Cheng Chen, Li Yao, Wang Chunyan, Yang Yanli, Guo Hao
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, No.79 Yingze West Street, Taiyuan City, Shanxi Province, China.
School of Software, Taiyuan University of Technology, No.79 Yingze West Street, Taiyuan City, Shanxi Province, China.
Brain Res Bull. 2025 Jan;220:111177. doi: 10.1016/j.brainresbull.2024.111177. Epub 2024 Dec 20.
Brain functional hypernetworks that can characterize the complex and multivariate interactions among multiple brain regions have been widely used in the diagnosis and prediction of brain diseases. However, there are few studies on the structure and dynamics of brain functional hypernetworks. Such studies can help to explore how the important functional features of brain functional hypernetworks characterize the working and pathological mechanisms of the human brain. Therefore, this article introduces the hypernetwork null model to analyze the dependencies between the features of interest. Specifically, first, based on the original brain functional hypernetwork, this article proposed the optimized hyper dK-series algorithm with hyperedges to construct null models that preserved the different node attributes and hyperedge attributes of the original brain functional hypernetwork, respectively. Next, based on the original hypernetwork model and the null model, multiple node attributes and hyperedge attributes were respectively introduced. Then, the level of similarity and correlation between the topological attributes of the original brain functional hypernetwork and the topological attributes of the brain functional hypernetwork null model were calculated to analyze the dependencies between the features of interest. The results showed that there were differences in the level of dependence between the features of interest. Node degree is the main dependency attribute for multiple metrics. Hyperedge degree, node degree-dependent redundancy coefficient, and hyperedge degree-dependent redundancy coefficient are partial dependency attributes for some metrics. The dependency attributes and level of dependency are the same for the hypernetwork clustering coefficients-HCC and HCC. This indicates that the node degree is redundant with respect to other attributes, while the hyperedge degree, node degree-dependent redundancy coefficient, and hyperedge degree-dependent redundancy coefficient perhaps contain other topology information. In addition, there is redundancy between HCC and HCC. Therefore, the effects of these redundant attributes need to be considered when performing network analysis.
能够表征多个脑区之间复杂多变量相互作用的脑功能超网络已被广泛应用于脑部疾病的诊断和预测。然而，关于脑功能超网络的结构和动力学的研究却很少。此类研究有助于探索脑功能超网络的重要功能特征如何表征人类大脑的工作机制和病理机制。因此，本文引入超网络空模型来分析感兴趣特征之间的依赖性。具体而言，首先，基于原始脑功能超网络，本文提出了带超边的优化超dK系列算法，分别构建保留原始脑功能超网络不同节点属性和超边属性的空模型。接下来，基于原始超网络模型和空模型，分别引入多个节点属性和超边属性。然后，计算原始脑功能超网络的拓扑属性与脑功能超网络空模型的拓扑属性之间的相似性和相关性水平，以分析感兴趣特征之间的依赖性。结果表明，感兴趣特征之间的依赖程度存在差异。节点度是多个指标的主要依赖属性。超边度、节点度相关冗余系数和超边度相关冗余系数是某些指标的部分依赖属性。超网络聚类系数-HCC和HCC的依赖属性和依赖程度相同。这表明节点度相对于其他属性是冗余的，而超边度、节点度相关冗余系数和超边度相关冗余系数可能包含其他拓扑信息。此外，HCC和HCC之间存在冗余。因此，在进行网络分析时需要考虑这些冗余属性的影响。
