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严超赣
认知与发展心理学研究室, 研究员
电 话:  86-10-64101582
传 真:  86-10-64101582
通讯地址:  北京市朝阳区林萃路16号院中国科学院心理研究所
邮政编码:  100101
电子邮件:  yancg@psych.ac.cn
课题组网站:  http://rfmri.org/yan

简历:

严超赣博士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年作为中国科学院“百人计划”研究员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脑疾病中的应用。他为静息态功能磁共振成像中的头动、标准化等方法学问题提出了解决方案。在脑自发活动的神经生物学意义上,揭示了脑自发活动与头动相关的干扰噪声与神经功能成份,揭示了不同静息状态对脑自发活动的影响。他在国际主流学术期刊共发表37篇学术论文,其中12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4000余次,h指数24(Google Scholar)。他开发的静息态功能磁共振数据处理与分析平台DPARSF和DPABI已成为该领域内主流的分析软件,其中DPARSF被引700余次。他发布的静息态功能磁共振网络教程已被访问十万余次。他是20余个国际主流学术期刊的学术审稿人,其中包括:Journal of Neuroscience、Biological Psychiatry、Cerebral Cortex、NeuroImage、Human Brain Mapping、PLoS ONE、Brain Connectivity、Journal of Neurophysiology等。 

教育与工作经历
2002-2006  北京科技大学  自动化                       学士
2
006-2011  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 –  中国科学院心理研究所                        研究员

课题组网站http://rfmri.org/yan

研究领域:

脑功能影像计算方法、静息态功能磁共振、脑自发活动及其在脑疾病中的应用

学术资源链接
REST: http://restfmri.net/REST 
DPARSF: http://rfmri.org/DPARSF 
DPABI: http://rfmri.org/DPABI 
The R-fMRI Course: http://rfmri.org/Course 
The R-fMRI Network: http://rfmri.org  

社会任职:
获奖及荣誉:

教育部博士学术新人奖(2010)
葛兰素史克明日之星奖(2011)
中国科学院“百人计划”(2015) 

代表论著:

1. Yan CG*, Wang XD, Zuo XN, Zang YF (2016) DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics. 14: 339-351. 
2. Yan CG*, Li Q, Gao L (2015) PRN: a preprint service for catalyzing R-fMRI and neuroscience related studies. F1000Research. 3: 313. 
3. Dai ZJ, Yan CG, Li KC, Wang ZQ, Wang JH, Cao M, Lin QX, Shu N, Xia MR, Bi YC, He Y (2015) Identifying and mapping connectivity patterns of brain network hubs in Alzheimer's Disease. Cerebral Cortex. 25: 3723-3742. 
4. 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. 
5. Di Martino A, Yan CG#, Li Q#, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M, Deen B, Delmonte S, Dinstein I, Ertl-Wagner B, Fair DA, Gallagher L, Kennedy DP, Keown CL, Keysers C, Lainhart JE, Lord C, Luna B, Menon V, Minshew N, Monk CS, Mueller S, Muller RA, Nebel MB, Nigg JT, O’Hearn K, Pelphrey KA, Peltier SJ, Rudie JD, Sunaert S, Thioux M, Tyszka JM, Uddin LQ, Verhoeven JS, Wenderoth N, Wiggins JL, Mostofsky SH, Milham MP (2014) The Autism Brain Imaging Data Exchange: towards large-scale evaluation of the intrinsic brain architecture in Autism. Molecular Psychiatry. 19: 659-667. 
6. 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. 
7. 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. 
8. 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. 
9. Craddock RC, Jbabdi S#, Yan CG#, Vogelstein J, Castellanos FX, Di Martino A, Kelly C, Heberlein K, Colcombe S, Milham MP (2013) Imaging human connectomes at the macroscale. Nature Methods. 10: 524-539. 
10. 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. 
11. 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. 
12. Yan CG and He Y. (2011) Driving and driven architectures of directed small-world human brain functional networks. PLoS ONE. 6(8): e23460. 
13. 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) 
14. Yan CG* and Zang YF* (2010) DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Frontiers in Systems Neuroscience. 4(13). 
15. 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. 
 (*Corresponding author #Equal contribution) 

承担科研项目情况: