Review of Community Detection in Complex Brain Networks

doi:10.3778/j.issn.1673-9418.2209007

Abstract

Abstract: The brain network community detection algorithm has become a highly regarded topic in recent years within the fields of neuroscience and network science, widely employed to unveil patterns of structural and functional connectivity in the brain. Due to the complexity of the brain networks and the need to handle multiple subjects and various task scenarios, it significantly increases the difficulty of community detection in this field. This paper focuses on functional magnetic resonance imaging (fMRI) technology and comprehensively reviews the advancements in research regarding algorithms for detecting communities within brain functional networks. Firstly, the basic process, task categories, and method types of brain network community detection algorithms are described. Next, various brain network community detection algorithms are classified in different task scenarios, including separate communities, overlapping communities, hierarchical communities, and dynamic community detection algorithms. A detailed analysis of the advantages and disadvantages of different methods is provided, along with their applicable scopes. Finally, the future directions of brain network community detection algorithms are discussed, including the problem of community detection in multi-subject networks, robustness issues in brain network community detection, and studies on brain network community detection algorithms for multimodal imaging data. This paper can serve as a methodological guide for future research on brain network community structures.

Key words: brain network, community detection, functional magnetic resonance imaging

摘要： 脑网络社区检测算法是近年来脑科学和网络科学领域备受关注的重要课题，被广泛用于揭示大脑结构和功能连接模式。由于大脑网络的复杂性以及需要处理多个被试、多种场景任务等因素，极大地增加了该领域社区检测的难度。聚焦功能磁共振成像技术，全面综述了面向脑功能网络社区检测算法的研究进展。首先，描述了脑网络社区检测算法的基本流程、任务类别和方法种类。然后，分类介绍了不同任务场景下的脑网络社区检测算法，包括分离社区、重叠社区、多层次社区和动态社区检测算法，深入分析了不同方法的优缺点，并给出了适用范围。最后，展望了未来脑网络社区检测算法的主要发展方向，包括多被试网络社区检测问题、脑网络社区检测的鲁棒性问题以及面向多模态影像数据的脑网络社区检测算法研究等。可为今后脑网络社区结构研究提供方法学指导。

关键词: 脑网络, 社区检测, 功能磁共振成像

WEN Xuyun, NIE Ziyu, CAO Qumei, ZHANG Daoqiang. Review of Community Detection in Complex Brain Networks[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(12): 2795-2807.

温旭云, 聂梓宇, 曹曲美, 张道强. 复杂脑网络社区检测算法综述[J]. 计算机科学与探索, 2023, 17(12): 2795-2807.

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