隋婧

中国科学院自动化研究所

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  • 隋婧
  • 研究员

简 历:

中国科学院自动化研究所教授、博导、百人计划

模式识别国家重点实验室/脑网络组中心

北京市海淀区中关村东路95号智能化大厦502室

E-mail: jing.sui@nlpr.ia.ac.cn办公电话:010-82544518

>> 教育背景

¨博士北京理工大学光学工程(图像信息处理方向)2002年9月 - 2007年3月

¨学士北京理工大学光电仪器与信息工程1998年9月 - 2002年7月

¨辅修北京理工大学计算机应用1999年3月 - 2002年1月

>>科研兴趣

¨多模态脑影像的联合分析(fMRI, DTI, sMRI, EEG).

¨模式识别,大规模数据挖掘,机器学习,多变量建模,分类.

¨精神神经疾病生物标志的提取和发现.

>>科研经历

2013-至今教授、博导,中国科学院自动化研究所,百人计划

多模态脑影像的分析与模式识别及其在精神疾病中的应用

2012-2013

助理教授, The Mind Research Network,阿尔伯克基,新墨西哥,美国

兼职助理教授,电子与计算机工程系,新墨西哥大学

多模式人脑成像联合分析项目的核心研发和项目负责人。开发多种联合分析算法以提取多元化信息,用于神经疾病的异常脑区鉴定,生物标志提取和分类。

2010- 2012

研究科学家, The Mind Research Network (MRN), 阿尔伯克基,新墨西哥,美国

多模式成像联合分析项目的研发和项目负责人。提出了一种通用的多变量联合算法,可以采取全脑数据快速的鉴定精神疾病的异常脑区,以及fMRI 图像的智能自动去伪影技术。

2007-2009

博士后, The Mind Research Network, 阿尔伯克基,新墨西哥,美国

提出多种基于数据源的多模式或多任务fMRI图像联合分析方法,可以引入先验知识用于帮助区分疾患与健康组。

2008.6-9

信号分析师 (实习生), 北京麦邦电子技术公司

采集并分析瞬时激发耳声信号。

2002-2007

助研,北京理工大学光电工程系

深入研究红外序列图像的非均匀性校正问题,提出了多种基于场景的自适应红外序列降噪方法,可用较少帧数实现高水平校正。同时开发的算法包括红外图像增强,图像注册,微光夜视成像系统性能评估,红外检测等。

>>荣誉奖励

2014中国神经科学学会精神疾病基础与临床分会年会优秀大会发言论文

2012国际人脑成像图谱大会(OHBM)优秀摘要奖(前5%),北京,中国

2010The Mind Research Network青年科学家奖

2009国际人脑成像图谱大会(OHBM)优秀摘要奖(前5%),旧金山,美国

2008国际核磁共振成像大会优秀摘要奖,多伦多,加拿大

>> 项目支持

中国科学院百人计划2013/8/1- 至今

标题: 多模态脑影像融合及其在精神疾病生物标志鉴定中的应用

项目负责人

>> 专业评审

¨为20余种国际期刊担任审稿人,包括:Biological Psychiatry,Neuroimage, Human Brain Mapping, IEEE TMI,IEEE TBME, IEEE TIP,IEEE SPM,JASA,人工智能与医学应用,医学运算与成像杂志,信号处理学会杂志,神经科学方法杂志。

¨担任国际生物信息学与生物医学大会(BIBM)程序委员会委员 2011-2013

¨Frontiers in Psychiatry 、Biomarker、《中国生物医学工程学报》期刊编委

>> 学术会员

2013-国际电子电气工程师协会高级会员(IEEE Senior Member)

2009-美国科学会(Sigma Xi) 会员

2008-国际人脑成像图谱协会会员(OHBM)

2004-2007国际光学工程学会北理分会副主席和学生会员(SPIE)

>> 专利

金伟其,高美静,王霞,隋婧,董立泉,王岭雪,刘广荣,红外显微成像仪器与应用专利号. CN101059459

>> 代表性期刊论文

1.Sui J, Huster R, Yu Q, Judith M. Segall, Vince D Calhoun. 2014. Function-Structure Associations of the Brain: Evidence from Multimodal Connectivity and Covariance Studies. Neuroimage. In press.

2.Sui J, He H, Pearlson GD, Adali T, Yu Q, Clark VP, White T, Mueller BA, Ho BC, AndreasenNC, Calhoun VD. 2013. Three-Way (N-way) Fusion of Brain Imaging Data Based on mCCA+jICA and Its Application to Discriminating Schizophrenia. Neuroimage. 2(66):119-132.

3.Sui J, He, H., Yu, Q., Chen, J., Rogers, J., Pearlson, G.D., Mayer, A., Bustillo, J., Canive, J., Calhoun, V.D., 2013. Combination of RestingState fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA. Front Hum Neurosci 7, 235.

4.Sui J, Adali T, Yu Q, Calhoun VD. 2012. A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data. Journal of Neuroscience Methods. 204(1): 68–81.

5.Sui J, Yu Q, He H, Pearlson GD, Calhoun VD. 2012. A Selective Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data. Frontiers in Human Neuroscience. 6:27.

6.Sui J, Pearlson GD, Adali T, Caprihan A, Liu J, Yamamoto J, Calhoun VD. 2011. Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in a CCA+ICA Based Model.Neuroimage. 57(7):839-855.

7.Sui J, Adali T, Pearlson GD, Yang H, Sponheim SR, White T, Calhoun VD 2010. A CCA+ICA Based Model for Multi-Task Brain Imaging Data Fusion And Its Application to Schizophrenia. Neuroimage. 51(5):123-134.

8.Sui J, Adali T, Pearlson GD, Calhoun VD. 2009. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage 46(1):73-86.

9.Sui J, Adali T, Pearlson GD, Clark VP, Calhoun VD. 2009. A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework. Hum Brain Mapping 30(9): 2953-2970.

10.Sui J, Jin W, Dong L. 2007. An adaptive nonuniformity correction algorithm for infrared line scanner based on local statistics. Chinese Journal of Electronics 16(2):11-15.

11.Sui J, Jin W, Dong L, Zhang Y. 2007. A new adaptive nonuniformity correction algorithm for infrared line scanner based on neural networks. Chinese Optics Letters 2:74-76.

12.Plis, S.M., Sui J., Lane, T., Roy, S., Clark, V.P., Potluru, V.K., Huster, R.J., Michael, A., Sponheim, S.R., Weisend, M.P., Calhoun, V.D., 2013. High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia. Neuroimage.

13.Yu Q, Sui J, Liu J, Plis SM, Kiehl KA, Pearlson GD, and Calhoun VD. 2013. Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia. Schizophrenia Research. 143(1):165-71.

14.Yu Q, Allen EA, Sui J, Fusar-Poli P, Arbabshirani MR, Pearlson GD, Calhoun VD.2013. Brain connectivity network in schizophrenia underlying resting state functional magnetic resonance imaging. Frontiers in Human Neuroscience. In press.

15.Yu, Q., Allen, E.A., Sui J., Arbabshirani, M.R., Pearlson, G., Calhoun, V.D., 2012. Brain connectivity networks in schizophrenia underlying resting state functional magnetic resonance imaging. Curr Top Med Chem 12, 2415-2425.

16.Chen, J., Calhoun, V.D., Pearlson, G.D., Perrone-Bizzozero, N., Sui J., Turner, J.A., Bustillo, J.R., Ehrlich, S., Sponheim, S.R., Canive, J.M., Ho, B.C., Liu, J., 2013. Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference. Neuroimage 83C, 384-396.

17.Calhoun VD, Sui J, Kiehl KA, Turner JA, Allen EA and Pearlson GD. 2012. Exploring the Psychosis Functional Connectone: Aberrant Intrinsic Networks in Schizophrenia and Bipolar Disorder. Frontiers in Psychiatry. 2:75.

18.He H, Sui J, Yu Q, Turner JA, Ho BC, Sponheim SR, Manoach DS, Clark VP, Calhoun VD. 2012. Altered Small-World Brain Networks in Schizophrenia Patients during Working Memory Performance. PLoS ONE, 7(6): e38195.

19.Yu Q., Sui J., Rachakonda S., He H.,Gruner. W, Pearlson, GD., Kiehl, KA., and Calhoun, VD. 2011. Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study, PLoS ONE, 6(9): e25423.

20.Yu Q, Sui J, Rachakonda S, He H, Pearlson GD, Calhoun VD 2011. Altered small-world brain networks in temporal lobe in patients with schizophrenia performing an auditory oddball task. Frontiers in System Neuroscience. 5:7.

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