Acupuncture has analgesic effect to most pain conditions. Many neuroimaging studies were conducted to explore acupoint specificity in pain and other condition, but till now there is still discrepancy. Based on our previous finding, this study investigated the brain metabolism changes of acupuncture analgesia induced by sub-specific acupoint and non-acupoint stimulation. 30 migraineurs were included and randomly assigned to 3 groups: Acupuncture Group (AG), Sham Acupuncture Group (SAG) and Migraine Group (MG). In AG, a combination sub-specific points of Shaoyang meridians, Luxi (TE19), San Yangluo (TE8), and Xi Yangguan(GB33) has been stimulated with electroacupuncture, while non-acupoints for SAG were used and MG received no treatment. Positron emission tomography with computed tomography (PET-CT) was used to identify differences in brain glucose metabolism between groups. In the AG, brain glucose metabolism increase compared with the MG was observed in the middle frontal gyrus, postcentral gyrus, the precuneus, parahippocampus, cerebellum and middle cingulate cortex (MCC), and decrease were observed in the left hemisphere of Middle Temporal Cortex (MTC).In the SAG, compared with MG, glucose metabolism increased in the poster cingulate cortex (PCC), insula, inferior temporal gyrus, MTC, superior temporal gyrus, postcentral gyrus, fusiform, inferior parietal lobe, superior parietal lobe, supramarginal gyrus, middle occipital lobe, angular and precuneus; while, decreased in cerebellum, parahippocampus. Acupuncture stimulation at both sub-specific acupoint and non-acupoint yields ameliorating effect to migraine pain, but with evidently differed central mechanism as measured by PET-CT. The pattern of brain glucose metabolism change in acupoint is pertinent and targeted, while in non-acupoint that was disordered and randomized. These finding may provide new perspectives into the validation of acupoint specificity, optimizing acupuncture analgesia and revealing central mechanism of acupuncture analgesia by neuroimaging measurement. This trial was registered in the Chinese Clinical Trial Registry, with registration no. http://ChiCTR-TRC-11001813 .

In clinical practice, sleep stage classification (SSC) is a crucial step for physicians in sleep assessment and sleep disorder diagnosis. However, traditional sleep stage classification relies on manual work by sleep experts, which is time-consuming and labor-intensive. Faced with this obstacle, computer-aided diagnosis (CAD) has the potential to become an intelligent assistant tool for sleep experts, aiding doctors in the assessment and decision-making process. In fact, in recent years, CAD supported by artificial intelligence, especially deep learning (DL) techniques, has been widely applied in SSC. DL offers higher accuracy and lower costs, making a significant impact. In this paper, we will systematically review SSC research based on DL methods (DL-SSC). We explores DL-SSC from several important perspectives, including signal and data representation, data preprocessing, deep learning models, and performance evaluation. Specifically, this paper addresses three main questions: (1) What signals can DL-SSC use? (2) What are the various methods to represent these signals? (3) What are the effective DL models? Through addressing on these questions, this paper will provide a comprehensive overview of DL-SSC.

Background: Ulcerative colitis (UC) and Crohn’s disease (CD) are 2 subtypes of inflammatory bowel disease (IBD). Several studies have reported brain abnormalities in IBD patients. This study aims to identify differences of gray matter volume (GMV) between patients with UC and healthy controls (HCs). Methods: Fifty-seven patients with UC and 40 HCs underwent structural magnetic resonance imaging. Voxel-based morphometry method was used to detect GMV differences. Receiver operating characteristic (ROC) curve was applied to investigate reliable biomarkers for identifying patients with UC from HCs. Regression analysis was used to examine relationships between the structure alternations and clinical symptoms. Results: Compared with HCs, patients with UC showed decreased GMV in the insula, thalamus, pregenual anterior cingulate cortex, hippocampus/parahippocampus, amygdala, and temporal pole; they showed increased GMV in the putamen, supplementary motor area, periaqueductal gray, hypothalamus, and precentral gyrus. Receiver operating characteristic analysis showed the highest classification power of thalamus. The inclusion of anxiety and depression as covariates eliminated the differences in the right insula, pregenual anterior cingulate cortex, supplementary motor area, and precentral gyrus. Most of the GMV changes were found in active patients with UC, with few changes in patients with UC in remission. We also found significantly negative correlation between UC duration and GMV in several regions. Conclusion: The current neuroimaging findings were involved in visceral sensory pathways and were partially associated with the levels of anxiety and depression and clinical stage of patients with UC. This study might provide evidence for possible neuromechanisms of UC.