Comparative Analysis of Robustness of Resting Human Brain Functional Hypernetwork Model

• Published in: Ji suan ji ke xue Vol. 49; no. 2; pp. 241 - 247
• Main Authors: Zhang, Cheng-Rui, Chen, Jun-Jie, Guo, Hao
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.02.2022; Editorial office of Computer Science
• Subjects: Brain; brain network|hypernetwork|lasso|group lasso|sparse group lasso|intentional attack|robustness; Brain research; Comparative analysis; Dynamic characteristics; Failure analysis; Graph theory; Medical imaging; Nodes; Regression models; Robustness
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.201200067

Robustness, as a dynamic behavior, is also a research hotspot in the field of supernetworks, and has important practical significance for building robust networks. Although there are more and more researches on supernetworks, there are relatively few studies on their dynamics, especially in In the field of neuroimaging. In the existing research on brain function supernetwork, most of them are to explore the static topological properties of the network, and there is no relevant research to analyze the dynamic characteristics of brain function supernetwork-robustness. In response to these problems, the paper Firstly, the lasso, group lasso and sparse group lasso methods are introduced to solve the sparse linear regression model to construct the hypernetwork; then two experimental models are attacked based on the node degree and the node betweenness in the deliberate attack, using the global efficiency and the relative size of the maximum connected subgraph The robustness of the brain function super-network in r