| Analysis of functional connectivity in depression based on a weighted hyper-network method | |
| Shao, Xuexiao1; Kong, Wenwen1; Sun, Shuting2; Li, Na1; Li, Xiaowei1,3; Hu, Bin1,4,5,6 | |
| 2023-02-01 | |
| Source Publication | Journal of Neural Engineering Impact Factor & Quartile |
| ISSN | 1741-2560 |
| Volume | 20Issue:1 |
| Abstract | Objective. Brain connectivity network is a vital tool to reveal the interaction between different brain regions. Currently, most functional connectivity methods can only capture pairs of information to construct brain networks which ignored the high-order correlations between brain regions. Approach. Therefore, this study proposed a weighted connectivity hyper-network based on resting-state EEG data, and then applied to depression identification and analysis. The hyper-network model was build based on least absolute shrinkage and selection operator sparse regression method to effectively represent the higher-order relationships of brain regions. On this basis, by integrating the correlation-based weighted hyper-edge information, the weighted hyper-network is constructed, and the topological features of the network are extracted for classification. Main results. The experimental results obtained an optimal accuracy compared to the traditional coupling methods. The statistical results on network metrics proved that there were significant differences between depressive patients and normal controls. In addition, some brain regions and electrodes were found and discussed to highly correlate with depression by analyzing of the critical nodes and hyper-edges. Significance. These may help discover disease-related biomarkers important for depression diagnosis. |
| Keyword | hyper-network weighted hyper-edge functional connectivity depression resting-state EEG data |
| Publisher | IOP Publishing Ltd |
| DOI | 10.1088/1741-2552/acb088 |
| Indexed By | SCIE |
| Language | 英语 |
| WOS Research Area | Engineering ; Neurosciences & Neurology |
| WOS Subject | Engineering, Biomedical ; Neurosciences |
| WOS ID | WOS:000918181100001 |
| Original Document Type | Article |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/499159 |
| Collection | 兰州大学 |
| Corresponding Author | Li, Xiaowei; Hu, Bin |
| Affiliation | 1.Lanzhou Univ, Sch Informat Sci & Engn, Gansu Prov Key Lab Wearable Comp, Lanzhou, Peoples R China; 2.Beijing Inst Technol, Inst Engn Med, Brain Hlth Engn Lab, Beijing, Peoples R China; 3.Shandong Acad Intelligent Comp Technol, Yantai, Shandong, Peoples R China; 4.Chinese Acad Sci, Shanghai Inst Biol Sci, CAS Ctr Excellence Brain Sci & Intelligence Techno, Shanghai, Peoples R China; 5.Chinese Acad Sci, Joint Res Ctr Cognit Neurosensor Technol Lanzhou U, Lanzhou, Peoples R China; 6.Lanzhou Univ, Engn Res Ctr Open Source Software & Real Time Syst, Minist Educ, Lanzhou, Peoples R China |
| Recommended Citation GB/T 7714 | Shao, Xuexiao,Kong, Wenwen,Sun, Shuting,et al. Analysis of functional connectivity in depression based on a weighted hyper-network method[J]. Journal of Neural Engineering,2023,20(1). |
| APA | Shao, Xuexiao,Kong, Wenwen,Sun, Shuting,Li, Na,Li, Xiaowei,&Hu, Bin.(2023).Analysis of functional connectivity in depression based on a weighted hyper-network method.Journal of Neural Engineering,20(1). |
| MLA | Shao, Xuexiao,et al."Analysis of functional connectivity in depression based on a weighted hyper-network method".Journal of Neural Engineering 20.1(2023). |
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