Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network

Brain functional hypernetworks have been successfully utilized in the diagnosis of brain diseases.In the previous study, the different hyper-edge generation method was mainly used to improve the construction of the hyper-network, which ignored the influence of different nodes definitions on the brain functional hyper-network topology.Therefore, in light of this problem, it is proposed to construct a brain functional hyper-network based on parcellation of different scales, so as to analyze its impact on brain functional hyper-network topology and classification performance.Specifically, firstly, based on the anatomical automatic labeling atlas, the brain was segmented by the method of clustering algorithm and the random dynamic seed point; secondly, based on the average time series obtained under each node scale, the brain functional hyper-network was constructed by the LASSO method respectively; then multiple sets of local features（node degree, shortest path, clustering coefficient） were extracted, and non-pa