Abstract：The aim of this work is to analyze the topological structure and properties of brain network with complex network analysis method based on graph theory exploring the characteristics of brain functional network in emotional conflict response on anxious population of college students. Sixteen volunteers in the anxiety group and the normal control group were selected for the emotional word-face stroop conflict task, and 64 EEG were recorded simultaneously. The EEG data with beta(14 ～ 30 Hz) and high gamma(50 ～ 80 Hz) were analyzed by synchronous likelihood analysis, and the appropriate threshold value was selected to construct the brain network topology, and the node degree and clustering coefficient of the network were calculated. Results showed that in beta and high-gamma rhythms, there were abnormal connections in the brain areas including frontal lobe, temporal lobe, and parietal lobe in anxiety group, and the node degree of frontal lobe and parietal lobe was lower than that of normal group (P<0.05), while the node degree of temporal lobe was higher than that of the normal group (P<0.05)(in beta rhythm,the node degrees of the frontal FP1, parietal CP1 and temporal T7 electrodes were 5.21±0.62, 6.25±0.53, 7.91±0.71 respectively in anxiety group and 10.42±1.53, 7.94±0.55, 3.55±0.36 in the normal group, indicating that the function of frontal lobe and parietal lobe were decreased in the anxiety group, while the function of temporal lobe was increased. The clustering coefficient of the brain network in the anxiety group was lower than that in the normal group (P<0.05) (the clustering coefficients of beta and high gamma rhythm in anxiety group were 0.523 8±0.039 2 and 0.586 4±0.055 8, respectively, while those in normal group were0.603 2±0.071 1 and 0.664 7±0.060 1), which indicated that the degree of internal clustering of the brain network in the anxious group and the information transmission ability of the network were decreased. This study can provide a new perspective for the neural mechanism research of psychological and mental diseases such as anxiety and depression.
季淑梅, 苏新乐, 荀兴苗, 步鑫鑫, 许全盛. 大学生焦虑人群情绪冲突反应的脑功能网络研究[J]. 中国生物医学工程学报, 2020, 39(2): 145-151.
Ji Shumei, Su Xinle, Xun Xingmiao, Bu Xinxin, Xu Quansheng. Study on Brain Function Network of Emotional Conflict Response inCollege Students with Anxiety. Chinese Journal of Biomedical Engineering, 2020, 39(2): 145-151.
[1] Etkin A, Egner T, Peraza DM, et al. Resolving emotional conflict: A role for the rostral anterior cingulated cortex in modulating activity in the amygdala[J]. Neuron, 2006,51(6): 871-882.
[2] Rocher ARD, Pickering AD. Trait anxiety, infrequent emotional conflict, and the emotional face Stroop task [J]. Personality & Individual Differences, 2017, 111:157-162.
[3] Dennis TA, Chen CC. Trait anxiety and conflict monitoring following threat: An ERP study [J]. Psychophysiology, 2010, 46(1):122-131.
[4] Larson MJ, Clawson A, Clayson PE, et al. Cognitive conflict adaptation in generalized anxiety disorder [J]. Biological Psychology, 2013, 94(2):408-418.
[5] Xue Song, Wang Shanshan, Kong Xia, et al. Abnormal neural basis of emotional conflict control in treatment-resistant depression: An event-related potential study [J]. Clinical EEG & Neuroscience, 2017, 48(2):103-110.
[6] Lewis AR, Zinbarg RE, Mineka S, et al. The relationship between anxiety sensitivity and latent symptoms of emotional problems: A structural equation modeling approach[J]. Behaviour Research & Therapy, 2010, 48(8):761-769.
[7] 刘丽, 郑涌, 杨集梅, 等. 特质焦虑者错误反馈下的情绪-认知偏向: 事件相关电位研究[J]. 中国心理卫生杂志, 2009,23(3):224-227.
[8] 张玲, 潘润德, 陈强, 等. 焦虑症认知行为治疗与药物治疗对照研究[J]. 临床精神医学杂志, 2008, 18(1):12-14.
[9] 童梓顺, 肖攀攀, 刘赟. 广泛性焦虑症首次发病患者治疗前后前额叶皮质及海马神经生化物质的变化[J]. 临床精神医学杂志, 2015(1):23-26.
[10] Qin Shaozheng, Young CB, Duan Xujun, et al. Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood[J]. Biological Psychiatry, 2014, 75(11):892-900.
[11] 王玮, 钱绍文, 刘锴, 等. 广泛性焦虑障碍发病机制的静息态功能磁共振成像[J]. 中国医学影像技术, 2016, 32(3):358-362.
[12] 阎锐, 姚志剑, 韩莉,等. 抑郁症患者静息态脑活动与焦虑症状的相关性[J]. 中华行为医学与脑科学杂志, 2012, 21(11):988-990.
[13] Engel AK,Singer W. Temporal binding and the neural correlates of sensory awareness[J]. Trends Cogn Sci,2001, 5(1): 16-25.
[14] Duan Lian, Zhang Yujin, Zhu Chaozhe. Quantitative comparison of restingstate functional connectivity derived from fNIRS and fMRI: A simultaneous recording study [J]. Neuroimage, 2012, 60 (4):2008-2016.
[15] Niendam TA, Laird AR, Ray KL, et al. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions[J]. Cognitive Affective & Behavioral Neuroscience, 2012, 12(2):241-268.
[16] Callejas A, Lupiàez J, Funes MJ, et al. Modulations among the alerting, orienting and executive control networks[J]. Experimental Brain Research, 2005, 167(1):27-37.
[17] Guangheng Dong, Xiao Lin, Marc N Potenza. Decreased functional connectivity in an executive control network is related to impaired executive function in Internet gaming disorder[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2015, 57(57):76-85.
[18] Sergerie K, Lepage M, Armony JL. A process-specific functional dissociation of the amygdala in emotional memory[J]. Journal of Cognitive Neuroscience, 2014, 18(8):1359-1367.
[19] Harris RJ, Young AW, Andrews TJ. Dynamic stimuli demonstrate a categorical representation of facial expression in the amygdala [J]. Neuropsychologia, 2014, 56(100):47-52.
[20] Kim H, Somerville LH, Johnstone T, et al. Inverse amygdala and medial prefrontal cortex responses to surprised faces[J]. Neuroreport, 2003, 14(18):2317-2322.
[21] Etkin A, Prater KE, Hoeft F, et al. Failure of anterior cingulate activation and connectivity with the amygdala during implicit regulation of emotional processing n generalized anxiety disorder[J]. American Journal of Psychiatry, 2010, 167(5): 545-554.
[22] Kim MJ, Gee DG, Loucks RA, et al. Anxiety dissociates dorsal and ventral medial prefrontal cortex functional connectivity with the amygdala at rest[J]. Cerebral Cortex, 2011, 21(7):1667-1673.
[23] Kaiser RH, Andrews-Hanna JR, Wager TD, et al. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity[J]. JAMA Psychiatry, 2015, 72(6):603-611.
