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.

Acupuncture Ameliorates Postoperative Ileus via IL-6-miR-19a-KIT Axis to Protect Interstitial Cells of Cajal.

Acupuncture is a therapy effective in treating postoperative ileus (POI); its underlying mechanisms remain unclear. MicroRNAs (miRNAs) participate in inflammation and injury to the interstitial cells of Cajal (ICCs), both of which are considered to be contributors to POI. C-kit, encoding KIT, a specific marker of ICCs, is predicted to be targeted by miR-19a, an inflammation-related miRNA. Therefore, we investigated a possible link between inflammation, miR-19a, and ICCs in POI, as well as the mechanism by which these factors are affected by acupuncture. The effects of acupuncture on POI were assessed in patients after colorectal resection and in colocolic anastomosis mice. Immunofluorescence staining demonstrated that KIT[Formula: see text]/ano1[Formula: see text] ICCs dramatically decreased around the colonic incision in mice, which was negatively correlated with the pronounced increase in macrophage. However, this decrease was not due to apoptosis. IL-6R was expressed in ICCs, and IL-6 level was significantly increased, as measured by ELISA, in accompaniment with high miR-19a expression. The increase in IL-6 and miR-19a levels was negatively correlated with the decrease in KIT[Formula: see text]/ano1[Formula: see text] ICCs. A luciferase reporter assay demonstrated that miR-19a directly targeted C-kit, indicating that miR-19a caused ICC damage. Interestingly, acupuncture inhibited macrophage activation, IL-6 release, and miR-19a upregulation, while promoting KIT and ano1 restoration in ICCs. High serum miR-19a level in patients after colorectal resection was also reduced by acupuncture. Conclusively, the IL-6 released by macrophages during gastrointestinal surgery upregulated miR-19a, which downregulated KIT in ICCs and finally resulted in POI. Acupuncture can interfere with the "IL-6-miR-19a-KIT" axis, suggesting that it may be a therapeutic mechanism that works against POI.