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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：IEEE Access

摘要：A functional brain network has attracted much attention due to its capability of characterizing the functional connectivity patterns of the brain. The existing methods for network construction usually rely on the conventional measures such as Pearson correlation to determine the pairwise similarity between each pair of brain regions, thus ignoring the global structure relationship and the information communication flow among different brain regions. To this end, this paper proposes a directional brain network construction method based on effective distance. Specifically, the effective distance can capture the hidden relevance pattern among all the brain regions and provide a directional functional network reflecting information propagation paths among the functional brain areas simultaneously. When estimating the probability of the direction and the strength of the connectivity between two regions, the structure information characterized by their neighbors is also considered in our method. The functional brain network produced by our method is more flexible in uncovering physiological mechanisms of the brain compared with the conventional undirected network. The experiments on two fMRI data analysis tasks, i.e., disease diagnosis and cognitive state detection, show that our method outperforms the conventional functional brain network approaches, including Pearson correlation, structural equation modeling, and sparse representation-based method. © 2013 IEEE.

是否译文：否

发表时间：2018-01-01

合写作者：Zhu, Jiuwen,Liu, Mingxia,Xu, Xijia,张道强

通讯作者：朱清华