Unsupervised Cross-Modality Adaptation via Dual Structural-Oriented Guidance for 3D Medical Image Segmentation.

Deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation; however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised domain adaptation (UDA) is a promising solution to tackle this problem. In this work, we present a novel UDA method, named dual adaptation-guiding network (DAG-Net), which incorporates two highly effective and complementary structural-oriented guidance in training to collaboratively adapt a segmentation model from a labelled source domain to an unlabeled target domain.

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

Background: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians.

Helping Roles of Artificial Intelligence (AI) in the Screening and Evaluation of COVID-19 Based on the CT Images.

Objective: The aim of this study was to explore the role of the AI system which was designed and developed based on the characteristics of COVID-19 CT images in the screening and evaluation of COVID-19.

Methods: The research team adopted an improved U-shaped neural network to segment lungs and pneumonia lesions in CT images through multilayer convolution iterations. Then the appropriate 159 cases were selected to establish and train the model, and Dice loss function and Adam optimizer were used for network training with the initial learning rate of 0.

A slice classification model-facilitated 3D encoder-decoder network for segmenting organs at risk in head and neck cancer.

For deep learning networks used to segment organs at risk (OARs) in head and neck (H&N) cancers, the class-imbalance problem between small volume OARs and whole computed tomography (CT) images results in delineation with serious false-positives on irrelevant slices and unnecessary time-consuming calculations. To alleviate this problem, a slice classification model-facilitated 3D encoder-decoder network was developed and validated. In the developed two-step segmentation model, a slice classification model was firstly utilized to classify CT slices into six categories in the craniocaudal direction.

Retinotopic Changes in the Gray Matter Volume and Cerebral Blood Flow in the Primary Visual Cortex of Patients With Primary Open-Angle Glaucoma.

Purpose: To assess the cortical structure and cerebral blood flow changes in the brain of patients with primary open-angle glaucoma (POAG).

Methods: High-resolution anatomical magnetic resonance imaging (MRI) and arterial spin labeling (ASL)-MRI were performed in 23 POAG patients and 29 controls. Patients were further divided into early-moderate and advanced groups based on mean deviation (MD) cutoff of 12 dB.

Resting spontaneous activity in the default mode network predicts performance decline during prolonged attention workload.

After continuous and prolonged cognitive workload, people typically show reduced behavioral performance and increased feelings of fatigue, which are known as "time-on-task (TOT) effects". Although TOT effects are pervasive in modern life, their underlying neural mechanisms remain elusive. In this study, we induced TOT effects by administering a 20-min continuous psychomotor vigilance test (PVT) to a group of 16 healthy adults and used resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to examine spontaneous brain activity changes associated with fatigue and performance.

Altered salience network connectivity predicts macronutrient intake after sleep deprivation.

Although insufficient sleep is a well-recognized risk factor for overeating and weight gain, the neural mechanisms underlying increased caloric (particularly fat) intake after sleep deprivation remain unclear. Here we used resting-state functional magnetic resonance imaging and examined brain connectivity changes associated with macronutrient intake after one night of total sleep deprivation (TSD). Compared to the day following baseline sleep, healthy adults consumed a greater percentage of calories from fat and a lower percentage of calories from carbohydrates during the day following TSD.

Gender differences in cerebral regional homogeneity of adult healthy volunteers: a resting-state FMRI study.

Objective: We sought to use the regional homogeneity (ReHo) approach as an index in the resting-state functional MRI to investigate the gender differences of spontaneous brain activity within cerebral cortex and resting-state networks (RSNs) in young adult healthy volunteers.

Methods: One hundred and twelve healthy volunteers (56 males, 56 females) participated in the resting-state fMRI scan. The ReHo mappings in the cerebral cortex and twelve RSNs of the male and female groups were compared.

Serotonin transporter genotype modulates functional connectivity between amygdala and PCC/PCu during mood recovery.

The short (S) allele of the serotonin transporter-linked polymorphic region (5-HTTLPR) has been associated with increased susceptibility to depression. Previous neuroimaging studies have consistently showed increased amygdala activity during the presentation of negative stimuli or regulation of negative emotion in the homozygous short allele carriers, suggesting the key role of amygdala response in mediating increased risk for depression. The brain default mode network (DMN) has also been shown to modulate amygdala activity.

Assessment of risk-taking and impulsive behaviors: A comparison between three tasks.

The balloon analogue risk task (BART), the delay discounting task (DDT), and the Iowa gambling task (IGT) are increasingly used for the assessment of risk-taking and impulsive behaviors. This study examined the reliability of and relationships between these three tasks in healthy Chinese subjects. The BART and DDT showed moderate to high test-retest reliability across three test sessions.

Resting state brain function analysis using concurrent BOLD in ASL perfusion fMRI.

The past decade has seen astounding discoveries about resting-state brain activity patterns in normal brain as well as their alterations in brain diseases. While the vast majority of resting-state studies are based on the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI), arterial spin labeling (ASL) perfusion fMRI can simultaneously capture BOLD and cerebral blood flow (CBF) signals, providing a unique opportunity for assessing resting brain functions with concurrent BOLD (ccBOLD) and CBF signals. Before taking that benefit, it is necessary to validate the utility of ccBOLD signal for resting-state analysis using conventional BOLD (cvBOLD) signal acquired without ASL modulations.

Changes in cerebral blood flow and anxiety associated with an 8-week mindfulness programme in women with breast cancer.

This study employed functional magnetic resonance imaging to evaluate changes in cerebral blood flow (CBF) associated with the Mindfulness-based Art Therapy (MBAT) programme and correlate such changes to stress and anxiety in women with breast cancer. Eighteen breast cancer patients were randomized to the MBAT or education control group. The patients received the diagnosis of breast cancer between 6 months and 3 years prior to enrollment and were not in active treatment.

Altered resting brain function and structure in professional badminton players.

Neuroimaging studies of professional athletic or musical training have demonstrated considerable practice-dependent plasticity in various brain structures, which may reflect distinct training demands. In the present study, structural and functional brain alterations were examined in professional badminton players and compared with healthy controls using magnetic resonance imaging (MRI) and resting-state functional MRI. Gray matter concentration (GMC) was assessed using voxel-based morphometry (VBM), and resting-brain functions were measured by amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity.