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Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease. Sequencing-based ST technologies provide whole-transcriptome profiles but are limited by the non–single cell–level resolution. Lack of knowledge in the number of cells or cell type composition at each spot can lead to invalid downstream analysis, which is a critical issue recognized in ST data analysis. Methods developed, however, tend to underuse histological images, which conceptually provide important and complementary information including anatomical structure and distribution of cells. To fill in the gaps, we present POLARIS, a versatile ST analysis method that can perform cell type deconvolution, identify anatomical or functional layer-wise differentially expressed (LDE) genes, and enable cell composition inference from histology images. Applied to four tissues, POLARIS demonstrates high deconvolution accuracy, accurately predicts cell composition solely from images, and identifies LDE genes that are biologically relevant and meaningful.

Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease. Sequencing-based ST technologies provide whole-transcriptome profiles, but are limited by the non-single cell level resolution. Lack of knowledge in the number of cells or cell type composition at each spot can lead to invalid downstream analysis, which is a critical issue recognized in ST data analysis. Methods developed, however, tend to under-utilize histological images, which conceptually provide important and complementary information including anatomical structure and distribution of cells. To fill in the gaps, we present POLARIS, a versatile ST analysis method that can perform cell type deconvolution, identify anatomical or functional layer-wise differentially expressed (LDE) genes and enable cell composition inference from histology images. Applied to four tissues, POLARIS demonstrates high deconvolution accuracy, accurately predicts cell composition solely from images, and identifies LDE genes that are biologically relevant and meaningful.

To investigate the change of cortical venous flow in acute ischemic stroke patients with large vessel occlusion (LVO-AIS) and its clinical value.Baseline whole-brain 4D-CTA/CTP and clinical data of LVO-AIS and a control group were collected from June 2020 to October 2021. Venous inflow time (VIT), venous peak time (VPT), and venous outflow time (VOT) were analyzed on both sides of patients and normal controls. The VIT/VPT/VOT were statistically described and compared between the patient group and normal controls, then, in patients with different collateral circulation and prognoses. Next, the correlation between cortical venous drainage time and collateral circulation grading was analyzed. Finally, logistic regression analysis was used to explore the relationship between the three venous times and prognosis, and receiver operating characteristic (ROC) curves were plotted to assess the value of delayed cortical venous imaging in predicting prognosis.149 LVO-AIS and 73 normal controls were collected. VIT, VPT, and VOT were significantly delayed on the affected side in the patient group compared with the healthy side (p0.05) and the controls (p0.05); VIT and VPT were also significantly delayed on the healthy side of patients compared with the controls (p0.05). Delayed VIT and VPT on the affected side in the patient group were more significant in patients with poor collateral circulation (p0.05), and VIT and VPT on the affected side in the patient group were negatively correlated with arterial collateral scores. VIT and VPT were significantly delayed in both sides of patients in the poor prognosis group compared with the good prognosis group (p0.05). logistic regression showed that patients' affected VPT, arterial collateral scores, and NIHSS were independent predictors of poor prognosis, with an accuracy of 79.6% in predicting poor prognosis. The affected VPT and NIHSS were independent predictors of poor prognosis for patients presenting within 24 hours, with an accuracy of 79.6% in predicting poor prognosis.Cortical venous flow was significantly slowed in both sides of LVO-AIS patients. delayed ipsilateral VPT in LVO-AIS patients can be used as an imaging indicator to determine poor collateral circulation and predict poor prognosis.

Brain ventricular and subcortical structures are heritable both in size and shape. Genetic influences on brain region size have been studied using conventional volumetric measures, but little is known about the genetic basis of ventricular and subcortical shapes. Here we developed pipelines to extract seven complementary shape measures for lateral ventricles, subcortical structures, and hippocampal subfields. Based on over 45,000 subjects in the UK Biobank and ABCD studies, 60 genetic loci were identified to be associated with brain shape features (P < 1.09 × 10-10), 19 of which were not detectable by volumetric measures of these brain structures. Ventricular and subcortical shape features were genetically related to cognitive functions, mental health traits, and multiple brain disorders, such as the attention-deficit/hyperactivity disorder. Vertex-based shape analysis was performed to precisely localize the brain regions with these shared genetic influences. Mendelian randomization suggests brain shape causally contributes to neurological and neuropsychiatric disorders, including Alzheimer’s disease and schizophrenia. Our results uncover the genetic architecture of brain shape for ventricular and subcortical structures and prioritize the genetic factors underlying disease-related shape variations.

Qi Li,1 Xiao-Qun He,1,&ast; Xiao Fan,2 Tian-You Luo,1 Ji-Wen Huo,1 Xing-Tao Huang3,&ast; 1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, 400014, People’s Republic of China; 3Department of Radiology, University of Chinese Academy of Sciences Chongqing Renji Hospital (Fifth People’s Hospital of Chongqing), Chongqing, 400062, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiao-Qun He Tel +86 18324179667Email 18324179667@163.comXing-Tao HuangDepartment of Radiology, University of Chinese Academy of Sciences Chongqing Renji Hospital (Fifth People’s Hospital of Chongqing), No. 24 Renji Road, Nan’an District, Chongqing, 400062, People’s Republic of ChinaTel +86 13896573793Fax +86 23 68811487Email hxt89011721@163.comBackground: Many delayed diagnoses of lung adenocarcinoma (LADC) are identified due to poor understanding of protean imaging findings. Moreover, clarifying the relationship between computed tomography (CT) morphological classification and epidermal growth factor receptor (EGFR) mutations of LADC might inform therapeutic decision-making while obtaining pathological specimens is difficult. Here, we retrospectively analyzed CT manifestations of LADC and investigated the morphological classification of tumors in relation to EGFR mutation status.Methods: We included 1075 LADC patients undergoing chest CT and EGFR genotype examinations from January 2013 to January 2019. CT morphological characteristics of tumors were carefully evaluated and their correlation with EGFR mutation status was analyzed using the chi-squared test.Results: Tumors were divided into eight types: I (peripheral solid nodule/mass; 526/1075, 48.93%), II (central solid nodule/mass; 220/1075, 20.47%), III (subsolid nodule/mass; 92/1075, 8.56%), IV (focal consolidation; 32/1075, 2.98%), V (cystic airspace; 14/1075, 1.30%), VI (multiple lesions with similar appearances to I–V; 85/1075, 7.91%), VII (diffuse consolidation; 53/1075, 4.93%), VIII (occult lesion usually obscured by nonobstructive atelectasis; 53/1075, 4.93%). Type III and IV tumors were more frequent in patients with EGFR mutation, whereas type II and VII tumors were more common in patients without EGFR mutation (all P < 0.05). However, we did not identify any significant associations between other tumor types and EGFR mutation status (all P > 0.05). Among patients with type VI tumors, EGFR mutation status was closely related to tumor density (all P < 0.05). Furthermore, type VII tumors were associated with 19 deletion mutation positive and non-L858R mutation positive (all P < 0.05).Conclusion: LADC can be categorized into eight types based on CT imaging. Improving our understanding of the morphological classification and correlation with EGFR mutation status may contribute to the accurate diagnosis of LADC, while suggesting the presence of underlying EGFR genetic mutations.Keywords: lung adenocarcinoma, morphology, computed tomography, epidermal growth factor receptor, mutation

Background Necrotic pulmonary lesions manifest as relatively low-density internally on contrast-enhanced computed tomography (CT). However, using CT to differentiate malignant and benign necrotic pulmonary lesions is challenging, as these lesions have similar peripheral enhancement. With the introduction of dual-energy spectral CT (DESCT), more quantitative parameters can be obtained and the ability to differentiate material compositions has been highly promoted. This study investigated the use of kVp-switching DESCT in differentiating malignant from benign necrotic lung lesions. Methods From October 2016 to February 2019, 40 patients with necrotic lung cancer (NLC) and 31 with necrotic pulmonary mass-like inflammatory lesion (NPMIL) were enrolled and underwent DESCT. The clinical characteristics of patients, CT morphological features, and DESCT quantitative parameters of lesions were compared between the two groups. Binary logistic regression analysis was performed to identify the independent prognostic factors differentiating NPMIL from NLC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analyses. Results Significant differences in age, C-reactive protein concentration, the slope of the spectral curve from 40 to 65 keV (K40–65 keV) of necrosis in non-contrast-enhanced scanning (NCS), arterial phase (AP) and venous phase (VP), effective atomic number of necrosis in NCS, and iodine concentration (IC) of the solid component in VP were observed between groups (all p 40–65 keV of necrosis in NCS, and IC of the solid component in VP were the most effective factors for differentiating NLC from NPMIL, with an AUC of 0.966 and percentage of correct class of 88.7%. Conclusions DESCT can differentiate malignant from benign necrotic lung lesions with a relatively high accuracy.

Background Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging has a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity changes and build the potential diagnostic biomarker in ET patients. Methods The histogram features extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs and the classification performance was compared in the testing set. Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. Results Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; 92.27%, 0.939, respectively. The most power discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were 2 histogram features negatively and 1 positively correlated with tremor severity. Conclusions Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs, and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

To investigate the correlation between choroid plexus volume and whole brain morphology in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).Fifty-one patients with MS, 42 patients with NMOSD, and 56 healthy controls (HC) were recruited. The morphological changes in choroid plexus and whole brain tissue were compared between three groups and the correlations between choroid plexus volume and brain atrophy were further investigated. The longitudinal alterations of brain morphology in 25 MS and 20 NMOSD patients were compared.Compared to the HC group, the choroid plexus volumes were increased in the MS group (p0.001) but not in the NMOSD group (p0.05). Compared to the HC group, the MS group showed reduced cortex thickness, deep gray matter volume, and increased ventricle system volume, and the NMOSD group showed increased third ventricle volume (all p0.05, false discovery rate corrected). In the MS group, there were widespread correlations between enlarged choroid plexus volume and reduced cerebral cortex thickness (p0.05, r = -0.292~-0.538, false discovery rate corrected). The interval time was not significantly different between the MS (median: 1.37 years) and NMOSD group (median: 1.25 years) (p0.05). In MS, compared with the baseline, the right hippocampus and nucleus accumbens volumes were decreased in long follow-up, and bilateral lateral ventricle volumes were increased both in short and long follow-up (all p0.05, false discovery rate corrected).The enlarged choroid plexus related to reduced cortical thickness and progressive local brain atrophy are shown in MS patients, but not obvious in NMOSD patients.• MS and NMOSD have different altered patterns in choroid plexus volume and brain atrophy. • The enlarged choroid plexus related to brain atrophy is shown in MS patients, but not obvious in NMOSD patients. • Progressive local brain atrophy is shown in MS patients, but not obvious in NMOSD patients.

To investigate cerebral amplitude of low-frequency fluctuations (ALFF) changes during a single hemodialysis (HD) in end-stage renal disease (ESRD) patients who need maintenance HD. A total of 24 patients and 27 healthy subjects were included. The patients underwent neuropsychological tests and took twice resting-state fMRI (rs-fMRI) (before and after HD). Healthy group had one rs-fMRI. The zALFF based on rs-fMRI was calculated. Paired t and independent t test was applied to compare zALFF among groups. The associations between zALFF and duration of HD, ultrafiltration volume, and neuropsychological tests was calculated by partial correlation. Compared to healthy group, patients before HD showed significant worse performances on digit symbol test (DST) and serial dotting test (SDT). Patients after HD performed DST better than before HD. The patients after HD showed higher zALFF in left putamen than before HD. Multiple regions of both HD groups showed significant lower zALFF than healthy group. The zALFF of left putamen of patients after HD was significant negative correlated with the ultrafiltration volume (R = −0.679). The zALFF in patients before HD exhibited significantly positive or negative correlations with DST and SDT in multiple regions. The zALFF of patients after HD significantly negative correlated with DST in right temporal, positive and negative correlated with ultrafiltration volume in right frontal, left putamen respectively. ESRD patients showed changed spontaneous brain activity and cognitive impairments. After a single HD session, patients performed better in neuropsychological test, and spontaneous brain activity changed in left putamen. Ultrafiltration volume might be associated with activity of left putamen.

Next-generation sequencing (NGS) of bulk cell populations is a useful and ubiquitous tool for the molecular characterization of clinical tumor samples. Bulk NGS reveals transcript abundance within a tumor sample and can further infer cell populations via deconvolution algorithms (PMID:31570899). However, it can’t ascribe the cellular context for a given gene’s expression or elucidate the spatial organization of tumor microenvironments. These additional features are critical to our understanding of tumor biology and are key to the development of immuno-oncology therapeutics. Spatial Transcriptomics (ST) is an emerging technology that characterizes gene expression within the spatial context of tissue. ST data can be generated directly from archival formalin fixed paraffin embedded samples, enabling the study of spatial gene expression in real-world clinical settings. We have studied a dataset comprising 6 samples from non-small cell lung cancer (NSCLC) patients and 1 core needle biopsy from a tumor of unknown origin. We used the 10X Visium CytAssist platform to generate ST data and additionally generated paired bulk RNAseq data. To test the interassay reliability of CytAssist on archival FFPE tissue sections, we compared ST results across 3 sample preparation conditions. We further studied the state of the tumor microenvironment by applying state-of-the-art computational approaches to deconvolve immune cell populations and produce super-resolution ST maps, validated using multiplex immunofluorescence (IF) via CODEX (PMID:30078711). We find key quality control metrics and spatial biomarkers are consistent across all 3 sample preparation conditions. When comparing deconvolution results between bulk and spatially-resolved methods we observe modest correlations for many cell types despite differences in sample preparation, supporting the idea that bulk and spatial samples contain complementary transcriptomic information. However, within samples, we find many of the correlations observed in bulk do not show strong spatial correlation. These comparisons indicate the importance of considering spatial context when studying the tumor immune microenvironment. Finally, we find an agreement between super-resolution ST and multiplex IF across key spatial biomarkers. These results demonstrate clinical archival FFPE samples yield high interassay reliability via the CytAssist platform. Results were consistent through 3 different H&E staining protocols and findings were consistent when superresolution deconvolution was utilized which further strongly correlated with high-resolution multiplex IF. Our findings demonstrate the feasibility and translational utility of ST to discover spatial signatures and the cellular context in retrospective clinical cohorts to empower discovery and translational efforts in precision oncology and therapeutic development. Citation Format: Mario G. Rosasco, Chi-Sing Ho, Tianyou Luo, Michelle M. Stein, Luca Lonini, Martin C. Stumpe, Jagadish Venkataraman, Sonal Khare, Ameen A. Salahudeen. Comparison of interassay similarity and cellular deconvolution in spatial transcriptomics data using Visum CytAssist. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4692.

Cardiovascular health interacts with cognitive and psychological health in complex ways. Yet, little is known about the phenotypic and genetic links of heart-brain systems. Using cardiac and brain magnetic resonance imaging (CMR and brain MRI) data from over 40,000 UK Biobank subjects, we developed detailed analyses of the structural and functional connections between the heart and the brain. CMR measures of the cardiovascular system were strongly correlated with brain basic morphometry, structural connectivity, and functional connectivity after controlling for body size and body mass index. The effects of cardiovascular risk factors on the brain were partially mediated by cardiac structures and functions. Using 82 CMR traits, genome-wide association study identified 80 CMR-associated genomic loci (P < 6.09 × 10-10), which were colocalized with a wide spectrum of heart and brain diseases. Genetic correlations were observed between CMR traits and brain-related complex traits and disorders, including schizophrenia, bipolar disorder, anorexia nervosa, stroke, cognitive function, and neuroticism. Our results reveal a strong heart-brain connection and the shared genetic influences at play, advancing a multi-organ perspective on human health and clinical outcomes.

To investigate the specific features of static functional connectivity (SFC) and dynamic functional connectivity (DFC) of networks related to cognition in patients with subcortical ischemic vascular disease (SIVD).In this retrospective study, resting-state functional MRI data and a series of cognitive scores were obtained from 38 patients with SIVD and 23 normal controls. Independent component analysis, sliding window method, k-means clustering analysis and graph theory method were used to examine FC between the default mode network (DMN), dorsal attention network (DAN), frontoparietal network (FPN), salience network (SN) and executive control network (ECN) in patients with SIVD. Then, correlations between abnormal FC features and cognition were assessed.Compared with normal controls, SFC within the DMN significantly increased and SFC between the DMN and DAN significantly decreased in patients with SIVD. The decreased DFC mainly occurred in weakly connected states, especially the DFC of the SN; but the increased DFC, global network efficiency and local network efficiency and the decreased mean dwell time (MDT) and frequency mainly occurred in strongly connected states in SIVD patients. Moreover, aberrant SFC, DFC and MDT were significantly correlated with patients' cognitive scores.The overall results are suggestive of abnormal functional segregation and integration of SFC and DFC among networks related to cognition, especially in the SN. This may advance our comprehensive understanding of the abnormal changes in brain network connectivity in patients with SIVD. Our findings also highlight DFC may be an effective neuroimaging marker for the clinical diagnosis of SIVD.

Background Pneumonic-type lung adenocarcinoma (PLADC) with different ranges might exhibit different imaging and clinicopathological features. This study divided PLADC into localized PLADC (L-PLADC) and diffuse PLADC (D-PLADC) based on imaging and aimed to clarify the differences in clinical, imaging, and pathologic characteristics between the two new subtypes. Results The data of 131 patients with L-PLADC and 117 patients with D-PLADC who were pathologically confirmed and underwent chest computed tomography (CT) at our institute from December 2014 to December 2020 were retrospectively collected. Patients with L-PLADC were predominantly female, non-smokers, and without respiratory symptoms and elevated white blood cell count and C-reactive protein level, whereas those with D-PLADC were predominantly male, smokers, and had respiratory symptoms and elevated white blood cell count and C-reactive protein level (all p p p Conclusions L-PLADC and D-PLADC have different clinical, imaging, and pathological characteristics. This new imaging-based classification may help improve our understanding of PLADC and develop personalized treatment plans, with concomitant implications for patient outcomes.

Purpose This study aimed to explore the differences in the magnetic resonance imaging (MRI) findings between intraspinal tuberculosis and metastatic cancer, which may aid in making the correct diagnosis. Patients and methods The clinical features and MRI findings of 15 patients with intraspinal tuberculosis and 11 patients with intraspinal metastatic cancers were retrospectively analyzed. Results The mean ages of the patients with intraspinal tuberculosis and metastatic cancer were 26.3 (15-42) and 52.1 (38-67) years, respectively. All intraspinal tuberculosis cases were secondary to primary extraspinal tuberculosis, including tuberculous meningitis (11/15), as well as pulmonary (9/15), vertebral (5/15), urinary tract (1/15), abdominal (1/15), cervical lymph node (1/15), and multisystem tuberculosis (9/15). The intraspinal metastases originated from the breast (5/11), lung (3/11), kidney (1/11), ovarian (1/11), and nasopharyngeal cancers (1/11). Both intraspinal tuberculosis and metastatic cancers presented with multiple intra- and extramedullary lesions throughout all regional segments of the spinal canal, accompanied by irregularly thickened meninges. Intraspinal tuberculous lesions had indistinct edges that integrated with each other, most of them exhibiting obvious enhancement on MRI. Conversely, intraspinal metastatic lesions were distinctly separated with clear edges and exhibited lesser enhanced MRI than intraspinal tuberculosis. Conclusion A combined analysis of clinical features and MRI findings may be helpful in differentiating intraspinal tuberculosis from metastatic cancer.

Individual variations of white matter (WM) tracts are known to be associated with various cognitive and neuropsychiatric traits. Diffusion tensor imaging (DTI) and genome-wide single-nucleotide polymorphism (SNP) data from 17,706 UK Biobank participants offer the opportunity to identify novel genetic variants of WM tracts and explore the genetic overlap with other brain-related complex traits. We analyzed the genetic architecture of 110 tract-based DTI parameters, carried out genome-wide association studies (GWAS), and performed post-GWAS analyses, including association lookups, gene-based association analysis, functional gene mapping, and genetic correlation estimation. We found that DTI parameters are substantially heritable for all WM tracts (mean heritability 48.7%). We observed a highly polygenic architecture of genetic influence across the genome (p value = 1.67 × 10−05) as well as the enrichment of genetic effects for active SNPs annotated by central nervous system cells (p value = 8.95 × 10−12). GWAS identified 213 independent significant SNPs associated with 90 DTI parameters (696 SNP-level and 205 locus-level associations; p value

BACKGROUND First-generation and second-generation dual-source computed tomography (DSCT) are useful for analyzing left ventricle (LV) structure and function. This pilot study aimed to investigate the feasibility and role of third-generation DSCT for the evaluation of dynamic changes in LV structural and functional characteristics in a Diannan small-ear pig model of acute myocardial infarction (AMI). MATERIAL AND METHODS The model of AMI was established by balloon occlusion of the distal third of the left anterior descending (LAD) coronary artery in 14 Diannan small-eared pigs. Third-generation DSCT was performed to observe dynamic changes in LV structure and function before and after AMI was induced, with a follow-up period of 30 days. RESULTS The mean structural measurements at baseline included interventricular septum thickness (8.50±0.90 mm), LV anterior wall thickness (8.40±1.30 mm), LV posterior wall thickness (7.80±1.20 mm), LV end-diastolic dimension (LVEDD) (45.00±4.90 mm), and LV end-systolic dimension (LVESD) (25.90±4.10 mm). The mean functional measurements at baseline included the LV end-diastolic volume (LVEDV) (74.62±13.54 ml), LV end-systolic volume (LVESV) (23.06±7.46 ml), LV ejection fraction (LVEF) (69.29±6.83%), LV mass (86.35±14.02 g), stroke volume (SV) (51.56±9.77 ml), and cardiac output (CO) (4.22±2.14 l/min). Trends of time-dependent changes were observed for LVESV, LVEF, SV, and CO, but not for LVEDV or LV mass. CONCLUSIONS Third-generation DSCT was validated as a tool for assessing dynamic changes in LV global function in a porcine model of AMI.

The human cerebral cortex plays a crucial role in brain functions. However, genetic influences on the human cortical functional organizations are not well understood. Using a parcellation-based approach with resting-state and task-evoked functional magnetic resonance imaging (fMRI) from 40,253 individuals, we identified 47 loci associated with functional areas and networks at rest, 15 of which also affected the functional connectivity during task performance. Heritability and locus-specific genetic effects patterns were observed across different brain functional areas and networks. Specific functional areas and networks were identified to share genetic influences with cognition, mental health, and major brain disorders (such as Alzheimer’s disease and schizophrenia). For example, in both resting and task fMRI, the APOE ε4 locus strongly associated with Alzheimer’s disease was particularly associated with the visual cortex in the secondary visual and default mode networks. In summary, by analyzing biobank-scale fMRI data in high-resolution brain parcellation, this study advances our understanding of the genetic determinants of cerebral cortex function and prioritizes genetically associated fMRI traits for clinical applications of specific brain disorders.

Neuromyelitis optica spectrum disorder (NMOSD) may sometimes be misdiagnosed as multiple sclerosis (MS) because both disorders have similar clinical presentations and commonly show white matter damage in the brain. Diffusion tensor imaging (DTI) is an advanced MRI technique to assess the microstructural organization of white matter and provides greater pathological specificity than conventional MRI. In the present combined cross-sectional and longitudinal study, the novel DTI technique of Track-Based Spatial Statistics (TBSS) was used to investigate the difference of DTI parameter abnormalities between NMOSD and MS.A total of 42 patients with NMOSD, 51 patients with MS and 56 health controls (HC) were recruited and of these 14 patients with NMOSD and 13 patients with MS were also studied at follow-up after an average interval of approximately one year. Measurements of fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD) and radial diffusivity (RD) were compared at baseline and follow-up in patients with NMOSD and MS.Significant reduction in FA, increase in MD, AD and RD were observed in patients with MS (p0.05) and reduced FA was shown in NMOSD (p0.05) compared to HC, with all the effects, together with lesion load on T1WI and T2WI, being greater in patients with MS than in patients with NMOSD (p0.05). There was no significant difference in the time interval to follow-up in patients with MS (1.37 years) and NMOSD (1.25 years) (p0.05), during which there were significant changes in EDSS score between baseline and follow-up in NMOSD and MS patients (p0.05). There was a significantly reduced FA, and increased MD and RD in patients with MS (p0.05), but no significant changes in patients with NMOSD (p0.05).Both MS and NMOSD have microstructure damage in white matter, while the progressive change in brain microstructural properties is observed in patients with MS but may not in patients with NMOSD in a short-term follow-up.

Brain regions communicate with each other via tracts of myelinated axons, commonly referred to as white matter. White matter microstructure can be measured in the living human brain using diffusion based magnetic resonance imaging (dMRI), and has been found to be altered in patients with neuropsychiatric disorders. Although under strong genetic control, few genetic variants influencing white matter microstructure have ever been identified. Here we identified common genetic variants influencing white matter microstructure using dMRI in 42,919 individuals (35,741 in the UK Biobank). The dMRIs were summarized into 215 white matter microstructure traits, including 105 measures from tract-specific functional principal component analysis. Genome-wide association analysis identified many novel white matter microstructure associated loci (P< 2.3 × 10−10). We identified shared genetic influences through genetic correlations between white matter tracts and 62 other complex traits, including stroke, neuropsychiatric disorders (e.g., ADHD, bipolar disorder, major depressive disorder, schizophrenia), cognition, neuroticism, chronotype, as well as non-brain traits. Common variants associated with white matter microstructure alter the function of regulatory elements in glial cells, particularly oligodendrocytes. White matter associated genes were enriched in pathways involved in brain disease pathogenesis, neurodevelopment process, and repair of white matter damage (P< 1.5 × 10−8). In summary, this large-scale tract-specific study provides a big step forward in understanding the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

Patients with subcortical ischemic vascular disease (SIVD) exhibit a high risk of cognitive impairment that might be caused by neurologic deficits and vascular injuries. However, the mechanism remains unknown. In current study, 24 normal controls (NC) and 54 SIVD patients, including 26 SIVD patients with no cognitive impairment (SIVD-NCI) and 28 SIVD patients with mild cognitive impairment (SIVD-MCI) underwent the resting-state functional MRI (rs-fMRI) and neuropsychological assessments. We combined regional homogeneity (ReHo) and cerebral blood flow (CBF) by using the global ReHo-CBF correlations coefficient and the ReHo/CBF ratio to detect the inner link between neuronal activity and vascular responses. Correlations between the ReHo/CBF ratio and neuropsychological assessments were explored in patients with SIVD. As a result, we identified significantly decreased global ReHo-CBF coupling in the SIVD-NCI group and SIVD- MCI group with respect to the NC. The SIVD-MCI group showed more serious decoupling of the global ReHo-CBF correlation. We also found a significantly abnormal ReHo/CBF ratio predominantly located in cognitive-related brain regions, including the left insula, right middle temporal gyrus, right precuneus, left precentral gyrus, and left inferior parietal lobule but not the supramarginal and angular gyri. The SIVD-MCI group showed more severe disorders of neurovascular coupling than the other two groups. Moreover, the ReHo/CBF ratio in the left precentral gyrus of the SIVD-NCI group exhibited a positive correlation with the MMSE scores. These findings suggested that patients with SIVD show abnormal neurovascular coupling at the early stage of the disease and during disease development. It might be associated with disease severity and cognitive impairment. Neurovascular decoupling in brain may be a possible neuropathological mechanism of SIVD.

BackgroundBased on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).MethodsRadiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.ResultsThirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691–0.927 in the training cohort and 0.700–0.951 in the validation cohort, respectively, thereby indicating good performance.ConclusionCT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.