Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates.

Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.

Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs).

Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.

Effectiveness and safety of different academic schools of traditional Chinese medicine in the treatment of obesity: A protocol for systematic review and meta-analysis.

BACKGROUND: Obesity is a global epidemic. Since 1975, the global obesity rate has almost tripled. Although many systematic reviews and clinical trials have shown that traditional Chinese medicine (TCM) can effectively treat obesity, the effectiveness and safety of different academic schools of TCM in treating obesity have not been systematically evaluated. METHODS: The retrieval language of this study was Chinese and English. From the date of creation of the following data to June 2023, the data of Medline, PubMed, Embase, Cochrane Science Network, China Biomedical Literature Database, Central Controlled Trial Registration Center, and China Science Journal Database were retrieved, respectively. This study included clinical randomized controlled trials related to the treatment of obesity by different academic schools of TCM. The main outcome measures were body mass index, waist circumference, hip circumference, waist hip ratio, body fat content, fasting blood glucose, glycosylated hemoglobin, and blood lipid level. In addition, we manually searched other resources, including reference lists of identified publications, conference articles, and gray literature. RESULTS: This study will provide a more diverse choice of treatment options. CONCLUSION: The purpose of this study is to summarize and evaluate the effectiveness and safety of different academic schools of TCM in improving and treating obese patients from clinical trials, so as to provide more options for obesity treatment.

Effectiveness and safety of acupuncture combined with hot compress in the treatment of obese adolescents with insulin resistance: A protocol for systematic review and meta-analysis.

BACKGROUND: In recent years, the incidence of obesity patients has become younger and younger, and adolescents are gradually becoming one of the groups with a high incidence of obesity. Although several systematic reviews and clinical trials suggest that acupuncture and warm compresses may be effective in the treatment of obesity, the effectiveness and safety of acupuncture combined with warm compresses in the treatment of obesity insulin resistance (IR) in adolescents have not been systematically reviewed. METHODS: The search language of this study is Chinese and English, and the data of Medline, PubMed, Embase, Cochrane Web of Science, China Biomedical Literature Database, Central Controlled Trial Registration Center, and China Scientific Journal Database were searched for this study respectively, from the date of creation of the above data to December 2022. Randomized controlled trials of acupuncture combined with warm compresses in adolescents with obese IR were included in this review. Main outcome measures were body mass index, waist circumference, hip circumference, waist-hip ratio, fasting blood glucose, glycosylated hemoglobin, IR index, body fat content, blood lipid level and blood pressure, etc. In addition, we manually retrieved other resources, including reference lists of identified publications, conference articles and gray literature. RESULTS: This study will provide more clinical treatment ideas and options for adolescent obese IR patients. CONCLUSION: The purpose of this study is to summarize and evaluate the efficacy and safety of acupuncture combined with hot compress in treating obesity IR in adolescents from clinical trials.