加权Fast Newman模块化算法在人脑结构网络中的应用

doi:10.11772/j.issn.1001-9081.2016.12.3347

计算机应用 ›› 2016, Vol. 36 ›› Issue (12): 3347-3352.DOI: 10.11772/j.issn.1001-9081.2016.12.3347

加权Fast Newman模块化算法在人脑结构网络中的应用

夏一丹, 王彬, 董迎朝, 刘辉, 熊新

昆明理工大学信息工程与自动化学院, 昆明 650504

收稿日期:2016-05-30 修回日期:2016-08-02 出版日期:2016-12-10 发布日期:2016-12-08
通讯作者: 王彬
作者简介:夏一丹(1994-),女,河南信阳人,硕士研究生,主要研究方向:人脑网络特性分析、图像处理;王彬(1977-),女,黑龙江哈尔滨人,副教授,博士,CCF会员,主要研究方向:人脑网络的动态特性分析、工业实时控制、实时数据分析、图像处理;董迎朝(1990-),男,河北唐山人,硕士研究生,主要研究方向:人脑网络重构的集群运算平台搭建、结合图形处理器技术的人脑网络特征提取;刘辉(1984-),男,陕西蒲城人,副教授,博士,CCF会员,主要研究方向:实时计算机控制、图像处理、模式识别;熊新(1977-),男,安徽金寨人,高级工程师,硕士,主要研究方向:电机控制、工业实时控制。
基金资助:
国家自然科学基金资助项目（61263017）；云南省自然科学基金资助项目（2011FZ060）。

Application of weighted Fast Newman modularization algorithm in human brain structural network

XIA Yidan, WANG Bin, DONG Yingzhao, LIU Hui, XIONG Xin

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 650504, China

Received:2016-05-30 Revised:2016-08-02 Online:2016-12-10 Published:2016-12-08
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61263017), the Provincial Natural Science Foundation of Yunnan (2011FZ060).

摘要/Abstract

摘要： 针对二值人脑结构网络的模块化方法不足以反映复杂的人脑生理特征这一问题，提出一种基于Fast Newman二值算法的加权脑网络模块化算法。该算法以凝聚节点的层次聚类思想为基础，以脑网络中单个脑区节点的权重值和脑网络总权重值为主要依据构建加权模块度评价指标，并将其增量作为度量值来确定加权脑网络中节点的合并从而实现模块划分。将该算法应用于60个健康人的组平均数据中的实验结果显示，与二值人脑网络模块化结果相对比，所提算法得到的模块度提高了28%，并且模块内部和模块外部的特征区分更加明显，所得到的人脑模块也更符合已知的人脑生理特性；而与现有的两种加权模块化算法实验对比结果表明，所提算法在合理划分人脑网络模块结构的同时也小幅提高了模块度。

关键词: 模块结构, Fast Newman算法, 加权网络, 模块度, 人脑结构网络

Abstract: The binary brain network modularization is not enough to describe physiological features of human brain. In order to solve the problem, a modularization algorithm for weighted brain network based on Fast Newman binary algorithm was presented. Using the hierarchical clustering idea of condensed nodes as the base, a weighted modularity indicator was built with the main bases of single node's weight and entire network's weight. Then the modularity increment was taken as the testing index to decide which two nodes should be combined in weighted brain network and realize module partition. The proposed method was applied to detect the modular structure of the group average data of 60 healthy people. The experiment results showed that, compared with the modular structure of the binary brain network, the brain network modularity of the proposed method was increased by 28% and more significant difference between inside and outside of modules could be revealed. Moreover, the modular structure found by the proposed method is more consistent with the physiological characteristics of human brain. Compared with the other two existing weighted modular algorithms, the proposed method can also slightly improve the modularity and guarantee a reasonable identification for human brain modular structure.

Key words: modular structure, Fast Newman algorithm, weighted network, modularity, human brain structural network

中图分类号:

夏一丹, 王彬, 董迎朝, 刘辉, 熊新. 加权Fast Newman模块化算法在人脑结构网络中的应用[J]. 计算机应用, 2016, 36(12): 3347-3352.

XIA Yidan, WANG Bin, DONG Yingzhao, LIU Hui, XIONG Xin. Application of weighted Fast Newman modularization algorithm in human brain structural network[J]. Journal of Computer Applications, 2016, 36(12): 3347-3352.

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] SPORNS O, TONONI G, KOTTER R. The human connectome:a structural description of the human brain[J]. Plos Computational Biology, 2005, 1(4):e42.
[3] SPORNS O. Discovering the Human Connectome[M]. Cambridge, MA:MIT Press, 2012:3.
[4] RUBINOV M, SPORNS O. Complex network measures of brain connectivity:uses and interpretations[J]. Neuroimage, 2010, 52(3):1059-1069.
[5] KERNⅡGHAN B W, LIN S. An efficient heuristic procedure for partitioning graphs[J]. Bell Labs Technical Journal, 1970, 49(2):291-307.
[6] FIEDLER M. Algebraic connectivity of graphs[J]. Czechoslovak Mathematical Journal, 1973, 23(98):298-305.
[7] POTHEN A, SIMON H D, LIOU K P. Partitioning sparse matrices with eigenvectors of graphs[J]. SIAM Journal on Matrix Analysis & Applications, 1990, 11(3):430-452.
[8] GIRVAN M, NEWMAN M E J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2001, 99(12):7821-7826.
[9] NEWMAN M E J. Fast algorithm for detecting community structure in networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2004, 69(6 Pt 2):066133.
[10] CLAUSET A, NEWMAN M E J, MOORE C. Finding community structure in very large networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2004, 70(Pt 2):066111.
[11] MEUNIER D, ACHARD S, MORCON A, et al. Age-related changes in modular organization of human brain functional networks[J]. Neuroimage, 2009, 44(3):715-23.
[12] HE Y, WANG J H, WANG W L, et al. Uncovering intrinsic modular organization of spontaneous brain activity in humans[J]. Plos One, 2009, 4(4):e5226.
[13] HAGMANN P, CAMMOUN L, GIGANDET X, et al. Mapping the structural core of human cerebral cortex[J]. Plos Biology, 2008, 6(7):e159.
[14] VAN DEN HEUVEL M P, SPORNS O. Rich-club organization of the human connectome[J]. Journal of Neuroscience:the Official Journal of the Society for Neuroscience, 2011, 31(44):15775-15786.
[15] DADUCCI A, GERHARD S, GRIFFA A, et al. The connectome mapper:an open-source processing pipeline to map connectomes with MRI[J]. Plos One, 2012, 7(12):e48121.
[16] FISCHL B, VAN DER KOUWE A, DESTRIEUX C, et al. Automatically parcellating the human cerebral cortex[J]. Cerebral Cortex, 2004, 14(1):11-22.
[17] WEDEEN V J, DAVIS T L, LAUTRUP B E, et al. Diffusion anisotropy and white matter tracts[J]. Neuroimage, 1996, 3(3):S146.
[18] WATTS D J, STROGATZ S H. Collective dynamics of "small-world" networks[J]. Nature, 1998, 393(6684):440-442.
[19] BLONDEL V D, GUILLAUME J-L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics:Theory and Experiment, 2008(10):P10008.
[20] NEWMAN M E J. Modularity and community structure in networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(23):8577-8582.

加权Fast Newman模块化算法在人脑结构网络中的应用

Application of weighted Fast Newman modularization algorithm in human brain structural network

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

编辑推荐

Metrics

[1]	李萍, 汪芬, 陈祺东, 孙俊. 求解多目标社区发现问题的离散化随机漂移粒子群优化算法[J]. 计算机应用, 2021, 41(3): 803-811.
[2]	盛俊, 李斌, 陈崚. 基于模块度和标签传递的推荐算法[J]. 计算机应用, 2020, 40(9): 2606-2612.
[3]	付立东, 郝伟, 李丹, 李凡. 基于共邻节点相似度的社区划分算法[J]. 计算机应用, 2019, 39(7): 2024-2029.
[4]	李雷, 闫光辉, 杨绍文, 张海韬. 基于孤立节点分离策略的改进鲁汶算法[J]. 计算机应用, 2017, 37(4): 970-974.
[5]	王天宏, 武星, 兰旺森. 改进的基于局部模块度的社团划分算法[J]. 计算机应用, 2016, 36(5): 1296-1301.
[6]	王班, 马润年, 王刚, 陈波. 改进的加权网络节点重要性评估的互信息方法[J]. 计算机应用, 2015, 35(7): 1820-1823.
[7]	陈骁, 黄曙光, 秦李. 基于微博转发的社交网络模型[J]. 计算机应用, 2015, 35(3): 638-642.
[8]	张嫱嫱, 黄廷磊, 张银明. 基于聚类分析的二分网络社区挖掘[J]. 计算机应用, 2015, 35(12): 3511-3514.
[9]	水超李慧. 基于“次中心”的社区结构探寻算法[J]. 计算机应用, 2012, 32(08): 2154-2158.
[10]	王艳群李海芳郭浩陈俊杰. 静息态脑功能网络的社团结构研究[J]. 计算机应用, 2012, 32(07): 2044-2048.
[11]	马杰良赵岳. TF法则嵌入机制的动态局域加权网络模型[J]. 计算机应用, 2012, 32(05): 1240-1243.
[12]	史伟赵政薛桂香. 基于节点类型的复杂网络模块探测算法[J]. 计算机应用, 2008, 28(10): 2590-2593.