温晓通
温晓通
Wen, Xiaotong
PhD., Professor
E-mail: wenxiaotong@163.com
办公电话: 010-82509716
教育背景Education
2008年7月毕业于北京师范大学认知神经科学与学习国家重点实验室并获基础心理学博士学位。
2005年7月硕士毕业于北京师范大学信息科学与技术学院，获通信与信息系统硕士学位。
2002年7月本科毕业于北京师范大学信息科学与技术学院，获电子学学士学位
科研工作经历 Scientific research and academic work experience:
2023年9月至今，任中国人民大学心理学系教授。
2013年10月-2023年8月，任中国人民大学心理学系副教授。
2012年9月-2013年10月，在美国佛罗里达大学生物医学工程系任研究助理科学家。
2008年9月-2012年8月，在美国佛罗里达大学生物医学工程系，任博士后研究员
研究方向Research
基础研究：认知控制与情绪的交互及其与行为的关系，心境障碍和情绪问题的认知神经基础和社会心理因素。
应用研究：健康促进，抑郁和焦虑的应对及干预，人因风险。
方法学：复杂网络分析，基于机器学习和人工智能融合心理-生理-神经多指标建模。
科研项目 Research project
主持项目
1. 国家自然科学基金青年项目：《注意控制的神经网络机制研究》（31400973）2015/01-2017/12，25万
2. 中国人民大学新教师启动金项目：《高级认知控制的神经网络机制研究》（14XNLF11） 2014/01-2016/12 ，25万
3. 中国人民大委托及决策咨询项目：《老化过程中的认知控制神经网络机制变化》 2015/05-2017/12 （15XNQ037）5万
参与项目
1. 国家自然科学基金重点项目《神经影像的时间信息提取及其在中枢药物疗效检测中的应用》2012/01-2016/12，280万
2. 中国人民大学决策咨询及预研委托项目团队基金：《社会比较对结果评价加工过程影响的神经机制》（15XNLQ05）2015.4-2017.12，81万
获得奖励
12022-09，北京市高等教育教学成果奖二等奖（“认知科学与哲学”交叉融合的心理学专业创新人才培养模式），二等奖，北京市教育委员会，第一获奖人
22021-12，中国人民大学教学成果奖一等奖，一等奖，中国人民大学，第一获奖人
32020-11国家一流本科课程，教育部，第四
42023-01中国人民大学课程思政师范课程，中国人民大学，负责人
52022，中国人民大学教学标兵提名，中国人民大学
62021，中国人民大学2020-2021年秋季学期本科教学常规督导推荐示范课程，中国人民大学
72020，中国人民大学2019-2020年春季学期线上教学优秀课程，中国人民大学
8 2018-06中国人民大学本科毕业论文（设计）优秀指导教师，中国人民大学
92015-06中国人民大学青年教师基本功大赛二等奖，中国人民大学
102015-06中国人民大学青年教师基本功大赛优秀教案奖，中国人民大学
开设课程
本科课程
普通心理学、认知科学与哲学通识讲坛、认知与脑、Matlab 技术、认知心理学、认知神经科学（研究型课程）、心智和脑-认识神经科学导论、时间与心智——脑的进化、发展、变化和衰老
研究生课程
脑与认知-基础感知觉、认知神经科学导论、研究规范与论文写作、高级心理实验技术、高级心理实验技术、数据分析进阶
代表成果（*为通讯作者 #为共同一作）
1. Wen, X. *, Han, B., Li, H., Dou, F., Wei, G., Hou, G., & Wu, X. (2023). Unbalanced amygdala communication in major depressive disorder. Journal of Affective Disorders, 329, 192–206. https://doi.org/10.1016/j.jad.2023.02.091
2. Yu R., Han B., Wu X., Wei G., Zhang J., Ding M., Wen, X* （2023）Dual-functional network regulation underlies the central executive system in working memory, Neuroscience, 524, 158-180, doi.org/10.1016/j.neuroscience.2023.05.025
3. Song, W., Li, H.*, Sun, F., Wei, S., Wen, X.*, & Ouyang, L. (2023). Fusion of pain avoidance and the contingent negative variation induced by punitive condition predict suicide ideation in a college population. Behavioural brain research, 438, 114210. https://doi.org/10.1016/j.bbr.2022.114210
4. Han, B., Wei, G., Dou, F., Zhang, J., & Wen, X.* (2023). Exploring the Lifelong Changes of Interaction between Cingulo-Opercular Network and Other Cognitive Control Related Functional Networks Based on Multiple Connectivity Indices. Journal of integrative neuroscience, 22(3), 74. https://doi.org/10.31083/j.jin2203074
5. 孙芳,宋巍,温晓通*,李欢欢*欧阳李晟 (2022)痛苦逃避和自我参照惩罚条件下脑电特征对自杀意念的分类效能, 心理学报, 54(9), 1-19, doi.org/10.3724/SP.J.1041.2022.00000
6. 陈钰莹，贾雅雯，温晓通*，李欢欢*，郝子雨，林亦轩，蒋松源 (2022) 情感激励延迟任务下抑郁症自杀未遂者痛苦加工的脑激活模式, 中国临床心理学杂志, 30(5) 1005-1012
7. Hao, Z., Li, H*., Ouyang, L., Sun, F., Wen, X*., & Wang, X. (2022). Pain avoidance and functional connectivity between insula and amygdala identifies suicidal attempters in patients with major depressive disorder using machine learning. Psychophysiology, e14136. Advance online publication. https://doi.org/10.1111/psyp.14136
8. Wen, X., Liu, Y., Zhao, P., Liu, Z., Li, H., Li, W., Zhu, Z., & Wu, X*. (2021). Disrupted communication of the temporoparietal junction in patients with major depressive disorder. Cognitive, Affective & Behavioral neuroscience, 21(6), 1276–1296.
9. Liu, Z., Liu, Y., Zhao, P., Li, W., Zhu, Z., Wen, X. *, & Wu, X.* (2021). Exploring Brain Dynamic Functional Connectivity Using Improved Principal Components Analysis Based on Template Matching. Brain topography, 34(2), 121-138.
10. Wen, X. *+, Li, W. +, Liu, Y., Liu, Z., Zhao, P., Zhu, Z., & Wu, X.* (2021). Exploring communication between the thalamus and control-related functional networks in the cerebral cortex. Cognitive Affective and Behavioral Neuroscience, 21(3), 656–677.
11. Zhao, P., Yu, R. S., Liu, Y., Liu, Z. H., Wu, X., Li, R., Ding, M. Z., & Wen, X.* (2021). The functional hierarchy of the task-positive networks indicates a core control system of top-down regulation in visual attention. Journal of integrative neuroscience, 20(1), 43–53. https://doi.org/10.31083/j.jin.2021.01.297
12. Wen, X., Wang, H., Liu, Z., Liu, C., Li, K., Ding, M., & Wu, X.* (2018). Dynamic Top-down Configuration by the Core Control System During Working Memory. Neuroscience, 391, 13-24.
13. Wu X, Wu T, Liu C, Wen X*, Yao Li (2017) Frequency Clustering Analysis for Resting State Functional Magnetic Resonance Imaging Based on Hilbert-Huang Transform, Frontiers in Human Neuroscience , 11: 61, doi: 10.3389/fnhum.2017.00061
14. Zhang Y#，Li Q#，Wen X#，Cai W Li G，Tian J，Zhang EY，Liu J，Yuan K，Zhao J，Wang W，Zhou Z，Ding M， Gold SM，Liu Y，Wang G，(2017) Granger causality reveals a dominant role of memory circuit in chronic opioid dependence，Addiction Biology, doi: 10.1111/adb.12390.
15. Wu X, Wu T, Zhan Z, Yao L, Wen X* (2016) A real-time method to reduce ballistocardiogram artifacts from EEG during fMRI based on optimal basis sets (OBS), Computer Methods and Programs in Biomedicine, 127: 114-125
16. Wen X, Kang M, Yao L, Zhao X* (2016) Real-Time Ballistocardiographic Artifact Reduction Using the k-Teager Energy Operator Detector and Multi-Channel Referenced Adaptive Noise Cancelling, International Journal of Imaging Systems and Technology, 26(3): 209-215
17. Wu X, Lai Y, Zhang Y, Yao L, Wen X* (2015) Breakdown of Sensorimotor Network Communication in Leukoaraiosis. Neurodegener Dis, 15(6): 322-330.
18. Wen X, Liu Y, Li Y, Ding M (2013) Top-down regulation of default mode activity in spatial visual attention. The Journal of Neuroscience 33(15): 6444-6453.
19. Wen X, Rangarajan G, Ding M (2013) Is Granger causality a viable technique for analyzing fMRI data? PLoS One. 8(7): e67428
20. Wen X, Rangarajan G, Ding M (2013) Multivariate Granger causality: an estimation framework based on factorization of spectral density matrix. Philos Transact A Math Phys Eng Sci, 371:20110610.
21. Wen X, Li Y, Liu Y, Ding M (2012) Causal interactions in attention networks predict behavioral performance. The Journal of Neuroscience, 32(4): 1284-1292.
22. Wen X, Mo J, Ding M (2012) Exploring resting-state functional connectivity with total interdependence. Neuroimage, 60:1587-1595.
23. Wen X, Yin K, Sun D, Yao L, Zhao X (2007) Application of time-varying coherence to coordinative connectivity based on event related EEG. ICNC 2007, Vol. 2: 216-220.
24. Wen X, Zhao X, Yao L (2006) Time-frequency analysis of EEG based on event related cognitive task. ISNN 2006, LNCS 3973: 579 -585.
25. Wen X, Zhao X, Yao L, Wu X (2006) Applications of Granger causality model to connectivity network based on fMRI time series. Lecture Notes Computer Science 4221: 205-213.
26. Wen X, Zhao X, Yao L (2005) Synchrony of basic neuronal network based on event related EEG. Lecture Notes Computer Science 3498: 725-730.
27. Zhang Y, Wang J, Zhang G, Zhu Q, Cai W, Tian J, Zhang YE, Miller JL, Wen X, Ding M, Gold MS, Liu Y. (2015) The neurobiological drive for overeating implicated in Prader-Willi syndrome. Brain Res, 1620: 72-80
28. Zhang C, Song S, Wen X, Yao L, Long Z (2015) Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data. J Neurosci Methods, 245:15-24.
29. Zhang X, Wang Z, Song S, Wen X, Yao L, Long Z (2014) Fast Voxel Selection of fMRI Data Based on Smoothed 10 Norm. 2014 International Workshop on Pattern Recognition in Neuroimaging, Tubingen, 2014, 1-4.
30. Wen X, Li R, Fleisher A, Reiman E, Wen X, Chen K, Yao L, Wu X (2013) Alzheimer's disease-related changes in regional spontaneous brain activity levels and inter-region interactions in the default mode network. Brain Research, 1509: 58-69.
31. Long Z, Li R, Wen X, Jin Z, Chen K, Yao L（2013）Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection. Magn Reson Imaging. 31(1):60-74.
32. Luo Q, Lu W, Cheng W, Valdes-Sosa P, Wen X, Ding M, Feng J (2013) Spatio-temporal Granger causality: a new framework. Neuroimage 79: 241-263
33. Zhang Y, Zhao H, Qiu S, Tian J, Wen X, Miller JL, von Deneen KM, Zhou Z, Gold MS, Liu Y (2013) Altered functional brain networks in Prader-Willi syndrome. NMR Biomed. 26(6):622-629.
34. Ding M, Mo J, Schroeder CE, Wen X (2011) Analyzing coherent brain networks with Granger causality. Conf Proc IEEE Eng Med Biol Soc. 2011:5916-5918.
35. Hui M, Li J, Wen X, Yao L, Long Z (2011) An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data. PLoS One. 6(12):e29274.（SCI）
36. Miao X, Chen K, Li R, Wen X, Yao L, Wu X (2010) Application of Granger causality analysis to effective connectivity of the default-mode network. 2010 IEEE/ICME: 156 -160.
37. Wu X, Chen K, Long Z, Wen X, Jin Z, Yao L (2008) Ipsilateral brain deactivation specific to the nondominant hand during simple finger movements. NeuroReport, Vol.19: 483-486.
38. Wu XC, Tang N, Yin K, Wu X, Wen X, Yao L, Zhao X (2007) Investigation of effective connectivity in the motor cortex of fMRI data using Granger causality model. Medical Imaging 2007: Physiology, Function, and Structure from Medical Images,Vol. 6511(1).