Module dividing for brain functional networks by employing betweenness efficiency

  • Zhuqing JiaoEmail author
  • Min Cai
  • Xuelian Ming
  • Yin Cao
  • Ling ZouEmail author
  • Shui-Hua WangEmail author


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.


Brain 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).


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringChangzhou UniversityChangzhouChina
  2. 2.Department of NeurologyChangzhou Second People’s Hospital Affiliated to Nanjing Medical UniversityChangzhouChina
  3. 3.School of Architecture Building and Civil EngineeringLoughborough UniversityLoughboroughUK
  4. 4.Department of InformaticsUniversity of LeicesterLeicesterUK

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