中国科学院心理研究所研究员，博士生导师，心理所所务委员、认知与发展心理学研究室主任，抑郁症大数据国际研究中心主任，磁共振成像研究中心主任，国家优秀青年科学基金获得者，入选爱思唯尔中国高被引学者（2019~2022年度），获国际人脑图谱学会青年科学家奖、北京市科学技术奖自然科学奖二等奖（第一完成人）和中国科学院优秀导师奖。2006年毕业于北京科技大学获学士学位，2011年毕业于北京师范大学获博士学位。随后赴美留学，在美国内森克兰精神病学研究所和纽约大学儿童与青少年精神病学系先后担任研究科学家(Research Scientist)和研究助理教授(Research Assistant Professor)职位。2015年加入中国科学院心理研究所工作。严超赣博士的主要研究领域集中在静息态功能磁共振方法学、数据分析软件平台、脑自发活动机制及其在抑郁症中的应用。他为一系列困扰静息态功能磁共振成像的方法学问题提出了广受领域认可和引用的解决方案，包括头动伪迹校正、标准化和多重比较校正等。他还对静息态功能磁共振成像的计算方法进行了规范化，建立了被引5000余次的脑成像流水线式计算平台DPARSF、脑成像分析与共享平台DPABI、基于大脑皮层的脑成像数据分析软件DPABISurf、脑网络分析平台DPABINet和脑结构纤维分析平台DPABIFiber。他牵头建立了抑郁症脑成像大数据联盟(DIRECT)，创建了抑郁症大数据国际研究中心，与ENIGMA-MDD等国际组织展开国际抑郁症脑成像大数据合作研究，推动抑郁症脑影像研究从小样本模式转向大数据开放合作模式，用严谨的多中心研究方法确定了抑郁症患者大脑默认网络的异常模式（PNAS，2019）。他发起了 “心花计划”抑郁症干预前瞻性研究，旨在提出抑郁症新型心理物理综合干预方案。他在国际主流学术期刊（如PNAS, Molecular Psychiatry, Science Bulletin, Cerebral Cortex, NeuroImage, Human Brain Mapping等）共发表100余篇学术论文，其中40余篇为第一作者和/或通讯作者。研究成果得到国际同行的高度关注，总引用2万余次，h指数43（Google Scholar）。有6篇第一/通讯作者论文入选ESI高被引论文（其中1篇为前万分之一，1篇为前千分之一），3篇单篇被引过千。他历任《NeuroImage: Reports》副主编和《NeuroImage》期刊编委，现任《Imaging Neuroscience》执行编辑。
教育与工作经历
2002-2006 北京科技大学 自动化 学士
2006-2011 北京师范大学 认知神经科学 博士
2011-2015 The Nathan S. Kline Institute for Psychiatric Research 研究科学家
2013-2015 Department of Child and Adolescent Psychiatry, New York University 研究助理教授
2015 –今 中国科学院心理研究所 研究员
课题组网站：http://rfmri.org/yan
脑影像计算方法、基于脑影像的抑郁症精确诊断和精准治疗、大脑活动的个体化神经调控、基于功能磁共振成像和深度学习的“读心”
2023- Imaging Neuroscience执行编辑
2020-2023 NeuroImage: Reports 副主编
2018-2023 NeuroImage 编委
2018-2022 Journal of Neuroscience Methods 编委
2022- 中国心理学会国际学术交流工作委员会副主任
2022- 中国心理学会心理学脑成像专业委员会副主任
2016-2019 国际人脑图谱学会（OHBM）通讯委员会委员（负责中国联系事务）
2023 中国科学院优秀党务工作者奖
2021 北京市科学技术奖自然科学奖二等奖 （第一完成人）
2021 国际人脑图谱学会青年科学家奖
2019~2023 斯坦福全球前2%顶尖科学家
2019~2022 爱思唯尔中国高被引学者
2020 中国科学院优秀导师奖
1. Wang, Y.W., Chen, X., Yan, C.G.*(2023). Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion. Neuroimage, 120089.
2. Li, H.X., Lu, B., Wang, Y.W., Li, X.Y., Chen, X., Yan, C.G.*(2023). Neural representations of self-Generated thought during think-aloud fMRI. Neuroimage, 119775.
3. Wang, J., Zhang, W., Xu, H., Ellenbroek, B., Dai, J., Wang, L., Yan, C.G.*, Wang, W.W.* (2023). The Changes of Histone Methylation Induced by Adolescent Social Stress Regulate the Resting-State Activity in mPFC. Research, 6, 0264.
4. Lu, B., Li, H.-X., Chang, Z.-K., Li, L., Chen, N.-X., Zhu, Z.-C., Zhou, H.-X., Li, X.-Y., Wang, Y.-W., Cui, S.-X., Deng, Z.-Y., Fan, Z., Yang, H., Chen, X., Thompson, P.M., Castellanos, F.X., Yan, C.G.* (2022). A practical Alzheimer’s disease classifier via brain imaging-based deep learning on 85,721 samples. Journal of Big Data, 9(1), 101.
5. Chen, X., Lu, B., Li, H.-X., Li, X.-Y., Wang, Y.-W., Castellanos, F.X., Cao, L.-P., Chen, N.-X., Chen, W., Cheng, Y.-Q., Cui, S.-X., Deng, Z.-Y., Fang, Y.-R., Gong, Q.-Y., Guo, W.-B., Hu, Z.-J.-Y., Kuang, L., Li, B.-J., Li, L., Li, T., Lian, T., Liao, Y.-F., Liu, Y.-S., Liu, Z.-N., Lu, J.-P., Luo, Q.-H., Meng, H.-Q., Peng, D.-H., Qiu, J., Shen, Y.-D., Si, T.-M., Tang, Y.-Q., Wang, C.-Y., Wang, F., Wang, H.-N., Wang, K., Wang, X., Wang, Y., Wang, Z.-H., Wu, X.-P., Xie, C.-M., Xie, G.-R., Xie, P., Xu, X.-F., Yang, H., Yang, J., Yao, S.-Q., Yu, Y.-Q., Yuan, Y.-G., Zhang, K.-R., Zhang, W., Zhang, Z.-J., Zhu, J.-J., Zuo, X.-N., Zhao, J.-P., Zang, Y.-F., consortium, t.D., Yan, C.G.* (2022). The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder. Psychoradiology, 2(1), 32-42.
6. Li, H.X., Lu, B., Chen, X., Li, X.Y., Castellanos, F.X., Yan, C.G.* (2022). Exploring self-generated thoughts in a resting state with natural language processing. Behavior Research Methods, 54(4):1725-1743.
7. Li, X.Y., Chen, X.,* Yan, C.G.* Altered cerebral activities and functional connectivity in depression: a systematic review of fMRI studies. Quantitative Biology, 10(4), 366-380.
8. Ma, Z.H., Lu, B., Li, X., Mei, T., Guo, Y.Q., Yang, L., Wang, H., Tang, X.Z., Ji, Z.Z., Liu, J.R., Xu, L.Z., Yang, Y.L., Cao, Q.J.*, Yan, C.G.*, Liu, J.* (2022). Atypicalities in the developmental trajectory of cortico-striatal functional connectivity in autism spectrum disorder. Autism, 26(5):1108-1122.
9. Mei, T., Ma, Z.H., Guo, Y.Q., Lu, B., Cao, Q.J., Chen, X., Yang, L., Wang, H., Tang, X.Z., Ji, Z.Z., Liu, J.R., Xu, L.Z., Wang, L.Q., Yang, Y.L., Li, X.*, Yan, C.G.*, Liu, J.* (2022). Frequency-specific age-related changes in the amplitude of spontaneous fluctuations in autism. Translational Pediatrics, 11(3), 349-358.
10. Yang, H., Chen, X., Chen, Z.B., Li, L., Li, X.Y., Castellanos, F.X., Bai, T.J., Bo, Q.J., Cao, J., Chang, Z.K., Chen, G.M., Chen, N.X., Chen, W., Cheng, C., Cheng, Y.Q., Cui, X.L., Duan, J., Fang, Y., Gong, Q.Y., Guo, W.B., Hou, Z.H., Hu, L., Kuang, L., Li, F., Li, H.X., Li, K.M., Li, T., Liu, Y.S., Liu, Z.N., Long, Y.C., Lu, B., Luo, Q.H., Meng, H.Q., Peng, D., Qiu, H.T., Qiu, J., Shen, Y.D., Shi, Y.S., Si, T.M., Tang, Y.Q., Wang, C.Y., Wang, F., Wang, K., Wang, L., Wang, X., Wang, Y., Wang, Y.W., Wu, X.P., Wu, X.R., Xie, C.M., Xie, G.R., Xie, H.Y., Xie, P., Xu, X.F., Yang, J., Yao, J.S., Yao, S.Q., Yin, Y.Y., Yuan, Y.G., Zang, Y.F., Zhang, A.X., Zhang, H., Zhang, K.R., Zhang, L., Zhang, Z.J., Zhao, J.P., Zhou, R., Zhou, Y.T., Zhu, J.J., Zhu, Z.C., Zou, C.J., Zuo, X.N., Yan, C.G.* (2021). Disrupted intrinsic functional brain topology in patients with major depressive disorder. Molecular Psychiatry, 26(12):7363-7371.
11. Yan, C.-G.*, Wang, X.-D., Lu, B. (2021). DPABISurf: data processing & analysis for brain imaging on surface. Science Bulletin, 66 (24), 2453-2455.
12. Chen, X., Yan, C.-G.* (2021). Hypostability in the default mode network and hyperstability in the frontoparietal control network of dynamic functional architecture during rumination. Neuroimage, 241, 118427.
13. Li, L., Su, Y.A., Wu, Y.K., Castellanos, F.X., Li, K., Li, J.T., Si, T.M.*, Yan, C.G.* (2021). Eight-week antidepressant treatment reduces functional connectivity in first-episode drug-naïve patients with major depressive disorder. Human Brain Mapping, 42(8):2593-2605.
14. Chen, N.-X., Fu, G., Chen, X., Li, L., Milham, M.P.*, Lui, S.*, Yan, C.-G.* (2021). The contributions of brain structural and functional variance in predicting age, sex and treatment. Neuroimage: Reports, 1(2), 100024.
15. Chen, X., Chen, N.X., Shen, Y.Q., Li, H.X., Li, L., Lu, B., Zhu, Z.C., Fan, Z., Yan, C.G.* (2020) The subsystem mechanism of default mode network underlying rumination: a reproducible neuroimaging study. Neuroimage, 221, 117185.
16. Li, L., Lu, B., Yan, C.G.* (2020) Stability of dynamic functional architecture differs between brain networks and states. Neuroimage, 216, 116230.
17. Zhou, H.-X., Chen, X., Shen, Y.-Q., Li, L., Chen, N.-X., Zhu, Z.-C., Castellanos, F.X., Yan, C.-G.* (2020) Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage, 206, 116287. （ESI前百分之一高被引论文）
18. Shen, Y.-Q., Zhou, H.-X., Chen, X., Castellanos, F.X., Yan, C.-G.* (2020). Meditation effect in changing functional integrations across large-scale brain networks: Preliminary evidence from a meta-analysis of seed-based functional connectivity. Journal of Pacific Rim Psychology, 14, e10.
19. Liang, S., Deng, W., Li, X., Greenshaw, A.J., Wang, Q., Li, M., Ma, X., Bai, T.J., Bo, Q.J., Cao, J., Chen, G.M., Chen, W., Cheng, C., Cheng, Y.Q., Cui, X.L., Duan, J., Fang, Y.R., Gong, Q.Y., Guo, W.B., Hou, Z.H., Hu, L., Kuang, L., Li, F., Li, K.M., Liu, Y.S., Liu, Z.N., Long, Y.C., Luo, Q.H., Meng, H.Q., Peng, D.H., Qiu, H.T., Qiu, J., Shen, Y.D., Shi, Y.S., Si, T.M., Wang, C.Y., Wang, F., Wang, K., Wang, L., Wang, X., Wang, Y., Wu, X.P., Wu, X.R., Xie, C.M., Xie, G.R., Xie, H.Y., Xie, P., Xu, X.F., Yang, H., Yang, J., Yu, H., Yao, J.S., Yao, S.Q., Yin, Y.Y., Yuan, Y.G., Zang, Y.F., Zhang, A.X., Zhang, H., Zhang, K.R., Zhang, Z.J., Zhao, J.P., Zhou, R.B., Zhou, Y.T., Zou, C.J., Zuo, X.N., Yan, C.G.*, Li, T.* (2020). Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns. NeuroImage: Clinical, 28, 102514.
20. 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.-B., Hou, Z.-H., Hu, L., Kuang, L., Li, F., Li, K.-M., Li, T., Liu, Y.-S., Liu, Z.-N., Long, Y.-C., Luo, Q.-H., Meng, H.-Q., Peng, D.-H., Qiu, H.-T., Qiu, J., Shen, Y.-D., Shi, Y.-S., Wang, C.-Y., Wang, F., Wang, K., Wang, L., Wang, X., Wang, Y., Wu, X.-P., Wu, X.-R., Xie, C.-M., Xie, G.-R., Xie, H.-Y., Xie, P., Xu, X.-F., Yang, H., Yang, J., Yao, J.-S., Yao, S.-Q., Yin, Y.-Y., Yuan, Y.-G., Zhang, A.-X., Zhang, H., Zhang, K.-R., Zhang, L., Zhang, Z.-J., Zhou, R.-B., Zhou, Y.-T., Zhu, J.-J., Zou, C.-J., Si, T.-M., Zuo, X.-N., Zhao, J.-P.*, Zang, Y.-F.* (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(18), 9078-9083.（ESI前百分之一高被引论文）
21. Fan, Z., Chen, X., Qi, Z.X., Li, L., Lu, B., Jiang, C.L., Zhu, R.Q., Yan, C.G.*, Chen, L.*, (2019) Physiological significance of R-fMRI indices: Can functional metrics differentiate structural lesions (brain tumors)? Neuroimage: Clinical, 22: 101741.
22. An, J., Li, L., Wang, L., Su, Y.A., Wang, Y., Li, K., Zeng, Y., Kong, Q., Yan, C.G.*, Si, T.M.* (2019) Striatal functional connectivity alterations after two-week antidepressant treatment associated to enduring clinical improvement in major depressive disorder. Frontiers in Psychiatry, 10, 884.
23. Chen X, Lu B, Yan CG* (2018) Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Human Brain Mapping. 39: 300-318.（ESI前百分之一高被引论文）
24. Kong QM, Qiao H, Liu CZ, Zhang P, Li K, Wang L, Li JT, Su YA, Li KQ, Yan CG*, Mitchell, PB, Si TM* (2018) Aberrant intrinsic functional connectivity in thalamo-cortical networks in major depressive disorder. CNS Neuroscience & Therapeutics. 24(11): 1063-1072.
25. Li Q, Wang L, Li XY, Chen X, Lu B, Cheng L, Yan CG*, Xu Y* (2018) Total salvianolic acid balances brain functional network topology in rat hippocampi overexpressing miR-30e. Frontiers in Neuroscience. 12: 448.
26. Yan CG*, Yang Z, Colcombe S, Zuo XN, Milham MP (2017) Concordance among indices of intrinsic brain function: insights from inter-individual variation and temporal dynamics. Science Bulletin. 62: 1572-1584.
27. Yan CG#, Rincon-Cortes M#, Raineki C, Sarro E, Colcombe S, Guilfoyle DN, Yang Z, Gerum S, Biswal BB, Milham MP, Sullivan RM, Castellanos FX (2017) Aberrant development of intrinsic brain activity in a rat model of caregiver maltreatment of offspring. Translational Psychiatry. 7: e100.
28. Wang L, Kong QM, Li K, Li XN, Zeng YW, Chen C, Qian Y, Feng SJ, Li JT, Su YA, Correll CU, Mitchell PB, Yan CG*, Zhang DR, Si TM* (2017) Altered intrinsic functional brain architecture in female patients with bulimia nervosa. Journal of Psychiatry & Neuroscience. 42: 414-423.
29. Yan CG*, Wang XD, Zuo XN, Zang YF (2016) DPABI: Data processing & analysis for (resting-state) brain imaging. Neuroinformatics. 14: 339-351.（ESI前万分之一高被引论文）
30. Yan CG*, Li Q, Gao L (2015) PRN: a preprint service for catalyzing R-fMRI and neuroscience related studies. F1000Research. 3: 313.
31. Qiu TM#, Yan CG#, Tang WJ, Wu JS, Zhuang DX, Yao CJ, Lu JF, Zhu FP, Mao Y, Zhou LF (2014) Localizing hand motor area using resting-state fMRI: validated with direct cortical stimulation. Acta Neurochir. 156, 2295-2302.
32. Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, Li Q, Zuo XN, Castellanos FX, Milham MP (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage. 76:183-201.（ESI前千分之一高被引论文）
33. Yan CG, Craddock RC, Zuo XN, Zang YF, Milham MP (2013) Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage. 80: 246-262.（ESI前百分之一高被引论文）
34. Yan CG*, Craddock RC, He Y, Milham MP* (2013) Addressing head motion dependencies for small-world topologies in functional connectomics. Frontiers in Human Neuroscience. 7: 910.
35. Dai ZJ#, Yan CG#, Wang ZQ, Wang JH, Xia MR, Li KC, He Y (2012) Discriminative analysis of early Alzheimer’s disease using multi-modal imaging and multi-level characterization with multi-classifier (M3). Neuroimage. 59: 2187-2195.
36. Yan CG, Gong GL, Wang JH, Wang DY, Liu DQ, Zhu CZ, Chen ZJ, Evans A, Zang YF, He Y (2011) Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. Cerebral Cortex. 21: 449-458.
37. Yan CG and He Y. (2011) Driving and driven architectures of directed small-world human brain functional networks. PLoS ONE. 6(8): e23460.
38. Wang ZQ#, Yan CG#, Zhao C, Qi ZG, Zhou WD, Lu J, He Y, Li KC (2011) Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer’s disease: A resting-state functional MRI study. Human Brain Mapping. 32: 1720-1740. (From the cover)
39. Yan CG* and Zang YF* (2010) DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Frontiers in Systems Neuroscience. 4(13).
40. Yan CG, Liu DQ, He Y, Zou QH, Zhu CZ, Zuo XN, Long XY, Zang YF (2009) Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS ONE. 4(5): e5743.
41. 严超赣*，李雪莹，鲁彬 (2020) 功能磁共振脑影像学. 科学观察. 15, 6.
42. 鲁彬，陈骁，李乐，沈杨千，陈宁轩，梅婷，周会霞，刘靖，严超赣* (2018) 孤独症脑自发活动动态性及其整合的异常机制. 科学通报. 63(15): 1452-1463
43. 严超赣* (2018) 大数据时代的静息态功能磁共振成像——走向精神疾病诊疗应用. 中华精神科杂志. 51(4): 224-227.
(*Corresponding author #Equal contribution)
专利
1. 严超赣, 鲁彬. 一种基于脑成像大数据深度学习的阿尔兹海默症分类器: 2020108206690. 授权年份: 2023
2. 严超赣、鲁彬. 一种基于脑成像大数据深度学习的性别分类器: 2020108214451. 授权年份: 2023
3. 严超赣、李乐、鲁彬. 一种脑动态功能模式稳定性计算方法: 2019106315652. 授权年份: 2023
软件
4. DPABI - a toolbox for Data Processing & Analysis of Brain Imaging (脑影像数据处理与分析工具箱)
5. DPABISurf - A Surface-Based Resting-State fMRI Data Analysis Toolbox (基于皮层的静息态功能磁共振数据分析工具箱)
6. DPABINet - a toolbox for Brain Network and Graph Theoretical Analyses (脑网络分析工具箱)
7. DPABIFiber - A Fiber Tractography and Structural Connectivity Analysis Toolbox (脑纤维和结构连接数据分析工具箱)
8. DPARSF - Data Processing Assistant for Resting-State fMRI (静息态功能磁共振数据处理助手)
9. The R-fMRI Network (RFMRI.ORG) - 静息态功能磁共振成像研究服务网络
10. The R-fMRI Course: 静息态功能磁共振在线教程
更多成果信息请见：http://ir.psych.ac.cn/yancg@psych.ac.cn