tea intake, coffee consumption, smoking, educational attainment, and certain BIDPs. Additionally, some BIDPs could serve as mediators between leisure/physical inactivity and major depressive disorder. Regarding diseases, BIDPs have been found to have causal relationships not only with Alzheimer’s disease, stroke, psychiatric disorders, and migraine, but also with cardiovascular diseases, diabetes, poor oral health, osteoporosis, and ankle sprain. In addition, there were causal relationships between certain biological markers and BIDPs, such as blood pressure, LDL-C, IL-6, telomere length, and more.

Background: The impediment to beta-amyloid (A beta ) clearance caused by the invalid intracranial lymphatic drainage in Alzheimer's disease is pivotal to its pathogenesis, and finding reliable clinical available solutions to address this challenge remains elusive. Methods: The potential role and underlying mechanisms of intranasal oxytocin administration, an approved clinical intervention, in improving intracranial lymphatic drainage in middle-old-aged APP/PS1 mice were investigated by live mouse imaging, ASL/CEST-MRI scanning, in vivo two-photon imaging, immunofluorescence staining, ELISA, RT-qPCR, Western blotting, RNA-seq analysis, and cognitive behavioral tests. Results: Benefiting from multifaceted modulation of cerebral hemodynamics, aquaporin-4 polarization, meningeal lymphangiogenesis and transcriptional profiles, oxytocin administration normalized the structure and function of both the glymphatic and meningeal lymphatic systems severely impaired in middle-old-aged APP/PS1 mice. Consequently, this intervention facilitated the efficient drainage of A beta from the brain parenchyma to the cerebrospinal fluid and then to the deep cervical lymph nodes for efficient clearance, as well as improvements in cognitive deficits. Conclusion: This work broadens the underlying neuroprotective mechanisms and clinical applications of oxytocin medication, showcasing its promising therapeutic prospects in central nervous system diseases with intracranial lymphatic dysfunction.

To develop and validate a prediction model based on imaging data for the prognosis of mild chronic subdural hematoma undergoing atorvastatin treatment. We developed the prediction model utilizing data from patients diagnosed with CSDH between February 2019 and November 2021. Demographic characteristics, medical history, and hematoma characteristics in non-contrast computed tomography (NCCT) were extracted upon admission to the hospital. To reduce data dimensionality, a backward stepwise regression model was implemented to build a prognostic prediction model. We calculated the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model by a tenfold cross-validation procedure. Maximum thickness, volume, mean density, morphology, and kurtosis of the hematoma were identified as the most significant predictors of good hematoma dissolution in mild CSDH patients undergoing atorvastatin treatment. The prediction model exhibited good discrimination, with an area under the curve (AUC) of 0.82 (95