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The population ageing process worldwide is leading to an increasing number of women in the perimenopausal phase. Many of the perimenopausal symptoms, such as headache, depression, insomnia, and cognitive decline, are neurological in nature. Therefore, the study of the perimenopausal brain is of great importance. In addition, relevant studies can also provide an imaging basis for multiple therapies to treat perimenopausal symptoms. Because of its non-invasive nature, magnetic resonance imaging (MRI) has now been widely applied to the study of perimenopausal brains, revealing alterations in the brain associated with symptoms during the menopause transition. In this review, we collected papers and works of literature on the perimenopausal brain using MRI techniques in the Web of Science database. We firstly described the general principles and analysis methods of different MRI modalities briefly and then reviewed the structural, functional, perfusion, and metabolic compounds changes in the brain of perimenopausal women respectively, and described the latest advances in probing the perimenopausal brain using MRI, resulting in summary diagrams and figures. Based on the summary of existing works of the literature, this review further provided a perspective on multi-modal MRI studies in the perimenopausal brain, suggesting that population-based, multi-center, and longitudinal studies will be beneficial to the comprehensive understanding of changes in the perimenopausal brain. In addition, we found a hint towards neural heterogeneity in the perimenopausal brain, which should be addressed by future MRI studies to provide more help for the precise diagnosis and personalized treatment of perimenopausal symptoms. Key Points: • Perimenopause is not only a physiological transition but also a period of neurological transition. • Multi-modal MRI studies have revealed that perimenopause is accompanied by alterations in the brain, which is implicated in many perimenopausal symptoms. • The diversity in the multi-modal MRI findings may give a hint to neural heterogeneity in the perimenopausal brain. [ABSTRACT FROM AUTHOR]

Attention‐deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age‐inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting‐state functional magnetic resonance (rs‐fMRI) have more discriminative power for the diagnosis of ADHD. The rs‐fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD‐200 Consortium. A total of four preprocessed rs‐fMRI images including regional homogeneity (ReHo), amplitude of low‐frequency fluctuation (ALFF), voxel‐mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs‐fMRI information to distinguish ADHD from healthy controls. The rs‐fMRI‐based radiomics features have the potential to be neuroimaging biomarkers for ADHD. [ABSTRACT FROM AUTHOR]

• Deep learning model can accurately evaluate high-risk tiles of WHMs-related according to Fazekas scales. • The model can predict the progression of WMHs on baseline T2-FLAIR images. • WMHs are associated with GM and WM atrophies, micro-structural changes, and perfusion alterations in high-risk areas. • Multi-sequence MRI measures of high-risk areas demonstrate differences in WMH-induced cognitive decline. White matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted with WMHs lacks comprehensive investigation. This study developed a WMHs risk prediction model to evaluate WHMs according to Fazekas scales, and to locate potential regions with high risks across the entire brain. We developed a WMHs risk prediction model, which consisted of the following steps: T2 fluid attenuated inversion recovery (T2-FLAIR) image of each participant was firstly segmented into 1000 tiles with the size of 32 × 32 × 1, features from the tiles were extracted using the ResNet18-based feature extractor, and then a 1D convolutional neural network (CNN) was used to score all tiles based on the extracted features. Finally, a multi-layer perceptron (MLP) was constructed to predict the Fazekas scales based on the tile scores. The proposed model was trained using T2-FLAIR images, we selected tiles with abnormal scores in the test set after prediction, and evaluated their corresponding gray matter (GM) volume, white matter (WM) volume, fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF) via longitudinal and multi-sequence Magnetic Resonance Imaging (MRI) data analysis. The proposed WMHs risk prediction model could accurately predict the Fazekas ratings based on the tile scores from T2-FLAIR MRI images with accuracy of 0.656, 0.621 in training data set and test set, respectively. The longitudinal MRI validation revealed that most of the high-risk tiles predicted by the WMHs risk prediction model in the baseline images had WMHs in the corresponding positions in the longitudinal images. The validation on multi-sequence MRI demonstrated that WMHs were associated with GM and WM atrophies, WM micro-structural and perfusion alterations in high-risk tiles, and multi-modal MRI measures of most high-risk tiles showed significant associations with Mini Mental State Examination (MMSE) score. Our proposed WMHs risk prediction model can not only accurately evaluate WMH severities according to Fazekas scales, but can also uncover potential markers of WMHs across modalities. The WMHs risk prediction model has the potential to be used for the early detection of WMH-related alterations in the entire brain and WMH-induced cognitive decline. [ABSTRACT FROM AUTHOR]

Purpose.Anisometropic amblyopia usually occurs during early childhood and results in monocular visual deficit. Recent neuroimaging studies have demonstrated structural and functional alterations in pediatric anisometropic amblyopia (PAA) patients. However, the relationship between structural and functional alterations remains largely unknown. The aim of this study was to investigate the relationship between structural and functional alterations in PAA patients. Materials and Methods. Eighteen PAA patients and 14 healthy children underwent a multimodal MRI scanning including T1WI and functional MRI (fMRI). Voxel-based morphometry was used to assess structural alterations between PAA patients and healthy children. Regional homogeneity (ReHo) was used to investigate changes in local spontaneous brain activity in the enrolled subjects. Correlations between structural, functional alterations, and clinical information were analyzed in the PAA group. Results. Compared with healthy children, PAA patients exhibited significantly reduced ReHo of spontaneous brain activity in the right superior temporal gyrus (STG) and right middle frontal gyrus (MFG) and increased gray matter volume in the right lobules 4 and 5 of the cerebellum. The gray matter volume of the right lobules 4 and 5 of the cerebellum was negatively correlated with the ReHo values of the right MFG. Conclusions. Our findings may suggest that PAA patients experience structural and functional abnormalities in brain regions related to oculomotor and visual-spatial information. In addition, the increased gray matter volume may compensate the decreased brain activity in the oculomotor regions, which reflects compensatory or neural plasticity in PAA patients. [ABSTRACT FROM AUTHOR]

Perimenopause is associated with increased risk of depression, vasomotor symptoms, and sexual dysfunction. To explore the effect of sex hormones on the functional connectivity (FC) of different brain regions related to sexual function in perimenopausal women. 32 premenopausal women (mean age, 47.75 ± 1.55 years) and 25 perimenopausal women (mean age, 51.60 ± 1.63 years) underwent sex hormone level measurements and resting-state fMRI. Serum levels of sex hormones, including prolactin (PRL), follicle-stimulating hormone (FSH), luteotropic hormone (LH), estradiol (E2), free testosterone (free-T), and progesterone (P), were measured. 10 brain regions related to sexual function were selected according to a meta-analysis, and FCs of the selected regions of interest were calculated as Pearson's correlation coefficient. Compared with premenopausal women, perimenopausal women showed increased FC between the right area 13 (A13_r) and the right medial superior frontal gyrus (mSFG), between the left dorsal granular insula (dIg_L) and the right superior frontal gyrus (SFG) (Gaussian random field-corrected at the voxel level, P <.001, and cluster level, P <.025). Furthermore, the PRL level was negatively correlated with the FC of A13_R with the right mSFG and the FC of dIg_L with the right SFG. These findings may be applicable to assessing brain dysfunction with FC changes in women approaching menopause. This study is the first to evaluate a direct relationship between sex hormone levels and brain FC changes in women approaching menopause. Sexual function was not assessed, which may weaken the conclusions related to sexual function. The results show that women approaching menopause suffered from aberrant intrinsic FC in regions related to sexual function, and reveal a direct relationship between serum sex hormone levels and FC changes related to sexual function. Lu W, Guo W, Cui D, et al. Effect of Sex Hormones on Brain Connectivity Related to Sexual Function in Perimenopausal Women: A Resting-State fMRI Functional Connectivity Study. J Sex Med 2019;16:711–720. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effects of high intraocular pressure (IOP) on central nervous system in patients with high‐tension glaucoma (HTG) by using resting‐state functional magnetic resonance imaging (rs‐fMRI). Methods: Thirty‐six patients with HTG and twenty age‐ and gender‐matched healthy controls (HCs) were recruited and underwent IOP measurement and rs‐fMRI scan. The whole brain regional homogeneity (ReHo) value was calculated among the enrolled subjects. Two‐sample t tests with permutation test and threshold‐free cluster enhancement was performed between HTG group and HCs. Correlation analyses between IOP and ReHo values were conducted. Results: Compared with HCs, HTG group showed increased ReHo values in the left lobule 8 of cerebellar hemisphere, left lobule 4 and 5 of cerebellar hemisphere and left fusiform gyrus (FG) (p < 0.05). HTG group showed decreased ReHo value in the left middle frontal gyrus (MFG) (p < 0.05). Intraocular pressure of the left eye in HTG group experienced a significant positive correlation with ReHo value of the left FG (r = 0.370, p = 0.026), IOP of the right eye in HTG group showed a significant negative correlation with ReHo value in the left MFG (r = −0.421, p = 0.011). Conclusion: Resting‐state fMRI ReHo analyses associated elevated IOP with abnormal regional activity in several brain regions related to higher visual function and visual memory consolidation. High‐tension glaucoma patients also showed diminished integration of visual information and cerebellar function. These results may provide imaging support for pathophysiological research of HTG and may reveal new targets for the accurate treatment of HTG. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have commonly been used for the differential diagnosis between benign and malignant thyroid nodules. The aim of the study described here was to develop an integrated system that combines a deep learning model and a clinical standard Thyroid Imaging Reporting and Data System (TI-RADS) for the simultaneous segmentation and risk stratification of thyroid nodules. Three hundred four ultrasound images from two independent sites with TI-RADS 4 thyroid nodules were collected. The edge connection and Criminisi algorithm were used to remove manually induced markers in ultrasound images. An integrated system based on TI-RADS and a mask region-based convolution neural network (Mask R-CNN) was proposed to stratify subclasses of TI-RADS 4 thyroid nodules and to segment thyroid nodules in the ultrasound images. Accuracy and the precision-recall curve were used to evaluate stratification performance, and the Dice similarity coefficient (DSC) between the segmentation of Mask R-CNN and the radiologist's contour was used to evaluate the segmentation performance of the model. The combined approach could significantly enhance the performance of the proposed integrated system. Overall stratification accuracy of TI-RADS 4 thyroid nodules, mean average precision and mean DSC of the proposed model in the independent test set was 90.79%, 0.8579 and 0.83, respectively. Specifically, stratification accuracy values for TI-RADS 4a, 4b and 4c thyroid nodules were 95.83%, 84.21% and 77.78%, respectively. An integrated system combining TI-RADS and a deep learning model was developed. The system can provide clinicians with not only diagnostic assistance from TI-RADS but also accurate segmentation of thyroid nodules, which improves the applicability of the system in clinical practice. [ABSTRACT FROM AUTHOR]

Background: The total-body positron emission tomography/computed tomography (PET/CT) system, with a long axial field of view, represents the state-of-the-art PET imaging technique. Recently, the total-body PET/CT system has been commercially available. The total-body PET/CT system enables high-resolution whole-body imaging, even under extreme conditions such as ultra-low dose, extremely fast imaging speed, delayed imaging more than 10 h after tracer injection, and total-body dynamic scan. The total-body PET/CT system provides a real-time picture of the tracers of all organs across the body, which not only helps to explain normal human physiological process, but also facilitates the comprehensive assessment of systemic diseases. In addition, the total-body PET/CT system may play critical roles in other medical fields, including cancer imaging, drug development and immunology. Main body: Therefore, it is of significance to summarize the existing studies of the total-body PET/CT systems and point out its future direction. This review collected research literatures from the PubMed database since the advent of commercially available total-body PET/CT systems to the present, and was divided into the following sections: Firstly, a brief introduction to the total-body PET/CT system was presented, followed by a summary of the literature on the performance evaluation of the total-body PET/CT. Then, the research and clinical applications of the total-body PET/CT were discussed. Fourthly, deep learning studies based on total-body PET imaging was reviewed. At last, the shortcomings of existing research and future directions for the total-body PET/CT were discussed. Conclusion: Due to its technical advantages, the total-body PET/CT system is bound to play a greater role in clinical practice in the future. [ABSTRACT FROM AUTHOR]

Background: Bone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination. Purpose: Machine learning analysis of [99mTc]‐methylene diphosphate (99mTc‐MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer. Methods: One hundred forty‐one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK‐SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics‐clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test. Results: The optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively. Conclusions: The machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics‐clinical integrated model demonstrated the best performance. [ABSTRACT FROM AUTHOR]

The present study aimed to explore brain morphological alterations associated with age and sex hormone levels between premenopausal and perimenopausal women using magnetic resonance imaging (MRI) T1‐weighted structural images. Thirty‐two premenopausal women aged (mean ± SD) 47.75 ± 1.55 years and twenty‐five recently perimenopausal women aged 51.60 ± 1.63 years were evaluated for sex hormone levels, including prolactin, follicle‐stimulating hormone, luteinising hormone, oestradiol, free testosterone and progesterone. A 3.0‐Tesla MRI scanner was utilised to acquire T1 images. Voxel‐based morphometry (VBM) was used to evaluate changes in grey matter volume between the two groups. The general linear model was applied with false discovery rate correction for between group voxel‐wise statistics. Spearman partial correlation analyses were conducted between age, sex hormone levels and regions of grey matter volume showing significant differences between the two groups. The VBM analysis revealed that age and menopause per se lead to grey matter volume reduction in certain brain structures. These structural changes might be potential causes of sexual dysfunction, nervous system degeneration and depression, which need to be examined in future studies. Our findings might provide evidence and guide future research in understanding the menopausal transition. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to explore the functional connectivity of the primary visual cortex (V1) in children with anisometropic amblyopia by using the resting-state functional connectivity analysis method and determine whether anisometropic amblyopia is associated with changes in brain function. Methods: Functional magnetic resonance imaging (fMRI) data were obtained from 16 children with anisometropia amblyopia (CAA group) and 12 healthy children (HC group) during the resting state. The Brodmann area 17 (BA17) was used as the region of interest, and the functional connection (FC) of V1 was analyzed in both groups. A two-sample t test was used to analyze the FC value between the two groups. Pearson's correlation was used to analyze the correlation between the mean FC value in the brain function change area of the CAA group and the best corrected visual acuity (BCVA) of amblyopia. p < 0.05 was considered statistically significant. Results: There were no significant differences in age and sex between the CAA and HC groups (p > 0.05). Compared to the HC group, the CAA group showed lower FC values in BA17 and the left medial frontal gyrus, as well as BA17 and the left triangle inferior frontal gyrus. Conversely, the CAA group showed higher FC values in BA17 and the left central posterior gyrus. Notably, BCVA in amblyopia did not correlate with the area of change in mean FC in the brain function of the CAA group. Conclusion: Resting-state fMRI-based functional connectivity analysis indicates a significant alteration in V1 of children with anisometropic amblyopia. These findings contribute additional insights into the neuropathological mechanisms underlying visual impairment in anisometropic amblyopia. [ABSTRACT FROM AUTHOR]

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA), the most common joint disease, is characterized by inflammation and cartilage degradation. Previous studies illustrated that Smad nuclear-interacting protein 1 (SNIP1) is an inhibitor of the TGF-β signal transduction pathway and SNIP1 has been reported as an anti-inflammatory factor. This study aimed to explore the role of SNIP1 in OA progression. In this study, the SNIP1 expression was evaluated in OA human and OA mice tissue and interleukin-1 beta (IL-1β)-induced chondrocytes. The Safranin-O (SO) staining and osteoarthritis research society international (OARSI) scoring system was used to evaluate cartilage injury. The gain- and loss-of-function studies for SNIP1 were performed in chondrocytes. The SNIP1 overexpression adenovirus was injected into mice by intra-articular injection. The SNIP1 expression was decreased in OA patients, OA mice, and IL-1β-stimulated chondrocytes. The cartilage injury of medial meniscus-induced OA (DMM-OA) mice at 8 weeks showed more severe than that at 4 weeks. The expression of SNIP1 was lower at 8 weeks than that at 4 weeks. In IL-1β-stimulated chondrocytes, SNIP1 overexpression reduced the expression of TNF-α and IL-6, alleviated ECM degradation, reduced the phosphorylation levels of p65 and IκBα, and decreased the p65 level in nuclear. Moreover, overexpression of SNIP1 alleviated cartilage injury in DMM-OA mice. In brief, our study suggested that SNIP1 alleviated OA and repressed inflammation by inhibiting the activation of NF-κB. This study might provide a new insight into OA treatment. [Display omitted] • SNIP1 expression was downregulated in OA tissues. • High expression of SNIP1 inhibited inflammation in chondrocytes. • SNIP1 inhibited the NF-κB pathway to reduced inflammation. [ABSTRACT FROM AUTHOR]

• A micro-sized porous Ni foil (MP Ni) with a thickness of ∼ 70.0 μm was prepared by powder metallurgy method. • A CoNiP/MP Ni electrocatalyst was constructed basing on the porous Ni foil. • The CoNiP/MP Ni catalyst is efficient and durable for both electrocatalytic hydrogen and oxygen evolution reactions. • The combination feature of active CoNiP particles and micro-sized porous Ni substrate were explored by DFT calculations. Constructing self-supported electrocatalysts is recognized as a practical implement to promote the electrocatalytic performance of water splitting. Herein, bimetallic phosphide CoNiP grown on a micro-sized porous nickel substrate (MP Ni) as electrocatalyst (CoNiP/MP Ni) has been developed to decrease the overpotentials of hydrogen evolution and oxygen evolution reaction. The micro-sized porous nickel substrate provides adequate specific surface area for loading active materials, and the hierarchically porous structure offers numerous interconnected channels, which together accelerates the mass transfer process in the electrocatalytic reaction. The CoNiP/MP Ni electrocatalyst achieves an ultra-low overpotentials of 37 mV at 10 mA∙cm−2 and 358 mV at 100 mA∙cm−2 for driving hydrogen evolution and oxygen evolution, respectively. The stable porous structure also enables the integrated CoNiP/MP Ni electrolyzer an excellent long-term operation stability. Meanwhile, first-principles calculations demonstrate that the CoNiP bimetallic phosphide possesses a low energy barrier and a strong interfacial bonding strength with micro-sized porous Ni from an atomic viewpoint. This work opens up a simple and new way of constructing low-cost, efficient and applicable self-supported electrocatalysts for water splitting. [ABSTRACT FROM AUTHOR]

Background: To establish a radiomics-clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients. Methods: We retrospectively analyzed 256 patients (122 with bone metastasis and 134 with benign bone disease) and randomized them in the ratio of 6:2:2 into training, test and validation sets. All patients underwent 99mTc-labeled methylene diphosphonate (99mTc-MDP) SPECT/CT. We manually outlined the volumes of interest (VOIs) of lesions using ITK-SNAP from SPECT and CT images. In the training set, radiomics features were extracted using PyRadiomics and selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we established three radiomics models (CT, SPECT and SPECT-CT models) using support vector machine (SVM). In addition, a radiomics-clinical model was constructed using multivariable logistic regression analysis. The four models' performance was assessed using the area under the receiver operating characteristic curve (AUC). Using DeLong test to make comparisons between the ROC (receiver operating characteristic) curves of different models. The clinical utility of the models was evaluated using decision curve analysis (DCA). Results: The radiomics-clinical displayed excellent performance, and its AUC was 0.941 and 0.879 in the training and test sets. The DCA of radiomics-clinical model showed the highest clinical utility. Conclusions: The radiomics-clinical nomogram for identifying bone metastasis and benign bone disease in tumor patients was suitable to assist in clinical decision. [ABSTRACT FROM AUTHOR]

Photoacoustic computed tomography (PACT) is a hybrid imaging modality that combines optical excitation and acoustic detection techniques. It obtains high‐resolution deep‐tissue images based on the deep penetration of light, the anisotropy of light absorption in objects, and the photoacoustic effect. Hence, PACT shows great potential in biomedical sample imaging. Recently, due to its advantages of high sensitivity to optical absorption and wide scalability of spatial resolution with the desired imaging depth, PACT has received increasing attention in preclinical and clinical practice. To date, there has been a proliferation of PACT systems designed for specific biomedical imaging applications, from small animals to human organs, from ex vivo to in vivo real‐time imaging, and from simple structural imaging to functional and molecular imaging with external contrast agents. Therefore, it is of great importance to summarize the previous applications of PACT systems in biomedical imaging and clinical practice. In this review, we searched for studies related to PACT imaging of biomedical tissues and samples over the past two decades; divided the studies into two categories, PACT imaging of preclinical animals and PACT imaging of human organs and body parts; and discussed the significance of the studies. Finally, we pointed out the future directions of PACT in biomedical applications. With the development of exogenous contrast agents and advances of imaging technique, in the future, PACT will enable biomedical imaging from organs to whole bodies, from superficial vasculature to internal organs, from anatomy to functions, and will play an increasingly important role in biomedical research and clinical practice. [ABSTRACT FROM AUTHOR]

Objective: The morphology of basilar artery (BA) may affect posterior circulation blood perfusion. We aimed to investigate whether different degrees of BA tortuosity could lead to the alterations of posterior circulation perfusion.We collected 138 subjects with different BA tortuosity scores, including 32 cases of score 0, 45 cases of score 1, 43 cases of score 2, and 18 cases of score 3. A higher score represented a higher degree of BA tortuosity. Ordered logistic regression analysis was performed to investigate the risk factors for BA tortuosity. We quantitatively measured the cerebral blood flow (CBF) in eight posterior circulation brain regions using arterial spin labeling. SPSS 25.0 was used for statistical analysis. The correlation between the CBF and BA tortuosity was corrected by the Bonferroni method. The significance level was set at 0.006 (0.05/8).Hypertension (HR: 2.39; 95%CI: 1.23–4.71; P = 0.01) and vertebral artery dominance (HR: 2.38; 95%CI: 1.10–4.67; P = 0.03) were risk factors for BA tortuosity. CBF in occipital gray matter (R = -0.383, P < 0.001), occipital white matter (R = -0.377, P < 0.001), temporal gray matter (R = -0.292, P = 0.001), temporal white matter (R = -0.297, P < 0.001), and cerebellum (R = -0.328, P < 0.001) were negatively correlated with BA tortuosity degree. No significant correlation was found between the BA tortuosity degree and CBF in hippocampus (R = -0.208, P = 0.014), thalamus (R = -0.001, P = 0.988) and brainstem (R = -0.204, P = 0.016).BA tortuosity could affect posterior circulation blood perfusion. CBF was negatively correlated with BA tortuosity degree. The morphology of BA may serve as a biomarker for posterior circulation and the severity of posterior circulation ischemia.Methods: The morphology of basilar artery (BA) may affect posterior circulation blood perfusion. We aimed to investigate whether different degrees of BA tortuosity could lead to the alterations of posterior circulation perfusion.We collected 138 subjects with different BA tortuosity scores, including 32 cases of score 0, 45 cases of score 1, 43 cases of score 2, and 18 cases of score 3. A higher score represented a higher degree of BA tortuosity. Ordered logistic regression analysis was performed to investigate the risk factors for BA tortuosity. We quantitatively measured the cerebral blood flow (CBF) in eight posterior circulation brain regions using arterial spin labeling. SPSS 25.0 was used for statistical analysis. The correlation between the CBF and BA tortuosity was corrected by the Bonferroni method. The significance level was set at 0.006 (0.05/8).Hypertension (HR: 2.39; 95%CI: 1.23–4.71; P = 0.01) and vertebral artery dominance (HR: 2.38; 95%CI: 1.10–4.67; P = 0.03) were risk factors for BA tortuosity. CBF in occipital gray matter (R = -0.383, P < 0.001), occipital white matter (R = -0.377, P < 0.001), temporal gray matter (R = -0.292, P = 0.001), temporal white matter (R = -0.297, P < 0.001), and cerebellum (R = -0.328, P < 0.001) were negatively correlated with BA tortuosity degree. No significant correlation was found between the BA tortuosity degree and CBF in hippocampus (R = -0.208, P = 0.014), thalamus (R = -0.001, P = 0.988) and brainstem (R = -0.204, P = 0.016).BA tortuosity could affect posterior circulation blood perfusion. CBF was negatively correlated with BA tortuosity degree. The morphology of BA may serve as a biomarker for posterior circulation and the severity of posterior circulation ischemia.Results: The morphology of basilar artery (BA) may affect posterior circulation blood perfusion. We aimed to investigate whether different degrees of BA tortuosity could lead to the alterations of posterior circulation perfusion.We collected 138 subjects with different BA tortuosity scores, including 32 cases of score 0, 45 cases of score 1, 43 cases of score 2, and 18 cases of score 3. A higher score represented a higher degree of BA tortuosity. Ordered logistic regression analysis was performed to investigate the risk factors for BA tortuosity. We quantitatively measured the cerebral blood flow (CBF) in eight posterior circulation brain regions using arterial spin labeling. SPSS 25.0 was used for statistical analysis. The correlation between the CBF and BA tortuosity was corrected by the Bonferroni method. The significance level was set at 0.006 (0.05/8).Hypertension (HR: 2.39; 95%CI: 1.23–4.71; P = 0.01) and vertebral artery dominance (HR: 2.38; 95%CI: 1.10–4.67; P = 0.03) were risk factors for BA tortuosity. CBF in occipital gray matter (R = -0.383, P < 0.001), occipital white matter (R = -0.377, P < 0.001), temporal gray matter (R = -0.292, P = 0.001), temporal white matter (R = -0.297, P < 0.001), and cerebellum (R = -0.328, P < 0.001) were negatively correlated with BA tortuosity degree. No significant correlation was found between the BA tortuosity degree and CBF in hippocampus (R = -0.208, P = 0.014), thalamus (R = -0.001, P = 0.988) and brainstem (R = -0.204, P = 0.016).BA tortuosity could affect posterior circulation blood perfusion. CBF was negatively correlated with BA tortuosity degree. The morphology of BA may serve as a biomarker for posterior circulation and the severity of posterior circulation ischemia.Conclusions: The morphology of basilar artery (BA) may affect posterior circulation blood perfusion. We aimed to investigate whether different degrees of BA tortuosity could lead to the alterations of posterior circulation perfusion.We collected 138 subjects with different BA tortuosity scores, including 32 cases of score 0, 45 cases of score 1, 43 cases of score 2, and 18 cases of score 3. A higher score represented a higher degree of BA tortuosity. Ordered logistic regression analysis was performed to investigate the risk factors for BA tortuosity. We quantitatively measured the cerebral blood flow (CBF) in eight posterior circulation brain regions using arterial spin labeling. SPSS 25.0 was used for statistical analysis. The correlation between the CBF and BA tortuosity was corrected by the Bonferroni method. The significance level was set at 0.006 (0.05/8).Hypertension (HR: 2.39; 95%CI: 1.23–4.71; P = 0.01) and vertebral artery dominance (HR: 2.38; 95%CI: 1.10–4.67; P = 0.03) were risk factors for BA tortuosity. CBF in occipital gray matter (R = -0.383, P < 0.001), occipital white matter (R = -0.377, P < 0.001), temporal gray matter (R = -0.292, P = 0.001), temporal white matter (R = -0.297, P < 0.001), and cerebellum (R = -0.328, P < 0.001) were negatively correlated with BA tortuosity degree. No significant correlation was found between the BA tortuosity degree and CBF in hippocampus (R = -0.208, P = 0.014), thalamus (R = -0.001, P = 0.988) and brainstem (R = -0.204, P = 0.016).BA tortuosity could affect posterior circulation blood perfusion. CBF was negatively correlated with BA tortuosity degree. The morphology of BA may serve as a biomarker for posterior circulation and the severity of posterior circulation ischemia. [ABSTRACT FROM AUTHOR]

• Terahertz time-domain spectroscopy contains noises and uncertainties. • The noises and uncertainties add difficulty to spectral signal analysis. • A recurrent quantum neural network was proposed as an adaptive filter. • The proposed network can eliminate the noise and maintain spectrum characteristics. System interferences such as noise and background information in terahertz time-domain spectroscopy (THz-TDS) add difficulty to spectral signal analysis and affect the accuracy of component identification. In this paper, we proposed an adaptive filter based on a recurrent quantum neural network (RQNN) to process the THz-TDS signal. RQNN is a network architecture guided by quantum mechanics, for adaptive filtering of THz-TDS signal. Six pure substances, ferric sulfate, ferrous sulfate, Prussian blue, agarose, polylysine and sodium of polyaspartic acid were used as test samples. The TDS signals of the six samples were filtered with RQNN and five competing filters. Optical constants including refractive index and absorption coefficient were calculated from raw THz signal and filtered THz signal. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were calculated between the optical constants calculated before and after filtering as evaluation indicators. Results revealed that RQNN could better eliminate the noise in THz-TDS signals, and retain the characteristic peaks in the calculated optical constants simultaneously. In terms of PSNR and SSIM, RQNN-filtered THz-TDS signals demonstrated higher values than those of the competing filters. The RQNN proposed in this paper can be used as an adaptive filter for noise filtering and signal extraction of THz-TDS signal, with advantages above traditional filtering algorithms. [ABSTRACT FROM AUTHOR]

Background: Clinical and etiological varieties remain major obstacles to decompose heterogeneity in autism spectrum disorders (ASD). Recently, neuroimaging raised new hope to identify neurosubtypes of ASD for further understanding the biological mechanisms behind the disorder. Methods: In this study, brain structural MRI data and clinical measures of 221 male subjects with ASD and 257 healthy controls were selected from 7 independent sites from the Autism Brain Image Data Exchange database (ABIDE). Heterogeneity through discriminative analysis (HYDRA), a recently-proposed semi-supervised clustering method was utilized to divide individuals with ASD into several neurosubtypes by regional volumetric measures of gray matter, white matter, and cerebrospinal fluid. Voxel-wise volume, clinical measures, dynamic resting-state functional magnetic resonance imaging (R-fMRI) measures among different neurosubtypes of ASD were explored. In addition, support vector machine (SVM) model was applied to test whether the neurosubtyping of ASD could improve diagnostic accuracy of ASD. Results: Two neurosubtypes of ASD with different voxel-wise volumetric patterns were revealed. The full-scale intelligence quotient (IQ), verbal IQ, Autism Diagnostic Observation Schedule (ADOS) total scores and ADOS severity scores were significantly different between the two neurosubtypes, the total intracranial volume was correlated with performance IQ in Subtype 1 and was correlated with ADOS communication score and ADOS social score in Subtype 2. Compared with Subtype 2, Subtype 1 showed lower dynamic R-fMRI measures, lower dynamic functional architecture stability, higher mean and lower standard deviation (SD) of concordance among dynamic R-fMRI measures in cerebellum. In addition, classification accuracies between ASD neurosubtypes and healthy controls were significantly improved compared with classification accuracy between entire ASD group and healthy controls. Limitations: The present study excluded female subjects and left-handed subjects, which limited the ability to investigate the associations between these factors and the heterogeneity of ASD. Conclusions: The two distinct neuroanatomical subtypes of ASD validated by other data modalities not only adds reliability of the result, but also bridges from brain phenomenology to clinical behavior. The current neurosubtypes of ASD could facilitate understanding the neuropathology of this disorder and could be potentially used to improve clinical decision-making process and optimize treatment. [ABSTRACT FROM AUTHOR]

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia. [ABSTRACT FROM AUTHOR]

Purpose: To investigate brain morphological alterations of high-tension glaucoma patients and explore the association between brain morphological changes and elevated intraocular pressure. Methods: Thirty-six patients with high-tension glaucoma and 20 healthy controls were collected and underwent structural MRI scan. Surface-based morphometry and voxel-based morphometry were applied to assess cortical thickness and subcortical gray matter volume of the enrolled subjects. The association between brain morphometry and intraocular pressure was assessed by partial correlation. Results: Compared with healthy controls, high-tension glaucoma patients showed decreased cortical thickness in the bilateral superior temporal gyrus, bilateral superior parietal gyrus, bilateral lateral occipital gyrus, left fusiform gyrus, left medial orbitofrontal gyrus, right precentral gyrus, and right superior frontal gyrus (p < 0.05). High-tension glaucoma patients also showed reduced gray matter volume in the right hippocampus, bilateral putamen, and bilateral thalamus (p < 0.05). In addition, brain morphological correlates of mean intraocular pressure were found in the left rostral middle frontal gyrus, right precentral gyrus, and left postcentral gyrus in high-tension glaucoma group (p < 0.05). Conclusion: High-tension glaucoma patients experienced morphological reduction in the visual and nonvisual areas throughout the entire brain. Elevated intraocular pressure may contribute to the reduction of cortical thickness in certain areas in the progression of the disease. [ABSTRACT FROM AUTHOR]

• The current clinical presentations of ADHD fail to reflect the underlying neurobiological mechanism. • We discovered two neurosubtypes of ADHD which were different from clinical phenotypes. • The two neurosubtypes of ADHD exhibited differences in clinical profiles and brain structural and functional patterns. • The current neurosubtypes could facilitate the understanding of neuropathological and neurobiological mechanism of ADHD. Clinical and etiological variability of attention deficit hyperactivity disorder (ADHD) presents an obstacle to understand the disorder. The aim of this study was to disentangle the heterogeneity of ADHD using neuroimaging and a semi-supervised machine learning algorithm. We collected brain structural and functional magnetic resonance imaging (MRI) data and clinical profiles of 183 children with ADHD and 396 neurotypical controls from 7 independent sites. We also used an external validation set with 750 subjects. We adopted a semi-supervised clustering method to subtype ADHD by regional volumetric measures of gray matter, white matter, and fractional amplitude of low frequency fluctuation (fALFF). In addition, split sample test, leave-one-site-out test and external validation were applied to evaluate the reproducibility and stability of ADHD subtypes. Two stable and reproducible neurosubtypes of ADHD were disclosed, which were proved by the split-sample test and leave-one-site-out validation. The structural and functional patterns of ADHD subtypes were also stable in the external validation set. The current two neurosubtypes differed in clinical manifestations and volumetric gray matter, white matter volume and fALFF patterns. The current neurosubtypes of ADHD which were different from clinical phenotypes could facilitate understanding the underlying neuropathological and neurobiological mechanism of the disorder. [ABSTRACT FROM AUTHOR]

Pseudomonas aeruginosa (PA)-induced keratitis is characterized by inflammatory epithelial edema, stromal infiltration, corneal ulceration and can lead to vision loss. The present study aimed to study the effect of ubiquitin-specific protease 22 (USP22) on PA-induced keratitis. Using RT-qPCR and western blotting, significantly increased expression of USP22 was identified in mouse corneas and cultured RAW264.7 cells following PA stimulation. In addition, the results of in vivo experiments, western blot assay and ELISA suggested that the silencing of USP22 attenuated disease progression, downregulated the NF-κB pathway and suppressed the expression of pro-inflammatory cytokines following PA stimulation. Notably, it was identified that the expression of tumor necrosis factor receptor-associated factor 6 (TRAF6) was decreased by silencing of USP22 and USP22 was found to remove lysine 48-linked poly-ubiquitination chains from TRAF6 to stabilize TRAF6 expression and these effects were clearly aggravated following PA infection. [ABSTRACT FROM AUTHOR]

• Edge-connection approach proposed accurately identified the manually induced artifacts in the ultrasound images of thyroid nodules. • Criminisi inpainting algorithm restored the ultrasound images with manually induced artifacts with high peak signal-to-noise ratio. • The joint approach combined edge-connection approach and Criminisi inpainting algorithm, could be used for ultrasound image restoration. There are various artificial markers in ultrasound images of thyroid nodules, which have impact on subsequent processing and computer-aided diagnosis. The purpose of this study was to develop an approach to automatically remove artifacts and restore ultrasound images of thyroid nodules. Fifty ultrasound images with manually induced artifacts were selected from publicly available and self-collected datasets. A combined approach was developed which consisted of two steps, artifacts detection and removal of the detected artifacts. Specifically, a novel edge-connection algorithm was used for artifact detection, detection accuracy and false discovery rate were used to evaluate the performance of artifact detection approaches. Criminisi algorithm was used for image restoration with peak signal-to-noise ratio (PSNR) and mean gradient difference to evaluate its performance. In addition, computation complexity was evaluated by execution time of relevant algorithms. Results revealed that the proposed joint approach with edge-connection and Criminisi algorithm could achieve automatic artifacts removal. Mean detection accuracy and mean false discovery rate of the proposed edge-connection algorithm for the 50 ultrasound images were 0.86 and 1.50. Mean PSNR of the 50 restored images by Criminisi algorithm was 36.64 dB, and mean gradient difference of the restored images was -0.002 compared with the original images. The proposed combined approach had a good detection accuracy for different types of manually induced artifacts, and could significantly improve PSNR of the ultrasound images. The proposed combined approach may have potential use for the repair of ultrasound images with artifacts. [ABSTRACT FROM AUTHOR]

Background: ADHD is one of the most common psychiatric disorders in children and adolescents. Altered functional connectivity has been associated with ADHD symptoms. This study aimed to investigate abnormal changes in the functional connectivity of resting-state brain networks (RSNs) among adolescent patients with different subtypes of ADHD.Methods: The data were obtained from the ADHD-200 Global Competition, including fMRI data from 88 ADHD patients (56 patients of ADHD-Combined, ADHD-C and 32 patients of ADHD-Inattentive, ADHD-I) and 67 typically developing controls (TD-C). Group ICA was utilized to research aberrant brain functional connectivity within the different subtypes of ADHD.Results: In comparison with the TD-C group, the ADHD-C group showed clusters of decreased functional connectivity in the left inferior occipital gyrus (p = 0.0041) and right superior occipital gyrus (p = 0.0011) of the dorsal attention network (DAN), supplementary motor area (p = 0.0036) of the executive control network (ECN), left supramarginal gyrus (p = 0.0081) of the salience network (SN), middle temporal gyrus (p = 0.0041), and superior medial frontal gyrus (p = 0.0055) of the default mode network (DMN), while the ADHD-I group showed decreased functional connectivity in the right superior parietal gyrus (p = 0.0017) of the DAN and left middle temporal gyrus (p = 0.0105) of the DMN. In comparison with the ADHD-I group, the ADHD-C group showed decreased functional connectivity in the superior temporal gyrus (p = 0.0062) of the AN, inferior temporal gyrus (p = 0.0016) of the DAN, and the dorsolateral superior frontal gyrus (p = 0.0082) of the DMN. All the clusters surviving at p < 0.05 (AlphaSim correction).Conclusion: The results suggested that decreased functional connectivity within the DMN and DAN was responsible, at least in part, for the symptom of inattention in ADHD-I patients. Similarly, we believed that the impaired functional connectivity within networks may contribute to the manifestations of ADHD-C patients, including inattention, hyperactivity/impulsivity, and unconscious movements. [ABSTRACT FROM AUTHOR]