| Analysis of functional connectivity in depression based on a weighted hyper-network method | |
Shao, Xuexiao1; Kong, Wenwen1; Sun, Shuting2; Li, Na1; Li, XW(李小伟)1,3 ; Hu, B(胡斌)1,4,5 | |
| 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. © 2023 IOP Publishing Ltd. |
| Keyword | Brain Classification (of information) Diagnosis Brain connectivity Brain networks Brain regions Depression Functional connectivity Hyper-network Network methods Resting state Resting-state EEG data Weighted hyper-edge |
| Publisher | Institute of Physics |
| DOI | 10.1088/1741-2552/acb088 |
| Indexed By | EI |
| Language | 英语 |
| EI Accession Number | 20230513468210 |
| EI Keywords | Regression analysis |
| EI Classification Number | 461.1 Biomedical Engineering ; 461.6 Medicine and Pharmacology ; 716.1 Information Theory and Signal Processing ; 903.1 Information Sources and Analysis ; 922.2 Mathematical Statistics |
| Original Document Type | Journal article (JA) |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/500875 |
| Collection | 信息科学与工程学院 |
| Corresponding Author | Li, Xiaowei; Hu, Bin |
| Affiliation | 1.Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; 2.Brain Health Engineering Laboratory, Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China; 3.Shandong Academy of Intelligent Computing Technology, Shandong, China; 4.CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; 5.Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University, Institute of Semiconductors, Chinese Academy of Sciences, Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China |
| First Author Affilication | School of Information Science and Engineering |
| Corresponding Author Affilication | School of Information Science and Engineering; Lanzhou University |
| 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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