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Children and adolescents are more susceptible than adults to developing posttraumatic stress disorder (PTSD). Pediatric PTSD is characterized by functional alterations in brain fear circuitry, but little is known about the underlying microstructural changes; previous work has mainly focused on the corpus callosum. This study is aimed at investigating brain-wide microstructural abnormalities in pediatric PTSD, their relationship to age and sex, and their potential diagnostic value. The microstructure of major white matter tracts was assessed from diffusion tensor images acquired from 24 treatment-naive non-comorbid PTSD patients

Background Schizophrenia is considered to be a disorder of dysconnectivity characterized by abnormal functional integration between distinct brain regions. Different brain connection abnormalities were found to be correlated with various clinical manifestations, but whether a common deficit in functional connectivity (FC) in relation to both clinical symptoms and cognitive impairments could present in first-episode patients who have never received any medication remains elusive. Objective To find a core deficit in the brain connectome that is related to both psychopathological and cognitive manifestations. Methods A total of 75 patients with first-episode schizophrenia and 51 healthy control participants underwent scanning of the brain and clinical ratings of behaviors. A principal component analysis was performed on the clinical ratings of symptom and cognition. Partial correlation analyses were conducted between the main psychopathological components and resting-state FC that were found abnormal in schizophrenia patients. Results Using the principal component analysis, the first principal component (PC1) explained 37% of the total variance of seven clinical features. The ratings of GAF and BACS contributed negatively to PC1, while those of PANSS, HAMD, and HAMA contributed positively. The FCs positively correlated with PC1 mainly included connections related to the insula, precuneus gyrus, and some frontal brain regions. FCs negatively correlated with PC1 mainly included connections between the left middle cingulate cortex and superior and middle occipital regions. Conclusion In conclusion, we found a linked pattern of FC associated with both psychopathological and cognitive manifestations in drug-naïve first-episode schizophrenia characterized as the dysconnection related to the frontal and visual cortex, which may represent a core deficit of brain FC in patients with schizophrenia.

Background Neuroimaging studies in posttraumatic stress disorder (PTSD) have identified various alterations in white matter (WM) microstructural organization. However, it remains unclear whether these are localized to specific regions of fiber tracts, and what diagnostic value they might have. This study set out to explore the spatial profile of WM abnormalities along defined fiber tracts in PTSD. Methods Diffusion tensor images were obtained from 77 treatment-naive noncomorbid patients with PTSD and 76 demographically matched trauma-exposed non-PTSD (TENP) controls. Using automated fiber quantification, tract profiles of fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were calculated to evaluate WM microstructural organization. Results were analyzed by pointwise comparisons, by correlation with symptom severity, and for diagnosis-by-sex interactions. Support vector machine analyses assessed the ability of tract profiles to discriminate PTSD from TENP. Results Compared to TENP, PTSD showed lower fractional anisotropy accompanied by higher radial diffusivity and mean diffusivity in the left uncinate fasciculus, and lower fractional anisotropy accompanied by higher radial diffusivity in the right anterior thalamic radiation. Tract profile alterations were correlated with symptom severity, suggesting a pathophysiological relevance. There were no significant differences in diagnosis-by-sex interaction. Tract profiles allowed individual classification of PTSD versus TENP with significant accuracy, of potential diagnostic utility. Conclusions These findings add to the knowledge of the neuropathological basis of PTSD. WM alterations based on a tract-profile quantification approach are a potential biomarker for PTSD.

Although a large number of case-control statistical and machine learning studies have been conducted to investigate structural brain changes in schizophrenia, how best to measure and characterize structural abnormalities for use in classification algorithms remains an open question. In the current study, a convolutional 3D autoencoder specifically designed for discretized volumes was constructed and trained with segmented brains from 477 healthy individuals. A cohort containing 158 first-episode schizophrenia patients and 166 matched controls was fed into the trained autoencoder to generate auto-encoded morphological patterns. A classifier discriminating schizophrenia patients from healthy controls was built using 80% of the samples in this cohort by automated machine learning and validated on the remaining 20% of the samples, and this classifier was further validated on another independent cohort containing 77 first-episode schizophrenia patients and 58 matched controls acquired at a different resolution. This specially designed autoencoder allowed a satisfactory recovery of the input. With the same feature dimension, the classifier trained with autoencoded features outperformed the classifier trained with conventional morphological features by about 10% points, achieving 73.44% accuracy and 0.8 AUC on the internal validation set and 71.85% accuracy and 0.77 AUC on the external validation set. The use of features automatically learned from the segmented brain can better identify schizophrenia patients from healthy controls, but there is still a need for further improvements to establish a clinical diagnostic marker. However, with a limited sample size, the method proposed in the current study shed insight into the application of deep learning in psychiatric disorders.

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.

Deep learning (DL) is a recently proposed subset of machine learning methods that has gained extensive attention in the academic world, breaking benchmark records in areas such as visual recognition and natural language processing. Different from conventional machine learning algorithm, DL is able to learn useful representations and features directly from raw data through hierarchical nonlinear transformations. Because of its ability to detect abstract and complex patterns, DL has been used in neuroimaging studies of psychiatric disorders, which are characterized by subtle and diffuse alterations. Here, we provide a brief review of recent advances and associated challenges in neuroimaging studies of DL applied to psychiatric disorders. The results of these studies indicate that DL could be a powerful tool in assisting the diagnosis of psychiatric diseases. We conclude our review by clarifying the main promises and challenges of DL application in psychiatric disorders, and possible directions for future research.

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. Qualitative and quantitative results demonstrate that the proposed methods achieve better performance than several existing state-of-the-art methods., Accepted by IEEE TMI

The neural basis underlying stereopsis defects in patients with amblyopia remains unclear, which hinders the development of clinical therapy. This study aimed to investigate visual network abnormalities in patients with amblyopia and their associations with stereopsis function. Spectral dynamic causal modeling methods were employed for resting-state functional magnetic resonance imaging data to investigate the effective connectivity (EC) among 14 predefined regions of interest in the dorsal and ventral visual pathways. We adopted two independent datasets, including a cross-sectional and a longitudinal dataset. In the cross-sectional dataset, we compared group differences in EC between 31 patients with amblyopia (mean age: 26.39 years old) and 31 healthy controls (mean age: 25.71 years old) and investigated the association between EC and stereoacuity. In addition, we explored EC changes after perceptual learning in a novel longitudinal dataset including 9 patients with amblyopia (mean age: 15.78 years old). We found consistent evidence from the two datasets indicating that the aberrant EC from V2v to LO2 is crucial for the stereoscopic deficits in the patients with amblyopia: it was weaker in the patients than in the controls, showed a positive linear relationship with the stereoscopic function, and increased after perceptual learning in the patients. In addition, higher-level dorsal (V3d, V3A, and V3B) and ventral areas (LO1 and LO2) were important nodes in the network of abnormal ECs associated with stereoscopic deficits in the patients with amblyopia. Our research provides insights into the neural mechanism underlying stereopsis deficits in patients with amblyopia and provides candidate targets for focused stimulus interventions to enhance the efficacy of clinical treatment for the improvement of stereopsis deficiency.

Background Isoflurane can induce anterograde amnesia. Hippocampal ripples are high-frequency oscillatory events occurring in the local field potentials of cornu ammonis 1 involved in memory processes. The authors hypothesized that isoflurane suppresses hippocampal ripples at a subanesthetic concentration by modulating the excitability of cornu ammonis 1 neurons. Methods The potencies of isoflurane for memory impairment and anesthesia were measured in mice. Hippocampal ripples were measured by placing recording electrodes in the cornu ammonis 1. Effects of isoflurane on the excitability of hippocampal pyramidal neurons and interneurons were measured. A simulation model of ripples based on the firing frequency of hippocampal cornu ammonis 1 neurons was used to validate the effects of isoflurane on neuronal excitability in vitro and on ripples in vivo. Results Isoflurane at 0.5%, which did not induce loss of righting reflex, impaired hippocampus-dependent fear memory by 97.4 ± 3.1% (mean ± SD; n = 14; P < 0.001). Isoflurane at 0.5% reduced ripple amplitude (38 ± 13 vs. 42 ± 13 μV; n = 9; P = 0.003), rate (462 ± 66 vs. 538 ± 81 spikes/min; n = 9; P = 0.002) and duration (36 ± 5 vs. 48 ± 9 ms; n = 9; P < 0.001) and increased the interarrival time (78 ± 7 vs. 69 ± 6 ms; n = 9; P < 0.001) and frequency (148.2 ± 3.9 vs. 145.0 ± 2.9 Hz; n = 9; P = 0.001). Isoflurane at the same concentration depressed action potential frequency in fast-spiking interneurons while slightly enhancing action potential frequency in cornu ammonis 1 pyramidal neurons. The simulated effects of isoflurane on hippocampal ripples were comparable to recordings in vivo. Conclusions The authors’ results suggest that a subanesthetic concentration of isoflurane can suppress hippocampal ripples by differentially modulating the excitability of pyramidal neurons and interneurons, which may contribute to its amnestic action. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Quantitative analysis of immunohistochemically stained breast cancer specimens by cell counting is important for prognosis and treatment planning. This paper presents a robust, accurate, and novel method to label immunopositive and immunonegative cells automatically. During preprocessing, we developed an adaptive method to correct the colour aberration caused by imaging conditions. Next, a pixel-level segmentation was performed on preprocessed images using a support vector machine with a radial basis function kernel in HSV colour space. The segmentation result was processed by mathematical morphology operations to correct error-segmented regions and extract the marker for each cell. Validation studies showed that the automated cell-counting method had divergences varying from –5.05% to 3.99% compared with manual counting by a pathologist, indicating considerable agreement of the present automated cell counting method with manual counting. Thus, this method can free pathologists from laborious work and can potentially improve the accuracy and the reproducibility of diagnosis.

Background Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. Methods This study enrolled 5-20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model. Results Lung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model. Conclusions Predictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.

Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography. Traditional compressed sensing focuses on the design of handcrafted prior regularizers, which are usually image-dependent and time-consuming. Inspired by recently proposed deep learning-based CT reconstruction models, we extend the state-of-the-art LEARN model to a dual-domain version, dubbed LEARN++. Different from existing iteration unrolling methods, which only involve projection data in the data consistency layer, the proposed LEARN++ model integrates two parallel and interactive subnetworks to perform image restoration and sinogram inpainting operations on both the image and projection domains simultaneously, which can fully explore the latent relations between projection data and reconstructed images. The experimental results demonstrate that the proposed LEARN++ model achieves competitive qualitative and quantitative results compared to several state-of-the-art methods in terms of both artifact reduction and detail preservation., 10 pages, 11 figures

Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy whose neurobiological underpinnings remain poorly understood. The present study aimed to identify possible neurochemical alterations in the dorsolateral prefrontal cortex (DLPFC) in participants with SHE using proton magnetic resonance spectroscopy (1 H MRS). Thirty-nine participants with SHE (mean age, 30.7 years ± 11.3 [standard deviation], 24 men) and 59 controls (mean age, 29.4 years ± 10.4, 29 men) were consecutively and prospectively recruited and underwent brain magnetic resonance imaging and 1 H MRS in the bilateral DLPFCs. Brain concentrations of metabolites, including N-acetyl aspartate (NAA), myo-inositol (mI), choline, creatine, the sum of glutamate and glutamine, glutathione (GSH) and γ-aminobutyric acid, were estimated with LCModel and corrected for the partial volume effect of cerebrospinal fluid using tissue segmentation. ANCOVA analyses revealed lower concentration of NAA in the left DLPFC in participants with SHE compared with controls. A significant difference of NAA concentration between DLPFC in the two hemispheres (left > right) was observed only in the control group. We further confirmed a higher GSH concentration in men than in women in SHE participants, which probably indicates that men are more susceptible to this disease. The mI concentration in the right DLPFC was negatively correlated with epilepsy duration. This study demonstrates that DLPFC is an important brain region involved in the pathophysiology of SHE, in which both neurons and astrocytes appear impaired, and the elevated GSH level may suggest an abnormality related to oxidative stress.

Background: Anorexia nervosa (AN) is a debilitating illness whose neural basis remains unclear. Studies using tract-based spatial statistics (TBSS) with diffusion tensor imaging (DTI) have demonstrated differences in white matter (WM) microarchitecture in AN, but the findings are inconclusive and controversial. Objectives: To identify the most consistent WM abnormalities among previous TBSS studies of differences in WM microarchitecture in AN. Methods: By systematically searching online databases, a total of 11 datasets were identified, including 245 patients with AN and 246 healthy controls (HC). We used Seed-based d Mapping to analyze fractional anisotropy (FA) differences between AN patients and HC, and performed meta-regression analysis to explore the effects of clinical characteristics on WM abnormalities in AN. Results: The pooled results of all AN patients showed robustly lower FA in the corpus callosum (CC) and the cingulum compared to HC. These two regions preserved significance in the sensitivity analysis as well as in all subgroup analyses. Fiber tracking showed that the WM tracts primarily involved were the body of the CC and the cingulum bundle. Meta-regression analysis revealed that the body mass index and mean age were not linearly correlated with the lower FA. Conclusions: The most consistent WM microstructural differences in AN were in the interhemispheric connections and limbic association fibers. These common "targets" advance our understanding of the complex neural mechanisms underlying the puzzling symptoms of AN, and may help in developing early treatment approaches.

The need for treatment response predictive biomarkers is being increasingly recognized in children and adolescents with psychiatric disorders. Structural gray matter abnormalities as a predictor of treatment outcome in pediatric bipolar disorder have not been systematically investigated, especially early in the illness course. With a prospective longitudinal study design, the present study enrolled 52 bipolar adolescents with no history of treatment with mood stabilizers or a therapeutic dose of antipsychotic drugs and 31 healthy controls. Patients were randomly assigned to treatment with quetiapine or lithium after pretreatment data collection. A hierarchical cluster analysis was performed using pretreatment cortical thickness data that identified two discrete patient subgroups. Compared to healthy subjects, patients in subgroup 1 (n = 16) showed widespread greater cortical thickness mainly across heteromodal cortex but also involving some regions of unimodal cortex, while those in subgroup 2 (n = 36) showed regional cortical thinning mainly in superior temporal and superior parietal regions. Patients within subgroup 1 showed a significantly higher response rate to quetiapine than those in subgroup 2 (100% vs 53%). No statistically significant difference was found in lithium response rate between the patient subgroups (63% vs 53%). Pretreatment clinical ratings and neuropsychological data did not differ across subgroups. Our findings suggest the existence of distinct and clinically relevant subgroups of pediatric bipolar patients, as defined by pattern of cortical thickness. These groups appear to differentially respond to antipsychotic treatment—notably with greater cortical thickness relative to controls predicting better treatment response.

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly use regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold a state-of-the-art "fields of experts" based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a Learned Experts' Assessment-based Reconstruction Network ("LEARN") for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a competitive performance with the well-known Mayo Clinic Low-Dose Challenge Dataset relative to several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 12, reducing the computational complexity of typical iterative algorithms by orders of magnitude., Comment: 18 pages, 15 figures, accepted by IEEE TMI

Purpose To identify cerebral radiomic features related to diagnosis and subtyping of attention deficit hyperactivity disorder (ADHD) and to build and evaluate classification models for ADHD diagnosis and subtyping on the basis of the identified features. Materials and Methods A consecutive cohort of 83 age- and sex-matched children with newly diagnosed and never-treated ADHD (mean age 10.83 years ± 2.30; range, 7-14 years; 71 boys, 40 with ADHD-inattentive [ADHD-I] and 43 with ADHD-combined [ADHD-C, or inattentive and hyperactive]) and 87 healthy control subjects (mean age, 11.21 years ± 2.51; range, 7-15 years; 72 boys) underwent anatomic and diffusion-tensor magnetic resonance (MR) imaging. Features representing the shape properties of gray matter and diffusion properties of white matter were extracted for each participant. The initial feature set was input into an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power for diagnosis and subtyping. Random forest classifiers were constructed and evaluated on the basis of identified features. Results No overall difference was found between children with ADHD and control subjects in total brain volume (1069830.00 mm3 ± 90743.36 vs 1079 213.00 mm3 ± 92742.25, respectively; P = .51) or total gray and white matter volume (611978.10 mm3 ± 51622.81 vs 616960.20 mm3 ± 51872.93, respectively; P = .53; 413532.00 mm3 ± 41 114.33 vs 418173.60 mm3 ± 42395.48, respectively; P = .47). The mean classification accuracy achieved with classifiers to discriminate patients with ADHD from control subjects was 73.7%. Alteration in cortical shape in the left temporal lobe, bilateral cuneus, and regions around the left central sulcus contributed significantly to group discrimination. The mean classification accuracy with classifiers to discriminate ADHD-I from ADHD-C was 80.1%, with significant discriminating features located in the default mode network and insular cortex. Conclusion The results of this study provide preliminary evidence that cerebral morphometric alterations can allow discrimination between patients with ADHD and control subjects and also between the most common ADHD subtypes. By identifying features relevant for diagnosis and subtyping, these findings may advance the understanding of neurodevelopmental alterations related to ADHD. © RSNA, 2017 Online supplemental material is available for this article.

Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and negatively impact subsequent diagnoses and treatment planning. With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT. Despite the encouraging results achieved by these methods, there is still much room to further improve performance. In this paper, a novel Dual-domain Adaptive-scaling Non-local network (DAN-Net) for MAR. We correct the corrupted sinogram using adaptive scaling first to preserve more tissue and bone details as a more informative input. Then, an end-to-end dual-domain network is adopted to successively process the sinogram and its corresponding reconstructed image generated by the analytical reconstruction layer. In addition, to better suppress the existing artifacts and restrain the potential secondary artifacts caused by inaccurate results of the sinogram-domain network, a novel residual sinogram learning strategy and nonlocal module are leveraged in the proposed network model. In the experiments, the proposed DAN-Net demonstrates performance competitive with several state-of-the-art MAR methods in both qualitative and quantitative aspects., Comment: 29 pages, 11 figures

Purpose To detect biophysical abnormalities in patients with postmedication treatment-resistant depression (TRD) with magnetization transfer imaging. Materials and Methods This study was approved by the local ethics committee, and written informed consent was obtained from all participants. Participants included 69 patients with major depressive disorder (MDD) (30 with TRD; 39 with non-TRD) and 41 healthy control subjects. Age and sex were examined with one-way analysis of variance and χ2 tests and were well matched among the three groups. Whole-brain voxel-based analysis was used to compare the magnetization transfer ratio (MTR) between the three groups. Regional MTR values were used to analyze the correlations with symptom severity and illness duration. Results MTR differences were identified in the bilateral precentral gyrus, left cerebellum posterior lobe, left middle occipital lobe, left precuneus, and left temporal lobe among the three groups. Relative to patients with non-TRD, those with TRD had significantly lower MTR in the task-positive network regions, including the bilateral precentral gyrus and left middle occipital lobe, and had lower MTR in the default mode network regions, including the left precuneus and left temporal lobe. Regional MTRs were not associated with symptom severity or illness duration. Conclusion These results suggest that treatment resistance in patients with MDD may be mediated by macromolecular abnormalities in the task-positive and default mode functional networks. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 29, 2017.

Background An overlap of clinical symptoms between major depressive disorder (MDD) and social anxiety disorder (SAD) suggests that the two disorders exhibit similar brain mechanisms. However, few studies have directly compared the brain structures of the two disorders. The aim of this study was to assess the gray matter volume (GMV) and cortical thickness alterations between non-comorbid medication-naive MDD patients and SAD patients. Methods High-resolution T1-weighted images were acquired from 37 non-comorbid MDD patients, 24 non-comorbid SAD patients and 41 healthy controls (HCs). Voxel-based morphometry analysis of the GMV (corrected with a false discovery rate of p, Highlights • MDD and SAD share common gray matter abnormalities in the orbitofrontal-striatal-thalamic circuit, salience and dorsal attention network. • MDD patients show disorder-specific involvement of the visual processing regions. • SAD patients show disorder-specific involvement of the precentral cortex. An overlap of clinical symptoms between major depressive disorder (MDD) and social anxiety disorder (SAD) suggests similar brain mechanisms for the two disorders. However, few studies have directly compared the brain structures of the two disorders. The aim of this study was to assess gray matter volume and cortical thickness alterations between non-comorbid medication-naive MDD patients and SAD patients. We found that MDD and SAD shared a common pattern of gray matter abnormalities in the orbitofrontal-striatal-thalamic circuit, salience network and dorsal attention network. MDD patients showed disorder-specific involvement of the visual processing regions. SAD patients showed disorder-specific involvement of the precentral cortex.

Background Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas. Methods This single-institution retrospective study included 100 patients with midline gliomas, including 40 patients with H3 K27M mutations and 60 wild-type patients. Radiomics features were extracted from fluid-attenuated inversion recovery images. Prior to autoML analysis, the dataset was randomly stratified into separate 75% training and 25% testing cohorts. The Tree-based Pipeline Optimization Tool (TPOT) was applied to optimize the machine learning pipeline and select important radiomics features. We compared the performance of 10 independent TPOT-generated models based on training and testing cohorts using the area under the curve (AUC) and average precision to obtain the final model. An independent cohort of 22 patients was used to validate the best model. Results Ten prediction models were generated by TPOT, and the accuracy obtained with the best pipeline ranged from 0.788 to 0.867 for the training cohort and from 0.60 to 0.84 for the testing cohort. After comparison, the AUC value and average precision of the final model were 0.903 and 0.911 in the testing cohort, respectively. In the validation set, the AUC was 0.85, and the average precision was 0.855 for the best model. Conclusions The autoML classifier using radiomics features of conventional MR images provides high discriminatory accuracy in predicting the H3 K27M mutation status of midline glioma.

Previous studies have shown that posttraumatic stress disorder (PTSD) is associated with dysfunction of the limbic system, in which the amygdala plays an important role. The purpose of this study was to evaluate whether the neurochemical concentrations assessed by proton magnetic resonance spectroscopy (1H-MRS) in the amygdala are abnormal in children and adolescents with PTSD. Twenty-eight pediatric PTSD patients (11 boys, 17 girls) and 24 matched trauma-exposed control subjects (9 boys, 15 girls) underwent magnetic resonance brain imaging and 1H-MRS of the bilateral amygdalae. The concentrations of N-acetylaspartate (NAA), myo-inositol (mI), total creatine (tCr) and total choline (tCho) in the right amygdala were significantly increased in PTSD patients compared with trauma-exposed control subjects. There were significant group-by-age interactions in the left amygdala NAA and right amygdala mI concentrations: older pediatric patients with PTSD had higher left amygdala NAA concentration and younger patients had higher right amygdala mI concentration than trauma-exposed control subjects. There was also a significant correlation between right mI concentration and time since trauma in PTSD patients. Finally, there was significant group-by-age interaction in the left amygdala volume; intragroup analysis revealed that the right amygdala volume was significantly lower than the left in the PTSD group, but not in the control group. These neurochemical abnormalities of the amygdala may indicate that dysfunctions of both neurons and glial cells are involved in the pathology of pediatric PTSD.

Anorexia nervosa (AN) is a severe psychiatric disorder with high mortality. The underlying neurobiological mechanisms are not well understood, and high-resolution structural magnetic resonance brain imaging studies have given inconsistent results. Here we aimed to psychoradiologically define the most prominent and replicable abnormalities of gray matter volume (GMV) in AN patients, and to examine their relationship to demographics and clinical characteristics, by means of a new coordinate-based meta-analytic technique called seed-based d mapping (SDM). In a pooled analysis of all AN patients we identified decreased GMV in the bilateral median cingulate cortices and posterior cingulate cortices extending to the bilateral precuneus, and the supplementary motor area. In subgroup analysis we found an additional decreased GMV in the right fusiform in adult AN, and a decreased GMV in the left amygdala and left anterior cingulate cortex in AN patients without comorbidity (pure AN). Thus, the most consistent GMV alterations in AN patients are in the default mode network and the sensorimotor network. These psychoradiological findings of the brain abnormalities might underpin the neuropathophysiology in AN.

The city of Wuhan, Hubei province in China is the focus of global attention due to the Corona Virus Disease 2019 (COVID-19) outbreak [1]. Sichuan, as a province near Hubei, also has been involved. As of February 12, 2020, 59 741 confirmed cases of COVID-19 have been reported in China, of which 451 cases have been identified in Sichuan province. This disease is caused by infection of a new coronavirus named COVID-19 by the World Health Organization (WHO). According to the latest research, the novel coronavirus is 96% identical at the whole-genome level to a bat coronavirus, leading to speculation that this new coronavirus may originate from bats [2, 3]. Current epidemiologic data indicate that person-to-person transmission of COVID-19 is occurring [4]. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: The authors reported no biomedical financial interests or potential conflicts of interest. Conflict of interest: The authors reported no biomedical financial interests or potential conflicts of interest. Conflict of interest: The authors reported no biomedical financial interests or potential conflicts of interest. Conflict of interest: The authors reported no biomedical financial interests or potential conflicts of interest. Conflict of interest: The authors reported no biomedical financial interests or potential conflicts of interest.

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder–decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.

This study tested for differences of white matter integrity between treated and never-treated long-term schizophrenia patients, matched on illness duration, and for differential changes in relation to age in these two groups relative to healthy comparison subjects.This cross-sectional diffusion tensor imaging study included 31 never-treated and 46 matched antipsychotic-treated patients with long-term schizophrenia and 58 healthy comparison subjects. Fractional anisotropy measures of white matter tracts were extracted and compared. Linear regression analysis was used to explore the association between age and fractional anisotropy among the three groups.Fractional anisotropy significantly differed among the three groups in 14 of 20 white matter tracts defined in the Johns Hopkins University white matter template. Never-treated patients displayed greater reduction of fractional anisotropy than antipsychotic-treated patients in the left anterior thalamic radiation, the left cingulum-hippocampus pathway, the splenium and genu of the corpus callosum, and the left superior longitudinal fasciculus, and greater fractional anisotropy in the right uncinate fasciculus. Both patient groups showed multiple reductions relative to healthy comparison subjects. Never-treated patients showed an accelerated and clinically relevant age-related reduction of fractional anisotropy in the genu of the corpus callosum.These psychoradiological findings provide insight into the regional distribution of white matter deficits in the years after illness onset in long-term schizophrenia. Findings of greater impairments in never-treated patients, and a greater age-related reduction in the genu of the corpus callosum in these patients, suggest that long-term antipsychotic treatment does not adversely affect white matter tracts over the longer-term course of illness and may confer benefits.

Presbycusis (PC) is characterized by bilateral sensorineural hearing loss at high frequencies and speech-perception difficulties in noisy environments and has a strikingly detrimental impact on cognitive function. As the neural consequences of PC may involve the whole brain, we hypothesized that patients with PC would show structural alterations not only in the auditory cortex but also in the cortexes involved in cognitive function. The purpose of this study was to use surface-based morphometry (SBM) analysis to elucidate whole-brain structural differences between patients with PC and age-matched normal hearing controls. Three-dimensional T1-weighted MR images of 26 patients with mild PC and 26 age-, sex- and education-matched healthy controls (HCs) were acquired. All participants underwent a battery of neuropsychological tests. Our results revealed gray matter atrophy in several auditory cortical areas, nodes of the default mode network (DMN), including the bilateral precuneus and inferior parietal lobule, the right posterior cingulate cortex (PCC), and the right insula of patients with PC compared to that in the HCs. Our findings also revealed that hearing loss was associated with reduced gray matter volume in the right primary auditory cortex of patients with PC. Moreover, structural alterations in the nodes of the DMN were associated with cognitive impairments in PC patients. Additionally, this study provides evidence that a thicker right insula is associated with better speech perception in patients with PC. Based on these findings, we argue that the onset of PC seems to trigger its own cascade of conditions, including a need for increased cognitive resources during speech comprehension, which might lead to auditory and cognition-related cortical reorganization.

Background Previous psychoradiological studies of posttraumatic stress disorder (PTSD) were mainly of patients at a chronic stage, focusing on brain regions outside the amygdala. The goals of this study were to investigate the early biochemical and structural changes of anterior cingulate cortex (ACC) and amygdala in patients with PTSD and to explore their relationships. Methods Seventy-eight drug-naive PTSD subjects and 71 non-PTSD age- and sex-matched control subjects were enrolled, all of whom had suffered the same earthquake about one year before. Single-voxel proton magnetic resonance spectroscopy (1H-MRS) was performed and absolute metabolite concentrations in ACC and bilateral amygdalae were estimated with LCModel. Bilateral amygdalae were manually outlined and their volumes were calculated and corrected for the total intracranial volume. Results The PTSD group showed significantly increased N-acetylaspartate (NAA) concentration in the ACC, increased creatine (Cr) concentration in the left amygdala, and increased myo-inositol (mI) concentration in the right amygdala, compared to non-PTSD controls. The NAA concentration in ACC was negatively correlated with the time since trauma. The PTSD group showed significantly decreased volumes of bilateral amygdalae compared to non-PTSD controls, but amygdala volumes were not correlated with metabolite concentrations. Limitations Longitudinal studies are needed to explore the metabolic and structural changes of PTSD at different stages. The volume of ACC was not measured. Conclusions This concurrent increase in some metabolite concentrations and decrease of amygdala volumes may represent a pattern of biochemical and morphological changes in recent-onset PTSD which is different from that reported in chronic PTSD.

Introduction: There are still uncertainties about the true nature of age related changes in topological properties of the brain functional network and its structural connectivity during various developmental stages. In this cross- sectional study, we investigated the effects of age and its relationship with regional nodal properties of the functional brain network and white matter integrity. Method: DTI and fMRI data were acquired from 458 healthy Chinese participants ranging from age 8 to 81 years. Tractography was conducted on the DTI data using FSL. Graph Theory analyses were conducted on the functional data yielding topological properties of the functional network using SPM and GRETNA toolbox. Two multiple regressions were performed to investigate the effects of age on nodal topological properties of the functional brain network and white matter integrity. Result: For the functional studies, we observed that regional nodal characteristics such as node betweenness were decreased while node degree and node efficiency was increased in relation to increasing age. Perversely, we observed that the relationship between nodal topological properties and fasciculus structures were primarily positive for nodal betweenness but negative for nodal degree and nodal efficiency. Decrease in functional nodal betweenness was primarily located in superior frontal lobe, right occipital lobe and the global hubs. These brain regions also had both direct and indirect anatomical relationships with the 14 fiber bundles. A linear age related decreases in the Fractional anisotropy (FA) value was found in the callosum forceps minor. Conclusion: These results suggests that age related differences were more pronounced in the functional than in structural measure indicating these measures do not have direct one-to-one mapping. Our study also indicates that the fiber bundles with longer fibers exhibited a more pronounced effect on the properties of functional network.

Since absorption of X-ray radiation has the possibility of inducing cancerous, genetic and other diseases to patients, researches usually attempt to reduce the radiation dose. However, reduction of the radiation dose associated with CT scans will unavoidably increase the severity of noise and artifacts, which can seriously affect diagnostic confidence. Due to the outstanding performance of deep neural networks in image processing, in this paper, we proposed a Stacked Competitive Network (SCN) approach to noise reduction, which stacks several successive Competitive Blocks (CB). The carefully handcrafted design of the competitive blocks was inspired by the idea of multi-scale processing and improvement the network's capacity. Qualitative and quantitative evaluations demonstrate the competitive performance of the proposed method in noise suppression, structural preservation, and lesion detection.

Background While suppressor of cytokine signaling 3 (SOCS3) plays a crucial role in suppressing dysplasia and tumorigenesis, it also offers a typical instance of DNA methylation in the regulation of gene transcription, since the promoter region of the SOCS3 gene is rich in CpG islands (CGIs). During liver regeneration initiated by partial hepatectomy, SOCS3 acts as a suppressor to balance the acute-phase response and terminate the regeneration. This study aimed to determine whether the variation of SOCS3 expression throughout liver regeneration is also regulated by its DNA methylation. Methods We established a 70% partial hepatectomy mouse model and the animals were sacrificed at indicated times to assess the SOCS3 expression. We performed bisulfite sequencing PCR and DNA sequencing to investigate the detailed cytosine methylation in the SOCS3 gene. Results Within the promoter sequence, 58 CGIs were identified and 30 were found variously methylated before or after operation; however, methylation remained at a very low level. No evidence indicated that the total methylation level or the methylation of any CpG site regularly changed throughout liver regeneration. Conclusion DNA methylation or demethylation seems to be a relatively stable modification of cytosine, but not a dynamic and reversible process to regulate gene transcription in daily and acute pathophysiological events.

The mechanisms by which hepatic function is maintained after extensive parenchymal loss are unclear. In this study, we propose a novel concept of “functional heterogeneity” of hepatocytes based on their different expression of acetylated histones, the markers of active gene transcription, to explain the powerful compensatory capability of the liver. In the healthy human liver, only a fraction of the hepatocytes were marked by acetylated histones (ac-H2AK5, ac-H2BK5, ac-H3K9, ac-H3K14, ac-H3K27, and ac-H3K9/14). With the progression of cirrhosis, the ratio of the positive cells was gradually elevated, accompanied by the gradual exhaustion of the negative cells. By examining the global transcriptome of the mouse hepatocytes, we observed that the primed genes in the positive cells were much more numerous than those in negative cells. In a 70% hepatectomized mouse, the remnant hepatocytes were extensively activated, and the liver function was well maintained even when regeneration was severely inhibited. The functional compensation was absolutely dependent on the elevated expression of acetyl-histones. Additionally, when liver regeneration was blocked, the metabolism-related genes seemed to be preferentially transcribed. In conclusion, we demonstrate that normally, part of the active hepatocytes are competent for routine physiological requirements. The inactive hepatocytes, delicately regulated by acetyl-histones, act as a functional reservoir for future activation to restore the liver function after massive parenchymal loss.

Objective: Large segmental bone defect repair remains a clinical and scientific challenge with increasing interest focusing on combining gene transfection with tissue engineering techniques. The aim of this study is to investigate the effect of connective tissue growth factor (CTGF) on the proliferation and osteogenic differentiation of the bone marrow mesenchymal stem cells (MSCs). Methods: A CTGF-expressing plasmid (pCTGF) was constructed and transfected into MSCs. Then expressions of bone morphogenesis-related genes, proliferation rate, alkaline phosphatase activity, and mineralization were examined to evaluate the osteogenic potential of the CTGF gene-modified MSCs. Results: Overexpression of CTGF was confirmed in pCTGF-MSCs. pCTGF transfection significantly enhanced the proliferation rates of pCTGF-MSCs (P

Importance Accumulating evidence supports the hypothesis that cerebral white matter abnormalities are involved in the pathophysiology of schizophrenia; however, findings from in vivo neuroimaging studies have been inconsistent. Besides confounding factors, including age, illness duration, and medication effects, an additional cause for the inconsistent results may be heterogeneity in the nature of white matter alterations associated with the disorder. Objective To investigate whether different patterns of white matter abnormalities exist in a large cohort of medication-naive patients with first-episode schizophrenia and the relationship between such patterns and clinical parameters. Design, Setting, and Participants A cross-sectional diffusion tensor imaging study of 113 medication-naive patients with first-episode schizophrenia and 110 demographically matched healthy control individuals. The study was conducted in the mental health center of West China Hospital, Sichuan University, Chengdu, China, from January 2006 to June 2014. Main Outcomes and Measures The patterns of white matter abnormalities revealed by tract-specific analysis in conjunction with hierarchical clustering. Results With diffusion features extracted from 18 fiber tracts, cluster analysis revealed 2 patterns of abnormalities. One pattern (42.5% of patient sample) showed widespread white matter abnormalities compared with matched healthy control individuals, while another pattern (57.5% of patient sample) only showed circumscribed regional white matter abnormalities, mainly in the left superior longitudinal fasciculus. Patients in these subgroups did not differ in demographic features; however, negative symptoms were more severe in patients with widespread white matter abnormalities. Conclusions and Relevance Two distinct patterns of white matter abnormalities exist at the early phase of schizophrenia, with those having global abnormalities experiencing more severe negative symptoms. The finding that distinct subgroups of patients with schizophrenia have different forms of white matter pathology may reflect qualitatively distinct genetic influences or neurodevelopmental alterations and thus represents a promising strategy for resolving neurobiological heterogeneity in the schizophrenia syndrome.

Background: Published meta-analyses of resting-state regional cerebral blood flow (rCBF) studies of major depressive disorder (MDD) have included patients receiving antidepressants, which might affect brain activity and thus bias the results. To our knowledge, no meta-analysis has investigated regional homogeneity changes in medication-free patients with MDD. Moreover, an association between regional homogeneity and rCBF has been demonstrated in some brain regions in healthy controls. We sought to explore to what extent resting-state rCBF and regional homogeneity changes co-occur in the depressed brain without the potential confound of medication. Methods: Using the effect-size signed differential mapping method, we conducted 2 meta-analyses of rCBF and regional homogeneity studies of medication-free patients with MDD. Results: Our systematic search identified 14 rCBF studies and 9 regional homogeneity studies. We identified conjoint decreases in resting-state rCBF and regional homogeneity in the insula and superior temporal gyrus in medication-free patients with MDD compared with controls. Other changes included altered resting-state rCBF in the precuneus and in the frontal–limbic–thalamic–striatal neural circuit as well as altered regional homogeneity in the uncus and parahippocampal gyrus. Meta-regression revealed that the percentage of female patients with MDD was negatively associated with resting-state rCBF in the right anterior cingulate cortex and that the age of patients with MDD was negatively associated with rCBF in the left insula and with regional homogeneity in the left uncus. Limitations: The analysis techniques, patient characteristics and clinical variables of the included studies were heterogeneous. Conclusion: The conjoint alterations of rCBF and regional homogeneity in the insula and superior temporal gyrus may be core neuropathological changes in medication-free patients with MDD and serve as a specific region of interest for further studies on MDD.

Previous structural imaging studies have found evidence of brain morphometric changes in patients with major depressive disorder (MDD), but these studies rarely excluded compounding effects of certain important factors, such as medications and long duration of illnesses. Furthermore, the neurobiological mechanism of the macroscopic findings of structural alterations in MDD patients remains unclear. In this study, we utilized magnetization transfer imaging, a quantitative measure of the macromolecular structural integrity of brain tissue, to identify biophysical alterations, which are represented by a magnetization transfer ratio (MTR), in MDD patients. To ascertain whether MTR changes occur independent of volume loss, we also conduct voxel-based morphometry (VBM) analysis. The participants included 27 first-episode, drug-naive MDD patients and 28 healthy controls matched for age and gender. Whole-brain voxel-based analysis was used to compare MTR and gray matter volume across groups and to analyse correlations between MTR and age, symptom severity, and illness duration. The patients exhibited significantly lower MTR in the left superior parietal lobule and left middle occipital gyrus compared with healthy controls, which may be related to the attentional and cognitive dysfunction in MDD patients. The VBM analysis revealed significantly increased gray matter volume in right postcentral gyrus in MDD patients. These findings in first-episode, drug-naive MDD patients may reflect microstructural gray matter changes in the parietal and occipital cortices close to illness onset that existed before volume loss, and thus potentially provide important new insight into the early neurobiology of depression.

The mechanisms by which hepatic function is maintained after extensive parenchymal loss are unclear. In this study, we propose a novel concept of “functional heterogeneity” of hepatocytes based on their different expression of acetylated histones, the markers of active gene transcription, to explain the powerful compensatory capability of the liver. In the healthy human liver, only a fraction of the hepatocytes were marked by acetylated histones (ac-H2AK5, ac-H2BK5, ac-H3K9, ac-H3K14, ac-H3K27, and ac-H3K9/14). With the progression of cirrhosis, the ratio of the positive cells was gradually elevated, accompanied by the gradual exhaustion of the negative cells. By examining the global transcriptome of the mouse hepatocytes, we observed that the primed genes in the positive cells were much more numerous than those in negative cells. In a 70% hepatectomized mouse, the remnant hepatocytes were extensively activated, and the liver function was well maintained even when regeneration was severely inhibited. The functional compensation was absolutely dependent on the elevated expression of acetyl-histones. Additionally, when liver regeneration was blocked, the metabolism-related genes seemed to be preferentially transcribed. In conclusion, we demonstrate that normally, part of the active hepatocytes are competent for routine physiological requirements. The inactive hepatocytes, delicately regulated by acetyl-histones, act as a functional reservoir for future activation to restore the liver function after massive parenchymal loss.

Innovative cell-based therapies, including hepatic tissue engineering following hepatocyte transplantation, are considered as theoretical alternatives to liver transplant or for partial replacement of liver function in patients. However, recent progress in hepatic tissue engineering has been hampered by low initial hepatocyte engraftment and insufficient blood supply in vivo. We developed an intact 3D scaffold of an extracellular matrix (ECM) derived from a decellularized liver lobe, with layer-by-layer (LbL) heparin deposition to avoid thrombosis, which we repopulated with hepatocytes and successfully implanted as a tissue-engineered liver (TEL) into the portal system. The TEL provided sufficient volume for transplantation of cell numbers representing up to 10% of whole-liver equivalents and was perfused by portal vein blood. Treatment of extended hepatectomized rats with a TEL improved liver function and prolonged survival; mean lifespan was extended from 16 to 72 h. At 72 h postoperation, the TEL sustained functional and viable hepatocytes. In conclusion, we propose the TEL as a state-of-the-art substitute for whole-liver transplantation and as a proof of concept for the technology that will eventually allow for the transplantation of a reconstituted liver.

To study age-related metabolic changes in different brain regions.Point-resolved spectroscopy (repetition time/echo time = 2000 ms/30 ms) was performed in the left and right hippocampus, the left thalamus and the left centrum semiovale of 80 healthy subjects (37 females and 43 males aged 7-64 years). Analysis of covariance and linear regression were used for statistical analysis. Both metabolite concentration ratios with respect to total creatine (tCr) and absolute metabolite concentrations were included for analysis.Ins (myo-inositol)/tCr (p 0.001) and absolute Ins concentration (p = 0.031) were significantly increased with age after adolescence. NAA (N-acetylaspartic acid)/tCr (p 0.001) and absolute NAA concentration (p = 0.010) significantly declined with age after adolescence.Age-related increase of Ins and decline of NAA are found in all three regions, especially at the hippocampus, indicating possible gliosis in the ageing brain.We could use NAA/tCr and Ins/tCr as an indicator to estimate the neurons-to-glial cells ratio at the thalamus. This may be an index to distinguish normal tissues from gliosis.