EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task.

Depressive emotion (DE) refers to clinically relevant depressive symptoms without meeting the diagnostic criteria for depression. Studies have demonstrated that DE can cause spatial cognition impairment. However, the brain network mechanisms underlying DE interference spatial cognition remain unclear.

Adversarial robustness in deep neural networks based on variable attributes of the stochastic ensemble model.

Deep neural networks (DNNs) have been shown to be susceptible to critical vulnerabilities when attacked by adversarial samples. This has prompted the development of attack and defense strategies similar to those used in cyberspace security. The dependence of such strategies on attack and defense mechanisms makes the associated algorithms on both sides appear as closely processes, with the defense method being particularly passive in these processes.

Extremely negative emotion interferes with cognition: Evidence from ERPs and time-varying brain network.

In recent years, the relationship between emotion and cognition was a hot topic. However, it remains unclear which specific emotions can significantly interfere with cognition and how they do so. In this study, we designed a novel Affective Stroop experiment paradigm to investigate these issues.

Geometric parameters sensitivity evaluation based on projection trajectories for X-ray cone-beam computed laminography.

Background: X-ray cone-beam computed laminography (CL) is widely used for large flat objects that computed tomography (CT) cannot investigate. The rotation angle of axis tilt makes geometric correction of CL system more complicated and has more uncertain factors. Therefore, it is necessary to evaluate sensitivity of the geometric parameters of CL system in advance.

Super-resolution image reconstruction from sparsity regularization and deep residual-learned priors.

Background: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work.

Objective: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

Purpose: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT.

Methods: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification.

ERP-WGAN: A data augmentation method for EEG single-trial detection.

Brain computer interaction based on EEG presents great potential and becomes the research hotspots. However, the insufficient scale of EEG database limits the BCI system performance, especially the positive and negative sample imbalance caused by oddball paradigm. To alleviate the bottleneck problem of scarce EEG sample, we propose a data augmentation method based on generative adversarial network to improve the performance of EEG signal classification.

ShapeEditor: A StyleGAN Encoder for Stable and High Fidelity Face Swapping.

With the continuous development of deep-learning technology, ever more advanced face-swapping methods are being proposed. Recently, face-swapping methods based on generative adversarial networks (GANs) have realized many-to-many face exchanges with few samples, which advances the development of this field. However, the images generated by previous GAN-based methods often show instability.

Improving the Transferability of Adversarial Examples With a Noise Data Enhancement Framework and Random Erasing.

Deep neural networks (DNNs) are proven vulnerable to attack against adversarial examples. Black-box transfer attacks pose a massive threat to AI applications without accessing target models. At present, the most effective black-box attack methods mainly adopt data enhancement methods, such as input transformation.

Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification.

The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN.

Childhood Psychological Maltreatment and Depression among Chinese Adolescents: Multiple Mediating Roles of Perceived Ostracism and Core Self-Evaluation.

Previous studies have primarily focused on the separate mediating role of interpersonal context or personal characteristics in the relationship between childhood psychological maltreatment and depression, neglecting the combined effects, which have limited ecological validity. Therefore, this study investigated the multiple mediating roles of perceived ostracism and core self-evaluation in the relationship between childhood psychological maltreatment and depression. A total of 1592 Chinese adolescents (51.

Multi-view features integrated 2D\3D Net for glomerulopathy histologic types classification using ultrasound images.

Background And Objective: Early diagnoses and rational therapeutics of glomerulopathy can control progression and improve prognosis. The gold standard for the diagnosis of glomerulopathy is pathology by renal biopsy, which is invasive and has many contraindications. We aim to use renal ultrasonography for histologic classification of glomerulopathy.

Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy.

Background: The aim of this study was to investigate the potential use of renal ultrasonography radiomics features in the histologic classification of glomerulopathy.

Methods: A total of 623 renal ultrasound images from 46 membranous nephropathy (MN) and 22 IgA nephropathy patients were collected. The cases and images were divided into a training group (51 cases with 470 images) and a test group (17 cases with 153 images).

Neural mechanism for dynamic distractor processing during video target detection: Insights from time-varying networks in the cerebral cortex.

In dynamic video target detection tasks, distractors may suddenly appear due to the dynamicity of the visual scene and the uncertainty of the visual information, strongly influencing participants' attention and target detection performance. Moreover, the neural mechanism that accounts for dynamic distractor processing remains unknown, which makes it difficult to compensate for in EEG-based video target detection. Here, cortical activities with high spatiotemporal resolution were reconstructed using the source localization method.

Neural Mechanism of Affective Perception: Evidence from Phase and Causality Analysis in the Cerebral Cortex.

Emotion plays an important role in people's lives. However, the neural mechanism of affective perception is still unclear. In this study, steady-state visual evoked potentials (SSVEPs) were used to explore information processing speed and interactions among cortical structures involved in affective perception.

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affecting the diagnosis. Therefore, in this study, we investigate a dual residual convolution neural network (DRCNN) for low-dose CT (LDCT) imaging, whereby the CT images are reconstructed directly from the sinogram by integrating analytical domain transformations, thus reducing the loss of projection information.

CNN-based qualitative detection of bone mineral density via diagnostic CT slices for osteoporosis screening.

Unlabelled: The features extracted from diagnostic computed tomography (CT) slices were used to qualitatively detect bone mineral density (BMD) through neural network models, and the evaluation results indicated that it may be a promising approach to perform osteoporosis screening in clinical practice.

Introduction: The purpose of this study is to design a novelty diagnostic method for osteoporosis screening by using the convolutional neural network (CNN), which can be incorporated into the procedure of routine CT diagnostic in medical examination thereby improving the osteoporosis diagnosis and reducing the patient burden.

Methods: The proposed CNN-based method mainly comprises two functional modules to perform qualitative detection of BMD by analyzing the diagnostic 2D CT slice.

A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs.

Nowadays, visual encoding models use convolution neural networks (CNNs) with outstanding performance in computer vision to simulate the process of human information processing. However, the prediction performances of encoding models will have differences based on different networks driven by different tasks. Here, the impact of network tasks on encoding models is studied.

BigGAN-based Bayesian Reconstruction of Natural Images from Human Brain Activity.

In the visual decoding domain, the most difficult task is the visual reconstruction aimed at reconstructing the presented visual stimuli given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI), especially when reconstructing viewed natural images. Recent research regarded the visual reconstruction as the conditional image generation on fMRI voxels and started to use the generative adversarial networks (GANs) to design computational models for this task. Despite the great improvement in previous GAN-based methods, the fidelity and naturalness of the reconstructed images are still unsatisfactory, the reasons include the small number of fMRI data samples and the instability of GAN training.

Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.

To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variability adaptation problem of lymph node data which is related to the problem of domain adaptation in deep learning differs from the general domain adaptation problem because of the typically larger CT image size and more complex data distributions. Therefore, domain adaptation for this problem needs to consider the shared feature representation and even the conditioning information of each domain so that the adaptation network can capture significant discriminative representations in a domain-invariant space.

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network.

High-frequency electroencephalography (EEG) signals play an important role in research on human emotions. However, the different network patterns under different emotional states in the high gamma band (50-80 Hz) remain unclear. In this paper, we investigate different emotional states using functional network analysis on various frequency bands.

Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information.

The widespread application of X-ray computed tomography (CT) in clinical diagnosis has led to increasing public concern regarding excessive radiation dose administered to patients. However, reducing the radiation dose will inevitably cause server noise and affect radiologists' judgment and confidence. Hence, progressive low-dose CT (LDCT) image reconstruction methods must be developed to improve image quality.

Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging.

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms.

Emotion Regulation of Hippocampus Using Real-Time fMRI Neurofeedback in Healthy Human.

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) is a prospective tool to enhance the emotion regulation capability of participants and to alleviate their emotional disorders. The hippocampus is a key brain region in the emotional brain network and plays a significant role in social cognition and emotion processing in the brain. However, few studies have focused on the emotion NF of the hippocampus.