Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders

作者：Jie Yang, Fang Wang, Zhen Li, Zhen Yang, Xishang Dong, Qinghua Han · 发表于：Frontiers in Neuroscience · 年份：2023 · DOI：10.3389/fnins.2023.1257982 · 被引用次数：16 · 研究领域：Functional Brain Connectivity Studies、Mental Health Research Topics、Neural dynamics and brain function

Introduction: High-order functional connectivity networks (FCNs) that reflect the connection relationships among multiple brain regions have become important tools for exploring the deep workings of the brain and revealing the mechanisms of brain diseases. The traditional high-order FCN constructed based on the "correlation of correlations" strategy, is a representative method for conducting whole-brain connectivity analysis and revealing global network characteristics. However, whole-brain connectivity analysis may be affected by noise carried by less important brain regions, resulting in redundant information and affecting the accuracy and reliability of the analysis. Moreover, this type of analysis has a high computational complexity. Methods: To address these issues, a new method for constructing high-order FCN based on hypergraphs is proposed in this article, which is used to accurately capture the real interaction relationships among brain regions. Specifically, first, a low-order FCN reflecting the connection relationships between pairs of brain regions based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) time series is constructed, the method first constructs the low-order FCN that reflects the connection relationships between pairs of brain regions based on rs-fMRI time series, and then selects the "good friends" of each brain region from hypergraph perspective, which refers to the local friend circles with closer relationships. Then, the rs-fMRI ti...