Research on depression recognition based on brain function network_Journal of Biomedical Engineering

Authors：

 ZHANG Bingtao ^1,2,3 , ZHOU Wenying ^2,3 , LI Yanlin ^3,4 , CHANG Wenwen ¹ , XU Binbin ¹

1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;
2. Key Laboratory of Opto-technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;
3. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, P. R. China;
4. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, P. R. China;

Corresponding author：

ZHANG Bingtao, Email: zhangbt14@lzu.edu.cn

Keywords：

Depression; Brain function network; Resting state electroencephalogram; Phase lag index

DOI：

10.7507/1001-5515.202108034

Video：

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Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of “small world” and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.

Citation： ZHANG Bingtao, ZHOU Wenying, LI Yanlin, CHANG Wenwen, XU Binbin. Research on depression recognition based on brain function network. Journal of Biomedical Engineering, 2022, 39(1): 47-55. doi: 10.7507/1001-5515.202108034 Copy

1.	Zhang Bingtao, Yan Guanghui, Yang Zhifei, et al. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans Neural Syst Rehabil Eng, 2021, 29(1): 215-229.
2.	World Health Organization (WHO). Depression. (2018-09-21)[2021-12-10]. https: //www.who.int/health-topics/depressiontab=tab_1.
3.	Zhang Xiaowei, Shen Jian, Din Z U, et al. Multimodal depression detection: fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble. IEEE J Biomed Health Inform, 2019, 23(6): 2265-2275.
4.	Yao Z J, Fu Y, Wu J F, et al. Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients. Brain Imaging Behav, 2020, 14(3): 653-667.
5.	Alageel A, Albalawi F F, Aldosary F. Magnetoencephalography (MEG)-based biomarkers for depression//The 21st Annual ISBD Conference: Global Advances in Bipolar Disorder and Depression, Sydney: Wiley, 2019, 2019(21): 20-23.
6.	Cai H S, Qu Z D, Hu B, et al. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 2020, 59(2020): 127-138.
7.	Li X W, Zhang X, Hu B, et al. Depression recognition using machine learning methods with different feature generation strategies. Artif Intell Med, 2019, 99: 101696.
8.	Zeng Lingli, Shen Hui, Liu Li, et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain, 2012, 135(5): 1498-1507.
9.	Yu Haitao, Lei Xinyu, Song Zhenxi, et al. Supervised network-based fuzzy learning of EEG signals for Alzheimer's disease identification. IEEE Transactions on Fuzzy Systems, 2020, 28(1): 60-71.
10.	Zhang B T, Lei T, Liu H, et al. EEG-based automatic sleep staging using ontology and weighting feature analysis, Comput Math Method M, 2018, 2018: 6534041.
11.	Fingelkurts A A, Kähkönen S. Functional connectivity in the brain-is it an elusive concept?. Neurosci Biobehav Rev, 2005, 28(8): 827-836.
12.	Cai H S, Gao Y W, Sun S T, et al. MODMA dataset: a multi-modal open dataset for mental-disorder analysis. (2019-11-01) [2021-12-10]. http: //modma.lzu.edu.cn/data/index/.
13.	Zhang Bingtao, Zhou Wenying, Cai Hanshu, et al. Ubiquitous depression detection of sleep physiological data by using combination learning and functional networks. IEEE Access, 2020, 8: 94220-94235.
14.	Peng H, Hu B, Shi Q X, et al. Removal of ocular artifacts in EEG-an improved approach combining DWT and ANC for portable applications. IEEE J Biomed Health Inform, 2013, 17(3): 600-607.
15.	司慧芳, 谢天, 高军峰, 等. 基于相位延迟指数的脑功能网络及测谎研究. 电子学报, 2018, 46(7): 1742-1747.
16.	Li X W, Zhu J, Hu B, et al. A resting-state brain functional network study in MDD based on minimum spanning tree analysis and the hierarchical clustering. Complexity, 2017, 2017: 9514369.
17.	Fraschini M, Demuru M, Crobe A, et al. The effect of epoch length on estimated EEG functional connectivity and brain network organisation. J Neural Eng, 2016, 13(3): 036015.
18.	梁夏, 王金辉, 贺永. 人脑连接组研究:脑结构网络和脑功能网络. 科学通报, 2010, 55(16): 1565-1583.
19.	Li N, Hou J C, Sha L. Design and analysis of an MST-based topology control algorithm. IEEE T Wirel Commun, 2005, 4(3): 1195-1206.
20.	Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
21.	Zhang Junran, Wang Jinhui, Wu Qizhu, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry, 2011, 70(4): 334-342.
22.	Sun S T, Li X W, Zhu J, et al. Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(3): 429-439.
23.	Bian Zhijie, Li Qiuli, Wang Lei, et al. Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes. Front Aging Neurosci, 2014, 6: 11.
24.	Cai Hanshu, Zhang Xiangzi, Zhang Yanhao, et al. A case-based reasoning model for depression based on three-electrode EEG data. IEEE Trans Affect Comput, 2020, 11(3): 383-392.
25.	Cai H S, Han J S, Chen Y F, et al. A pervasive approach to EEG-based depression detection. Complexity, 2018, 2018: 5238028.
26.	Li X W, Hu B, Sun S T, et al. EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed, 2016, 136: 151-161.
27.	Cavanagh J F, Bismark A W, Frank M J, et al. Multiple dissociations between comorbid depression and anxiety on reward and punishment processing: evidence from computationally informed EEG. Comput Psychiatr, 2019, 3: 1-17.
28.	Zhou Yuan, Yu Chunshui, Zheng Hua, et al. Increased neural resources recruitment in the intrinsic organization in major depression. J Affect Disord, 2010, 121(3): 220-230.
29.	Connolly C G. Wu J, Ho T C, et al. Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biological Psychiatry, 2013, 74(12): 898-907.
30.	Joormann J, Gotlib I H. Emotion regulation in depression: relation to cognitive inhibition. Cogn Emot, 2010, 24(2): 281-298.
31.	Leistedt S J, Nathalie C, Martine D, et al. Altered sleep brain functional connectivity in acutely depressed patients. Hum Brain Mapp, 2009, 30(7): 2207-2219.
32.	Liu Yong, Liang Meng, Zhou Yuan, et al. Disrupted small-world networks in schizophrenia. Brain, 2008, 131(4): 945-961.

1. Zhang Bingtao, Yan Guanghui, Yang Zhifei, et al. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans Neural Syst Rehabil Eng, 2021, 29(1): 215-229.
2. World Health Organization (WHO). Depression. (2018-09-21)[2021-12-10]. https: //www.who.int/health-topics/depressiontab=tab_1.
3. Zhang Xiaowei, Shen Jian, Din Z U, et al. Multimodal depression detection: fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble. IEEE J Biomed Health Inform, 2019, 23(6): 2265-2275.
4. Yao Z J, Fu Y, Wu J F, et al. Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients. Brain Imaging Behav, 2020, 14(3): 653-667.
5. Alageel A, Albalawi F F, Aldosary F. Magnetoencephalography (MEG)-based biomarkers for depression//The 21st Annual ISBD Conference: Global Advances in Bipolar Disorder and Depression, Sydney: Wiley, 2019, 2019(21): 20-23.
6. Cai H S, Qu Z D, Hu B, et al. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 2020, 59(2020): 127-138.
7. Li X W, Zhang X, Hu B, et al. Depression recognition using machine learning methods with different feature generation strategies. Artif Intell Med, 2019, 99: 101696.
8. Zeng Lingli, Shen Hui, Liu Li, et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain, 2012, 135(5): 1498-1507.
9. Yu Haitao, Lei Xinyu, Song Zhenxi, et al. Supervised network-based fuzzy learning of EEG signals for Alzheimer's disease identification. IEEE Transactions on Fuzzy Systems, 2020, 28(1): 60-71.
10. Zhang B T, Lei T, Liu H, et al. EEG-based automatic sleep staging using ontology and weighting feature analysis, Comput Math Method M, 2018, 2018: 6534041.
11. Fingelkurts A A, Kähkönen S. Functional connectivity in the brain-is it an elusive concept?. Neurosci Biobehav Rev, 2005, 28(8): 827-836.
12. Cai H S, Gao Y W, Sun S T, et al. MODMA dataset: a multi-modal open dataset for mental-disorder analysis. (2019-11-01) [2021-12-10]. http: //modma.lzu.edu.cn/data/index/.
13. Zhang Bingtao, Zhou Wenying, Cai Hanshu, et al. Ubiquitous depression detection of sleep physiological data by using combination learning and functional networks. IEEE Access, 2020, 8: 94220-94235.
14. Peng H, Hu B, Shi Q X, et al. Removal of ocular artifacts in EEG-an improved approach combining DWT and ANC for portable applications. IEEE J Biomed Health Inform, 2013, 17(3): 600-607.
15. 司慧芳, 谢天, 高军峰, 等. 基于相位延迟指数的脑功能网络及测谎研究. 电子学报, 2018, 46(7): 1742-1747.
16. Li X W, Zhu J, Hu B, et al. A resting-state brain functional network study in MDD based on minimum spanning tree analysis and the hierarchical clustering. Complexity, 2017, 2017: 9514369.
17. Fraschini M, Demuru M, Crobe A, et al. The effect of epoch length on estimated EEG functional connectivity and brain network organisation. J Neural Eng, 2016, 13(3): 036015.
18. 梁夏, 王金辉, 贺永. 人脑连接组研究:脑结构网络和脑功能网络. 科学通报, 2010, 55(16): 1565-1583.
19. Li N, Hou J C, Sha L. Design and analysis of an MST-based topology control algorithm. IEEE T Wirel Commun, 2005, 4(3): 1195-1206.
20. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
21. Zhang Junran, Wang Jinhui, Wu Qizhu, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry, 2011, 70(4): 334-342.
22. Sun S T, Li X W, Zhu J, et al. Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(3): 429-439.
23. Bian Zhijie, Li Qiuli, Wang Lei, et al. Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes. Front Aging Neurosci, 2014, 6: 11.
24. Cai Hanshu, Zhang Xiangzi, Zhang Yanhao, et al. A case-based reasoning model for depression based on three-electrode EEG data. IEEE Trans Affect Comput, 2020, 11(3): 383-392.
25. Cai H S, Han J S, Chen Y F, et al. A pervasive approach to EEG-based depression detection. Complexity, 2018, 2018: 5238028.
26. Li X W, Hu B, Sun S T, et al. EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed, 2016, 136: 151-161.
27. Cavanagh J F, Bismark A W, Frank M J, et al. Multiple dissociations between comorbid depression and anxiety on reward and punishment processing: evidence from computationally informed EEG. Comput Psychiatr, 2019, 3: 1-17.
28. Zhou Yuan, Yu Chunshui, Zheng Hua, et al. Increased neural resources recruitment in the intrinsic organization in major depression. J Affect Disord, 2010, 121(3): 220-230.
29. Connolly C G. Wu J, Ho T C, et al. Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biological Psychiatry, 2013, 74(12): 898-907.
30. Joormann J, Gotlib I H. Emotion regulation in depression: relation to cognitive inhibition. Cogn Emot, 2010, 24(2): 281-298.
31. Leistedt S J, Nathalie C, Martine D, et al. Altered sleep brain functional connectivity in acutely depressed patients. Hum Brain Mapp, 2009, 30(7): 2207-2219.
32. Liu Yong, Liang Meng, Zhou Yuan, et al. Disrupted small-world networks in schizophrenia. Brain, 2008, 131(4): 945-961.

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Research on depression recognition based on brain function network

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