Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

被引:8
|
作者
Xi, Zhengtao [1 ]
Song, Chaofan [2 ]
Zheng, Jiahui [3 ]
Shi, Haifeng [3 ]
Jiao, Zhuqing [1 ,2 ]
机构
[1] Changzhou Univ, Sch Microelect & Control Engn, Changzhou 213164, Peoples R China
[2] Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou 213164, Peoples R China
[3] Nanjing Med Univ, Changzhou Peoples Hosp 2, Dept Radiol, Changzhou 213003, Peoples R China
来源
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES | 2023年 / 135卷 / 03期
基金
中国国家自然科学基金;
关键词
End-stage renal disease; mild cognitive impairment; brain functional network; dynamic hypergraph manifold regularization; classification; NEUROLOGICALLY ASYMPTOMATIC PATIENTS; ALZHEIMERS-DISEASE; CONNECTIVITY; COMPLICATIONS; DIALYSIS; FUSION; DEMENTIA;
D O I
10.32604/cmes.2023.023544
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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 Pearson'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 L-1 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.
引用
收藏
页码:2243 / 2266
页数:24
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