Multi-task DL for predicting EGFR mutation status Epidermal growth factor receptor (EGFR) mutation status is a critical biomarker in the management of non-small cell lung cancer (NSCLC), playing an essential role in selecting patients for EGFR-targeted treatment. With advancements in deep learning (DL), there is a growing interest in developing non-invasive methods for predicting EGFR mutation status. In this study, we present a multi-task deep learning (MTDL) model that utilizes CT images to predict EGFR mutation status (ChiCTR2400083082 in the WHO International Clinical Trials Registry). Our MTDL model achieved promising performance in accurately predicting EGFR mutation status. Additionally, the MTDL score was significantly associated with survival in patients receiving EGFR-targeted treatment, as well as relevant gene expression patterns and tumor microenvironment. These findings suggest that our method has the potential to serve as an accurate and non-invasive biomarker for predicting EGFR mutation status, thereby facilitating personalized treatment decisions for NSCLC patients.

Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

BACKGROUND:Prior neuroimaging studies and meta-analyses investigating brain correlates of placebo analgesia (PA) have yielded neuroanatomically heterogeneous findings, which may be reconciled from a connectomics perspective. The objective of this study was to examine network localization of brain functional alterations related to PA. METHODS:We initially identified PA-induced brain activation alterations (hyper-activation and hypo-activation separately) during experimental pain from 29 published studies with 674 individuals. By combining these implicated dysfunctional brain regions with large-scale discovery (N = 1113) and validation (N = 1093) resting-state functional magnetic resonance imaging datasets, we then employed a novel functional connectivity network mapping approach to construct PA hyper-activation and hypo-activation networks, respectively. RESULTS:The PA hyper-activation network manifested as a pattern of circumscribed brain regions mainly involving the limbic, default, and frontoparietal networks. By contrast, the PA hypo-activation network comprised a broadly distributed set of brain regions primarily implicating the ventral attention, somatomotor, and subcortical networks. CONCLUSIONS:Our findings regarding the brain network representations of PA may contribute to a deeper understanding of its action mechanisms and provide a neural framework that may inform future clinical translation.

Purpose To evaluate the diagnostic performance of endogenous T1ρ mapping, compared with native T1 and T2 mapping, for identifying late gadolinium enhancement (LGE)-defined peri-infarct zones in acute ST-elevation myocardial infarction (STEMI) and chronic myocardial infarction (MI). Methods This retrospective study included 35 patients with acute STEMI, 21 with chronic MI, and 35 healthy controls who underwent cardiac MRI with T1ρ, native T1, and T2 mapping between March 2022 and August 2025. LGE was used to delineate peri-infarct zones in patients with MI. A linear mixed-effects model accounting for within-participant clustering compared T1ρ values across myocardial segments. Receiver operating characteristic analysis was used to assess diagnostic performance. Results Among the 56 patients with MI (mean age, 57 ± 13 years; 49 men), global T1ρ values were elevated in both acute STEMI and chronic MI compared with controls (both p < 0.001). T1ρ values were higher in peri-infarct zones than in control myocardium (all p < 0.05). For discriminating peri-infarct zones from control myocardium in acute STEMI, T1ρ (AUC, 0.95) performed comparably to T2 (AUC, 0.97; p = 0.11) and better than native T1 (AUC, 0.88; p = 0.02). In chronic MI, T1ρ (AUC, 0.91) outperformed native T1 (AUC, 0.81; p = 0.04) and T2 (AUC, 0.68; p < 0.001). Conclusion Endogenous T1ρ mapping may provide a contrast-free biomarker for myocardial tissue characterization in MI, with diagnostic performance comparable to T2 in acute STEMI and superior to native T1 and T2 in chronic MI.

BACKGROUND AND HYPOTHESIS:Visual hallucinations (VH), a key symptom in neurodegenerative and psychiatric disorders, are associated with a more severe psychopathological profile and less favorable outcome. Neuroimaging research has revealed widespread brain regions involved in VH, echoing the updated notion that neuropsychiatric symptoms correspond more closely to interconnected brain networks than to single brain regions. However, there is still a dearth of studies examining brain network localization of VH. STUDY DESIGN:We initially identified brain structural and functional alterations specific to VH from 21 published neuroimaging studies with 418 VH and 522 non-VH individuals. By applying novel functional connectivity network mapping to large-scale discovery (n = 1113) and validation (n = 1093) resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 2 specific networks. STUDY RESULTS:The VH structural alteration network comprised a broadly distributed set of brain regions principally implicating the frontoparietal and dorsal attention networks. The VH functional alteration network also consisted of widely distributed brain areas predominantly involving the ventral attention and frontoparietal networks. CONCLUSIONS:Our findings may not only draw a more refined picture of the neurobiology of VH from a network perspective, but also potentially contribute to more targeted and effective treatment for VH.

Cortical morphological alteration patterns differ between adolescent and adult psychiatric disorders. However, the biological factors contributing to the divergence are unclear. Cortical thickness (CT) alterations in adolescents and adults with attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), major depressive disorder (MDD), and obsessive-compulsive disorder (OCD) were derived from the ENIGMA. We examined whether the structural connectome constrains disease-related CT alterations, followed by identifying likely epicenter regions and testing the hub vulnerability hypothesis. Using neurotransmitter, transcriptome, and mitochondria atlases, we furthermore investigated the neurochemical basis, genetic architecture, and molecular energetic landscape related to the CT alterations. Results showed that the structural connectome constrained CT alterations in adult psychiatric disorders rather than their adolescent counterparts. The epicenters were largely consistent in adolescents and adults for ADHD and MDD, while divergent for BD and OCD. The demonstration of CT alterations in adolescent BD, adult BD, and adult OCD as a function of connectome degree centrality was consistent with the hub vulnerability hypothesis. We also found distinct neurotransmitter systems linked to CT alterations in psychiatric adolescents and adults. Transcriptomic contextualization showed that CT alterations in adult ADHD, adult MDD, and adolescent OCD were related to genes involving essential components of the cerebral cortex, signal pathway, and nervous system development, while those in adolescent and adult BD to synapse and catabolic process. Additionally, mitochondrial features were associated with CT alterations in almost all conditions. Our findings may elucidate the biological factors associated with the differential cortical abnormalities between psychiatric adolescents and adults.

HER2 expression status reflects the heterogeneity of breast cancer and is closely associated with variations in the tumor microenvironment. Noninvasive imaging approaches capable of capturing this spatial heterogeneity may improve subtype stratification in HER2-negative breast cancer. The aim of this study was to develop a global tumor habitat model combining habitat signatures derived from multiparametric MRI (mpMRI) with intratumoral and peritumoral radiomic features, and to evaluate its feasibility for predicting subtypes of HER2-negative breast cancer. In this multicenter retrospective analysis, 432 patients diagnosed with breast cancer were divided into training (n = 259), validation (n = 112), and test (n = 61) cohorts. Each voxel within the annotated region of interest (ROI) from both dynamic contrast-enhanced (DCE) and T2-weighted image (T2WI) sequences was characterized using a set of localized features. Voxel-wise feature vectors were subsequently clustered via the K-means algorithm to partition tumor ROIs into morphologically distinct subregions. Peritumoral regions were generated by radial expansion of the original ROI by 3 and 5 mm. Independent machine learning models were developed for intratumoral radiomics, peritumoral (PeriXmm), habitat (Habitat, HabitatT2, HabitatDCE), and clinical signatures. A combined predictive model integrating the optimal peritumoral features, habitat-derived signatures, and clinical parameters was constructed. Compared with the intratumoral radiomics model, the habitat model demonstrated superior predictive performance across all cohorts, with area under the ROC curve (AUC) values of 0.890, 0.841, and 0.820 in the training, validation, and test cohorts, respectively, versus 0.839, 0.723, and 0.639 for the intratumoral model. The Peri3mm model provided a more reliable representation of the peritumoral microenvironment than the Peri5mm model across external cohorts (AUC: 0.749 vs. 0.735). The combined model achieved the highest predictive overall performance, with AUCs of 0.906, 0.899, and 0.824. The combined intratumoral-peritumoral habitat-based model demonstrated the most robust and generalizable performance in the accurate and noninvasive prediction of HER2-negative breast cancer subtypes across multicenter cohorts.

Elucidating how resting-state functional connectivity relates to task-evoked neural activation is an important topic in systems neuroscience. We analyzed task-based and resting-state functional magnetic resonance imaging data from 1,005 participants from the Human Connectome Project. On the basis of connectome-constrained predictive modeling, we calculated a neural activation constraint index (NACI) to assess the extent to which intrinsic functional connectome architecture constrains task-evoked neural activation. NACIs showed task-dependent variations, indicating differential constraint effects of the intrinsic functional connectome across distinct tasks. Spectral clustering based on the NACI classified participants into the high- and low-constraint groups. The high-constraint group exhibited superior cognitive functions in several domains and better performance across multiple tasks. Higher NACIs from working memory, language, and relational tasks correlated with greater cognitive functions and better task performance. These findings support the neurobiological and behavioral relevance of NACI and suggest its utility for characterizing individual differences in functional brain organization.

The widespread use of smartphones, particularly among young adults, has raised concerns about their impact on cognitive functioning, leading to a growing interest in understanding the neurobiological underpinnings of problematic smartphone use (PSU). Despite evidence linking PSU to negative cognitive and emotional outcomes, the neurobiological mechanisms underlying cognitive impairments in PSU individuals remain underexplored. This study aimed to investigate the relationship between cognitive fatigue (CF) and brain activity in individuals with PSU. Eighty-one healthy adults underwent functional magnetic resonance imaging (fMRI) while performing cognitively demanding tasks designed to induce CF. Brain activation patterns, functional connectivity, and correlations with behavioral performance and self-reported fatigue were analyzed using brain imaging analyses. The PSU group showed significant activation in the ventromedial prefrontal cortex (vmPFC) compared to the non-problematic smartphone use (nonPSU) group, and vmPFC activity was positively correlated with fatigue scores. In addition, there was also an increase in functional connectivity between the vmPFC and left middle frontal gyrus (MFG) in the PSU group. Correlations between task performance and activation in the nucleus accumbens (NAcc) and middle cingulate cortex (MCC) further indicated the engagement of compensatory mechanisms related to reward sensitivity and cognitive control. These results define specific neural markers of cognitive fatigue in people with PSU, indicating that increased activity and connectivity in key brain areas require greater cognitive resources to maintain functioning. Therefore, this highlights the need for targeted interventions to minimize cognitive fatigue and mitigate the neurocognitive impact of PSU.

Background Gut microbial dysbiosis and inflammation have been implicated in the pathophysiology of major depressive disorder (MDD). However, no attempts have been made to comprehensively investigate the potential relationship between gut microbiota, inflammation, brain function, and clinical features in MDD. Methods We conducted an integrative multi-omics study to examine the multi-dimensional differences in gut microbiome, inflammatory cytokines, brain functional connectivity, and clinical features between 60 MDD patients and 70 healthy controls. Furthermore, the potential associations between these multi-omics alterations were assessed using correlation and serial mediation analyses. Results MDD patients exhibited both depleted beneficial gut microbes and enriched detrimental bacteria, elevated interleukin-1 beta (IL-1β) level, a mix of decreased and increased functional connectivity of multiple brain regions. More important, we found that decreased abundance of anti-inflammatory bacterium (i.e., Blautia) led to increased IL-1β level, which in turn resulted in functional abnormalities in fronto-parietal regions that were associated with clinical symptoms and executive dysfunction. Conclusion Our findings may corroborate the gut microbiota-inflammation-brain axis hypothesis in depression, as well as highlight the potential use of targeting gut microbiota as anti-inflammatory intervention strategies in the prevention or treatment for MDD patients.

Purpose To evaluate the effectiveness of deep learning image reconstruction (DLIR) with metal artifact reduction (MAR) (DLIR-MAR) for improving carotid dual-energy CT angiography (DECTA) in patients with dental hardware. Methods This retrospective study included 49 patients with dental hardware who underwent carotid DECTA. Virtual monochromatic images (VMIs) were reconstructed with DLIR-MAR, adaptive statistical iterative reconstruction-Veo (ASIR-V), and ASIR-V with MAR (ASIRV-MAR) at 40-keV and 50-keV energy levels. We quantitatively compared image noise, CT attenuation, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and artifact index (AI) among different reconstructions, and performed a qualitative assessment of internal carotid artery (ICA) visualization affected by metal artifacts and overall image quality using a 5-point scale. Statistical analyses used repeated measures ANOVA or Friedman tests, with paired t-tests or Wilcoxon signed-rank tests as appropriate. Results Both ASIRV-MAR and DLIR-MAR reduced metal artifacts compared with ASIR-V, with lower AI and higher vascular visualization scores (P < 0.001). Compared to ASIRV-MAR, DLIR-MAR reduced image noise at both keV levels (P < 0.001). DLIR-MAR 40-keV VMIs exhibited comparable image noise (P > 0.05), higher CT attenuation, SNR, CNR, and overall image quality compared with ASIRV-MAR 50-keV VMIs (P < 0.05). Although DLIR-MAR 40-keV VMIs had higher AI values than ASIRV-MAR 50-keV VMIs (P < 0.001), this did not significantly affect ICA visualization (P > 0.05). Conclusions DLIR reconstructed with MAR improves carotid DECTA images in patients with dental hardware, reducing image noise in lower-keV VMIs to capture their contrast enhancement benefits.

Background: Major depressive disorder (MDD) is increasingly recognized as a highly heterogeneous disorder. Although the person-based similarity index (PBSI) provides a useful framework for characterizing individualized brain structural similarity, existing studies in MDD remain limited by either small samples or a lack of integration across different morphological features. Methods: We used structural MRI data from 1442 patients with MDD and 1277 healthy controls to calculate PBSI scores of cortical morphology measures based on cortical thickness (CT), cortical volume (CV), cortical surface area (SA), and sulcal depth (SD). Group comparisons of whole-brain PBSI and regional contributions to PBSI scores were then performed, and a subgroup analysis in 243 first-episode, drug-naive (FEDN) patients with MDD was further conducted. Results: Patients with MDD showed significant alterations in PBSI. Specifically, PBSI scores were significantly reduced for CT, CV, and SD, whereas no significant group difference was observed for SA in the main analysis. Analyses of regional contributions to PBSI further revealed significant between-group differences across multiple cortical regions. These alterations were mainly distributed in the default mode, ventral attention, and visual networks for CT; in the default mode, ventral attention, sensorimotor, and visual networks for CV; and in the default mode, dorsal attention, frontoparietal, and sensorimotor networks for SD. Similar patterns were also observed in the FEDN MDD subgroup. Conclusions: These findings provide neurobiological evidence for the marked structural heterogeneity of MDD and highlight the potential of PBSI as an individualized neuroimaging marker for more precise diagnosis and personalized intervention.

Psoriasis is a chronic autoimmune disease characterized by systemic inflammation and skin involvement, affecting millions of individuals worldwide. However, few studies have evaluated whether psoriasis and cardiac magnetic resonance imaging (CMR) traits share a common genetic basis. This study aimed to investigate the genetic correlation and bidirectional causal relationships between psoriasis and CMR traits using Mendelian randomization (MR). We conducted a bidirectional two-sample MR analysis to assess causal links between psoriasis and CMR traits. In the forward analysis, psoriasis was treated as the exposure and CMR traits as the outcomes; in the reverse analysis, CMR traits were used as the exposures and psoriasis as the outcome. Several MR methods were applied, including inverse variance-weighted (IVW), MR-Egger, weighted median, weighted mode, and simple mode. Finally, selected CMR traits were compared between patients with psoriasis and healthy controls. Psoriasis showed a causal effect on certain CMR traits, including regional peak circumferential strains (specifically, regional peak circumferential strain_13 (Ecc_AHA_13) and Ecc_AHA_14). Conversely, several CMR traits demonstrated causal effects on the risk of psoriasis, including right ventricular stroke volume (RVSV), left atrium ejection fraction (LAEF), regional peak circumferential strains (Ecc_AHA_12), and regional radial strains (regional radial strains_1(Err_AHA_1), Err_AHA_2, Err_AHA_15, and Err_global). In addition, circumferential strains (Ecc_AHA_13, Ecc_AHA_14) were higher in patients with psoriasis than in healthy controls. This research explored the genetic and causal connections between psoriasis and CMR traits, revealing the systemic influence of psoriasis on cardiovascular health.

Background Rumination, a recurrent and passive focus of thoughts on depressed mood and its possible causes and consequences, constitutes a characteristic feature of major depressive disorder (MDD). Despite considerable recent efforts to map neuropsychiatric symptoms to specific brain networks, little attention has been paid to network localization of rumination. Methods We initially conducted a systematic review of 49 published neuroimaging studies to identify rumination-related brain structural and functional alterations. Subsequently, by integrating these affected brain locations with large-scale discovery (1113 healthy individuals) and validation (1093 healthy individuals and 255 MDD patients) resting-state functional magnetic resonance imaging datasets, we applied novel functional connectivity network mapping to construct 3 rumination networks corresponding to different imaging modalities. Results The rumination gray matter volume abnormality network comprised widely distributed brain regions, primarily involving the ventral attention, subcortical, frontoparietal, limbic, somatomotor, and dorsal attention networks. The resting-state activity abnormality network mainly implicated the default network. The task-induced activation abnormality network was similar to the gray matter volume abnormality network, but the spatial extent was much smaller, chiefly involving the ventral attention and somatomotor networks. Conclusion Our findings help establish an integrative framework that may explain the neurobiology of rumination from a network perspective, laying the foundation for developing reliable biomarkers and targeted treatments for rumination.

BACKGROUND The hypothalamic suprachiasmatic nucleus, as the master circadian pacemaker, coordinates circadian homeostasis via neuroendocrine signaling networks. Circadian rhythm disruption (CRD) denotes functional impairment of this system, driven by etiologies ranging from environmental stressors to intrinsic insults, triggering pathophysiological cascades. AIM To delineate neuroendocrine-immune circuits in female textile workers with CRD by integrating diffusion spectrum imaging (DSI)-based neurite orientation dispersion and density imaging (NODDI) with biochemical indices, in order to identify specific biomarkers linked to circadian-hormonal-inflammatory crosstalk. METHODS This prospective observational study included 55 female patients with CRD (>= 10 annual night-shift cycles) and 41 age-, sex-, and education-matched controls. A comprehensive multimodal assessment was performed, including quantitative DSI-based NODDI analysis of gray and white matter regions, serum biomarker profiling, standardized neuropsychological evaluations, and statistical analysis of group differences. Within the CRD group, interrelations among multimodal variables were explored using correlation analyses. RESULTS Compared with controls, the CRD group showed significantly higher volume fraction of the isotropically diffusing water and intracellular volume fraction (ICVF) values in seven gray matter regions (P < 0.05), primarily within the default mode network and visual network (VN); white matter ICVF was also elevated in three fiber tracts associated with the VN. The CRD group had poorer neurofunctional performance compared to controls. The CRD group also demonstrated increased triiodothyronine (T3; 2.33 nmol/L vs 2.02 nmol/L), prolactin (303.20 & micro;IU/mL vs 249.60 & micro;IU/mL), neutrophil-to-lymphocyte ratio (2.02 vs 1.80); and decreased thyrotropin receptor antibody (0.75 IU/L vs 0.98 IU/L) and luteinizing hormone (14.77 mIU/mL vs 24.29 mIU/mL) compared with the control group. Correlation analysis showed that ICVF in the left middle occipital gyrus and pontine crossing tract correlated positively with T3 (r = 0.268 and 0.321 respectively), while ICVF in the left cuneus and left superior occipital gyrus correlated with cortisol (r = 0.278 and 0.268 respectively). CONCLUSION Prolonged night-shift causes CRD via neurostructural remodeling, endocrine dysregulation, neuroinflammation; hypothalamic-pituitary-thyroid/hypothalamic-pituitary-gonadal axis dysfunction mediates neuroimmune crosstalk. Multimodal biomarkers enable precise diagnosis/intervention.

BACKGROUND:Considerable neuroimaging effort has been dedicated to investigate the neural correlates of episodic memory, but the micro-scale molecular mechanisms that underlie the macro-scale neuroimaging correlates of episodic memory are still unclear. METHODS:Resting-state functional MRI data were obtained from a large cohort of 510 healthy young adults to calculate regional homogeneity (ReHo) to reflect spontaneous intrinsic brain activity. We then explored the relationship between California Verbal Learning Test-Ⅱ performance and ReHo across participants to delineate the neural substrates of episodic memory. Finally, we conducted the spatial relationship analyses between the neural correlates with gene expression and neurotransmitter atlases to further explore their potential genetic architecture and neurochemical underpinnings. RESULTS:Our analysis revealed a significant negative correlation between episodic memory and ReHo in the bilateral precuneus. Additional spatial correlation analyses revealed that the identified neural correlates of episodic memory were associated with expression of gene categories predominantly implicating signal transduction, immune system process, cellular metabolic process and anatomical structure development, as well as were linked to serotonin transporter. CONCLUSIONS:These findings may not only offer novel insights into the molecular substrates underlying the neural basis of episodic memory, but also help inform prevention and intervention strategies for individuals in at-risk and early phases of dementia.

Anhedonia, encompassing a broad spectrum of deficits in reward processing, is highly prevalent in major depressive disorder (MDD) and constitutes one of its core symptoms. While substantial progress has recently been made in mapping neuropsychiatric symptoms to specific brain networks, focused efforts to examine network localization of anhedonia are limited. We initially synthesized extant neuroimaging literature to identify brain locations with structural or functional alterations related to anhedonia. By integrating these affected brain locations with large-scale discovery (1113 healthy individuals) and validation (1093 healthy individuals and 255 MDD patients) resting-state functional magnetic resonance imaging datasets, we then applied novel functional connectivity network mapping to construct an anhedonia network. The anhedonia network was composed of the dorsal anterior cingulate cortex, insula, lateral prefrontal cortex, and striatum, principally implicating the canonical ventral attention and subcortical networks. Further analyses revealed that the trait and state anhedonia networks preferentially involved the default and limbic networks respectively, in addition to the commonly affected ventral attention and subcortical networks. Our findings may not only advance the understanding of the neurobiology underlying anhedonia from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for anhedonia.

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.

INTRODUCTION Individuals with similar white matter hyperintensities (WMH) burden show heterogeneous cognitive outcomes, yet the biological mechanisms underlying this variability remain incompletely understood.METHODS We integrated 16S rDNA sequencing, untargeted metabolomics, and multi-modal magnetic resonance imaging (MRI) to comprehensively characterize gut microbiome, plasma metabolome, and brain glymphatic function in 56 healthy controls, 40 WMH with normal cognition (WMH-NC), and 49 WMH with cognitive impairment (WMH-CI).RESULTS Group comparisons revealed differences in six bacterial genera, three plasma metabolites, and five glymphatic markers across three groups, with Acetivibrio, 1,5-naphthalenediamine, beta-uridine, free water fraction within the white matter, and index of diffusivity along the perivascular spaces (ALPS index) showing differences between WMH-CI and WMH-NC. Correlation and mediation analyses demonstrated associations between microbiota and cognition, mediated by tetradecyldiethanolamine and ALPS index.DISCUSSION These findings provide preliminary insights into plausible microbiota-metabolites-glymphatic function-cognition associations in WMH, potentially informing more targeted interventions for vascular cognitive impairment.

OBJECTIVE:Early risk stratification may support clinical decision-making in spontaneous intracerebral hemorrhage (ICH). We aimed to develop and internally validate HAGIV, a score integrating frequency of imaging markers (FIM), a time-adjusted non-contrast computed tomography (CT) metric of hematoma expansion, with established predictors for 90-day functional outcome in supratentorial ICH. METHODS:This prespecified prognostic modeling study used a multicenter retrospective cohort of consecutive supratentorial ICH patients with baseline non-contrast CT within 6 h of onset (January 2018-August 2022). The HAGIV score was constructed by assigning integer points according to regression coefficients from multivariable logistic regression. Discrimination for poor functional outcome (modified Rankin Scale score 3-6) was assessed using area under the curve (AUC) and compared with established ICH prognostic scores. RESULTS:HAGIV incorporated baseline hematoma volume (H), age (A), Glasgow Coma Scale score (G), frequency of imaging markers (I), and presence of intraventricular hemorrhage (V). In the derivation cohort, HAGIV achieved an AUC of 0.86, significantly outperforming the ICH (0.81), MICH (0.81), Outcome (0.72), and Landseed ICH (0.79) scores (all p < 0.001, DeLong's test). This superiority was confirmed in the validation cohort, where HAGIV maintained an AUC of 0.84 compared with 0.76, 0.78, 0.75, and 0.75, respectively. INTERPRETATION:In this predominantly small-to-moderate, supratentorial ICH cohort, HAGIV integrated FIM with established prognostic variables and improved discrimination for 90-day outcome. It may support interpretable early risk stratification for counseling and trial design, but prospective external validation is required before broader clinical implementation.