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神经影像与人脑连接组学团队

团队任务简介:

  基于神经影像的人脑连接组学(Human Connectomics),通过系统刻画人脑网络连接模式,为揭示人脑认知加工的工作机制和精神疾病受损机理及其临床辅助诊断和疗效评估提供了新理念和新技术。拟开展多模态神经影像(包括MRI和SPECT等)人脑连接组学计算方法及其在脑发育和精神疾病的应用研究,涉及多模态影像大数据预处理、脑网络构建以及网络拓扑属性计算分析方法,儿童脑网络发育模式的解析和评估,精神疾病脑网络受损机制及其在临床诊疗评估中的作用,评价不同中心和不同计算分析策略的选取对研究结果的可重复性。

  结合神经影像大数据,探索新型神经影像连接组学计算方法及其在脑发育和精神疾病的应用研究,建立脑发育和脑精神疾病脑网络活动图谱,揭示不同精神疾病(如抑郁症、精神分裂症和自闭症等)在脑网络中不同功能系统和核心脑区中的异常模式,为探索精神疾病的神经连接受损机制和建立基于脑影像的精神疾病诊疗标志物等提供新技术和新方法。

  

PI:贺永 教授 

  北京师范大学二级教授,长江学者特聘教授,国家杰出青年基金获得者,国家万人计划领军人才,科睿唯安全球高被引学者。现为认知神经科学与学习国家重点实验室副主任,神经影像大数据与人脑连接组学北京市重点实验室创始主任,麦戈文脑科学研究院课题组长。担任国际影像学权威期刊Human Brain Mapping副主编和Neuroimage期刊编委。主要研究领域为计算神经影像、人脑连接组学、脑发育等。近十年,在基于多模态神经影像(结构、功能和弥散磁共振等)人脑复杂网络计算方法、计算建模及应用基础研究方面,特别是在活体人脑结构和功能网络模型构建和解析、脑网络认知和生理基础及脑疾病模型验证方面做出了具有重要国际影响力的工作,建立的系列原创性计算方法已被国内外研究者广泛用于脑认知、脑医学、脑发育研究。带领团队开发了具有自主知识产权的神经影像连接组分析和可视化计算平台(“北京平台”,Gretna, BrainNet Viewer, PAGANI),已被哈佛大学、剑桥大学等1000余个国内外研究机构广泛采用,产生了重要的国际影响。承担国家自然科学基金重点项目2项和重点国际合作项目1项。在PNAS、Biol Psychiatry、Brain等发表SCI论文200余篇,总引用超过30000次,H指数80(Google Scholar)。

  

Co-PI:温俊海 教授

  温俊海,北京理工大学生命学院教授,主要研究方向包括医学图像处理、逆问题与图像重建、模式识别与三维可视化等。在国际上率先研究并发展了非均匀衰减扇型投影和可变焦扇型投影SPECT解析重建方案,这些方案可以同时对非均匀衰减,散射,检测器模糊效应进行解析校正,并能去除泊松噪声。正在研究非均匀衰减锥形投影SPECT解析重建方案,及将DNA计算应用于有限角图像重建。在国内外重要学术刊物及会议上发表学术论文50篇,其中被SCI和EI收录20余篇。担任多家国际著名期刊和国际会议审稿人,及CME2007国际会议出版主席。承担国家自然科学基金,教育部留学回国基金,北京理工大学优秀青年教师资助计划等多个项目。

  

团队成员:

  王安聪、高天欣、张春雨、仲苏玉

  

基本研发平台:

  认知神经科学与学习国家重点实验室

  神经影像大数据与人脑连接组学北京市重点实验室

  IDG/麦戈文脑科学研究院

  北师大脑成像中心

  融合医工系统与健康工程工信部重点实验室

  

科研成果:

  部分已发表论文

  [1] Ma Q, Y Tang, F Wang, X Liao, X Jiang, S Wei, A Mechelli, Y He, M Xia. Transdiagnostic Dysfunctions in Brain Modules Across Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Connectome-Based Study.[J]. Schizophrenia bulletin, 2020, 46(3): 699-712.

  [2] Sha Z, TD Wager, A Mechelli, Y He. Common Dysfunction of Large-Scale Neurocognitive Networks Across Psychiatric Disorders.[J]. Biological psychiatry, 2019, 85(5): 379-388.

  [3] Xia M, T Si, X Sun, Q Ma, B Liu, L Wang, J Meng, M Chang, X Huang, Z Chen, Y Tang, K Xu, Q Gong, F Wang, J Qiu, P Xie, L Li, Y He. Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals.[J]. NeuroImage, 2019, 189700-714.

  [4] Zhao T, X Liao, VS Fonov, Q Wang, W Men, Y Wang, S Qin, S Tan, JH Gao, A Evans, S Tao, Q Dong, Y He. Unbiased age-specific structural brain atlases for Chinese pediatric population.[J]. NeuroImage, 2019, 18955-70.

  [5] Zhao T, Y Xu, Y He. Graph theoretical modeling of baby brain networks.[J]. NeuroImage, 2019, 185711-727.

  [6] Yongbin Wei, Liao Xuhong, Yan Chaogan, He Yong, Xia Mingrui. Identifying Topological Motif Patterns of Human Brain Functional Networks[J]. Human Brain Mapping, 2017, 38(5): 2734-2750.

  [7] Yong Zhang, Liu Hao, Li Li, Ai Min, Teng Zhaowei. Cholecystectomy can increase the risk of colorectal cancer: A meta-analysis of 10 cohort studies[J]. Plos One, 2017, 12(8): e181852.

  [8] Ling-Yan Ma, Chen Xiao-Dan, He Yong, Ma Hui-Zi, Feng Tao. Disrupted Brain Network Hubs in Subtype-Specific Parkinson/"s Disease[J]. European Neurology, 2017, 78(3-4): 200-209.

  [9] Xiuqing Li, Lei Bingzhen, Zhang Jun, Wen Junhai. Positioning Verification of Irradiation Target in Gamma Knife Radiosurgery Based on Minimum Projection Error[A]//2018.

  [10] Bingzhen Lei, Li Xiuqing, Zhang Jun, Wen Junhai. Calculation of Projection Matrix in Image Reconstruction Based on Neural Network[A]//2018.

  [11] Feng C, H Zhao, M Tian, M Lu, J Wen. Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness.[J]. Biomedical engineering online, 2020, 19(1): 13.

  [12] He, Yong. Imaging Brain Networks in Neurodegenerative Diseases[J]. Cns Neuroscience & Therapeutics, 2015, 21(10): 751-753.

  [13] Sun Yan, Wang Gui-Bin, Lin Qi-Xiang, Lin Lu, Jie Shi. Disrupted white matter structural connectivity in heroin abusers[J]. Addiction Biology, 2015, 22(1): 184-195.

  [14] Zan Wang, Dai Zhengjia, Shu Hao, Liu Duan, Guo Qihao, He Yong, Zhang Zhijun. Cortical Thickness and Microstructural White Matter Changes Detect Amnestic Mild Cognitive Impairment[J]. Journal of Alzheimers Disease, 2017.

  [15] Hua Guo, Huang Zhi-Lin, Wang Wei, Zhang Shu-Xiao, Li Juan, Cheng Ke, Xu Ke, He Yong, Gui Si-Wen, Li Peng-Fei. iTRAQ-Based Proteomics Suggests Ephb6 as a Potential Regulator of the ERK Pathway in the Prefrontal Cortex of Chronic Social Defeat Stress Model Mice[J]. Proteomics Clinical Applications, 2017, 1700115.

  [16] Zhiqiang Sha, Xia Mingrui, Lin Qixiang, Miao Cao, Tang Yanqing, Ke Xu, Song Haiqing, Wang Zhiqun, Fei Wang, Fox Peter-T. Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders[J]. Cerebral Cortex, 2017, (12): 12.

  [17] Liang Xia, Li-Ming Hsu, Lu Hanbing, Akira Sumiyoshi, Yong He, Yang Yihong. The Rich-Club Organization in Rat Functional Brain Network to Balance Between Communication Cost and Efficiency[J]. Cerebral Cortex, 2017, (3): 3.

  [18] Jin Liu, Liao Xuhong, Xia Mingrui, He Yong. Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns[J]. Human Brain Mapping, 2017.

  [19] Wang, Xindi, Lin, Qixiang, Xia, Mingrui, He, Yong. Differentially categorized structural brain hubs are involved in different microstructural, functional, and cognitive characteristics and contribute to individual identification[J]. Human Brain Mapping, 2018.

  [20] Xiaodan Chen, Liao Xuhong, Dai Zhengjia, Lin Qixiang, Wang Zhiqun, Li Kuncheng, Yong He. Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models[J]. Human Brain Mapping, 2018.