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Module dividing for brain functional networks by employing betweenness efficiency

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. School of Information Science and Engineering, Changzhou University, Changzhou, 213164, China

    Zhuqing Jiao, Min Cai, Xuelian Ming & Ling Zou

  2. Department of Neurology, Changzhou Second People’s Hospital Affiliated to Nanjing Medical University, Changzhou, 213003, China

    Yin Cao

  3. School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Shui-Hua Wang

  4. Department of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, UK

    Shui-Hua Wang

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  1. Zhuqing Jiao
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  5. Ling Zou
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  6. Shui-Hua Wang
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Correspondence to Zhuqing Jiao, Ling Zou or Shui-Hua Wang.

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Jiao, Z., Cai, M., Ming, X. et al. Module dividing for brain functional networks by employing betweenness efficiency. Multimed Tools Appl 79, 15253–15271 (2020). https://doi.org/10.1007/s11042-018-7125-8

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  • DOI: https://doi.org/10.1007/s11042-018-7125-8

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