A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering
A large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often bias...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/9514369 |
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| author | Xiaowei Li Zhuang Jing Bin Hu Jing Zhu Ning Zhong Mi Li Zhijie Ding Jing Yang Lan Zhang Lei Feng Dennis Majoe |
| author_facet | Xiaowei Li Zhuang Jing Bin Hu Jing Zhu Ning Zhong Mi Li Zhijie Ding Jing Yang Lan Zhang Lei Feng Dennis Majoe |
| author_sort | Xiaowei Li |
| collection | DOAJ |
| description | A large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG) sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls. |
| format | Article |
| id | doaj-art-075f0fd5e50d45b69e8cbd18b76be3d7 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-075f0fd5e50d45b69e8cbd18b76be3d72025-02-03T05:45:48ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/95143699514369A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical ClusteringXiaowei Li0Zhuang Jing1Bin Hu2Jing Zhu3Ning Zhong4Mi Li5Zhijie Ding6Jing Yang7Lan Zhang8Lei Feng9Dennis Majoe10School of Information Science & Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou, ChinaInternational WIC Institute, Beijing University of Technology, Beijing, ChinaInternational WIC Institute, Beijing University of Technology, Beijing, ChinaThe Third People’s Hospital of Tianshui City, Tianshui, ChinaLanzhou University Second Hospital, Lanzhou, ChinaLanzhou University Second Hospital, Lanzhou, ChinaBeijing Anding Hospital of Capital Medical University, Beijing, ChinaComputer Systems Institute, ETH Zürich, Zürich, SwitzerlandA large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG) sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.http://dx.doi.org/10.1155/2017/9514369 |
| spellingShingle | Xiaowei Li Zhuang Jing Bin Hu Jing Zhu Ning Zhong Mi Li Zhijie Ding Jing Yang Lan Zhang Lei Feng Dennis Majoe A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering Complexity |
| title | A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering |
| title_full | A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering |
| title_fullStr | A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering |
| title_full_unstemmed | A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering |
| title_short | A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering |
| title_sort | resting state brain functional network study in mdd based on minimum spanning tree analysis and the hierarchical clustering |
| url | http://dx.doi.org/10.1155/2017/9514369 |
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