Research Progress in Electroencephalography of Depression
Liu Xiaoya1 , Liu Shuang1* , Guo Dongyue1, An Xingwei1 , Yang Jiajia2, Ming Dong1, 2#
1 Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; 2 Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
Abstract:Depression is an affective disease with significant and prolonged mood depression as the main symptoms, having a high incidence and spreading across all age groups. With the rapid development of the world economy and the ever-increasing competition in social life, the incidence of global depression has also rapidly risen. At the same time, diseased and suicide has showed a trend of younger age. Therefore, attention must be paid to the prevention and treatment of depression. Currently, diagnosis and treatment of depression mainly depend on subjective scale evaluation and doctor's experience, with poor consistency, while high misdiagnosis rate and missed diagnosis rate, not objective and effective enough, and lacking of convenient and rapid quantitative diagnostic indicators and methods. Electroencephalography (EEG) is a non-invasive measure to detect changes in cerebral cortical neural activity, which has high time resolution and rich information on central neurocognitive and physiological activities. And it is an objective and effective method to obtain brain pathological changes in depression. In recent years, the specificity of EEG for depression has achieved progress. This paper comprehensively reviewed the progress of EEG rhythm, nonlinear dynamic parameters, event-related potentials (ERPs) response and specificity of brain neural network research, existing problems, solutions for these problems, and discussed future visions, in order to promote the diagnosis and treatment of depression and to develop more effective anti-depression techniques.
作者简介: # 中国生物医学工程学会会员 (Member,Chinese Society of Biomedical Engineering)
引用本文:
刘潇雅, 刘爽, 郭冬月, 安兴伟, 杨佳佳, 明东. 抑郁症脑电特异性研究进展[J]. 中国生物医学工程学报, 2020, 39(3): 351-361.
Liu Xiaoya, Liu Shuang, Guo Dongyue, An Xingwei, Yang Jiajia, Ming Dong. Research Progress in Electroencephalography of Depression. Chinese Journal of Biomedical Engineering, 2020, 39(3): 351-361.
[1] Vos T, Abajobir AA, Abate KH, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016[J]. Lancet, 2017. 390(10100): 1211-1259. [2] Foti D, Carlson JM, Sauder CL, et al. Reward dysfunction in major depression: Multimodal neuroimaging evidence for refining the melancholic phenotype[J]. NeuroImage, 2014, 101(2014):50-58. [3] Bora E, Berk M. Theory of mind in major depressive disorder: A meta-analysis[J]. Journal of Affective Disorders, 2016, 191:49-55. [4] Paunio T, Korhonen T, Hublin C, et al. Poor sleep predicts symptoms of depression and disability retirement due to depression[J]. Journal of Affective Disorders, 2015, 172(2015):381-389. [5] Chen Y, Bennett D, Clarke R, et al. Patterns and correlates of major depression in Chinese adults: A cross-sectional study of 0.5 million men and women[J]. Psychological Medicine, 2017. 47(5): 958-970. [6] Kan DPX, Lee PF. Decrease alpha waves in depression: An electroencephalogram (EEG) study[C]//International Conference on Biosignal Analysis, Processing and Systems (ICBAPS). Kuala Lumpur: IEEE, 2015:156-161. [7] Friedrich MJ. Depression is the leading cause of disability around the world[J]. JAMA, 2017, 317(15):1517-1518. [8] World Health Organization. Depression and other common mental disorders: global health estimates[J]. World Health Organization, 2017, 2: 5-13. Lancet T. Mental health in China: what will be achieved by 2020? [J]. The Lancet, 2015, 385(9987):2548-2548. [9] Mendenhall E, Kohrt BA, Norris SA, et al. Non-communicable disease syndemics: Poverty, depression, and diabetes among low-income populations[J]. Lancet, 2017, 389(10072):951-963. [10] 陈建军. “联合代谢组学”筛选抑郁症性别差异性诊断标志物[D]. 重庆:重庆医科大学, 2016. [11] Woo CJ, Hee JM, Jin HS, et al. Abnormal sleep delta rhythm and interregional phase synchrony in patients with restless legs syndrome and their reversal by dopamine agonist treatment[J]. Journal of Clinical Neurology, 2017, 13(4):340-350. [12] Armitage R, Emslie GJ, Hoffmann RF, et al. Delta sleep EEG in depressed adolescent females and healthy controls[J]. Journal of Affective Disorders, 2001, 63(1-3):139-148. [13] Duncan WC, Selter J, Brutsche N, et al. Baseline delta sleep ratio predicts acute ketamine mood response in major depressive disorder[J]. Journal of Affective Disorders, 2012, 145(1):115-119. [14] Epp JR, Beasley CL, Galea LA. Increased hippocampal neurogenesis and p21 expression in depression: dependent on antidepressants, sex, age, and antipsychotic exposure[J]. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology, 2013, 38(11):2297-2306. [15] Dimitriadis SI, Sun Y, Kwok K, et al. Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions[J]. Annals of Biomedical Engineering, 2015, 43(4):977-989. [16] Fingelkurts AA, Fingelkurts AA. Altered structure of dynamic electroencephalogram oscillatory pattern in major depression[J]. Biological Psychiatry, 2015, 77(12):1050-1060. [17] Li Yuezhi, Kang Cheng, Qu Xingda, et al. Depression-related brain connectivity analyzed by EEG event-related phase synchrony measure[J]. Frontiers in Human Neuroscience, 2016, 10(477):1-15. [18] Heo JS, Park JS, Ahn JS, et al. PS208. Preliminary study on asymmetry of theta quantitative EEG activity in patients with depression and anxiety disorders[J]. The International Journal of Neuropsychopharmacology, 2016, 19(Suppl 1):76-76. [19] Korb AS, Cook IA, Hunter AM, et al. Brain electrical source differences between depressed subjects and healthy controls[J]. Brain Topography, 2008, 21(2):138-146. [20] Pawlowski MA, Gazea M, Wollweber B, et al. Heart rate variability and cordance in rapid eye movement sleep as biomarkers of depression and treatment response[J]. Journal of Psychiatric Research, 2017, 92:64-73. [21] Arns M, Etkin A, Hegerl U, et al. Frontal and rostral anterior cingulate (rACC) theta EEG in depression: implications for treatment outcome? [J]. European Neuropsychopharmacology, 2015, 25(8):1190-1200. [22] Baskaran A, Farzan F, Milev R, et al. The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study[J]. Journal of Affective Disorders, 2017, 227:542-549. [23] Suzuki M, Dallaspezia S, Locatelli C, et al. Does early response predict subsequent remission in bipolar depression treated with repeated sleep deprivation combined with light therapy and lithium? [J]. Journal of Affective Disorders, 2018, 229:371-376. [24] Tenke CE, Kayser J, Manna CG, et al. Current source density measures of EEG alpha predict antidepressant treatment response[J]. Biological Psychiatry, 2011, 70(4):388-394. [25] Hosseinifard B, Moradi MH, Rostami R. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal[J]. Computer Methods and Programs in Biomedicine, 2013, 109(3):339-345. [26] Cantisani A, Koenig T, Horn H, et al. Psychomotor retardation is linked to frontal alpha asymmetry in major depression[J]. Journal of Affective Disorders, 2015, 188:167-172. [27] Jesulola E, Sharpley CF, Bitsika V, et al. Frontal alpha asymmetry as a pathway to behavioural withdrawal in depression: Research findings and issues[J]. Behavioural Brain Research, 2015, 292(11):56-67. [28] Arns M, Bruder G, Hegerl U, et al. EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study[J]. Clinical Neurophysiology, 2016, 127(1):509-519. [29] Bruder GE, Tenke CE, Warner V, et al. Electroencephalographic measures of regional hemispheric activity in offspring at risk for depressive disorders[J]. Biological Psychiatry, 2005, 57(4):328-335. [30] Bruder GE, Bansal R, Tenke CE, et al. Relationship of resting EEG with anatomical MRI measures in individuals at high and low risk for depression[J]. Human Brain Mapping, 2012, 33(6):1325-1333. [31] Kemp AH, Griffiths K, Felmingham KL, et al. Disorder specificity despite comorbidity: Resting EEG alpha asymmetry in major depressive disorder and post-traumatic stress disorder[J]. Biological Psychology, 2010, 85(2):350-354. [32] Li Yuezhi, Kang Cheng, Wei Zhaoguo, et al. Beta oscillations in major depression - signalling a new cortical circuit for central executive function[J]. Scientific Reports, 2017, 7(1):1-15. [33] Clark DL, Brown EC, Ramasubbu R, et al. Intrinsic local beta oscillations in the subgenual cingulate relate to depressive symptoms in treatment-resistant depression[J]. Biological Psychiatry, 2016, 80(11): e93-e94. [34] Li Hui, Zhao Qihua, Huang Fang, et al. Increased beta activity links to impaired emotional control in ADHD adults with high IQ[J]. Journal of Attention Disorders, 2017, 23(7):754-764. [35] Nofzinger EA, Price JC, Meltzer CC, et al. Towards a neurobiology of dysfunctional arousal in depression: the relationship between beta EEG power and regional cerebral glucose metabolism during NREM sleep[J]. Psychiatry Research Neuroimaging, 2000, 98(2):71-91. [36] Knott V, Mahoney C, Kennedy S, et al. EEG power, frequency, asymmetry and coherence in male depression[J]. Psychiatry Research, 2001, 106(2):123-140. [37] Fingelkurts AA, Fingelkurts AA. Altered structure of dynamic electroencephalogram oscillatory pattern in major depression[J]. Biological Psychiatry, 2015, 77(12):1050-1060. [38] 罗跃嘉, 吴健辉. 情绪的心理控制与认知研究策略[J]. 西南大学学报(社会科学版), 2005, 31(2):26-29. [39] Li Yingjie, Cao Dan, Wei Ling, et al. Abnormal functional connectivity of EEG gamma band in patients with depression during emotional face processing[J]. Clinical Neurophysiology, 2015, 126(11):2078-2089. [40] Siegle GJ, Condray R, Thase ME, et al. Sustained gamma-band EEG following negative words in depression and schizophrenia[J]. International Journal of Psychophysiology, 2010, 75(2):107-118. [41] Roh SC, Park EJ, Shim M, et al. EEG beta and low gamma power correlates with inattention in patients with major depressive disorder[J]. Journal of Affective Disorders, 2016, 204(2016):124-130. [42] Berger C, Koo PC, Bartz J, et al. P123. Correlation between EEG and clinical symptoms in depression[J]. Clinical Neurophysiology, 2015, 126(8): e151-e151. [43] Zhao Qinglin, Jiang Hua, Hu Bin, et al. Nonlinear dynamic complexity and sources of resting-state EEG in abstinent heroin addicts[J]. IEEE Transactions on Nanobioscience, 2017, 16(5):349-355. [44] Hosseinifard B, Moradi MH, Rostami R. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal[J]. Computer Methods and Programs in Biomedicine, 2013, 109(3):339-345. [45] Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder[J]. International Journal of Psychophysiology, 2012, 85(2):206-211. [46] Bachmann M, Lass J, Suhhova A, et al. Spectral asymmetry and Higuchi′s fractal dimension measures of depression electroencephalogram[J]. Computational and Mathematical Methods in Medicine, 2013, 2013(1394):251638-251646. [47] Linkenkaer-hansen K, Monto S, RytslH, et al. Breakdown of long-range temporal correlations in theta oscillations in patients with major depressive disorder[J]. Journal of Neuroscience, 2005, 25(44):10131-10137. [48] Lee JS, Yang BH, Lee JH, et al. Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls[J]. Clinical Neurophysiology, 2007, 118(11):2489-2496. [49] 王春方, 张力新, 刘爽, 等. 基于去趋势波动分析(DFA)的脑卒中后抑郁症静息脑电特征提取与识别[J]. 中国生物医学工程学报, 2013, 32(5):11-16. [50] 张胜, 乔世妮, 王蔚. 抑郁症患者脑电复杂度的小波熵分析[J]. 计算机工程与应用, 2012, 48(4):143-145. [51] Abásolo D, Hornero R, Gómez C, et al. Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure[J]. Medical Engineering and Physics, 2006, 28(4):315-322. [52] Al-Nuaimi A, Jammeh E, Sun L, et al. Complexity measures for quantifying changes in electroencephalogram in Alzheimer′s disease[J]. Journal of Complexity, 2018, 2018:1-12. [53] Li Yingjie, Tong Shanbao, Liu Dan, et al. Abnormal EEG complexity in patients with schizophrenia and depression[J]. Clinical Neurophysiology, 2008, 119(6):1232-1241. [54] Mendez MA, Zuluaga P, Hornero R, et al. Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment[J]. Journal of Psychopharmacology, 2012, 26(5):636-643. [55] 孙长城, 王春方, 王勇军, 等. 脑卒中后抑郁症静息脑电信号非线性特征提取与分析[J]. 国际生物医学工程杂志, 2013, 36(3):143-146. [56] Zhang Ying, Wang Chunfang, Sun Changcheng, et al. Neural complexity in patients with poststroke depression: A resting EEG study[J]. Journal of Affective Disorders, 2015, 188(2015):310-318. [57] Dietrich A, Kanso R. A review of EEG, ERP, and neuroimaging studies of creativity and insight[J]. Psychological Bulletin, 2010, 136(5):822-848. [58] Gotlib IH, Joormann J. Cognition and depression: current status and future directions[J]. Annual Review of Clinical Psychology, 2010, 6(1):285-312. [59] Dai Qin, Feng Zhengzhi. More excited for negative facial expressions in depression: Evidence from an event-related potential study[J]. Clinical Neurophysiology, 2012, 123(11):2172-2179. [60] Zhong Mingtian, Zhu Xiongzhao, Yi Jinyao, et al. Do the early attentional components of ERPs reflect attentional bias in depression? It depends on the stimulus presentation time[J]. Clinical Neurophysiology, 2011, 122(7):1371-1381. [61] Yao Shuqiao, Liu Mingfan, Liu Jianping, et al. Inhibition dysfunction in depression: Event-related potentials during negative attictive priming[J]. Psychiatry Research-Neuroimaging, 2010, 182(2): l72-l79. [62] Yang Wenhui, Zhu Xiongzhao, Wang Xiang, et al. Time course of affective processing bias in major depression: An ERP study[J]. Neuroscience Letters, 2011, 487(3):372-377. [63] Hou Zhenghua, Cai Changqun, Liang Kemei, et al. Correlation of visual evoked potential P300 and the change of cognitive function in patients with major depression disorder[J]. Journal of Clinical Psychiatry, 2017, 27(1):47-49. [64] 李岳峙, 刘铁榜, 吴冬凌. 抑郁症患者的多导联听觉P3a和P3b事件相关电位研究[J]. 中国医学物理学杂志, 2008, 25(6):921-925. [65] Deveney CM, Deldin PJ. Memory of Faces: A Slow Wave ERP Study of Major Depression[J]. Emotion, 2004, 4(3):295-304. [66] Cao Miao, He Yong, Dai Zhengjia, et al. Early Development of functional network segregation revealed by connectomic analysis of the preterm human brain[J]. Cerebral Cortex, 2016, 27(3):1-15. [67] 高云, 曹丹, 李颖洁, 等. 抑郁症情绪面孔加工的大脑功能网络研究[J]. 中国生物医学工程学报, 2014, 33(5):556-563. [68] 刘耀中, 柳昀哲, 林碗君, 等. 抑郁障碍的核心脑机制——基于fMRI元分析的证据[J]. 中国科学:生命科学, 2015(12):1214-1223. [69] Luking KR, Repovs G, Belden AC, et al. Functional connectivity of the amygdala in early childhood onset depression[J]. Journal of the American Academy of Child and Adolescent Psychiatry, 2011, 50(10):1027-1041. [70] Chenwang Jin, Gao Chengge, Chen Ce, et al. A preliminary study of the dysregulation of the resting networks in first-episode medication-naive adolescent depression[J]. Neuroscience Letters, 2011, 503(2):105-109. [71] 朱雪玲, 袁福来, 姚树桥. 抑郁症脑功能网络度中心度研究[J]. 中国临床心理学杂志, 2015, 23(6):951-953. [72] 李淮周, 周海燕, 杨阳, 等. 基于内在功能连接推定抑郁症脑网络效率的改变[J]. 生物化学与生物物理进展, 2018(1):43-50. [73] Tao Haojuan, Guo Shuixia, Ge Tian, et al. Depression uncouples brain hate circuit[J]. Molecular Psychiatry, 2013, 18(1):101-111. [74] Alexopoulos GS, Hoptman M J, Kanellopoulos D, et al. Functional connectivity in the cognitive control network and the default mode network in late-life depression[J]. Journal of Affective Disorders, 2012, 139(1):56-65. [75] 王祖新. 抑郁障碍的分类[J]. 中国社区医师, 2006(2):11-11. [76] Allen JJB, Urry HL, Hitt SK, et al. The stability of resting frontal electroencephalographic asymmetry in depression[J]. Psychophysiology, 2004, 41(2):269-280. [77] Peckham AD, Mchugh RK, Otto MW. A meta-analysis of the magnitude of biased attention in depression[J]. Depression and Anxiety, 2013, 30(4):407-408. [78] Ibáezmolina AJ, Iglesiasparro S, Soriano MF, et al. Multiscale Lempel-Ziv complexity for EEG measures[J]. Clinical Neurophysiology, 2015, 126(3):541-548. [79] 王玲, 杨佳佳, 王发颀, 等. 经颅磁刺激对抑郁模型动物的作用研究进展[J]. 中国生物医学工程学报, 2018, 179(4):117-126. [80] 杨虎, 汪毓铎, 陈新华, 等. 多参数可调经颅微电流刺激仪的研制和基于脑电的效果评测[J]. 中国生物医学工程学报, 2015, 34(6):77-82. [81] 刘爽. 情绪诱发脑电的响应、识别与刺激调节机制[D]. 天津:天津大学, 2017. [82] 张力新, 郭冬月, 刘爽, 等. 经颅直流电刺激(tDCS)用于抑郁症治疗研究进展[J]. 中国生物医学工程学报, 2018, 37(5):107-115.