抑郁症的人格类型及其脑功能连接基础

doi:10.3724/SP.J.1041.2023.00740

摘要/Abstract

摘要：

本研究采用功能随机森林的方法, 将聚类过程与抑郁症诊断相结合, 分别在抑郁症和控制组中识别了人格类型(神经质和外向性的组合), 并进一步探究了不同人格类型的静息态功能连接差异。聚类分析结果显示, 抑郁症以高神经质和低外向性趋势的个体为主, 但同样有低神经质和高外向性趋势的个体。控制组样本则以低神经质和高外向性个体为主。静息态功能连接的结果显示：在不考虑人格亚型的情况下, 抑郁症和控制组在杏仁核/海马/脑岛−边缘网络/默认网络/控制网络的功能连接上均无显著差异。在纳入聚类分析所划分的亚型进行统计后, 多种人格类型在左侧杏仁核/脑岛−边缘网络(以眶额皮质区域为主)的功能连接强度上呈现出显著差异。本研究基于个人视角识别的抑郁症人格类型更符合现实情况与个体认知模式, 具有潜在的临床应用价值, 并且其功能连接的差异对理解抑郁症异质性提供了神经层面的参考。

关键词: 神经质, 外向性, 静息态功能连接, 抑郁症, 以个体为中心

Abstract:

Heterogeneity among mental health issues has always attracted considerable attention, thereby restricting research on mental health and cognitive neuroscience. Additionally, the person-centred approach to personality research, which emphasizes population heterogeneity, has received more attention. On the other hand, the heterogeneity among depressive patients has been a problem that cannot be ignored (most studies ignored the actual situation and directly assumed sample homogeneity). A large number of empirical studies have provided evidence that isolated personality traits are often associated with depression. Only a few studies have considered the probable effect from a taxonomy perspective. Moreover, the neural mechanisms of personality types in depression remain unclear. This study aimed to reveal different personality subtypes of depressive disorders and elucidate subtypes from the perspective of resting-state functional connectivity.

Personality and resting-state functional imaging data of 135 depressive patients and 133 controls were collected. First, combined with “depression diagnosis”, the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected nodes of the subcortical network as regions of interest according to the power-264 template and calculated the functional connectivity map of the region of interest to the whole brain. Based on the functional connectivity map, the differences in resting-state functional connectivity between the main types were compared.

Personality and resting-state functional imaging data of 159 depressive patients and 156 controls were collected. First, combined with “depression diagnosis”, the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected the amygdala, hippocampus, insula (AAL atlas) and limbic network, default network, and control network (Schaefer-Yeo template) as regions of interest and calculated the functional connectivity of the subcortical regions to the networks. ANOVA was used to compare resting-state functional connectivity between the personality types.

The results showed the following. (1) Depression was more common among individuals with high neuroticism and low extraversion tendencies, but there were also individuals with low neuroticism and high extraversion tendencies. The controls were more likely to be individuals with low neuroticism and high extraversion. (2) The results of resting-state functional connectivity showed no significant difference between depression and controls. (3) The functional connectivity strength of the left amygdala/insula-limbic network was significantly different across personality subtypes.

In summary, the personality subtypes of depression identified by person-centred perspectives are more in line with reality and individual cognitive patterns, and they have potential clinical adaptive value. The findings of this study enhance the understanding of heterogeneity among depressive disorders.

Key words: neuroticism, extraversion, resting-state functional connectivity, depressive disorders, person-centred

中图分类号:

B845
R395

李彧, 位东涛, 邱江. (2023). 抑郁症的人格类型及其脑功能连接基础. 心理学报, 55(5), 740-751.

LI Yu, WEI Dongtao, QIU Jiang. (2023). Personality subtypes of depressive disorders and their functional connectivity basis. Acta Psychologica Sinica, 55(5), 740-751.

图/表 10

参考文献 79

[1]	Abbasi, M., Mirderikvand, F., Adavi, H., & Hojati, M. (2018). The relationship between personality traits (neuroticism and extraversion) and self-efficacy with aging depression. Iranian Journal of Ageing, 12(4), 458-466.
[2]	Alexopoulos, G. S. (2002). Frontostriatal and limbic dysfunction in late-life depression. The American Journal of Geriatric Psychiatry, 10(6), 687-695. doi: 10.1097/00019442-200211000-00007 URL
[3]	Allen, T. A., Carey, B. E., McBride, C., Bagby, R. M., deYoung, C. G., & Quilty, L. C. (2018). Big Five aspects of personality interact to predict depression. Journal of Personality, 86(4), 714-725. doi: 10.1111/jopy.12352 pmid: 28921930
[4]	Altman, N., & Krzywinski, M. (2017). Points of significance: Clustering. Nature Methods, 14(6), 545-547. doi: 10.1038/nmeth.4299 URL
[5]	Asendorpf, J. B., Borkenau, P., Ostendorf, F., & van Aken, M. A. (2001). Carving personality description at its joints: Confirmation of three replicable personality prototypes for both children and adults. European Journal of Personality, 15(3), 169-198. doi: 10.1002/per.408 URL
[6]	Banjongrewadee, M., Wongpakaran, N., Wongpakaran, T., Pipanmekaporn, T., Punjasawadwong, Y., & Mueankwan, S. (2020). The role of perceived stress and cognitive function on the relationship between neuroticism and depression among the elderly: A structural equation model approach. BMC Psychiatry, 20(1), 1-8. doi: 10.1186/s12888-019-2374-2
[7]	Blondel, V. D., Guillaume, J. -L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
[8]	Bohane, L., Maguire, N., & Richardson, T. (2017). Resilients, overcontrollers and undercontrollers: A systematic review of the utility of a personality typology method in understanding adult mental health problems. Clinical Psychology Review, 57, 75-92. doi: S0272-7358(16)30294-X pmid: 28850932
[9]	Bowden, S., Saklofske, D., van de Vijver, F., Sudarshan, N., & Eysenck, S. (2016). Cross-cultural measurement invariance of the Eysenck Personality Questionnaire across 33 countries. Personality and Individual Differences, 103, 53-60. doi: 10.1016/j.paid.2016.04.028 URL
[10]	Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. doi: 10.1023/A:1010933404324 URL
[11]	Bryan, C. J., Tipton, E., & Yeager, D. S. (2021). Behavioural science is unlikely to change the world without a heterogeneity revolution. Nature Human Behaviour, 5(8), 980-989. doi: 10.1038/s41562-021-01143-3 pmid: 34294901
[12]	Cale, E. M. (2006). A quantitative review of the relations between the “Big 3” higher order personality dimensions and antisocial behavior. Journal of Research in Personality, 40(3), 250-284. doi: 10.1016/j.jrp.2005.01.001 URL
[13]	Canli, T. (2004). Functional brain mapping of extraversion and neuroticism: :Learning from individual differences in emotion processing. Journal of Personality, 72(6), 1105-1132. doi: 10.1111/j.1467-6494.2004.00292.x URL
[14]	Chand, G. B., Dwyer, D. B., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., ... Dazzan, P. (2020). Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain, 143(3), 1027-1038. doi: 10.1093/brain/awaa025 pmid: 32103250
[15]	Chen, Z. -G. (1983). Item analysis of Eysenck Personality Questionnaire tested in Beijing-district. Acta Psychologica Sinica, 15(2), 85-92.
	[陈仲庚. (1983). 艾森克人格问卷的项目分析. 心理学报, 15(2), 85-92.]
[16]	Cheng, W., Rolls, E. T., Qiu, J., Xie, X., Lyu, W., Li, Y., ... Feng, J. F. (2018). Functional connectivity of the human amygdala in health and in depression. Social Cognitive and Affective Neuroscience, 13(6), 557-568. doi: 10.1093/scan/nsy032 pmid: 29767786
[17]	Cheng, W., Rolls, E. T., Qiu, J., Xie, X., Wei, D., Huang, C. -C., ... Feng, J. F. (2018). Increased functional connectivity of the posterior cingulate cortex with the lateral orbitofrontal cortex in depression. Translational Psychiatry, 8(1), 1-10. doi: 10.1038/s41398-017-0025-2
[18]	Cohen, M. X., Young, J., Baek, J. -M., Kessler, C., & Ranganath, C. (2005). Individual differences in extraversion and dopamine genetics predict neural reward responses. Cognitive Brain Research, 25(3), 851-861. pmid: 16289773
[19]	Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238-251. doi: 10.1016/j.neuron.2014.05.014 pmid: 24991964
[20]	Dollinger, S. J., Urban, K. K., & James, T. A. (2004). Creativity and openness: Further validation of two creative product measures. Creativity Research Journal, 16(1), 35-47. doi: 10.1207/s15326934crj1601_4 URL
[21]	Donnellan, M. B., & Robins, R. W. (2010). Resilient, overcontrolled, and undercontrolled personality types: Issues and controversies. Social and Personality Psychology Compass, 4(11), 1070-1083. doi: 10.1111/j.1751-9004.2010.00313.x URL
[22]	Duggan, C., Sham, P., Lee, A., Minne, C., & Murray, R. (1995). Neuroticism: A vulnerability marker for depression evidence from a family study. Journal of Affective Disorders, 35(3), 139-143. pmid: 8749842
[23]	Etkin, A., Egner, T., & Kalisch, R. (2011). Emotional processing in anterior cingulate and medial prefrontal cortex. Trends in Cognitive Sciences, 15(2), 85-93. doi: 10.1016/j.tics.2010.11.004 pmid: 21167765
[24]	Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019). The heterogeneity problem: Approaches to identify psychiatric subtypes. Trends in Cognitive Sciences, 23(7), 584-601. doi: S1364-6613(19)30092-0 pmid: 31153774
[25]	Fournier, J., Jones, N., Chase, H., Cummings, L., Graur, S., & Phillips, M. (2017). Personality dysfunction in depression and individual differences in effortful emotion regulation. Biological Psychiatry, 81(10), S336-S337.
[26]	Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35(3), 346-355. doi: 10.1002/mrm.1910350312 pmid: 8699946
[27]	Gerlach, M., Farb, B., Revelle, W., & Amaral, L. A. N. (2018). A robust data-driven approach identifies four personality types across four large data sets. Nature Human Behaviour, 2(10), 735-742. doi: 10.1038/s41562-018-0419-z pmid: 31406291
[28]	Haas, B. W., Omura, K., Constable, R. T., & Canli, T. (2007). Emotional conflict and neuroticism: Personality-dependent activation in the amygdala and subgenual anterior cingulate. Behavioral Neuroscience, 121(2), 249-256. pmid: 17469914
[29]	Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56-62. doi: 10.1136/jnnp.23.1.56 URL
[30]	Herzberg, P. Y., & Roth, M. (2006). Beyond resilients, undercontrollers, and overcontrollers? An extension of personality prototype research. European Journal of Personality, 20(1), 5-28. doi: 10.1002/per.557 URL
[31]	Hooker, C. I., Verosky, S. C., Miyakawa, A., Knight, R. T., & D’Esposito, M. (2008). The influence of personality on neural mechanisms of observational fear and reward learning. Neuropsychologia, 46(11), 2709-2724. doi: 10.1016/j.neuropsychologia.2008.05.005 pmid: 18573512
[32]	Isler, L., Fletcher, G. J., Liu, J. H., & Sibley, C. G. (2017). Validation of the four-profile configuration of personality types within the Five-Factor Model. Personality and Individual Differences, 106, 257-262. doi: 10.1016/j.paid.2016.10.058 URL
[33]	Jones, L., Scott, J., Cooper, C., Forty, L., Smith, K. G., Sham, P., ... Jones, I. (2010). Cognitive style, personality and vulnerability to postnatal depression. British Journal of Psychiatry, 196(3), 200-205. doi: 10.1192/bjp.bp.109.064683 URL
[34]	Kehoe, E. G., Toomey, J. M., Balsters, J. H., & Bokde, A. L. (2012). Personality modulates the effects of emotional arousal and valence on brain activation. Social Cognitive and Affective Neuroscience, 7(7), 858-870. doi: 10.1093/scan/nsr059 pmid: 21948954
[35]	Kendler, K. S., Kuhn, J., & Prescott, C. A. (2004). The interrelationship of neuroticism, sex, and stressful life events in the prediction of episodes of major depression. American Journal of Psychiatry, 161(4), 631-636. doi: 10.1176/appi.ajp.161.4.631 pmid: 15056508
[36]	Kercher, A. J., Rapee, R. M., & Schniering, C. A. (2009). Neuroticism, life events and negative thoughts in the development of depression in adolescent girls. Journal of Abnormal Child Psychology, 37(7), 903-915. doi: 10.1007/s10802-009-9325-1 pmid: 19437113
[37]	Khoo, S., & Simms, L. J. (2018). Links between depression and openness and its facets. Personality and Mental Health, 12(3), 203-215. doi: 10.1002/pmh.1417 pmid: 29611346
[38]	Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A. N., & Whalen, P. J. (2011). The structural and functional connectivity of the amygdala: From normal emotion to pathological anxiety. Behavioural Brain Research, 223(2), 403-410. doi: 10.1016/j.bbr.2011.04.025 pmid: 21536077
[39]	Kim, S. Y., Stewart, R., Bae, K. Y., Kim, S. W., Shin, I. S., Hong, Y. J., ... Kim, J. M. (2016). Influences of the Big Five personality traits on the treatment response and longitudinal course of depression in patients with acute coronary syndrome: A randomised controlled trial. Journal of Affective Disorders, 203, 38-45. doi: 10.1016/j.jad.2016.05.071 URL
[40]	Klein, D. N., Kotov, R., & Bufferd, S. J. (2011). Personality and depression: Explanatory models and review of the evidence. Annual Review of Clinical Psychology, 7, 269-295. doi: 10.1146/annurev-clinpsy-032210-104540 pmid: 21166535
[41]	Knyazev, G. G. (2006). EEG correlates of personality types. Netherlands Journal of Psychology, 62(2), 78-87. doi: 10.1007/BF03061054 URL
[42]	Kokkonen, M., & Pulkkinen, L. (2001). Extraversion and neuroticism as antecedents of emotion regulation and dysregulation in adulthood. European Journal of Personality, 15(6), 407-424. doi: 10.1002/per.425 URL
[43]	Kong, R., Li, J., Orban, C., Sabuncu, M. R., Liu, H., Schaefer, A., ... Eickhoff, S. B. (2019). Spatial topography of individual- specific cortical networks predicts human cognition, personality, and emotion. Cerebral Cortex, 29(6), 2533-2551. doi: 10.1093/cercor/bhy123
[44]	Leikas, S., & Salmela-Aro, K. (2014). Personality types during transition to young adulthood: How are they related to life situation and well-being? Journal of Adolescence, 37(5), 753-762. doi: 10.1016/j.adolescence.2014.01.003 pmid: 24529315
[45]	Lorenzetti, V., Allen, N. B., Fornito, A., & Yücel, M. (2009). Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies. Journal of Affective Disorders, 117(1-2), 1-17. doi: 10.1016/j.jad.2008.11.021 pmid: 19237202
[46]	Mantini, D., Corbetta, M., Romani, G. L., Orban, G. A., & Vanduffel, W. (2013). Evolutionarily novel functional networks in the human brain? Journal of Neuroscience, 33(8), 3259-3275. doi: 10.1523/JNEUROSCI.4392-12.2013 pmid: 23426655
[47]	McDonnell, S., & Semkovska, M. (2020). Resilience as mediator between extraversion, neuroticism, and depressive symptoms in university students. Journal of Positive School Psychology, 4(1), 26-40.
[48]	Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214(5-6), 655-667. doi: 10.1007/s00429-010-0262-0 pmid: 20512370
[49]	Miller, C., Brady, M., & Dean, S. (2020). Is Conscientiousness a proxy for Depression? A study of Big Five Con-scientiousness predictive power above depression. Journal of Mental Health and Social Behaviour, 2(1), 112.
[50]	Milne, A. M., MacQueen, G. M., & Hall, G. B. (2012). Abnormal hippocampal activation in patients with extensive history of major depression: An fMRI study. Journal of Psychiatry and Neuroscience, 37(1), 28-36. doi: 10.1503/jpn.110004 URL
[51]	Mobbs, D., Hagan, C. C., Azim, E., Menon, V., & Reiss, A. L. (2005). Personality predicts activity in reward and emotional regions associated with humor. Proceedings of the National Academy of Sciences, 102(45), 16502-16506. doi: 10.1073/pnas.0408457102 URL
[52]	Murray, L. E., & O'Neill, L. (2019). Neuroticism and extraversion mediate the relationship between having a sibling with developmental disabilities and anxiety and depression symptoms. Journal of Affective Disorders, 243, 232-240. doi: S0165-0327(18)30316-1 pmid: 30248634
[53]	Ormel, J., Bastiaansen, A., Riese, H., Bos, E. H., Servaas, M., Ellenbogen, M., ... Aleman, A. (2013). The biological and psychological basis of neuroticism: Current status and future directions. Neuroscience & Biobehavioral Reviews, 37(1), 59-72. doi: 10.1016/j.neubiorev.2012.09.004 URL
[54]	Pang, Y. J. (2020). Brain connectivity study of depressive disorder and neuroticism personality (Unpublished doctorial dissertation). University of Electronic Science and Technology of China.
	[庞雅静. (2020). 抑郁症和神经质人格的脑连接研究 (博士学位论文). 电子科技大学, 成都.]
[55]	Paulus, M. P., Rogalsky, C., Simmons, A., Feinstein, J. S., & Stein, M. B. (2003). Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. NeuroImage, 19(4), 1439-1448. pmid: 12948701
[56]	Peluso, M. A., Glahn, D. C., Matsuo, K., Monkul, E. S., Najt, P., Zamarripa, F., ... Soaress, J. C. (2009). Amygdala hyperactivation in untreated depressed individuals. Psychiatry Research: Neuroimaging, 173(2), 158-161. doi: 10.1016/j.pscychresns.2009.03.006 URL
[57]	Peters, A. T., Burkhouse, K., Feldhaus, C. C., Langenecker, S. A., & Jacobs, R. H. (2016). Aberrant resting-state functional connectivity in limbic and cognitive control networks relates to depressive rumination and mindfulness: A pilot study among adolescents with a history of depression. Journal of Affective Disorders, 200, 178-181. doi: 10.1016/j.jad.2016.03.059 pmid: 27136416
[58]	Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320-341. doi: 10.1016/j.neuroimage.2013.08.048 pmid: 23994314
[59]	Quirk, G. J., Russo, G. K., Barron, J. L., & Lebron, K. (2000). The role of ventromedial prefrontal cortex in the recovery of extinguished fear. Journal of Neuroscience, 20(16), 6225-6231. pmid: 10934272
[60]	Rai, S., Griffiths, K. R., Breukelaar, I. A., Barreiros, A. R., Chen, W., Boyce, P., ... Korgaonkar, M. S. (2021). Default- mode and fronto-parietal network connectivity during rest distinguishes asymptomatic patients with bipolar disorder and major depressive disorder. Translational Psychiatry, 11(1), 547.
[61]	Rocca, M. A., Pravata, E., Valsasina, P., Radaelli, M., Colombo, B., Vacchi, L., ... Filippi, M. (2015). Hippocampal- DMN disconnectivity in MS is related to WM lesions and depression. Human Brain Mapping, 36(12), 5051-5063. doi: 10.1002/hbm.v36.12 URL
[62]	Rolls, E. T. (2015). Limbic systems for emotion and for memory, but no single limbic system. Cortex, 62, 119-157. doi: 10.1016/j.cortex.2013.12.005 pmid: 24439664
[63]	Scalabrini, A., Vai, B., Poletti, S., Damiani, S., Mucci, C., Colombo, C., ... Northoff, G. (2020). All roads lead to the default-mode network-global source of DMN abnormalities in major depressive disorder. Neuropsychopharmacology, 45(12), 2058-2069. doi: 10.1038/s41386-020-0785-x
[64]	Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. -N., Holmes, A. J., ... Yeo, B. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28(9), 3095-3114. doi: 10.1093/cercor/bhx179 URL
[65]	Spielberg, J. M., Forbes, E. E., Ladouceur, C. D., Worthman, C. M., Olino, T. M., Ryan, N. D., & Dahl, R. E. (2015). Pubertal testosterone influences threat-related amygdala- orbitofrontal cortex coupling. Social Cognitive and Affective Neuroscience, 10(3), 408-415. doi: 10.1093/scan/nsu062 pmid: 24795438
[66]	Spinhoven, P., de Rooij, M., Heiser, W., Smit, J. H., & Penninx, B. W. (2012). Personality and changes in comorbidity patterns among anxiety and depressive disorders. Journal of Abnormal Psychology, 121(4), 874-884. doi: 10.1037/a0028234 pmid: 22564181
[67]	Sprengelmeyer, R., Steele, J. D., Mwangi, B., Kumar, P., Christmas, D., Milders, M., & Matthews, K. (2011). The insular cortex and the neuroanatomy of major depression. Journal of Affective Disorders, 133(1-2), 120-127. doi: 10.1016/j.jad.2011.04.004 pmid: 21531027
[68]	Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences, 105(34), 12569-12574. doi: 10.1073/pnas.0800005105 URL
[69]	Surguladze, S. A., El-Hage, W., Dalgleish, T., Radua, J., Gohier, B., & Phillips, M. L. (2010). Depression is associated with increased sensitivity to signals of disgust: A functional magnetic resonance imaging study. Journal of Psychiatric Research, 44(14), 894-902. doi: 10.1016/j.jpsychires.2010.02.010 pmid: 20307892
[70]	Suslow, T., Konrad, C., Kugel, H., Rumstadt, D., Zwitserlood, P., Schöning, S., ... Dannlowski, U. (2010). Automatic mood- congruent amygdala responses to masked facial expressions in major depression. Biological Psychiatry, 67(2), 155-160. doi: 10.1016/j.biopsych.2009.07.023 URL
[71]	Tang, S., Lu, L., Zhang, L., Hu, X., Bu, X., Li, H., ... Huang, X. (2018). Abnormal amygdala resting-state functional connectivity in adults and adolescents with major depressive disorder: A comparative meta-analysis. EBioMedicine, 36, 436-445. doi: S2352-3964(18)30368-2 pmid: 30316866
[72]	Wardenaar, K. J., Conradi, H. J., Bos, E. H., & de Jonge, P. (2014). Personality modulates the efficacy of treatment in patients with major depressive disorder. The Journal of Clinical Psychiatry, 75(9), e916-923. doi: 10.4088/JCP.13m08855 URL
[73]	Wang, J., Wang, N. N., & Zhang, Y. (Eds). (2013). Students' Mental Health. Beijing Normal University Press.
	[王洁, 王宁宁, 张艳. (主编). (2013). 大学生心理健康. 北京: 北京师范大学出版社.]
[74]	Yan, C., & Zang, Y. (2010). DPARSF: A MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.
[75]	Yan, R., Geng, J. T., Huang, Y. H., Zou, H. W., Wang, X. M., Xia, Y., ... Lu, Q. (2022). Aberrant functional connectivity in insular subregions in somatic depression: A resting-state fMRI study. BMC Psychiatry, 22(1), 146.
[76]	Yin, K., Zhao, J., Zhou, J., & Nie, Q. (2021). The big-five personality profiles: A person-centered approach. Advances in Psychological Science, 29(10), 1866-1877. doi: 10.3724/SP.J.1042.2021.01866
	[尹奎, 赵景, 周静, 聂琦. (2021). “大五”人格剖面: 以个体为中心的研究路径. 心理科学进展, 29(10), 1866-1877.] doi: 10.3724/SP.J.1042.2021.01866
[77]	Yoon, K. L., Maltby, J., & Joormann, J. (2013). A pathway from neuroticism to depression: Examining the role of emotion regulation. Anxiety, Stress & Coping, 26(5), 558-572.
[78]	Zhang, T., Bai, T., Xie, W., Wei, Q., Lv, H., Wang, A., ... Wang, K. (2021). Abnormal connectivity of anterior-insular subdivisions and relationship with somatic symptom in depressive patients. Brain Imaging and Behavior, 15(4), 1760-1768. doi: 10.1007/s11682-020-00371-x
[79]	Zuroff, D. C., Mongrain, M., & Santor, D. A. (2004). Conceptualizing and measuring personality vulnerability to depression: Comment on Coyne and Whiffen (1995). Psychological Bulletin, 130(3), 489-511. pmid: 15122935

组别	年龄			性别		教育年限		HAMD
组别	均值	标准差	范围	男	女	均值	标准差	均值	标准差
抑郁症	38.9	13.3	18~71	61	98	11.7	3.69	21.1	5.02
控制组	41.7	15.9	19~70	54	102	12.3	4.53	2.17	1.87
统计值	t(313) = 1.68 p = 0.094			χ²(1) = 0.478 p = 0.490		t(313) = 1.23 p = 0.22		t(313) = 44.08 p < 0.001

组别	年龄			性别		教育年限		HAMD
组别	均值	标准差	范围	男	女	均值	标准差	均值	标准差
抑郁症	38.9	13.3	18~71	61	98	11.7	3.69	21.1	5.02
控制组	41.7	15.9	19~70	54	102	12.3	4.53	2.17	1.87
统计值	t(313) = 1.68 p = 0.094			χ²(1) = 0.478 p = 0.490		t(313) = 1.23 p = 0.22		t(313) = 44.08 p < 0.001

组间比较			神经质					外向性
组间比较			t	p_holm	Cohen's d	95% CI		t	p_holm	Cohen's d	95% CI
DD1	−	DD2	−13.91	<0.001	−3.087	−3.588	−2.585	12.85	<0.001	2.851	2.359	3.344
	−	DD3	−13.76	<0.001	−3.076	−3.579	−2.572	−0.03	1.000	−0.006	−0.446	0.433
	−	DD4	−15.45	<0.001	−3.654	−4.204	−3.105	6.25	<0.001	1.478	0.998	1.958
	−	CON1	5.78	<0.001	1.247	0.811	1.683	−4.44	<0.001	−0.958	−1.389	−0.527
	−	CON2	−3.07	0.024	−0.729	−1.200	−0.257	2.63	0.124	0.626	0.156	1.097
	−	CON3	3.92	0.001	0.946	0.466	1.426	0.58	1.000	0.139	−0.335	0.614
	−	CON4	−2.49	0.106	−0.756	−1.355	−0.156	−2.26	0.267	−0.687	−1.286	−0.088
	−	CON5	−7.69	<0.001	−2.729	−3.460	−1.998	0.88	1.000	0.313	−0.385	1.012
DD2	−	DD3	0.05	1.000	0.011	−0.415	0.437	−13.20	<0.001	−2.858	−3.341	−2.374
	−	DD4	−2.41	0.110	−0.567	−1.033	−0.102	−5.83	<0.001	−1.373	−1.849	−0.897
	−	CON1	19.44	<0.001	4.334	3.775	4.893	−17.08	<0.001	−3.809	−4.343	−3.275
	−	CON2	9.29	<0.001	2.358	1.824	2.892	−8.76	<0.001	−2.225	−2.755	−1.695
	−	CON3	18.15	<0.001	4.033	3.490	4.576	−12.21	<0.001	−2.712	−3.200	−2.224
	−	CON4	7.77	<0.001	2.331	1.713	2.950	−11.80	<0.001	−3.538	−4.192	−2.884
	−	CON5	1.04	1.000	0.358	−0.318	1.034	−7.39	<0.001	−2.538	−3.243	−1.833
DD3	−	DD4	−2.43	0.110	−0.579	−1.049	−0.108	6.24	<0.001	1.485	1.001	1.968
	−	CON1	19.36	<0.001	4.323	3.764	4.882	−4.26	<0.001	−0.952	−1.397	−0.506
	−	CON2	9.29	<0.001	2.347	1.816	2.878	2.51	0.166	0.633	0.133	1.132
	−	CON3	17.56	<0.001	4.022	3.468	4.575	0.64	1.000	0.146	−0.305	0.597
	−	CON4	7.68	<0.001	2.320	1.698	2.943	−2.25	0.267	−0.680	−1.277	−0.083
	−	CON5	0.99	1.000	0.347	−0.340	1.033	0.92	1.000	0.320	−0.367	1.006
DD4	−	CON1	21.03	<0.001	4.901	4.298	5.505	−10.45	<0.001	−2.436	−2.934	−1.938
	−	CON2	11.43	<0.001	2.925	2.370	3.481	−3.33	0.017	−0.852	−1.360	−0.344
	−	CON3	18.30	<0.001	4.600	3.984	5.217	−5.33	<0.001	−1.339	−1.845	−0.833
	−	CON4	9.23	<0.001	2.899	2.239	3.559	−6.89	<0.001	−2.165	−2.807	−1.523
	−	CON5	2.56	0.098	0.925	0.210	1.640	−3.22	0.022	−1.165	−1.882	−0.448
CON1	−	CON2	−8.88	<0.001	−1.976	−2.442	−1.510	7.12	<0.001	1.584	1.128	2.040
	−	CON3	−1.21	1.000	−0.301	−0.791	0.189	4.41	<0.001	1.097	0.600	1.595
	−	CON4	−6.63	<0.001	−2.003	−2.618	−1.387	0.90	1.000	0.271	−0.324	0.866
	−	CON5	−11.06	<0.001	−3.976	−4.752	−3.201	3.54	0.008	1.271	0.556	1.986
CON2	−	CON3	5.85	<0.001	1.675	1.096	2.254	−1.70	0.810	−0.487	−1.052	0.078
	−	CON4	−0.08	1.000	−0.027	−0.661	0.608	−4.07	0.001	−1.313	−1.956	−0.670
	−	CON5	−5.19	<0.001	−2.000	−2.775	−1.225	−0.81	1.000	−0.313	−1.072	0.446
CON3	−	CON4	−5.51	<0.001	−1.702	−2.324	−1.079	−2.68	0.118	−0.826	−1.437	−0.215
	−	CON5	−10.88	<0.001	−3.675	−4.402	−2.948	0.52	1.000	0.174	−0.491	0.839
CON4	−	CON5	−4.88	<0.001	−1.974	−2.784	−1.163	2.47	0.167	1.000	0.201	1.799

组间比较			神经质					外向性
组间比较			t	p_holm	Cohen's d	95% CI		t	p_holm	Cohen's d	95% CI
DD1	−	DD2	−13.91	<0.001	−3.087	−3.588	−2.585	12.85	<0.001	2.851	2.359	3.344
	−	DD3	−13.76	<0.001	−3.076	−3.579	−2.572	−0.03	1.000	−0.006	−0.446	0.433
	−	DD4	−15.45	<0.001	−3.654	−4.204	−3.105	6.25	<0.001	1.478	0.998	1.958
	−	CON1	5.78	<0.001	1.247	0.811	1.683	−4.44	<0.001	−0.958	−1.389	−0.527
	−	CON2	−3.07	0.024	−0.729	−1.200	−0.257	2.63	0.124	0.626	0.156	1.097
	−	CON3	3.92	0.001	0.946	0.466	1.426	0.58	1.000	0.139	−0.335	0.614
	−	CON4	−2.49	0.106	−0.756	−1.355	−0.156	−2.26	0.267	−0.687	−1.286	−0.088
	−	CON5	−7.69	<0.001	−2.729	−3.460	−1.998	0.88	1.000	0.313	−0.385	1.012
DD2	−	DD3	0.05	1.000	0.011	−0.415	0.437	−13.20	<0.001	−2.858	−3.341	−2.374
	−	DD4	−2.41	0.110	−0.567	−1.033	−0.102	−5.83	<0.001	−1.373	−1.849	−0.897
	−	CON1	19.44	<0.001	4.334	3.775	4.893	−17.08	<0.001	−3.809	−4.343	−3.275
	−	CON2	9.29	<0.001	2.358	1.824	2.892	−8.76	<0.001	−2.225	−2.755	−1.695
	−	CON3	18.15	<0.001	4.033	3.490	4.576	−12.21	<0.001	−2.712	−3.200	−2.224
	−	CON4	7.77	<0.001	2.331	1.713	2.950	−11.80	<0.001	−3.538	−4.192	−2.884
	−	CON5	1.04	1.000	0.358	−0.318	1.034	−7.39	<0.001	−2.538	−3.243	−1.833
DD3	−	DD4	−2.43	0.110	−0.579	−1.049	−0.108	6.24	<0.001	1.485	1.001	1.968
	−	CON1	19.36	<0.001	4.323	3.764	4.882	−4.26	<0.001	−0.952	−1.397	−0.506
	−	CON2	9.29	<0.001	2.347	1.816	2.878	2.51	0.166	0.633	0.133	1.132
	−	CON3	17.56	<0.001	4.022	3.468	4.575	0.64	1.000	0.146	−0.305	0.597
	−	CON4	7.68	<0.001	2.320	1.698	2.943	−2.25	0.267	−0.680	−1.277	−0.083
	−	CON5	0.99	1.000	0.347	−0.340	1.033	0.92	1.000	0.320	−0.367	1.006
DD4	−	CON1	21.03	<0.001	4.901	4.298	5.505	−10.45	<0.001	−2.436	−2.934	−1.938
	−	CON2	11.43	<0.001	2.925	2.370	3.481	−3.33	0.017	−0.852	−1.360	−0.344
	−	CON3	18.30	<0.001	4.600	3.984	5.217	−5.33	<0.001	−1.339	−1.845	−0.833
	−	CON4	9.23	<0.001	2.899	2.239	3.559	−6.89	<0.001	−2.165	−2.807	−1.523
	−	CON5	2.56	0.098	0.925	0.210	1.640	−3.22	0.022	−1.165	−1.882	−0.448
CON1	−	CON2	−8.88	<0.001	−1.976	−2.442	−1.510	7.12	<0.001	1.584	1.128	2.040
	−	CON3	−1.21	1.000	−0.301	−0.791	0.189	4.41	<0.001	1.097	0.600	1.595
	−	CON4	−6.63	<0.001	−2.003	−2.618	−1.387	0.90	1.000	0.271	−0.324	0.866
	−	CON5	−11.06	<0.001	−3.976	−4.752	−3.201	3.54	0.008	1.271	0.556	1.986
CON2	−	CON3	5.85	<0.001	1.675	1.096	2.254	−1.70	0.810	−0.487	−1.052	0.078
	−	CON4	−0.08	1.000	−0.027	−0.661	0.608	−4.07	0.001	−1.313	−1.956	−0.670
	−	CON5	−5.19	<0.001	−2.000	−2.775	−1.225	−0.81	1.000	−0.313	−1.072	0.446
CON3	−	CON4	−5.51	<0.001	−1.702	−2.324	−1.079	−2.68	0.118	−0.826	−1.437	−0.215
	−	CON5	−10.88	<0.001	−3.675	−4.402	−2.948	0.52	1.000	0.174	−0.491	0.839
CON4	−	CON5	−4.88	<0.001	−1.974	−2.784	−1.163	2.47	0.167	1.000	0.201	1.799

		左脑岛- LimbicB	左脑岛- DMNA	左脑岛- DMNB	右脑岛- LimbicB	右脑岛- DMNA	右脑岛- DMNB	外向性
左脑岛-LimbicB	皮尔逊相关系数	—
	p	—
	95% CI	—
	95% CI	—
左脑岛-DMNA	皮尔逊相关系数	0.689	—
	p	<0.001	—
	95% CI	0.749	—
	95% CI	0.617	—
左脑岛-DMNB	皮尔逊相关系数	0.688	0.863	—
	p	<0.001	<0.001	—
	95% CI	0.748	0.892	—
	95% CI	0.616	0.828	—
右脑岛-LimbicB	皮尔逊相关系数	0.902	0.617	0.614	—
	p	<0.001	<0.001	<0.001	—
	95% CI	0.923	0.689	0.686	—
	95% CI	0.875	0.533	0.53	—
右脑岛-DMNA	皮尔逊相关系数	0.649	0.913	0.795	0.698	—
	p	<0.001	<0.001	<0.001	<0.001	—
	95% CI	0.716	0.932	0.837	0.757	—
	95% CI	0.571	0.89	0.744	0.627	—
右脑岛-DMNB	皮尔逊相关系数	0.658	0.809	0.921	0.686	0.862	—
	p	<0.001	<0.001	<0.001	<0.001	<0.001	—
	95% CI	0.724	0.848	0.938	0.747	0.891	—
	95% CI	0.581	0.761	0.9	0.614	0.826	—
外向性	皮尔逊相关系数	0.204	0.208	0.208	0.214	0.182	0.223	—
	p	0.001	0.001	<0.001	<0.001	0.004	<0.001	—
	95% CI	0.32	0.324	0.325	0.33	0.3	0.339	—
	95% CI	0.081	0.085	0.086	0.092	0.059	0.101	—