Effect of electroconvulsive therapy on brain functional network in major depressive disorder_Journal of Biomedical Engineering

Authors：

TIAN Shuxiang ^1,2,3,4 ,  XU Guizhi ^1,2,3 ,  YANG Xinsheng ^1,2,3 , Paul B Fitzgerald ⁵ , Alan Wang ^6,7,8

1. Key Laboratory of Bioelectromagnetic and Neural Engineering of Hebei Province, Hebei University of Technology, Tianjin 300401, P. R. China;
2. Tianjin Key Laboratory of Bioelectricity and Intelligent Health, Hebei University of Technology, Tianjin 300401, P. R. China;
3. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;
4. National Institute on Drug Dependence, Peking University, Beijing 100191, P.R. China;
5. School of Medicine and Psychology, The Australian National University, Canberra, Australian Capital Territory 2601, Australia;
6. Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand;
7. Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1023, New Zealand;
8. Centre for Brain Research, The University of Auckland, Auckland 1042, New Zealand;

Corresponding author：

XU Guizhi, Email: gzxu@hebut.edu.cn; YANG Xinsheng, Email: xsyang@hebut.edu.cn

Keywords：

Major depressive disorder; Electroconvulsive therapy; Resting-state electroencephalogram; Brain functional network; Minimum spanning tree

DOI：

10.7507/1001-5515.202201032

Video：

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Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.

Citation： TIAN Shuxiang, XU Guizhi, YANG Xinsheng, Paul B Fitzgerald, Alan Wang. Effect of electroconvulsive therapy on brain functional network in major depressive disorder. Journal of Biomedical Engineering, 2023, 40(3): 426-433. doi: 10.7507/1001-5515.202201032 Copy

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2.	Beppi C, Ribeiro Violante I, Scott G, et al. EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions. Brain Cogn, 2021, 148(1): 105677.
3.	Chen G, Zhang Y, Li X, et al. Distinct inhibitory circuits orchestrate cortical beta and gamma band oscillations. Neuron, 2017, 96(6): 1403-1418.
4.	邸新, 饶恒毅. 人脑功能连通性研究进展. 生物化学与生物物理进展, 2007, 34(1): 5-12.
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7.	Li X, Jing Z, Hu B, et al. A resting-rtate brain functional network study in MDD based on minimum spanning tree analysis and the hierarchical clustering. Complexity, 2017, 2017(1): 1-11.
8.	Friston K J, Frith C D, Liddle P F, et al. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab, 1993, 13(1): 5-14.
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11.	Hill A T, Hadas I, Zomorrodi R, et al. Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis. Sci Rep, 10(1): 17057.
12.	Ghosh P, Roy D, Banerjee A. Organization of directed functional connectivity among nodes of ventral attention network reveals the common network mechanisms underlying saliency processing across distinct spatial and spatio-temporal scales. NeuroImage, 2021, 231(1): 117869.
13.	Lin Y, Chang C C, Huang C C Y, et al. Efficacy and neurophysiological predictors of treatment response of adjunct bifrontal transcranial direct current stimulation(tDCS) in treating unipolar and bipolar depression. J Affective Disord, 2021, 280(1): 295-304.
14.	Gurel S C, Mutlu E, Basar K, et al. Bitemporal electroconvulsive therapy efficacy in bipolar and unipolar depression: A retrospective comparison. Asian J Psychiatr, 2021, 55(1): 102503.
15.	Ainsworth N J, Sepehry A A, Vila-Rodriguez F J T J O E. Effects of ketamine anesthesia on efficacy, tolerability, seizure response, and neurocognitive outcomes in electroconvulsive therapy: A comprehensive meta-analysis of double-blind randomized controlled trials. J ECT, 2020, 36(2): 94-105.
16.	Zhang J, Cheng W, Wang Z G, et al. Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy. PLoS One, 2012, 7(5): e36733.
17.	van Dellen E, Sommer I E, Bohlken M M, et al. Minimum spanning tree analysis of the human connectome. Hum Brain Mapp, 2018, 39(6): 2455-2471.
18.	Borgheai S B, Mclinden J, Mankodiya K, et al. Frontal functional network disruption associated with amyotrophic lateral sclerosis: An fNIRS-based minimum spanning tree analysis. Front Neurosci, 2020, 14(1376): 613990.
19.	Wang R, Liu M, Cheng X, et al. Segregation, integration and balance of large-scale resting brain networks configure different cognitive abilities. Proc Natl Acad Sci USA, 2021, 118(23): e2022288118.
20.	Tian S, Xu G, Yang H, et al. The effect of brain functional network following electroconvulsive therapy in major depressive disorder. COMPEL, 2023, 42(1): 149-158.
21.	Voineskos D, Levinson A J, Sun Y, et al. The relationship between cortical inhibition and electroconvulsive therapy in the treatment of major depressive disorder. Sci Rep, 2016, 6(1): 37461.
22.	Bruder G E, Sedoruk J P, Stewart J W, et al. Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: Pre- and post-treatment findings. Biol Psychiat, 2008, 63(12): 1171-1177.
23.	刘潇雅, 刘爽, 郭冬月, 等. 抑郁症脑电特异性研究进展. 中国生物医学工程学报, 2020, 39(3): 351-361.
24.	柏同健. 电休克疗法调控抑郁症情绪与记忆的神经机制研究. 合肥: 安徽医科大学, 2019.
25.	Farzan F, Atluri S, Mei Y, et al. Brain temporal complexity in explaining the therapeutic and cognitive effects of seizure therapy. Brain, 2017, 140(4): 1011-1025.
26.	Scangos K F, Weiner R D, Weiner R D, et al. An electrophysiological biomarker that may predict treatment response to ECT. J ECT, 2019, 35(2): 95-102.
27.	史丹丹. 首发重性抑郁症患者电休克治疗前后默认网络的fMRI研究. 重庆: 重庆医科大学, 2016.
28.	Hakulinen C, Fried E I, Pulkki-Råback L, et al. Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000–2011 study. Soc Psychiatry Psychiatr Epidemiol, 2020, 55(10): 1273-1282.
29.	Sun S, Yang P, Chen H, et al. Electroconvulsive therapy-induced changes in functional brain network of major depressive disorder patients: A longitudinal resting-sate electroencephalography study. Front Hum Neurosci, 2022, 16(1): 852657.

1. World Health Organization. Depression (2021) [2022-01-13]. https: //www.who.int/zh/news-room/fact-sheets/detail/depression.
2. Beppi C, Ribeiro Violante I, Scott G, et al. EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions. Brain Cogn, 2021, 148(1): 105677.
3. Chen G, Zhang Y, Li X, et al. Distinct inhibitory circuits orchestrate cortical beta and gamma band oscillations. Neuron, 2017, 96(6): 1403-1418.
4. 邸新, 饶恒毅. 人脑功能连通性研究进展. 生物化学与生物物理进展, 2007, 34(1): 5-12.
5. Takamiya A, Hirano J, Yamagata B, et al. Electroconvulsive therapy modulates resting-state EEG oscillatory pattern and phase synchronization in nodes of the default mode network in patients with depressive disorder. Front Hum Neurosci, 2019, 13(1): 1-9.
6. Fingelkurts A A, Fingelkurts A A. Altered structure of dynamic electroencephalogram oscillatory pattern in major depression. Biol Psychiat, 2015, 77(12): 1050-1060.
7. Li X, Jing Z, Hu B, et al. A resting-rtate brain functional network study in MDD based on minimum spanning tree analysis and the hierarchical clustering. Complexity, 2017, 2017(1): 1-11.
8. Friston K J, Frith C D, Liddle P F, et al. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab, 1993, 13(1): 5-14.
9. Kirsten A, Seifritz E, Olbrich S. Electroencephalogram source connectivity in the prediction of electroconvulsive therapy outcome in major depressive disorder. Clin EEG Neurosci, 2020, 51(1): 10-18.
10. Tsolaki E, Narr K L, Espinoza R, et al. Subcallosal cingulate structural connectivity differs in responders and nonresponders to electroconvulsive therapy. Biol Psychiatry Cogn Neurosci Neuroimaging, 2021, 6(1): 10-19.
11. Hill A T, Hadas I, Zomorrodi R, et al. Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis. Sci Rep, 10(1): 17057.
12. Ghosh P, Roy D, Banerjee A. Organization of directed functional connectivity among nodes of ventral attention network reveals the common network mechanisms underlying saliency processing across distinct spatial and spatio-temporal scales. NeuroImage, 2021, 231(1): 117869.
13. Lin Y, Chang C C, Huang C C Y, et al. Efficacy and neurophysiological predictors of treatment response of adjunct bifrontal transcranial direct current stimulation(tDCS) in treating unipolar and bipolar depression. J Affective Disord, 2021, 280(1): 295-304.
14. Gurel S C, Mutlu E, Basar K, et al. Bitemporal electroconvulsive therapy efficacy in bipolar and unipolar depression: A retrospective comparison. Asian J Psychiatr, 2021, 55(1): 102503.
15. Ainsworth N J, Sepehry A A, Vila-Rodriguez F J T J O E. Effects of ketamine anesthesia on efficacy, tolerability, seizure response, and neurocognitive outcomes in electroconvulsive therapy: A comprehensive meta-analysis of double-blind randomized controlled trials. J ECT, 2020, 36(2): 94-105.
16. Zhang J, Cheng W, Wang Z G, et al. Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy. PLoS One, 2012, 7(5): e36733.
17. van Dellen E, Sommer I E, Bohlken M M, et al. Minimum spanning tree analysis of the human connectome. Hum Brain Mapp, 2018, 39(6): 2455-2471.
18. Borgheai S B, Mclinden J, Mankodiya K, et al. Frontal functional network disruption associated with amyotrophic lateral sclerosis: An fNIRS-based minimum spanning tree analysis. Front Neurosci, 2020, 14(1376): 613990.
19. Wang R, Liu M, Cheng X, et al. Segregation, integration and balance of large-scale resting brain networks configure different cognitive abilities. Proc Natl Acad Sci USA, 2021, 118(23): e2022288118.
20. Tian S, Xu G, Yang H, et al. The effect of brain functional network following electroconvulsive therapy in major depressive disorder. COMPEL, 2023, 42(1): 149-158.
21. Voineskos D, Levinson A J, Sun Y, et al. The relationship between cortical inhibition and electroconvulsive therapy in the treatment of major depressive disorder. Sci Rep, 2016, 6(1): 37461.
22. Bruder G E, Sedoruk J P, Stewart J W, et al. Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: Pre- and post-treatment findings. Biol Psychiat, 2008, 63(12): 1171-1177.
23. 刘潇雅, 刘爽, 郭冬月, 等. 抑郁症脑电特异性研究进展. 中国生物医学工程学报, 2020, 39(3): 351-361.
24. 柏同健. 电休克疗法调控抑郁症情绪与记忆的神经机制研究. 合肥: 安徽医科大学, 2019.
25. Farzan F, Atluri S, Mei Y, et al. Brain temporal complexity in explaining the therapeutic and cognitive effects of seizure therapy. Brain, 2017, 140(4): 1011-1025.
26. Scangos K F, Weiner R D, Weiner R D, et al. An electrophysiological biomarker that may predict treatment response to ECT. J ECT, 2019, 35(2): 95-102.
27. 史丹丹. 首发重性抑郁症患者电休克治疗前后默认网络的fMRI研究. 重庆: 重庆医科大学, 2016.
28. Hakulinen C, Fried E I, Pulkki-Råback L, et al. Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000–2011 study. Soc Psychiatry Psychiatr Epidemiol, 2020, 55(10): 1273-1282.
29. Sun S, Yang P, Chen H, et al. Electroconvulsive therapy-induced changes in functional brain network of major depressive disorder patients: A longitudinal resting-sate electroencephalography study. Front Hum Neurosci, 2022, 16(1): 852657.

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Effect of electroconvulsive therapy on brain functional network in major depressive disorder

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