Neural mechanism of disgust based on event-related brain network analysis

  • ZANG Qian ,
  • ZHAO Xiaoming ,
  • LI Yongzhi ,
  • LOU Cunguang ,
  • LI Ruichang
Expand
  • 1. Center for Student Mental Health and Development, Hebei University, Baoding 071002, China; 2. Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China; 3. College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; 4. College of Humanity and Management, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China

Received date: 2024-04-30

  Online published: 2024-11-19

Abstract

Disgust, as a fundamental negative emotion, is universally present in human life and profoundly influences individuals cognition and behavioral patterns. While high temporal resolution event-related potential(ERP)analysis has shown advantages in extracting disgust emotion processing, its limitations in revealing interactions between different brain regions should not be overlooked. In this study, through in-depth analysis of early posterior negativity(EPN)and its neural sources, electroencephalogram(EEG)brain networks were constructed based on the time-frequency maximal information coefficient(TFMIC)method, and complex network analysis was introduced to explore the generation and modulation mechanisms of disgust emotion induced by pictures stimuli. The results indicate that compared to neutral emotions, disgust emotion not only elicits more significant EPN waveforms but also- DOI:10.3969/j.issn.1000-1565.2024.06.012基于事件相关脑网络分析的厌恶情绪神经机制研究臧倩1, 2,赵小茗2,3,李勇志2,3,娄存广2,3,李瑞昌2,4(1. 河北大学 学生心理健康与发展中心,河北 保定 071002;2.河北大学 河北省数字医疗工程重点实验室,河北 保定 071002;3.河北大学 电子信息工程学院,河北 保定 071002;4.福建中医药大学 人文与管理学院,福建 福州 350122)摘 要:厌恶情绪作为一种基本的负性情绪,在人类生活中普遍存在并深刻影响着个体的认知与行为模式.尽管高时间分辨率的事件相关电位分析在提取厌恶情绪信息处理进程方面展现出优势,但其在揭示大脑各区域间交互作用方面的局限性亦不容忽视.本研究在深入分析早期后部负波(early posterior negativity,EPN)及其神经来源的基础上,基于时频最大信息系数(TFMIC)方法构建EEG(electroencephalogram)脑网络,并引入复杂网络分析,探索了图片刺激诱发的厌恶情绪的产生与调制机制.结果显示,相较于中性情绪,厌恶情绪不仅诱发了更为显著的EPN波形,还显示出激活脑区的差异性.进一步的脑网络分析表明,厌恶情绪状态下的大脑网络连接更为紧密,特征路径长度显著缩短,而聚类系数则显著增大.结果表明,结合事件相关电位的脑网络分析能够有效揭示厌恶情绪与中性情绪在时空层面的差异,为深入理解负性情绪的神经机制提供了新的视角与思路.关键词:厌恶; 早期后部负波; 脑网络; 源分析; 负性情绪中图分类号: TP391 文献标志码:A 文章编号:1000-1565(2024)06-0666-07Neural mechanism of disgust based on event-related brain network analysisZANG Qian1,2, ZHAO Xiaoming2,3, LI Yongzhi2,3, LOU Cunguang2,3, LI Ruichang2,4(1. Center for Student Mental Health and Development, Hebei University, Baoding 071002, China; 2. Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China;3. College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; 4. College of Humanity and Management, Fujian University of Traditional Chinese Medicine, Fuzhou 350122,China)Abstract: Disgust, as a fundamental negative emotion, is universally present in human life and profoundly influences individuals cognition and behavioral patterns. While high temporal resolution event-related potential(ERP)analysis has shown advantages in extracting disgust emotion processing, its limitations in revealing interactions between different brain regions should not be overlooked. In this study, through in-depth analysis of early posterior negativity(EPN)and its neural sources, electroencephalogram(EEG)brain networks were constructed based on the time-frequency maximal information coefficient(TFMIC)method, and complex network analysis was introduced to explore the generation and modulation mechanisms of disgust emotion induced by pictures stimuli. The results indicate that compared to neutral emotions, disgust emotion not only elicits more significant EPN waveforms but also- 收稿日期:2024-04-30;修回日期:2024-06-06 基金项目:河北省自然科学基金资助项目(F2021201002);河北省高等学校科学技术研究项目(ZD2020146) 第一作者:臧倩(1988—),女,河北大学讲师,主要从事高校心理咨询以及负性情绪机制研究.E-mail:hbuzangqian@163.com 通信作者:李瑞昌(1993—),男,福建中医药大学讲师,主要从事医疗数据处理与分析.E-mail:ruilicary@163.com 第6期臧倩等:基于事件相关脑网络分析的厌恶情绪神经机制研究河北大学学报(自然科学版) 第44卷demonstrates differences in activating brain areas. Further brain network analysis revealed that under the state of disgust emotion, brain network connections were more tightly linked, with a significantly shortened characteristic path length and an increased clustering coefficient. These findings suggest that combining event-related potential brain network analysis can effectively reveal the spatiotemporal differences between disgust and neutral emotions, providing a new perspective and approach for understanding the neural mechanisms of negative emotions.

Cite this article

ZANG Qian , ZHAO Xiaoming , LI Yongzhi , LOU Cunguang , LI Ruichang . Neural mechanism of disgust based on event-related brain network analysis[J]. Journal of Hebei University(Natural Science Edition), 2024 , 44(6) : 666 -672 . DOI: 10.3969/j.issn.1000-1565.2024.06.012

References

[1] 李招贤.厌恶的神经基础:来自大脑诱发和自发神经活动模式的证据[D].新乡: 新乡医学院, 2021.
[2] 黄好,罗禹,冯廷勇,等.厌恶加工的神经基础[J].心理科学进展, 2010, 18(9): 1449-1457.
[3] 肖婷炜,董洁,梁飞,等.厌恶情绪与自杀行为的关系[J].心理科学进展, 2023, 31(1): 87-98. DOI: 10.3724/SP.J.1042.2023.00087.
[4] OLATUNJI B O, KIM J. Examining reciprocal relations between disgust proneness and OCD symptoms: a four-wave longitudinal study[J]. J Behav Ther Exp Psychiatry, 2024, 82: 101907. DOI: 10.1016/j.jbtep.2023.101907.
[5] CULICETTO L, FERRAIOLI F, LUCIFORA C, et al. Disgust as a transdiagnostic index of mental illness: a narrative review of clinical populations[J]. Bull Menninger Clin, 2023, 87(Supplement A): 53-91. DOI: 10.1521/bumc.2023.87.suppA.53.
[6] STARK R, SCHIENLE A, WALTER B, et al. Hemodynamic responses to fear and disgust-inducing pictures: an fMRI study[J]. Int J Psychophysiol, 2003, 50(3): 225-234. DOI: 10.1016/s0167-8760(03)00169-7.
[7] MCCABE C, ROCHA-REGO V. Investigating the predictive value of functional MRI to appetitive and aversive stimuli: a pattern classification approach[J]. PLoS One, 2016, 11(11): e0165295. DOI: 10.1371/journal.pone.0165295.
[8] MOSCARELLO J M, MAREN S. Flexibility in the face of fear: hippocampal-prefrontal regulation of fear and avoidance[J]. Curr Opin Behav Sci, 2018, 19: 44-49. DOI: 10.1016/j.cobeha.2017.09.010.
[9] GARRETT A S, MADDOCK R J. Time course of the subjective emotional response to aversive pictures: relevance to fMRI studies[J]. Psychiatry Res, 2001, 108(1): 39-48. DOI: 10.1016/s0925-4927(01)00110-x.
[10] 曾庆,郑希付.高低厌恶敏感性个体对不同情绪刺激的注意时程: 来自ERP研究的证据[J].心理与行为研究, 2019, 17(6): 743-749. DOI: 10.3969/j.issn.1672-0628.2019.06.004.
[11] YAO Y J, XU D. Unconscious cognitive reappraisal and unconscious expression suppression regulate emotional responses: an ERP study[J]. Curr Psychol, 2024, 43(9): 7772-7784. DOI: 10.1007/s12144-023-04943-0.
[12] ALDUNATE N, LóPEZ V, BOSMAN C A. Early influence of affective context on emotion perception: EPN or early-N400?[J]. Front Neurosci, 2018, 12: 708. DOI: 10.3389/fnins.2018.00708.
[13] 魏玲,李颖洁,姚旭峰.恐惧和悲伤情绪认知重评过程中的脑电活动差异[J].中国生物医学工程学报, 2021, 40(1): 44-52. DOI: 10.3969/j.issn.0258-8021.2021.01.05.
[14] ZHOU F, ZHAO W H, QI Z Y, et al. A distributed fMRI-based signature for the subjective experience of fear[J]. Nat Commun, 2021, 12(1): 6643. DOI: 10.1038/s41467-021-26977-3.
[15] SI Y J, JIANG L, TAO Q, et al. Predicting individual decision-making responses based on the functional connectivity of resting-state EEG[J]. J Neural Eng, 2019, 16(6): 066025. DOI: 10.1088/1741-2552/ab39ce.
[16] BRANCO D, GONÇALVESÓó F, BADIA S B. A systematic review of international affective picture system(iaps)around the world[J]. Sensors, 2023, 23(8): 3866. DOI: 10.3390/s23083866.
[17] 梁铁,张清愉,洪磊,等.一种改进的最大信息系数算法在脑卒中患者的皮质肌功能耦合分析中的应用[J].生物医学工程学杂志, 2021, 38(6): 1154-1162. DOI: 10.7507/1001-5515.202106062.
[18] LIANG T, ZHANG Q Y, LIU X G, et al. Time-frequency maximal information coefficient method and its application to functional corticomuscular coupling[J]. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(11): 2515-2524. DOI: 10.1109/TNSRE.2020.3028199.
[19] XIA M, WANG J, HE Y. BrainNet Viewer: a network visualization tool for human brain connectomics[J]. PloS one, 2013, 8(7): e68910. DOI: 10.1371/journal.pone.0068910.
[20] RUBINOV M, KÖTTER R, HAGMANN P, et al. Brain connectivity toolbox: a collection of complex network measurements and brain connectivity datasets[J]. NeuroImage, 2009, 47: S169. DOI: 10.1016/S1053-8119(09)71822-1.
[21] SCHUPP H, CUTHBERT B, BRADLEY M, et al. Brain processes in emotional perception: motivated attention[J]. Cogn Emot, 2004, 18(5): 593-611. DOI: 10.1080/02699930341000239.
[22] ALDUNATE N, LóPEZ V, BOSMAN C A. Early influence of affective context on emotion perception: EPN or early-N400?[J]. Front Neurosci, 2018, 12: 708. DOI: 10.3389/fnins.2018.00708.
[23] SCHINDLER S, BRUCHMANN M, STRAUBE T. Feature-based attention interacts with emotional picture content during mid-latency and late ERP processing stages[J]. Biol Psychol, 2022, 170: 108310. DOI: 10.1016/j.biopsycho.2022.108310.
[24] WHEATON M G, HOLMAN A, RABINAK C A, et al. Danger and disease: electrocortical responses to threat- and disgust-eliciting images[J]. Int J Psychophysiol, 2013, 90(2): 235-239. DOI: 10.1016/j.ijpsycho.2013.08.001.
[25] MCCABE C, ROCHA-REGO V. Investigating the predictive value of functional MRI to appetitive and aversive stimuli: a pattern classification approach[J]. PloS One, 2016, 11(11): e0165295. DOI: 10.1371/journal.pone.0165295.
[26] LI Z, LI Y, LI X, et al. The spontaneous brain activity of disgust: perspective from resting state fMRI and resting state EEG[J]. Behav Brain Res, 2021, 403: 113135. DOI: 10.1016/j.bbr.2021.113135.
[27] MOSCARELLO J M, MAREN S. Flexibility in the face of fear: hippocampal-prefrontal regulation of fear and avoidance[J]. Curr Opin Behav Sci, 2018, 19: 44-49. DOI: 10.1016/j.cobeha.2017.09.010.
[28] SHAHABI H, MOGHIMI S. Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity[J]. Comput Hum Behav, 2016, 58: 231-239. DOI: 10.1016/j.chb.2016.01.005.
[29] LIM J, KURNIANINGSIH Y A, ONG H H, et al. Moral judgment modulation by disgust priming via altered fronto-temporal functional connectivity[J]. Sci Rep, 2017, 7(1): 10887. DOI: 10.1038/s41598-017-11147-7.
[30] RUBINOV M, SPORNS O. Complex network measures of brain connectivity: Uses and interpretations[J]. Neuroimage, 2010, 52(3): 1059-1069. DOI: 10.1016/j.neuroimage.2009.10.003. (
Outlines

/

〈 〉