基本信息

• 职称：教授（博士生导师）

• 研究方向：智能信息处理

• 电话: 010-5880-0427

• 邮箱：gxj@bnu.edu.cn

教育背景

• 2016.11-2017.11        美国Banner Alzheimer’s Institute      访问学者

• 2003.9-2006.7    北京师范大学认知神经科学与学习研究所基础心理学    博士

• 2000.9-2003.7    北京师范大学信息科学学院电子学系通信与信息系统    硕士

• 1994.9-1998.7    山西省太原理工大学电子信息工程系通信工程专业     学士

研究领域

主要从事智能信息处理的研究工作，应用计算机科学和信息科学的理论和方法，研究脑成像数据处理和分析的方法论及其应用。重点是基于脑连接组学，融合多模态数据，构建深度学习模型，探讨人脑的功能-结构网络关系以及随年龄发展变化的神经机制。

承担课程

• 本科生：《模式识别》

• 研究生：《应用数理统计》

主持项目

• 国家自然科学基金面上项目：基于多模态影像深度学习的脑年龄预测模型研究(2021/01-2024/12)。

• 国家自然科学基金面上项目：基于多变量方法的脑网络动态轨迹研究(2017/01-2020/12)。

• 中央高校基本科研业务费专项资金重点项目：基于多变量分析的脑老化效应研究(2015/01-2017/12)。

• 国家自然科学基金青年科学基金项目：基于静息态功能连接的大脑结构网络老化效应研究(2011/01-2013/12)。

• 中央高校基本科研业务费专项资金一般项目：阿尔茨海默氏症脑解剖结构连接网络的磁共振成像研究(2011/01-2012/12)。

• 北京师范大学教学建设与改革项目：电路理论基础课程有效教学策略研究(2013/03-2015/03)。

• 国家级大学生创新创业训练计划项目：基于独立成分分析构建脑白质网络(2013/01-2015/01)。

参与项目

• 国家自然科学基金重大国际(地区)合作研究项目：神经影像信息处理及应用-老年痴呆的鉴别、早期预测与预防(2013/01-2017/12)。

• 国家自然科学基金面上项目：大脑中数字领域特异性加工的本质：数量还是形状(2013/01-2016/12)。

• 国家自然科学基金重点项目：基于认知的实时功能磁共振成像的理论与关键技术(2010/01-2013/12)。

• 国家自然科学基金重大研究计划：结合功能磁共振成像技术的脑机接口研究与实现(2009/01-2011/12)。

奖励与荣誉

• 北京师范大学优质研究生课程优秀奖(2015、2014年)

• 北京师范大学优秀共产党员(2016、2010年)

• 北京师范大学优秀辅导员(2016年)

• 北京师范大学2014-2015学年优秀新生导师(2015年)

• 北京师范大学信息学院建设贡献奖(社会工作先进个人)(2016、2010、2009)

• 北京师范大学“京师英才”奖励(2014、2013年)

• 北京师范大学信息学院青年教师教学基本功比赛(优秀奖)(2008年)

代表性期刊论文

• [1]  Li JM, Wang Q, Li K, Yao L, Guo XJ*. Tracking age-related topological changes in individual brain morphological networks across the human lifespan.J. Magn. Reson. Imaging 2023. DOI: 10.1002/jmri.28984.

• [2]  Guo XJ, Chen KW, Chen YH, Xiong CJ, Su Y, Yao L, Reiman EM. A computational Monte Carlo simulation strategy to determine the temporal ordering of abnormal age onset among biomarkers of Alzheimers disease. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022,19(5):2613-2622. doi: 10.1109/TCBB.2021.3106939
  

• [3]  Chen KW, Guo XJ, Pan R, Xiong CJ, Harvey DJ, Chen YH, Yao L, Su Y, Reiman EM. Limitations of clinical trial sample size estimate by subtraction of two measurements. Statistics in Medicine. 2022;41(7):1137-1147. doi: 10.1002/sim.9244.
  

• [4]  Liu K, Li Q, Yao L, Guo XJ*. The coupled representation of hierarchical features for Mild Cognitive Impairment and Alzheimer’s Disease classification. Front. Neurosci. 2022, 16:902528.doi: 10.3389/fnins.2022.902528
  

• [5]  Jiang HT, Lu N, Chen KW, Yao L, Li K, Zhang JC, Guo XJ*. Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks. Front. Neurol. 2020, 10:1346. (SCI)
  

• [6]  Zhao XY, Yao L, Chen KW, Li K, Zhang JC, Guo XJ*. Changes in the functional and structural default mode network across the adult lifespan based on partial least squares. IEEE Access. 2019, 7:82256-82265. (SCI)
  

• [7]  Liu K, Yao SX, Chen KW, Zhang JC, Yao L, Li K, Jin Z, Guo XJ*. Structural brain network changes across the adult lifespan. Frontiers in Aging Neuroscience 2017，9(275):1-10. doi: 10.3389/fnagi.2017.00275. (SCI)
  

• [8]  Liu K, Chen KW, Yao L, Guo XJ*. Prediction of Mild Cognitive Impairment conversion using a combination of Independent Component Analysis and the Cox model. Frontiers in Human Neuroscience 2017, 11(33): 1-11. doi: 10.3389/fnhum.2017.00033. (SCI)
  

• [9]  Wang PY, Chen KW, Yao L, Hu B, Wu X, Zhang JC, Ye Q, Guo XJ*. Multimodal classification of mild cognitive impairment based on partial least squares. Journal of Alzheimer's Disease (2016 ). (SCI)
  

• [10]  Zhan Y, Chen KW, Wu X, Zhang DQ, Zhang JC, Yao L, Guo XJ*. Identification of conversion from normal elderly cognition to Alzheimer's disease using multimodal support vector machine. Journal of Alzheimer's Disease 2015, 47(4):1057-1067. DOI: 10.3233/JAD-142820. (SCI)
  

• [11]  OuYang X, Chen KW, Yao L, Hu B, Wu X, Ye Q, Guo XJ*. Simultaneous changes in gray matter volume and white matter fractional anisotropy in Alzheimer's disease revealed by multimodal CCA and joint ICA. Neuroscience 2015, 301: 553-562. DOI: 10.1016/j.neuroscience.2015.06.031. (SCI)
  

• [12]  OuYang X, Chen KW, Yao L, Wu X, Zhang JC, Li K, Jin Z, Guo XJ*. Independent component analysis-based identification of covariance patterns of microstructural white matter damage in Alzheimer's disease. PLOS ONE 2015, 10(3): e0119714. DOI:10.1371/journal.pone.0119714. (SCI)
  

• [13]  Guo XJ, Wang Y, Guo TM, Chen KW, Zhang JC, Li K, Jin Z, Yao L*. Structural covariance networks across healthy young adults and their consistency. Journal of Magnetic Resonance Imaging 2015, 42(2): 261-268. DOI: 10.1002/jmri.24780. (SCI)
  

• [14]  Li K, Guo XJ, Jin Z, Ouyang X, Zeng YW, Feng JS, Wang Y, Li Yao, Ma L*. Effect of simulated microgravity on human brain gray matter and white matter - Evidence from MRI. PLoS ONE 2015, 10(8): e0135835. doi:10.1371/journal.pone.0135835 (SCI)
  

• [15]  Guo XJ, Wang Y, Chen KW, Wu X, Zhang JC, Li K, Jin Z, Yao L*. Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian Networks. Frontiers in Computational Neuroscience 2014, 8(122):1-10. DOI: 10.3389/fncom.2014.00122. (SCI)
  

• [16]  Wang Y, Chen KW, Zhang JC, Yao L, Li K, Jin Z, Ye Q, Guo XJ*. Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling. Frontiers in Aging Neuroscience 2014, 6(105):1-7. DOI:10.3389/ fnagi.2014.00105. (SCI)
  

• [17]  Guo XJ, Chen KW, Zhang YM, Wang Y, Yao L*. Regional covariance patterns of gray matter alterations in Alzheimer’s disease and its replicability evaluation. Journal of Magnetic Resonance Imaging 2014, 39(1):143-149. (SCI)
  

• [18]  Wang Y, Chen KW, Yao L, Jin Z, Guo XJ*. Structural Interactions within the default mode network identified by Bayesian Network analysis in Alzheimer’s disease. PLOS ONE 2013, 8(8): e74070. (SCI)
  

• [19]  Liu L*, You WP, Wang WJ, Guo XJ, Peng DL, Booth J. Altered brain structure in Chinese dyslexic children. Neuropsychologia 2013, 51(7):1169-1176. (SCI)
  

• [20]  Wu X*, Guo XJ, Chen KW, Lai YZ, Yao L. Biomarker research of preclinical Alzheimer’s disease and MCI based on neuroimage techniques. Neuroscience and Biomedical Engineering 2013,1(2):92-101.
  

• [21]  Guo XJ, Han Y, Chen KW, Wang Y, Yao L*. Mapping joint grey and white matter reductions in Alzheimer’s disease using joint independent component analysis. Neuroscience Letters 2012, 531(2):136-141. (SCI)
  

• [22]  Wang ZQ, Guo XJ, Qi ZG, Yao L, Li KC. Whole-brain voxel-based morphometry of white matter in mild cognitive impairment. European Journal of Radiology 2010, 75(2):129-133. (SCI)
  

• [23]  Guo XJ, Wang ZQ, Li KC, Li ZY, Qi ZG, Jin Z, Yao L, Chen KW. Voxel-based assessment of gray and white matter volumes in Alzheimer’s disease. Neuroscience Letters 2010, 468(2): 146-150. (SCI)
  

会议论文

• [1]  Liu K, Yao SX, Chen KW, Zhang JC, Yao L, Guo XJ*. Age-related changes of structural brain network across the adult lifespan. The 19th Annual Conference of Arizona Alzheimer's Consortium (AACAC 2017). May 18, 2017, Phoenix, USA.

• [2]  Liu K, Guo XJ*, Zhang JC, Yao L, Chen KW. Combining Multivariate Cox Model and Independent Component Analysis to Predict MCI Conversion. The 22nd Annual Meeting of the Organization for Human Brain Mapping (HBM 2016), June 24-30, Geneva, Switzerland.

• [3]  Zhou LQ, Guo XJ*, Zhang JC, Yao L, Chen KW. Classification of MCI from Structural Default Mode Network Using Bayesian Classifiers. The 22nd Annual Meeting of the Organization for Human Brain Mapping (HBM 2016), June 24-30, Geneva, Switzerland.

• [4]  Liu K, Chen KW, Hu B, Yao L, Guo XJ*. Prediction of MCI conversion based on multivariate Cox proportional hazards regression model. International Conference on Brain Informatics and Health (BIH 2015). August 30 – September 2. London, UK.

• [5]  Zhan Y, Wu X, Yao L, Chen KW, Guo XJ*. Analysis of multimodal neuroimaging data for classification in mild cognitive impairment. IEEE/ICME International Conference on Complex Medical Engineering, 2014. (EI)

• [6]  OuYang X, Sun XY, Guo T, Sun QY, Chen KW, Wu X, Yao L, Guo XJ*. Independent component analysis of DTI data reveals white matter covariances in Alzheimer’s disease. Proc. of SPIE, Medical Imaging, 2014. Vol. 9038 90380B: 1-7. (EI)

• [7]  OuYang X, Yan CF, Yao L, Guo XJ*. Classifying spatial patterns of fMRI activity for object category based on information mapping. Proc. of SPIE, Medical Imaging, 2013. Vol. 8672 86721V: 1-8. (EI)

• [8]  Wang Y, Yao L, Guo XJ*, Chen KW. Structural correlation in the default mode network in Alzheimer's disease. IEEE/ICME International Conference on Complex Medical Engineering, 2012. 235-240. (EI)

• [9]  Yan CF, Song ST, Yao L, Guo XJ*. Object category classification of fMRI data using support vector machine combined with deactivation voxel selection. Proc. of SPIE, Medical Imaging, 2012. Vol. 8317 83171O:1-7. (EI)

• [10]  Liu JC, Li ZY, Chen KW, Yao L, Wang ZQ, Li KC, Guo XJ*. Comparison of gray matter volume and thickness for analysis of cortical changes in Alzheimer's Disease. Proc. of SPIE, Medical Imaging, 2011. Vol. 7965 79652E: 1-7. (EI)

• [11]  Guo XJ, Li ZY, Chen KW, Yao L, Wang ZQ, Li KC. Mapping gray matter volume and cortical thickness in Alzheimer's Disease. Proc. of SPIE, Medical Imaging, 2010. Vol. 7626 76260B:1-9 (EI)

参编教材

• 1. 林捷,杨绪业,郭小娟主编.《模拟电路与数字电路(第2版)》, 人民邮电出版社(2011.2).

• 2. 郭小娟,姚力,彭聃龄.脑发育研究(第2 章).见于: 汉语儿童语言发展与促进.彭聃龄等主编.北京:人民教育出版社,2007,p359-372.

• 3. 郭小娟,姚力.人类脑图谱(第8 章).见于: 神经信息学及其应用. 唐焕文,唐一源,郭崇慧,陈克伟主编.北京:科学出版社,2007,p131-160.

其他

• 科学技术成果鉴定(阿尔茨海默病的神经影像辅助诊断系统[教NF2007]第011号])

• BIP脑磁共振成像数据处理软件(2007SR14196)

• 阿尔茨海默病神经影像辅助诊断系统(2007SR14197)