Despite that deep learning (DL) methods have presented tremendous potential in many medical image analysis tasks, the practical applications of medical DL models are limited due to the lack of enough data samples with manual annotations. By noting that the clinical radiology examinations are associated with radiology reports that describe the images, we propose to develop a foundation model for multi-model head MRI by using contrastive learning on the images and the corresponding radiology findings. In particular, a contrastive learning framework is proposed, where a mixed syntax and semantic similarity matching metric is integrated to reduce the thirst of extreme large dataset in conventional contrastive learning framework. Our proposed similarity enhanced contrastive language image pretraining (SeLIP) is able to effectively extract more useful features. Experiments revealed that our proposed SeLIP performs well in many downstream tasks including image-text retrieval task, classification task, and image segmentation, which highlights the importance of considering the similarities among texts describing different images in developing medical image foundation models.

Abstract Magnetic resonance imaging (MRI) is a widely used clinical diagnostic modality; however, conventional metal-based contrast agents, particularly gadolinium-based contrast agents, have raised concerns about long-term tissue retention and potential metal-associated toxicity. Nitroxide radical contrast agents (NRCAs) have been investigated as promising metal-free alternatives because persistent nitroxide radicals can shorten proton longitudinal relaxation times and thereby produce positive contrast on T1-weighted MRI without metal deposition. Moreover, nitroxide radical–based Overhauser-enhanced MRI (OMRI) can be used as a complementary approach to redox-sensitive functional imaging via dynamic nuclear polarization. Nevertheless, small-molecule nitroxides are limited by rapid bioreduction, short circulation times, and relatively low relaxivity. To address these limitations, various nanoscale engineering strategies, including polymer conjugation, dendrimer construction, supramolecular assembly, biomacromolecular scaffolding, and biomimetic delivery, have been developed to improve radical stability, increase relaxivity, and prolong in vivo retention. This review summarizes recent advances in the molecular design of NRCAs, radical stabilization, nanomaterial engineering, and their biomedical applications in cancer, inflammation, and ischemia–reperfusion (I/R) injury. Current challenges and future perspectives regarding the development of next-generation metal-free MRI contrast agents are also discussed.

This study aimed to explore the prognostic value of body composition (BC) parameters derived from CT imaging and their derived phenotypes following resection of hepatocellular carcinoma (HCC). Retrospective collection of HCC patients who underwent liver resection at 5 medical centers. TotalSegmentator was employed to segment adipose and muscle tissues on CT images. Manual corrections were performed at the L3 level to extract tissue area and CT density parameters. Cox proportional hazards models were used to identify potential prognostic parameters for 2-year recurrence-free survival (RFS) and construct BC phenotypes. Further exploration was conducted on the prognostic value of the BC phenotypes. A total of 497 patients were included (mean age, 59.3 ± 11.0 years; 396 men; cirrhosis prevalence, 53.50

BACKGROUND:Transjugular intrahepatic portosystemic shunt (TIPS) manages portal hypertension complications in cirrhosis, but predicting post-TIPS outcomes remains challenging, especially in viral hepatitis-dominated populations. PURPOSE:To systematically evaluate the predictive performance of the novel Viral-Associated Index of Post-TIPS Score (VIPs) for post-TIPS prognosis. We also comprehensively compare it with six established clinical prognostic models and one imaging-based model (the spleen volume-based model, SvBM). MATERIALS AND METHODS:We retrospectively analyzed 247 cirrhotic patients undergoing TIPS (56.7 % viral hepatitis). Baseline data calculated prognostic scores (VIPs, MELD, MELD-Na, FIPS, Child-Pugh, ALBI, MOTS, and SvBM). The primary endpoint was transplant-free survival (TFS). Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC) at 6, 12, 36, and 60 months post-TIPS. Calibration (Brier score), explanatory power (R2), and decision curve analysis (DCA) were also evaluated. RESULTS:VIPs demonstrated good-to-moderate discrimination for TFS, with AUROCs (95 % CI) of 0.794 (0.689-0.899), 0.753 (0.649-0.858), 0.721 (0.645-0.797), and 0.692 (0.617-0.767) at 6, 12, 36, and 60 months, respectively. This advantage was most pronounced in the viral hepatitis subgroup, with AUROCs ranging from 0.699 (0.603-0.796) to 0.822 (0.715-0.930) across follow-up. VIPs significantly outperformed Child-Pugh, ALBI, FIPS, MOTS and SvBM at all timepoints (all p < 0.05), and surpassed MELD and MELD-Na for long-term predictions (36/60 months, both p < 0.05). It also exhibited the best calibration (lowest Brier scores: 0.076-0.217) and the highest explanatory power (R2 = 0.121-0.142). Subgroup analyses further confirmed robust performance in females and patients with variceal bleeding. CONCLUSIONS:VIPs demonstrates superior predictive accuracy for post-TIPS survival in a viral hepatitis-dominated cohort and may serve as a preferred prognostic tool to guide individualized decision-making.

Contrast-induced acute kidney injury (CI-AKI) poses a significant clinical challenge and contributes to a considerable healthcare burden. Ferroptosis has been increasingly recognized as an important mechanism of renal tubular epithelial cell injury in CI-AKI. Salvianolic acid B (SalB), a natural compound with anti-inflammatory and antioxidant properties, has shown protective effects in various kidney diseases. However, its role in CI-AKI-associated ferroptosis has not been fully clarified. In this study, we established a rat model of CI-AKI by subcutaneous injection of carbon tetrachloride for 6 weeks followed by iopamidol administration, and an in vitro model using iopamidol-treated HK-2 cells. Effects of tubular injury and ferroptosis were examined both in vivo and in vitro. Cycloheximide chase assay, cellular thermal shift assay, and molecular docking were used to assess the binding capacity of SalB to SIRT1. Our results showed that SalB significantly alleviated renal injury, reduced iron accumulation, oxidative stress levels, and lipid peroxidation, upregulated the expression of SLC7A11 and GPX4, and downregulated ACSL4 expression in both iopamidol-treated rat kidneys and HK-2 cells. Mechanistically, SalB targeted and bound to SIRT1, enhancing its stability, thereby promoting Nrf2 upregulation and nuclear translocation, which in turn enhanced the expression of SLC7A11 and GPX4 and attenuated ferroptosis. Silencing either SIRT1 or Nrf2 in HK-2 cells partially abrogated the protective effect of SalB. Collectively, our results support SalB as a viable treatment strategy for CI-AKI.

Non-invasive and accurate staging of hepatic fibrosis remains an unmet clinical challenge due to the lack of molecularly specific MRI contrast agents. Here, we report a legumain-activatable gadolinium-loaded liposomal nanoprobe (Gd@Lipo-TAT-AAN) for quantitative MRI staging of fibrosis via targeted imaging of activated hepatic stellate cells (HSCs). Guided by single-cell transcriptomic analysis, we identified legumain as a plasma membrane biomarker progressively upregulated on activated HSCs in direct correlation with fibrosis severity in a CCl4-induced mouse model (Ishak stages 0–5). The nanoprobe was constructed by conjugating a legumain-cleavable TAT-AAN peptide onto Gd-loaded liposomes, wherein the cell-penetrating TAT domain remains sterically shielded until proteolytic activation by legumain. This design creates a dynamically regulated bio-interface that enhances hepatic accumulation of the nanoprobe in fibrotic liver upon intravenous administration, with peak T₁-weighted MRI signal enhancement at 20 min post-injection. Quantitative ΔSNR values, which reflected stage-dependent contrast enhancement, increased stepwise with advancing Ishak stage (stage 0: 45.00 ± 5.93; stage 1: 77.58 ± 16.15; stage 3: 120.41 ± 3.47; stage 5: 149.03 ± 12.29), enabling clear discrimination of early-stage fibrosis (stage 1) from healthy liver (P < 0.001), with 2.11-fold higher ΔSNR than clinical Gd-DTPA at this stage. The probe exhibited excellent biosafety with no acute toxicity or abnormal tissue Gd retention. Collectively, this work establishes legumain as a quantitative imaging biomarker for fibrosis staging and demonstrates that bio-interface engineering of enzyme-responsive nanomaterials provides a sensitive, targeted MRI platform with significant potential for early detection and longitudinal monitoring of chronic liver diseases.

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.

PURPOSE:To evaluate the monitoring value of T1 and T2 mapping in assessing kidney injury associated with chronic liver disease and the therapeutic efficacy of bone marrow mesenchymal stem cells (BMSCs) treatment. METHODS:Thirty-six rats were divided into 6 subgroups (n = 6/group) and underwent MRI scanning at 0, 2, 4, 6, 8, and 12 weeks, respectively, followed by biochemical and histological analyses. Seven rats underwent continuous MRI scanning to monitor changes in imaging parameters. Twenty-four rats divided into BMSC and control group. Six rats per group were subjected to serial MRI scans at weeks 13, 14, 15, and 16, another six rats per group were scanned at week 14 and then sacrificed for biochemical and renal histological analysis. RESULTS:From baseline to 12 weeks, renal hematoxylin and eosin (HE) scores and α-smooth muscle actin (α-SMA) levels increased significantly, and similar trends were found in renal T1 and T2 values. Following BMSCs injection, both BMSC and control groups exhibited reductions in HE scores and α-SMA levels, with BMSC group demonstrating more substantial decreases. Renal T1 and T2 values declined in both groups, with the BMSC group showing significantly lower T2 values than the control group. Strong correlations were found between renal T1/T2 values and HE scores, α-SMA levels (|r|=0.419-0.724). The area under the curve values for T1 and T2 in differentiating renal injury severity across different renal strips were from 0.793 to 0.930. CONCLUSION:T1 and T2 mapping can effectively monitor renal injury progression in chronic liver disease, with T2 values demonstrating greater potential for assessing the therapeutic efficacy of BMSCs.

Obtaining pixel-level expert annotations is expensive and labor-intensive in the medical imaging field, especially for multi-modality imaging data like MR. Most conventional cross-modality segmentation methods rely on unsupervised domain adaptation to achieve efficient cross-domain segmentation. However, these methods are often hindered by discrepancies between the source and target domains. In this paper, we propose a new scheme for cross-modality segmentation based on foundation models, which uses spatial consistency across multiple modalities and is not affected by discrepancies between the source and target domains. This scheme allows us to use annotated data from one imaging modality to train a network capable of performing accurate segmentation on other target imaging modalities, without the need for labels or registration processes. Specifically, we propose using a SAM-based model that uses segmentation results from one imaging modality as pseudo labels and prompts to guide training and testing in the target imaging modality. Moreover, we introduce consistency-based prompt tuning and hybrid representation learning to address potential unregistered issues and noisy label problems that may arise in cross-modality segmentation. We conducted extensive validation experiments on two internal datasets and one public dataset, including liver lesion segmentation and liver segmentation. Our method demonstrates significant improvement compared to current state-of-the-art approaches.

Background Vessels that encapsulate tumor clusters (VETC) is a powerful predictor of aggressive hepatocellular carcinoma (HCC) and associated with poor outcomes of HCC. Imaging surrogates of VETC potentially help predict postsurgical recurrence. Purpose To explore the noninvasive predictive potential of contrast-enhanced computed tomography (CE-CT) for VETC of HCC (VETC-HCC). A web-based prognostic nomogram model including VETC-associated imaging markers was subsequently created to predict postoperative recurrence-free survival (RFS) in HCC patients. Methods A retrospective evaluation was performed on 393 patients with HCC who underwent CE-CT and immunohistochemical staining for CD34 at three different institutions. Patients from institution 1 (n = 241) were split into training (n = 169) and internal test (n = 72) sets. The remaining 152 patients from institutions 2 and 3 were used as the external test set. Univariate logistic regression analyses and six machine learning algorithms were performed on the training set, and the performance of 6 ML models (AUC, DeLong test, etc.) was compared to identify VETC-associated imaging markers (which were calculated as the VETC score), which were validated in the internal and external test sets. An interactive prognostic nomogram model including VETC-associated imaging markers and clinical data was used to predict RFS in the training and internal test sets. The association between the model's stratification and postoperative recurrence after radical resection or liver transplantation was also assessed. Results Nonsmooth margins (P = 0.011), tumor size > 5 cm (P = 0.030), and intratumoral necrosis (P = 0.038) were identified as independent predictors of VETC-HCC, and were combined into 6 machine learning models. Logistic regression (LR) was the final selected model and the VETC score was calculated. In the training, internal test, and external test sets, the VETC score demonstrated effective predictive performance for VETC (AUC: 0.768, 0.742, and 0.724, respectively). The interactive prognostic nomogram model(https://radiology.shinyapps.io/DynNomapp/) including the neutrophil-to-lymphocyte ratio (NLR), serum alpha-foetoprotein (AFP), and VETC score yielded C-index values ranging from 0.805 to 0.783 in the training and internal test sets and produced three prognostically distinct groups. Among patients classified as those associated with medium risk by the model, those who underwent liver transplantation had significantly improved RFS (P < 0.05). In contrast, radical resection had no significant effect on RFS in patients classified as having either low or high risk (both P > 0.05). Conclusion Preoperative CE-CT features can be used to characterize VETC-HCCs. The prognostic nomogram model has prognostic value for the preoperative prediction of RFS can aid in the selection of the appropriate surgical approach.

BACKGROUND:As an extension of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) quantifies non-Gaussian water diffusion and has been applied to explore brain disorders. However, the genetic architecture of brain DKI phenotypes remains unknown. METHODS:Here, we estimated heritability and conducted genome-wide association studies (GWASs) for 804 DKI phenotypes across 188 brain structures in 4183 participants. To determine whether DKI-GWASs provides genetic insights beyond DTI-GWASs, we compared results from 804 DKI-GWASs and 752 DTI-GWASs in the same cohort. To clarify the biological significance of DKI phenotypes, we examined associations between DKI phenotypes and brain health-related outcomes within the CHIMGEN, and explored associations between polygenic risk scores (PRSs) of DKI phenotypes and mental disorders in the UK Biobank. FINDINGS:Of 804 DKI phenotypes, 275 showed significant heritability (P < 0.05; h2 range: 0.143-0.602). We detected 280 significant associations (P < 5 × 10-8), with 38 surviving Bonferroni correction (P < 1.54 × 10-10). These associations were unevenly distributed across chromosomes, DKI phenotype subgroups, and brain structures. Among 229 independent variant-structure associations for DKI, 175 (76.4%) were DKI-specific. We observed 930 associations between DKI phenotypes and brain health-related outcomes (P < 0.05; ten Bonferroni-significant with P < 1.02 × 10-5), and 200 between PRSs and mental disorders (P < 0.05; one Bonferroni-significant with P < 9.61 × 10-5). INTERPRETATION:This study delineates the genetic architecture of brain DKI phenotypes, identifies complementary genetic insights into brain microstructure, and provides biologically relevant endophenotypes for investigating neural mechanisms underlying brain disorders. FUNDING:National Natural Science Foundation of China, National Key Research and Development Program of China, Tianjin Key Medical Discipline Construction Project, and Tianjin Natural Science Foundation.

Background:The early postoperative period is critical for patients who have undergone renal transplantation. This study aimed to quantify cortical perfusion in renal allografts during this period using arterial spin labeling (ASL) magnetic resonance imaging (MRI). Methods:Between May and December 2014, ASL MRI was performed on 53 patients with renal allografts who were 2 to 4 weeks post-transplantation, as well as on 20 healthy volunteers. Recipients were stratified into two groups according to estimated glomerular filtration rate (eGFR): those with good allograft function (eGFR ≥60 mL/min/1.73 m2), and those with impaired allograft function (eGFR <60 mL/min/1.73 m2). Renal blood flow (RBF) values were compared across healthy controls and the two recipient groups. The correlation between RBF and eGFR was also evaluated. Results:Mean cortical RBF was 389.9±61.2 mL/100 g/min in healthy controls, 295.0±67.9 mL/100 g/min in recipients with good allograft function, and 195.5±88.9 mL/100 g/min in recipients with impaired allograft function. Statistically significant differences in cortical RBF were observed among the three groups (P<0.001). A moderate positive correlation between eGFR and cortical RBF was identified among renal allograft recipients (R=0.62, P<0.01). Conclusions:Renal cortical perfusion in allografts during the early postoperative period was significantly reduced compared to healthy controls, particularly in cases of impaired allograft function. ASL MRI demonstrates potential as a non-contrast imaging modality for quantifying renal cortical perfusion in the post-transplant setting.

Cerebral asymmetry is a core principle of human brain organization, showing dynamic changes across the lifespan and alterations in brain disorders. However, it remains unclear whether lifespan trajectories of asymmetry differ across populations. We compared lifespan structural asymmetry normative charts of 221 cerebral imaging phenotypes from 43,037 Chinese and 56,339 Western participants aged 0–100 years. The two populations showed distinct lifespan asymmetry patterns in 26.2% of the phenotypes. Chinese-minus-Western asymmetry difference curves displayed distinct patterns across brain phenotypes: rightward (45.7%), leftward (26.2%), rightward-to-leftward (11.8%), leftward-to-rightward (10.0%), and unclassified (6.3%). Population-matched normative models outperformed population-unmatched normative models in capturing normal asymmetry variability among healthy individuals and in detecting abnormal asymmetry deviations in patients with Alzheimer’s disease, mild cognitive impairment, schizophrenia, and major depressive disorder. These findings indicate that population mismatch can bias chart-based individual-level asymmetry assessment and underscore the need for population-representative brain asymmetry normative charts.

The mechanism by which the increasing environmental challenge of urbanicity impacts the brain, personality and mental disorders remains unclear. Here, grounded in life history theory, we tested the hypothesis that age at menarche (AAM) mediates the effects of early-life urbanicity on adult regional brain volumes and personality traits associated with mental disorders. In a sample of 2,950 young Chinese women, we discovered that higher levels of early-life urbanicity were associated with earlier AAM, which in turn correlated with reduced medial prefrontal volume and lower levels of agreeableness and reward dependence in adulthood. Urbanicity-related factors, particularly family socioeconomic status, also influenced these neurobehavioral traits through AAM. The urbanicity- and AAM-related brain and personality traits were changed in patients with major depressive disorder and schizophrenia. These findings suggest that life history theory may serve as a mechanism through which early-life urbanicity influences the adult brain and personality traits associated with mental disorders in women. It is unclear how early-life urbanicity influences adult neurobehavioral traits. This study reveals that earlier menarche mediates the relationship between early-life urbanicity and adult neurobehavioral traits associated with mental disorders.

Aims:Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant risk factor for chronic kidney disease. There is a lack of an accurate and comprehensive technique for detecting MASLD-related renal injury. This study aims to evaluate the efficacy of arterial spin labeling (ASL), blood oxygen level-dependent (BOLD) imaging, and proton density fat fraction (PDFF) for assessing renal injury in an animal model of MASLD. Methods:An animal model of MASLD was established using a high-fat diet. Forty-nine 6-week-old male Sprague-Dawley rats were divided into the pathology (14, 16, 18, 20, 22, and 24 weeks, n = 7 per subgroup) and continuous-scanning (n = 7) groups. Renal alterations at different time points were quantified through the application of ASL-renal blood flow (RBF), BOLD-T2*, and Fat Fraction (FF), alongside pathological indices and blood biochemical markers. Results:RBF did not change significantly from 14-24 weeks, consistent with the peritubular capillary density. Compared with those at week 14, renal T2* significantly decreased at week 20, FF increased at week 20, and serum creatine levels increased at week 24. Renal T2* and FF were significantly correlated with renal H&E scores and HIF-1α expression (|r| = 0.3552-0.7745). Kidney BOLD-T2*, liver and kidney FF enabled detecting renal injury in an animal model of MASLD (area under the curve = 0.76-0.86). Conclusion:During fatty liver disease progression, renal blood oxygen levels decreased, fat deposition increased, and blood flow remained unchanged. BOLD and PDFF allowed accurately quantifying these changes to facilitate early detection of kidney injury.

Background: Predicting the recurrence risk of NMIBC after TURBT is crucial for individualized clinical treatment. Objective: The objective of this study is to evaluate the ability of radiomic feature analysis of intratumoral and peritumoral regions based on computed tomography (CT) imaging to predict recurrence in non-muscle-invasive bladder cancer (NMIBC) patients who underwent transurethral resection of bladder tumor (TURBT). Methods: A total of 233 patients with NMIBC who underwent TURBT were retrospectively analyzed. Within the intratumoral and peritumoral regions of the venous phase images, 1316 radiomics features were extracted. Feature selection was used to identify a set of top recurrence-associated features within the training cohort. Three models were constructed to predict recurrence for a given patient using Random Forest (RF): Model 1 was based on the radiomics features set from the intratumoral region, Model 2 was based on a combination of intratumoral and peritumoral regions, and Model 3 combined the radiomics features from Model 2 and clinical factors. The three models were then independently tested on internal and external cohorts, and their performance was evaluated. We also employed the bootstrap method on the internal cohort to further validate the performance of the model. Results: Combining intratumoral and peritumoral regions, Model 2 yielded a higher area under the receiver operator characteristic curves (AUC) than Model 1, with 0.826 AUCs of the training cohort. After adding clinical factors, the predictive performance of Model 3 for postoperative recurrence of NMIBC was further improved, and the AUCs of the training, internal, and external validation cohorts of Model 3 were 0.860 (95% CI: 0.829-0.954), 0.829 (0.812-0.863), and 0.805 (0.652-0.840), respectively (all p>0.05). The bootstrap value of Model 3 on the internal cohort was 0.852. Model 3 stratified patients into high- and low-risk groups with significantly different recurrence-free survival (RFS) (p<0.001). Conclusion: Radiomic features derived from intratumoral regions can predict the 2-year recurrence risk following TURBT in patients with NMIBC. The predictive performance is further enhanced when combined with radiomic features from peritumoral regions and clinical risk factors.

With the worldwide increase in only-child families, it is crucial to understand the effects of growing up without siblings (GWS) on the adult brain, behaviour and the underlying pathways. Using the CHIMGEN cohort, we investigated the associations of GWS with adult brain structure, function, connectivity, cognition, personality and mental health, as well as the pathway from GWS to GWS-related growth environments to brain and to behaviour development, in 2,397 pairs of individuals with and without siblings well matched in covariates. We found associations linking GWS to higher language fibre integrity, lower motor fibre integrity, larger cerebellar volume, smaller cerebral volume and lower frontotemporal spontaneous brain activity. Contrary to the stereotypical impression of associations between GWS and problem behaviours, we found positive correlations of GWS with neurocognition and mental health. Despite direct effects, GWS affects most brain and behavioural outcomes through modifiable environments, such as socioeconomic status, maternal care and family support, suggesting targets for interventions to enhance children's healthy growth.

The brainstem houses numerous nuclei and tracts that serve vital functions. Genome-wide associations with brainstem substructure volumes have been explored in European individuals, yet other ancestries remain under-represented. Here, we conduct cross-ancestry genome-wide association meta-analyses in 103,098 individuals for brainstem and 78,062 individuals for eight substructure volumes, including 7094 Chinese Han individuals. We identify 713 locus-trait associations with brainstem and substructure volumes at P < 5.56 ×10−9, comprising 569 new associations. Two associations show different effect sizes, while 496 associations have similar effect sizes between ancestries. We prioritize 186 genes associated with brainstem volumetric traits. We find both shared and distinct genetic loci, genes, and pathways for midbrain, pons, and medulla volumes, along with the shared genetic architectures related to disease phenotypes and physiological functions. The results provide new insights into the genetic architectures of brainstem and substructure volumes and their genetic associations with brainstem physiologies and pathologies. A cross-ancestry GWAS meta-analyses of brainstem structures identify 713 associations. It reveals shared/distinct genetic architectures across ancestries/substructures and overlaps with neuropsychiatric disorders and physiological functions.

OBJECTIVES:To investigate the cross-sectional and longitudinal dynamics of the thalamic subregion microstructure in patients with liver cirrhosis by diffusion kurtosis imaging (DKI) and explore its relationship with cognitive function. MATERIALS AND METHODS:DKI was performed on 50 with hepatic encephalopathy (HE), 46 without hepatic encephalopathy (non-HE), and 41 healthy controls (HC). 61 of these cirrhotic patients underwent liver transplantation (LT) surgery and DKI again one month later to compare the diffusion parameters before and after surgery. Correlations between neuroimaging changes and neuropsychological clinical features were analyzed. RESULTS:We noticed increased axial diffusion (AD), radial diffusion (RD) and mean diffusion (MD), and decreased axial kurtosis (AK), radial kurtosis (RK), mean kurtosis (MK), and fractional anisotropy (FA) in most thalamic subregions of patients with liver cirrhosis, indicating low-grade cerebral edema and microstructural damage. We observed a compensatory increase in MK in the PPtha_r subregion in the patient group. The reduction in the MK from the non-HE to HE group may relate to the promotion of astrocyte apoptosis by HE attack. One month after LT, most thalamic subregional parameters tended to deteriorate, and lPFtha was the first subregion to exhibit increased DKI parameters. CONCLUSION:DKI can better detect the dynamic changes in the microstructure of the thalamus after HE and LT surgery. The decrease in DKI parameter in the HE group compared to the non-HE group may relate to neuronal apoptosis. lPFtha is the thalamic subregion where structural recovery first occurs 1-month after LT.

Early diagnosis of mild hepatic encephalopathy is important for the reversion of hepatic encephalopathy. Brain hyper-connectivity networks with hyperedges have showed good performance for diagnosis of neurological disorders. However, the previous hyper-connectivity networks is essentially low-level since the temporal synchronization of regional signal fluctuation is merely considered. Here, we propose a novel high-level hyper-connectivity network based on the resting state functional magnetic resonance imaging to capture the complex interactions among brain regions for better diagnosis of neurological disorders. Resting-state functional magnetic resonance imaging data from 36 mild hepatic encephalopathy patients and 36 cirrhotic patients with no mild hepatic encephalopathy are included in the study. Multi-level high-level hyper-connectivity networks are constructed firstly. Then, we define and extract node hyperdegree, hyperedge global importance and hyperedge dispersion from both low-level and high-level hyper-connectivity networks and combine them. Finally, gradient boosting decision tree is used for feature selection and classification. The leave-one-out cross-validation is used to evaluate the performance. The public ASD resting state functional magnetic resonance imaging datasets from 3 sites are also used as testing set to evaluate the generalization power of our method. Our method showed considerable performance in both experiments which confirms the effectiveness and generalization ability of the model. Besides, important regions and hyperedge features are identified for the interpretability.