Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (2): 150-155.DOI: 10.3778/j.issn.1002-8331.1909-0351

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Research on Spatial Dynamics Analysis and Classification of Resting-State Functional Brain Connections

GAO Jin, ZHAO Yunpeng, Godfred Kim Mensah, LI Xinyun, LIU Zhifen, CHEN Junjie, GUO Hao   

  1. 1.College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
    2.College of Art, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
    3.Department of Mental Health, First Hospital of Shanxi Medical University, Taiyuan 030000, China
  • Online:2021-01-15 Published:2021-01-14


高晋,赵云芃,Godfred Kim Mensah,李欣芸,刘志芬,陈俊杰,郭浩   

  1. 1.太原理工大学 信息与计算机学院,太原 030024
    2.太原理工大学 艺术学院,山西 晋中 030600
    3.山西医科大学第一医院 精神卫生科,太原 030000


The existing classification model of mental diseases uses the static index of brain network as the characteristic while ignoring the spatial dynamic information of brain network, which will result in an inferior classification performance. To overcome this limitation and improve the performance of the classification model, a classification method based on the spatial dynamic of resting-state functional brain connections is proposed. The spatial dynamic characteristics of brain connections are extracted by analyzing the brain connections with high-dimensional templates. By selecting characteristics through the statistical analysis, a classification model based on resting-state functional brain connections can be constructed. The conducted experiments distinguish between depression patients and normal subjects and the results show that the classification accuracy of model utilized spatial dynamic characteristics (83.0%) is 5.2 percentage points higher than that with static index (77.8%).

Key words: spatial dynamics, functional magnetic resonance imaging, support vector machine, depression



关键词: 空间动态, 功能磁共振成像, 支持向量机, 抑郁症