复杂脑网络社区检测算法综述

doi:10.3778/j.issn.1673-9418.2209007

摘要/Abstract

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

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

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.

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.

参考文献

[1] BULLMORE E, SPORNS O. Complex brain networks: graph theoretical analysis of structural and functional systems[J]. Nature Reviews Neuroscience, 2009, 10(3): 186-198.
[2] BULLMORE E T, BASSETT D S. Brain graphs: graphical models of the human brain connectome[J]. Annual Review of Clinical Psychology, 2011, 7: 113-140.
[3] RUBINOV M, SPORNS O. Complex network measures of brain connectivity: uses and interpretations[J]. Neuroimage, 2010, 52(3): 1059-1069.
[4] SPORNS O, BETZEL R F. Modular brain networks[J]. Annual Review of Psychology, 2016, 67: 613-640.
[5] YEO B T, KRIENEN F M, SEPULCRE J, et al. The organiza-tion of the human cerebral cortex estimated by intrinsic functional connectivity[J]. Journal of Neurophysiology, 2011, 106(3): 1125-1165.
[6] COLE M W, REYNOLDS J R, POWER J D, et al. Multi-task connectivity reveals flexible hubs for adaptive task control[J]. Nature Neuroscience, 2013, 16(9): 1348-1355.
[7] DAI Z, LIN Q, LI T, et al. Disrupted structural and functional brain networks in Alzheimer’s disease[J]. Neurobiology of Aging, 2019, 75: 71-82.
[8] GARCIA J O, ASHOURVAN A, MULDOON S, et al. Applica-tions of community detection techniques to brain graphs: algorithmic considerations and implications for neural function[J]. Proceedings of the IEEE, 2018, 106(5): 846-867.
[9] VANGIMALLA R R, SREEVALSAN-NAIR J. Comparing community detection methods in brain functional connectivity networks[C]//Proceedings of the 2019 International Con-ference on Computational Intelligence, Cyber Security, and Computational Models, Coimbatore, Dec 19-21, 2019. Singapore: Springer, 2020: 1213.
[10] PARK H, FRISTON K. Structural and functional brain networks: from connections to cognition[J]. Science, 2013, 342(6158): 1238411.
[11] POWER J D, COHEN A L, NELSON S M, et al. Functional network organization of the human brain[J]. Neuron, 2011, 72(4): 665-678.
[12] HUTCHISON R M, WOMELSDORF T, ALLEN E A, et al. Dynamic functional connectivity: promise, issues, and inter-pretations[J]. Neuroimage, 2013, 80: 360-378.
[13] LYNN C W, BASSETT D S. The physics of brain network structure, function and control[J]. Nature Reviews Physics, 2019, 1(5): 318-332.
[14] BETZEL R F, MI?I? B, HE Y, et al. Functional brain modules reconfigure at multiple scales across the human lifespan[J]. arXiv:1510.08045, 2015.
[15] MEUNIER D, ACHARD S, MORCOM A, et al. Age-related changes in modular organization of human brain functional networks[J]. Neuroimage, 2009, 44(3): 715-723.
[16] BLONDEL VINCENT D, JEAN-LOUP G, RENAUD L, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 10: P10008.
[17] NEWMAN M E. Modularity and community structure in networks[J]. Proceedings of the National Academy of Sciences, 2006, 103(23): 8577-8582.
[18] CLAUSET A, NEWMAN M E, MOORE C. Finding comm-unity structure in very large networks[J]. Physical Review E, 2004, 70(6): 66111.
[19] YEH J Y, FU J C. Double simulated annealing for functional MRI analysis[J]. Journal of the Chinese Institute of Industrial Engineers, 2005, 22(6): 497-508.
[20] DUCH J, ARENAS A. Community detection in complex networks using extremal optimization[J]. Physical Review E, 2005, 72(2): 27104.
[21] BERGSTROM C T, ROSVALL M. Maps of random walks on complex networks reveal community structure[J]. Pro-ceedings of the National Academy of Sciences, 2008, 105(4): 1118-1123.
[22] JIANG Y, JIA C, YU J. An efficient community detection algorithm using greedy surprise maximization[J]. Journal of Physics A: Mathematical and Theoretical, 2014, 47(16): 165101.
[23] BRYANT C, ZHU H, AHN M, et al. LCN: a random graph mixture model for community detection in functional brain networks[J]. Statistics and Its Interface, 2017, 10(3): 369.
[24] ZHANG H, STANLEY N, MUCHA P J, et al. Multi-layer large-scale functional connectome reveals infant brain developmental patterns[C]//LNCS 11072: Proceedings of the 2018 International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Sep 16-20, 2018. Cham: Springer, 2018: 136-144.
[25] GUPTA S, RAJAPAKSE J C. Iterative consensus spectral clustering improves detection of subject and group level brain functional modules[J]. Scientific Reports, 2020, 10(1): 7590.
[26] WEN X, ZHANG H, LI G, et al. First-year development of modules and hubs in infant brain functional networks[J]. NeuroImage, 2019, 185: 222-235.
[27] NAJAFI M, MCMENAMIN B W, SIMON J Z, et al. Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions[J]. Neuroimage, 2016, 135: 92-106.
[28] GU Y, LI L, ZHANG Y, et al. The overlapping modular organization of human brain functional networks across the adult lifespan[J]. NeuroImage, 2022, 253: 119125.
[29] LI X, HU Z, WANG H. Overlapping community structure detection of brain functional network using non-negative matrix factorization[C]//LNCS 9949: Proceedings of the 23rd International Conference on Neural Information Pro-cessing, Kyoto, Oct 16-21, 2016. Cham: Springer, 2016: 140-147.
[30] LI X, HU Z, WANG H. Combining non-negative matrix factorization and sparse coding for functional brain overlapping community detection[J]. Cognitive Computation, 2018, 10: 991-1005.
[31] LI X, GAN J Q, WANG H. Collective sparse symmetric non-negative matrix factorization for identifying overlapping communities in resting-state brain functional networks[J]. NeuroImage, 2018, 166: 259-275.
[32] MIRZAEI S, SOLTANIAN-ZADEH H. Overlapping brain community detection using Bayesian tensor decomposition[J]. Journal of Neuroscience Methods, 2019, 318: 47-55.
[33] LIN Y, MA J, GU Y, et al. Intrinsic overlapping modular organization of human brain functional networks revealed by a multiobjective evolutionary algorithm[J]. NeuroImage, 2018, 181: 430-445.
[34] WEN X, CHEN W, LIN Y, et al. A maximal clique based multiobjective evolutionary algorithm for overlapping com-munity detection[J]. IEEE Transactions on Evolutionary Computation, 2016, 21(3): 363-377.
[35] YOLDEMIR B, NG B, ABUGHARBIEH R. Stable over-lapping replicator dynamics for brain community detection[J]. IEEE Transactions on Medical Imaging, 2015, 35(2): 529-538.
[36] HAN H, LI X, GAN J Q, et al. Biomarkers derived from alterations in overlapping community structure of resting-state brain functional networks for detecting Alzheimer’s disease[J]. Neuroscience, 2022, 484: 38-52.
[37] HAN H, GE S, WANG H. Prediction of brain age based on the community structure of functional networks[J]. Biomedical Signal Processing and Control, 2023, 79: 104151.
[38] MEUNIER D, LAMBIOTTE R, BULLMORE E T. Modular and hierarchically modular organization of brain networks[J]. Frontiers in Neuroscience, 2010, 4: 200.
[39] MEUNIER D, LAMBIOTTE R, FORNITO A, et al. Hier-archical modularity in human brain functional networks[J]. Frontiers in Neuroinformatics, 2009, 3: 571.
[40] ASHOURVAN A, TELESFORD Q K, VERSTYNEN T, et al. Multi-scale detection of hierarchical community architecture in structural and functional brain networks[J]. PLoS One, 2019, 14(5): e215520.
[41] VANGIMALLA R R, SREEVALSAN-NAIR J. A multiscale consensus method using factor analysis to extract modular regions in the functional brain network[C]//Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Montreal, Jul 20-24, 2020. Piscataway: IEEE, 2020: 2824-2828.
[42] SANCHEZ-RODRIGUEZ L M, ITURRIA-MEDINA Y, MOUCHES P, et al. Detecting brain network communities: considering the role of information flow and its different temporal scales[J]. NeuroImage, 2021, 225: 117431.
[43] FASKOWITZ J, ESFAHLANI F Z, JO Y, et al. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture[J]. Nature Neuroscience, 2020, 23(12): 1644-1654.
[44] FAN Y, WANG R, YI C, et al. Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification[J]. iScience, 2023, 26(5): 106575.
[45] BRAUN U, SCH?FER A, BASSETT D S, et al. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function[J]. Proceedings of the National Academy of Sciences, 2016, 113(44): 12568-12573.
[46] MUCHA P J, RICHARDSON T, MACON K, et al. Com-munity structure in time-dependent, multiscale, and multiplex networks[J]. Science, 2010, 328(5980): 876-878.
[47] MUELLER J M, PRITSCHET L, SANTANDER T, et al. Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle[J]. Network Neuroscience, 2021, 5(1): 125-144.
[48] RIZKALLAH J, BENQUET P, KABBARA A, et al. Dynamic reshaping of functional brain networks during visual object recognition[J]. Journal of Neural Engineering, 2018, 15(5): 56022.
[49] TING C, SAMDIN S B, TANG M, et al. Detecting dynamic community structure in functional brain networks across individuals: a multilayer approach[J]. IEEE Transactions on Medical Imaging, 2020, 40(2): 468-480.
[50] AL-SHAROA E, AL-KHASSAWENEH M, AVIYENTE S. Tensor based temporal and multilayer community detection for studying brain dynamics during resting state fMRI[J]. IEEE Transactions on Biomedical Engineering, 2018, 66(3): 695-709.
[51] WEN X, ZHANG D. A multi-layer random walk method for local dynamic community detection in brain functional network[C]//Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, Houston, Dec 9-12, 2021. Piscataway: IEEE, 2021: 1098-1103.
[52] WEN X, YANG M, HSU L, et al. Test-retest reliability of modular-relevant analysis in brain functional network[J]. Frontiers in Neuroscience, 2022, 16: 1000863.
[53] DIMITRIADIS S I, MESSARITAKI E, K JONES D. The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks[J]. Human Brain Mapping, 2021, 42(13): 4261-4280.
[54] PUXEDDU M G, FASKOWITZ J, SPORNS O, et al. Multi-modal and multi-subject modular organization of human brain networks[J]. NeuroImage, 2022, 264: 119673.