A study on electroencephalogram characteristics of depression in patients with aphasia based on resting state and emotional Stroop task_Journal of Biomedical Engineering

Authors：

DING Siyuan ¹ ,  ZHU Yan ² , SHI Chang ² ,  YANG Banghua ^1,3,4

1. Medical School, Shanghai University, Shanghai 200444, P. R. China;
2. Neurorehabilitation Department, The Second Rehabilitation Hospital of Shanghai, Shanghai 200431, P. R. China;
3. School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, P. R. China;
4. Engineering Research Center for TCM Intelligent Rehabilitation, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P. R. China;

Corresponding author：

ZHU Yan, Email: 80743682@qq.com; YANG Banghua, Email: yangbanghua@shu.edu.cn

Keywords：

Electroencephalogram; Depression; Aphasia; Microstate

DOI：

10.7507/1001-5515.202503034

Video：

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Post-stroke aphasia is associated with a significantly elevated risk of depression, yet the underlying mechanisms remain unclear. This study recorded 64-channel electroencephalogram data and depression scale scores from 12 aphasic patients with depression, 8 aphasic patients without depression, and 12 healthy controls during resting state and an emotional Stroop task. Spectral and microstate analyses were conducted to examine brain activity patterns across conditions. Results showed that depression scores significantly negatively explained the occurrence of microstate class C and positively explained the transition probability from microstate class A to B. Furthermore, aphasic patients with depression exhibited increased alpha-band activation in the frontal region. These findings suggest distinct neural features in aphasic patients with depression and offer new insights into the mechanisms contributing to their heightened vulnerability to depression.

Citation： DING Siyuan, ZHU Yan, SHI Chang, YANG Banghua. A study on electroencephalogram characteristics of depression in patients with aphasia based on resting state and emotional Stroop task. Journal of Biomedical Engineering, 2025, 42(3): 488-495. doi: 10.7507/1001-5515.202503034 Copy

1.	Xian Z, Liu H, Gu Y, et al. EEG biomarkers of behavioral inhibition in patients with depression who committed violent offenses: a Go/NoGo ERP study. Cereb Cortex, 2024, 34(2): bhae010.
2.	Otte C, Gold S M, Penninx B W, et al. Major depressive disorder. Nat Rev Dis Primers, 2016, 2: 16065.
3.	Yang C-Y, Lee H-M. Effects of the Hyperparameters on CNNs for MDD classification using Resting-State EEG. Electronics, 2024, 13(1): 186.
4.	Chou P H, Tu C H, Chen C M, et al. Bilateral theta-burst stimulation on emotional processing in major depressive disorder: A functional neuroimaging study from a randomized, double-blind, sham-controlled trial. Psychiatry Clin Neurosci, 2023, 77(4): 233-240.
5.	Panterliyon N A, Subasinghe S. Assessing depression and its correlates among post-stroke survivors with aphasia in Sri Lanka. J Psychosoc Rehabil Ment Health, 2024, 11(3): 307-316.
6.	Kao S-K, Chan C-T. Increased risk of depression and associated symptoms in poststroke aphasia. Sci Rep, 2024, 14(1): 21352.
7.	Tatti E, Cinti A, Serbina A, et al. Resting-state EEG alterations of practice-related spectral activity and connectivity patterns in depression. Biomedicines, 2024, 12(9): 2054.
8.	Wei C, Yang Q, Chen J, et al. EEG microstate as a biomarker of post-stroke depression with acupuncture treatment. Front Neurol, 2024, 15: 1452243.
9.	Murphy N, Tamman A J F, Lijffijt M, et al. Neural complexity EEG biomarkers of rapid and post-rapid ketamine effects in late-life treatment-resistant depression: a randomized control trial. Neuropsychopharmacology, 2023, 48(11): 1586-1593.
10.	Lucarini V, Alouit A, Yeh D, et al. Neurophysiological explorations across the spectrum of psychosis, autism, and depression, during wakefulness and sleep: protocol of a prospective case–control transdiagnostic multimodal study (DEMETER). BMC Psychiatry, 2023, 23(1): 860.
11.	Xue S, Shen X, Zhang D, et al. Unveiling frequency-specific microstate correlates of anxiety and depression symptoms. Brain Topogr, 2025, 38(1): 12.
12.	Li J, Li N, Shao X, et al. Altered brain dynamics and their ability for major depression detection using EEG microstates analysis. IEEE T Affect Comput, 2023, 14(3): 2116-2126.
13.	De Carvalho Costa T D, Da Silva Machado C B, Lemos Segundo R P, et al. Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson’s disease?. Neurophysiol Clin, 2023, 53(1): 102839.
14.	陈晓丽, 宋玉萍, 孙宏伟, 等. 冠心病与抑郁症共患者情绪Stroop效应任务的事件相关电位研究. 中国全科医学, 2016, 19(23): 2757-2762.
15.	Williams J M G, Mathews A, Macleod C. The emotional stroop task and psychopathology. Psychol Bull, 1996, 120(1): 3-24.
16.	You L, Yang B, Lu X, et al. Similarities and differences between chronic primary pain and depression in brain activities: Evidence from resting-state microstates and auditory Oddball task. Behav Brain Res, 2025, 477: 115319.
17.	Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 2004, 134(1): 9-21.
18.	Tait L, Zhang J. +microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG. Neuroimage, 2022, 258: 119346.
19.	Khanna A, Pascual-Leone A, Michel C M, et al. Microstates in resting-state EEG: Current status and future directions. Neurosci Biobehav Rev, 2015, 49: 105-113.
20.	Lao J, Zeng Y, Wu Z, et al. Abnormalities in electroencephalographic microstates in patients with Late-Life Depression. Neuropsychiatr Dis Treat, 2024, 20: 1201-1210.
21.	Li J, Hao Y, Xu H. Abnormal cortical functional network and microstates alterations in depression: insights from effective connectivity and EEG microstates. Proceedings// 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Istanbul: IEEE, 2023: 2535-2542.
22.	Qin X, Xiong J, Cui R, et al. EEG microstate temporal dynamics predict depressive symptoms in college students. Brain Topogr, 2022, 35(4): 481-494.
23.	Lee P F, Kan D P X, Croarkin P, et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci, 2018, 47: 315-322.
24.	Tarailis P, Koenig T, Michel C M, et al. The functional aspects of resting EEG microstates: A systematic review. Brain Topogr, 2024, 37(2): 181-217.
25.	Uddin L Q. Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci, 2015, 16(1): 55-61.
26.	Michel C M, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. Neuroimage, 2018, 180: 577-593.
27.	Seeley W W, Menon V, Schatzberg A F, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci, 2007, 27(9): 2349-2356.
28.	Korn U, Krylova M, Heck K L, et al. EEG-microstates reflect auditory distraction after attentive audiovisual perception recruitment of cognitive control networks. Front Syst Neurosci, 2021, 15: 751226.
29.	Tomescu M I, Papasteri C C, Sofonea A, et al. Spontaneous thought and microstate activity modulation by social imitation. Neuroimage, 2022, 249: 118878.
30.	Dixon M L, Christoff K. The lateral prefrontal cortex and complex value-based learning and decision making. Neurosci Biobehav Rev, 2014, 45: 9-18.
31.	Funahashi S, Andreau J M. Prefrontal cortex and neural mechanisms of executive function. J Physiol Paris, 2013, 107(6): 471-482.
32.	Tomescu M I, Rihs T A, Rochas V, et al. From swing to cane: Sex differences of EEG resting-state temporal patterns during maturation and aging. Dev Cogn Neurosci, 2018, 31: 58-66.

1. Xian Z, Liu H, Gu Y, et al. EEG biomarkers of behavioral inhibition in patients with depression who committed violent offenses: a Go/NoGo ERP study. Cereb Cortex, 2024, 34(2): bhae010.
2. Otte C, Gold S M, Penninx B W, et al. Major depressive disorder. Nat Rev Dis Primers, 2016, 2: 16065.
3. Yang C-Y, Lee H-M. Effects of the Hyperparameters on CNNs for MDD classification using Resting-State EEG. Electronics, 2024, 13(1): 186.
4. Chou P H, Tu C H, Chen C M, et al. Bilateral theta-burst stimulation on emotional processing in major depressive disorder: A functional neuroimaging study from a randomized, double-blind, sham-controlled trial. Psychiatry Clin Neurosci, 2023, 77(4): 233-240.
5. Panterliyon N A, Subasinghe S. Assessing depression and its correlates among post-stroke survivors with aphasia in Sri Lanka. J Psychosoc Rehabil Ment Health, 2024, 11(3): 307-316.
6. Kao S-K, Chan C-T. Increased risk of depression and associated symptoms in poststroke aphasia. Sci Rep, 2024, 14(1): 21352.
7. Tatti E, Cinti A, Serbina A, et al. Resting-state EEG alterations of practice-related spectral activity and connectivity patterns in depression. Biomedicines, 2024, 12(9): 2054.
8. Wei C, Yang Q, Chen J, et al. EEG microstate as a biomarker of post-stroke depression with acupuncture treatment. Front Neurol, 2024, 15: 1452243.
9. Murphy N, Tamman A J F, Lijffijt M, et al. Neural complexity EEG biomarkers of rapid and post-rapid ketamine effects in late-life treatment-resistant depression: a randomized control trial. Neuropsychopharmacology, 2023, 48(11): 1586-1593.
10. Lucarini V, Alouit A, Yeh D, et al. Neurophysiological explorations across the spectrum of psychosis, autism, and depression, during wakefulness and sleep: protocol of a prospective case–control transdiagnostic multimodal study (DEMETER). BMC Psychiatry, 2023, 23(1): 860.
11. Xue S, Shen X, Zhang D, et al. Unveiling frequency-specific microstate correlates of anxiety and depression symptoms. Brain Topogr, 2025, 38(1): 12.
12. Li J, Li N, Shao X, et al. Altered brain dynamics and their ability for major depression detection using EEG microstates analysis. IEEE T Affect Comput, 2023, 14(3): 2116-2126.
13. De Carvalho Costa T D, Da Silva Machado C B, Lemos Segundo R P, et al. Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson’s disease?. Neurophysiol Clin, 2023, 53(1): 102839.
14. 陈晓丽, 宋玉萍, 孙宏伟, 等. 冠心病与抑郁症共患者情绪Stroop效应任务的事件相关电位研究. 中国全科医学, 2016, 19(23): 2757-2762.
15. Williams J M G, Mathews A, Macleod C. The emotional stroop task and psychopathology. Psychol Bull, 1996, 120(1): 3-24.
16. You L, Yang B, Lu X, et al. Similarities and differences between chronic primary pain and depression in brain activities: Evidence from resting-state microstates and auditory Oddball task. Behav Brain Res, 2025, 477: 115319.
17. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 2004, 134(1): 9-21.
18. Tait L, Zhang J. +microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG. Neuroimage, 2022, 258: 119346.
19. Khanna A, Pascual-Leone A, Michel C M, et al. Microstates in resting-state EEG: Current status and future directions. Neurosci Biobehav Rev, 2015, 49: 105-113.
20. Lao J, Zeng Y, Wu Z, et al. Abnormalities in electroencephalographic microstates in patients with Late-Life Depression. Neuropsychiatr Dis Treat, 2024, 20: 1201-1210.
21. Li J, Hao Y, Xu H. Abnormal cortical functional network and microstates alterations in depression: insights from effective connectivity and EEG microstates. Proceedings// 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Istanbul: IEEE, 2023: 2535-2542.
22. Qin X, Xiong J, Cui R, et al. EEG microstate temporal dynamics predict depressive symptoms in college students. Brain Topogr, 2022, 35(4): 481-494.
23. Lee P F, Kan D P X, Croarkin P, et al. Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. J Clin Neurosci, 2018, 47: 315-322.
24. Tarailis P, Koenig T, Michel C M, et al. The functional aspects of resting EEG microstates: A systematic review. Brain Topogr, 2024, 37(2): 181-217.
25. Uddin L Q. Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci, 2015, 16(1): 55-61.
26. Michel C M, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. Neuroimage, 2018, 180: 577-593.
27. Seeley W W, Menon V, Schatzberg A F, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci, 2007, 27(9): 2349-2356.
28. Korn U, Krylova M, Heck K L, et al. EEG-microstates reflect auditory distraction after attentive audiovisual perception recruitment of cognitive control networks. Front Syst Neurosci, 2021, 15: 751226.
29. Tomescu M I, Papasteri C C, Sofonea A, et al. Spontaneous thought and microstate activity modulation by social imitation. Neuroimage, 2022, 249: 118878.
30. Dixon M L, Christoff K. The lateral prefrontal cortex and complex value-based learning and decision making. Neurosci Biobehav Rev, 2014, 45: 9-18.
31. Funahashi S, Andreau J M. Prefrontal cortex and neural mechanisms of executive function. J Physiol Paris, 2013, 107(6): 471-482.
32. Tomescu M I, Rihs T A, Rochas V, et al. From swing to cane: Sex differences of EEG resting-state temporal patterns during maturation and aging. Dev Cogn Neurosci, 2018, 31: 58-66.

Journal of Biomedical Engineering

A study on electroencephalogram characteristics of depression in patients with aphasia based on resting state and emotional Stroop task

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