脑网络分析方法及其应用
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1.南通大学信息科学技术学院, 南通 226019;2.安徽师范大学计算机与信息学院, 芜湖 241002;3.南京航空航天大学计算机科学与技术学院, 南京 211106

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国家自然科学基金(61861130366;61876082;61976120;62006128;61976006;61573023)资助项目。


Brain Network Analysis:Method and Application
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Affiliation:

1.School of Information Science and Technology, Nantong University, Nantong 226019, China;2.School of Computer and Information, Anhui Normal University, Wuhu 241002, China;3.College of Computer Science and Technology, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China

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    摘要:

    网络结构作为一种常见的数据关系表示方法被大量运用在各类研究中。人的大脑也可通过定义节点和连接边的方式抽象成一个复杂的网络结构。这个网络通常被简称为脑网络,其结构与人类的认知功能和脑疾病存在密切联系。分析和研究脑网络可以为人类探索大脑工作方式、研究神经性退化疾病的病理机制、改善心理疾病及大脑损伤的诊断治疗提供有力的工具。目前,脑网络分析及其应用已成为计算机与生物信息、医学等交叉学科中的研究热点。本文旨在回顾脑网络分析中的典型方法和应用,并按照脑网络构建、脑网络表示、脑网络分析3个部分加以介绍。最后,总结全文并展望未来研究方向。

    Abstract:

    The network is a popular way to model the interactions among elements in nature, and it has been widely used in many studies. The human brain can be considered as a complex network after defining nodes and edges. In such a network, human cognitive functions and some brain diseases are closely related to the structure of the network. Brian network analysis is a popular research area with important applications in a variety of disciplines. It provides a powerful approach to many works, including exploring working mechanisms of the brain, understanding pathological underpinnings of neurological disorders, and improving the efficiency of the therapeutic and diagnostic in clinical. This paper reviews the concepts, methods, and applications of brain network analysis, and it is divided into three parts with a introduction, i.e., brain network construction, brain network representation, and brain network analysis. Finally, we summarize this paper and discuss some new directions and problems for future research.

    表 1 脑网络模式分类分析方法及性能Table 1 Classification methods and performances in brain networks
    图1 Schematic of capture process by STSFig.1
    图2 Closed-loop control structure of the systemFig.2
    图3 Critic matrix P of controllerFig.3
    图4 Error norms of matrices P and KFig.4
    图5 Dimensionless state variables under IRL controlFig.5
    图6 Variation curve of tether tension in the control processFig.6
    图7 Cost function and integral reinforcement of the controllerFig.7
    图8 Comparison between policy iteration and LQRFig.8
    图1 脑网络分析基本框架Fig.1 Framework of brain network analysis
    图2 超图示意图[26]Fig.2 Schematic of hyper-graph[26]
    图3 动态脑网络构建示意图Fig.3 Schematic of dynamic brain network construction
    图4 多层脑网络示意图[37]Fig.4 Schematic of multilayer brain network[37]
    图5 有序模式示意图[49]Fig.5 Schematic of ordinal patterns[49]
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黄嘉爽,接标,丁卫平,张道强.脑网络分析方法及其应用[J].数据采集与处理,2021,36(4):648-663

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  • 收稿日期:2021-01-14
  • 最后修改日期:2021-05-26
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  • 在线发布日期: 2021-07-25