亚临床抑郁和临床抑郁症患者认知改变相关的大尺度功能脑网络的变化
Changes in Large-Scale Functional Brain Networks Associated with Cognitive Change in Patients with Subclinical Depression and Clinical Depression
DOI: 10.12677/AP.2020.1011213, PDF,   
作者: 裘吉成, 刘 永, 袁 宏：西南大学心理学部，重庆；吴国伟：江苏师范大学语言科学与艺术学院，江苏 徐州
关键词: 抑郁症；亚临床抑郁；独立成分分析；功能网络连接；Depression； Subclinical Depression； Dependent Functional Network Connection
摘要: 目的：基于静息态功能磁共振的网络内和网络间的功能连接方法探索亚抑郁与临床抑郁患者脑功能的变化，为进一步探索亚临床抑郁和抑郁症患者认知改变的神经基础提供更多的科学证据。方法：采用独立成分分析方法对抑郁组(26名)、亚临床抑郁组(25名)和健康对照组(25名)进行脑网络成分划分，划分为6个子成分网络，然后使用网络内和网络间的分析方法进行进一步分析，并与抑郁量表评分结合进行探索性的相关分析。结果：楔前叶脑区与多个功能网络的网络内连接存在组间差异，是临床抑郁症和亚临床抑郁相对于健康人脑网络连接变化的重要节点。亚临床抑郁组的腹侧默认网络与右侧执行控制网络的网络间连接相对于对照组显著增强，且与亚临床抑郁组被试的抑郁症状显著负相关，而临床抑郁症组无显著变化。结论：亚临床抑郁和临床抑郁症患者认知状态相关的功能网络变化发生在网络内、网络间静态连接的多个方面，并且与抑郁症状的变化存在显著相关。
Abstract: Purpose: The purpose of this study was to explore the changes of brain function in patients with sub depression and clinical depression through in-network and inter-network functional connectivity methods based on resting-state FMRI. Then, it will provide a solid scientific basis for further exploring the neural basis of cognitive changes in patients with subclinical depression and de-pression. Method: Brain network components were divided into 26 patients with depression, 25 patients with subclinical depression and 25 healthy controls by dependent component analysis. First, it was divided into six sub-component networks. Then, the data were further analyzed by using in-network and inter-network analysis methods. At the same time, it was combined with depression scale score to explore the correlation analysis. Result: The results showed that there were intergroup differences in intra-network connections between precuneus and multiple func-tional networks. These are important nodes in the connection between clinical depression and subclinical depression compared to healthy brain networks. In addition, the connections between the ventral default network and the right executive control network were much closer in the sub-clinical depression group than in the control group which was significantly negative related to de-pression symptoms in the subclinical depression group. However, there was no significant change in the clinical depression group. Conclusion: In conclusion, functional networks associated with subclinical depression and the cognitive status of patients with clinical depression changed in multiple aspects of static connections within and between networks, which was significantly related to changes in depressive symptoms.
文章引用：裘吉成, 吴国伟, 刘永, 袁宏 (2020). 亚临床抑郁和临床抑郁症患者认知改变相关的大尺度功能脑网络的变化. 心理学进展, 10(11), 1821-1841. https://doi.org/10.12677/AP.2020.1011213

参考文献

[1] Beckmann, C. F., & Smith, S. M. (2004). Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging, 23, 137-152.[CrossRef] 
[2] Brdar, I. (2014). Positive and Negative Affect Schedule (PANAS). In Encyclopedia of Quality of Life and Well-Being Research. Berlin: Springer.[CrossRef] 
[3] Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis. Human Brain Mapping, 14, No. 3.[CrossRef] [PubMed]
[4] Cuijpers, P., Koole, S. L., Van Dijke, A., Roca, M., Li, J., & Reynolds, C. F. (2014). Psychotherapy for Subclinical Depression: Meta-Analysis. British Journal of Psychiatry, 205, 268-274.[CrossRef] [PubMed]
[5] Diener, C., Kuehner, C., Brusniak, W., Ubl, B., Wessa, M., & Flor, H. (2012). A Meta-Analysis of Neurofunctional Imaging Studies of Emotion and Cognition in Major Depression. NeuroImage, 61, 677-685.[CrossRef] [PubMed]
[6] Friedrich, M. J. (2017). Depression Is the Leading Cause of Disability Around the World. JAMA, 317, 1517.[CrossRef] [PubMed]
[7] Gao, C., Wenhua, L., Liu, Y., Ruan, X., Chen, X., Liu, L., & Jiang, X. (2016). Decreased Subcortical and Increased Cortical Degree Centrality in a Nonclinical College Student Sample with subclinical Depressive Symptoms: A Resting-State fMRI Study. Frontiers in Human Neuroscience, 10, 617.[CrossRef] [PubMed]
[8] Gotlib, I. H., & Joormann, J. (2010). Cognition and Depression: Current Status and Future Directions. In Annual Review of Clinical Psychology, 6, 285-312.[CrossRef] [PubMed]
[9] Guo, W., Liu, F., Zhang, J., Zhang, Z., Yu, L., Liu, J., Chen, H., & Xiao, C. (2014). Abnormal Default-Mode Network Homogeneity in First-Episode, Drug-Naive Major De-pressive Disorder. PLoS ONE, 9, e91102.[CrossRef] [PubMed]
[10] He, H., Yu, Q., Du, Y., Vergara, V., Victor, T. A., Drevets, W. C., Savitz, J. B., Jiang, T., Sui, J., & Calhoun, V. D. (2016). Resting-State Functional Network Connectivity in Prefrontal Regions Differs between Unmedicated Patients with Bipolar and Major Depressive Disorders. Journal of Affective Dis-orders, 190, 483-493.[CrossRef] [PubMed]
[11] Hwang, J. W., Egorova, N., Yang, X. Q., Zhang, W. Y., Chen, J., Yang, X. Y., Hu, L. J., Sun, S., Tu, Y., & Kong, J. (2015). Subthreshold Depression Is Associated with Impaired Restingstate Functional Connectivity of the Cognitive Control Network. Translational Psychiatry, 5, e683.[CrossRef] [PubMed]
[12] Hwang, J. W., Xin, S. C., Ou, Y. M., Zhang, W. Y., Liang, Y. L., Chen, J., Yang, X. Q., Chen, X. Y., Guo, T. W., Yang, X. J., Ma, W. H., Li, J., Zhao, B. C., Tu, Y., & Kong, J. (2016). Enhanced Default Mode Network Connectivity with Ventral Striatum in Subthreshold Depression Individuals. Journal of Psychiatric Research, 76, 111-120.[CrossRef] [PubMed]
[13] Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015). Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-Analysis of Resting-State Functional Connectivity. JAMA Psychiatry, 72, 603-611.[CrossRef] [PubMed]
[14] Kaiser, R. H., Whitfield-Gabrieli, S., Dillon, D. G., Goer, F., Beltzer, M., Minkel, J., Smoski, M., Dichter, G., & Pizzagalli, D. A. (2016). Dynamic Resting-State Functional Connectivity in Major Depression. Neuropsychopharmacology, 41, 1822-1830.[CrossRef] [PubMed]
[15] Koshiyama, D., Fukunaga, M., Okada, N., Morita, K., Nemoto, K., Usui, K., Yamamori, H., Yasuda, Y., Fujimoto, M., Kudo, N., Azechi, H., Watanabe, Y., Hashimoto, N., Narita, H., Kusumi, I., Ohi, K., Shimada, T., Kataoka, Y., Yamamoto, M. et al. (2020). White Matter Microstructural Alterations across Four Major Psychiatric Disorders: Mega-Analysis Study in 2937 Individuals. Molecular Psychiatry, 25, 883-895.[CrossRef] [PubMed]
[16] Liu, F., Wang, Y., Li, M., Wang, W., Li, R., Zhang, Z., Lu, G., & Chen, H. (2017). Dynamic Functional Network Connectivity in Idiopathic Generalized Epilepsy with Generalized Ton-ic-Clonic Seizure. Human Brain Mapping, 38, No. 2.[CrossRef] [PubMed]
[17] Malhi, G. S., Das, P., Outhred, T., Gessler, D., John Mann, J., & Bryant, R. (2019). Cognitive and Emotional Impairments Underpinning Suicidal Activity in Patients with Mood Disorders: An fMRI Study. Acta Psychiatrica Scandinavica, 139, No. 5.[CrossRef] [PubMed]
[18] Manoliu, A., Meng, C., Brandl, F., Doll, A., Tahmasian, M., Scherr, M., Schwerthöffer, D., Zimmer, C., Förstl, H., Bäuml, J., Riedl, V., Wohlschläger, A. M., & Sorg, C. (2014). Insular Dysfunction within the Salience Network Is Associated with Severity of Symptoms and Aberrant Inter-Network Connectivity in Major Depressive Disorder. Frontiers in Human Neuroscience, 7, 930.[CrossRef] [PubMed]
[19] Mulders, P. C., van Eijndhoven, P. F., Schene, A. H., Beckmann, C. F., & Tendolkar, I. (2015). Resting-State Functional Connectivity in Major Depressive Disorder: A Review. Neuroscience and Biobehavioral Reviews, 56, 330-344.[CrossRef] [PubMed]
[20] Price, J. L., & Drevets, W. C. (2012). Neural Circuits Underlying the Pathophysiology of Mood Disorders. Trends in Cognitive Sciences, 16, 61-71.[CrossRef] [PubMed]
[21] Raichle, M. E. (2015). The Brain’s Default Mode Network. Annual Review of Neuroscience, 15, 433-447.[CrossRef] [PubMed]
[22] Rashid, B., Arbabshirani, M. R., Damaraju, E., Cetin, M. S., Miller, R., Pearlson, G. D., & Calhoun, V. D. (2016). Classification of Schizophrenia and Bipolar Patients Using Static and Dynamic Resting-State fMRI Brain Connectivity. NeuroImage, 134, 645-657.[CrossRef] [PubMed]
[23] Rashid, B., Chen, J., Rashid, I., Damaraju, E., Liu, J., Miller, R., Agcaoglu, O., van Erp, T. G. M., Lim, K. O., Turner, J. A., Mathalon, D. H., Ford, J. M., Voyvodic, J., Mueller, B. A., Belger, A., McEwen, S., Potkin, S. G., Preda, A., Bustillo, J. R., et al. (2019). A Framework for Linking Resting-State Chronnectome/Genome Features in Schizophrenia: A Pilot Study. NeuroImage, 184, 843-854.[CrossRef] [PubMed]
[24] Richter, P., Werner, J., Heerlein, A., Kraus, A., & Sauer, H. (1998). On the Validity of the Beck Depression Inventory. A Review. Psychopathology, 31, 160-168.[CrossRef] [PubMed]
[25] Rucci, P., Gherardi, S., Tansella, M., Piccinelli, M., Berardi, D., Bisoffi, G., Corsino, M. A., & Pini, S. (2003). Subthreshold Psychiatric Disorders in Primary Care: Prevalence and Associated Cha-racteristics. Journal of Affective Disorders, 76, 171-181.[CrossRef] 
[26] Salman, M. S., Du, Y., Lin, D., Fu, Z., Fedorov, A., Damaraju, E., Sui, J., Chen, J., Mayer, A. R., Posse, S., Mathalon, D. H., Ford, J. M., Van Erp, T., & Calhoun, V. D. (2019). Group ICA for Identifying Biomarkers in Schizophrenia: “Adaptive” Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-Temporal Regression. NeuroImage: Clinical, 22, 101747.[CrossRef] [PubMed]
[27] Segal, D. L. (2010). Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR). In: The Corsini Encyclopedia of Psychology.[CrossRef] 
[28] Sexton, C. E., Allan, C. L., Le Masurier, M., McDermott, L. M., Kalu, U. G., Herrmann, L. L., Mäurer, M., Bradley, K. M., Mackay, C. E., & Ebmeier, K. P. (2012). Magnetic reson-ance imaging in late-life depression: Multimodal examination of network disruption. Archives of General Psychiatry, 69, 680-689.[CrossRef] [PubMed]
[29] Szymkowicz, S. M., Woods, A. J., Dotson, V. M., Porges, E. C., Nissim, N. R., O’Shea, A., Cohen, R. A., & Ebner, N. C. (2019). Associations between Subclinical De-pressive Symptoms and Reduced Brain Volume in Middle-Aged to Older Adults. Aging and Mental Health, 23, 819-830.[CrossRef] [PubMed]
[30] Tahmasebi, A. M., Abolmaesumi, P., Zheng, Z. Z., Munhall, K. G., & Johnsrude, I. S. (2009). Reducing Inter-Subject Anatomical Variation: Effect of Normalization Method on Sensitivity of Functional Magnetic Resonance Imaging Data Analysis in Auditory Cortex and the Superior Temporal Region. NeuroImage, 47, 1522-1531.[CrossRef] [PubMed]
[31] Veer, I. M., Beckmann, C. F., van Tol, M. J., Ferrarini, L., Milles, J., Veltman, D. J., Aleman, A., van Buchem, M. A., van der Wee, N. J., & Rombouts, S. A. R. B. (2010). Whole Brain Resting-State Analysis Reveals Decreased Functional Connectivity in Major Depression. Frontiers in Systems Neu-roscience, 4, 41.[CrossRef] [PubMed]
[32] Wang, L., Liu, Q., Shen, H., Li, H., & Hu, D. (2015). Large-Scale Functional Brain Network Changes in Taxi Drivers: Evidence from Resting-State fMRI. Human Brain Mapping, 36, No. 3.[CrossRef] [PubMed]
[33] WHO ICD-10. (2016). International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). World Health Organization.
[34] Yan, C. G., Chen, X., Li, L., Castellanos, F. X., Bai, T. J., Bo, Q. J., Cao, J., Chen, G. M., Chen, N. X., Chen, W., Cheng, C., Cheng, Y. Q., Cui, X. L., Duan, J., Fang, Y. R., Gong, Q. Y., Guo, W. Bin, Hou, Z. H., Hu, L. et al. (2019). Reduced Default Mode Network Functional Connectivity in Patients with Recurrent Major Depressive Disorder. Proceedings of the National Academy of Sciences of the United States of America, 116, 9078-9083.[CrossRef] [PubMed]
[35] Yokoyama, S., Okamoto, Y., Takagaki, K., Okada, G., Takamura, M., Mori, A., Shiota, S., Ichikawa, N., Jinnin, R., & Yamawaki, S. (2018). Effects of Behavioral Activation on Default Mode Network Connectivity in Subthreshold Depression: A Preliminary Resting-State fMRI Study. Journal of Affective Disorders, 227, 156-163.[CrossRef] [PubMed]
[36] Zhi, D., Calhoun, V. D., Lv, L., Ma, X., Ke, Q., Fu, Z., Du, Y., Yang, Y., Yang, X., Pan, M., Qi, S., Jiang, R., Yu, Q., & Sui, J. (2018). Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder. Frontiers in Psychiatry, 9, 1-11.[CrossRef] [PubMed]