Module dividing for brain functional networks by employing betweenness efficiency
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Traditional researches assume that brain functional networks are static during the entire scanning process of functional magnetic resonance image (fMRI) in the resting state. However, it is not difficult to ignore the dynamic interaction patterns of brain regions that essentially change across time. In this study, we take the internal weight information of the brain functional network as a calculation condition of module dividing for brain functional networks. The concept of betweenness efficiency is firstly proposed to improve Girvan-Newman (GN) algorithm for a better module dividing result, and the maximum modularity is used as a criterion to classify the brain functional network modules of normal subjects. The effect of the improved method is verified by controlling subjects, parameters, environment and other conditions. Then, the improved method was utilized to separate the modules in brain functional networks of normal subjects, and the template was used to divide the functional network of Alzheimer’s disease (AD) patients and mild cognitive impairment (MCI) sufferers. The shortest path length of each module is calculated, and the experimental results are compared with the original GN and weighted GN algorithm improved by Newman. The experimental results demonstrate that, the maximum modularity of the improved method is higher while the dividing effect is better under the same conditions. Meanwhile, the conclusion is consistent with the existing research results when the proposed method is applicable to the analysis of the shortest path length. These results illustrate the viewpoint that the proposed method of module dividing is feasible in the analysis of modular structure of brain functional network.
KeywordsBrain functional network Betweenness efficiency Girvan-Newman algorithm Modularity Module dividing
This work was supported by the National Natural Science Foundation of China (Grant No. 51877013, No. 51307010), the Natural Science Foundation of Jiangsu Province (Grant No. BK20181463), the Key Research and Development Plan of Jiangsu Science and Technology Department (Grant No. BE2018638), the Science and Technology Program of Changzhou City (Grant No. CE20185038) and the University Natural Science Research Program of Jiangsu Province (Grant No. 17KJB510003, No. 13KJB510002).
- 19.Laurienti P, Hugenschmidt C, Hayasaka S (2009) Modularity maps reveal community structure in the resting human brain. Nature Publishing GroupGoogle Scholar
- 22.Li XJ, Zhang P, Di ZR, Fan W (2008) Community structure in complex networks. J Complex Syst Complexity 5(3):19–42Google Scholar
- 34.Stam CJ, de Haan W, Daffertshofer A, Jones BF, Manshanden I, van Cappellen van Walsum AM, Montez T, Verbunt JPA, de Munck JC, van Dijk BW, Berendse HW, Scheltens P (2009) Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain 132(Pt1):213–224CrossRefGoogle Scholar
- 36.Sun JF, Hong XF, Tong SB (2011) Research progress of complex brain networks-structure, function, calculation and application. J Complex Syst Complexity 7(4):74–90Google Scholar
- 40.Wang JH, Zuo X, He Y (2010) Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci 4:16Google Scholar
- 41.Wang KC, Wu GB, Hou X, Wei DT, Liu HS, Qiu J (2016) Segmentation and application of functional network from group to individual. Sci Bull 61(27):3022–3035Google Scholar
- 42.Wang X, Ren YS, Zhang WS (2017) Multi-task fused lasso method for constructing dynamic functional brain network of resting-state fMRI. J Image Graphics 22(7):0978–0987Google Scholar
- 57.Zhao XW, Yan JZ, Liang PP (2016) Human brain function partitioning for fMRI data. Chin Sci Bull 61(18):2035–2052Google Scholar