TY  - EJOU
AU  - Xi, Zhengtao 
AU  - Song, Chaofan 
AU  - Zheng, Jiahui 
AU  - Shi, Haifeng 
AU  - Jiao, Zhuqing 

TI  - Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2023
VL  - 135
IS  - 3
SN  - 1526-1506

AB  - The structure and function of brain networks have been altered in patients with end-stage renal disease (ESRD). Manifold regularization (MR) only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions. To solve this issue, we developed a method to construct a dynamic brain functional network (DBFN) based on dynamic hypergraph MR (DHMR) and applied it to the classification of ESRD associated with mild cognitive impairment (ESRDaMCI). The construction of DBFN with <i>Pearson</i>’s correlation (PC) was transformed into an optimization model. Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure, and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph. The DHMR and <b><i>L</i></b><sub>1</sub> norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN (DDBFN). Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods. Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI, providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.
KW  - End-stage renal disease; mild cognitive impairment; brain functional network; dynamic hypergraph manifold regularization; classification

DO  - 10.32604/cmes.2023.023544