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Rich club characteristics of dynamic brain functional networks in resting state

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Abstract

Conventional brain functional networks are constructed by extracting the entire time series from functional Magnetic Resonance Imaging (fMRI). Yet such a method is easy to ignore the dynamic interaction patterns of brain regions that essentially change across time. In this study, we analyze the functional connectivity characteristics of Rich Club in resting-state brain functional networks, and study the dynamic functional differences of core brain regions at different time periods. First, the time series is extracted from resting-state fMRI to construct a dynamic brain functional network. Then, Rich Clubs of different time periods are determined by the Rich Club coefficients. In particular, the efficiency of each Rich Club is calculated to examine the influences of the Rich Connections, Feeder Connections and Local Connections. Finally, the node degree, clustering coefficient and efficiency for Rich Club nodes are calculated to quantify the dynamic processes of Rich Clubs, and the functional connectivity of Rich Clubs are compared with those of the functional networks constructed by the entire fMRI time series. Experimental results demonstrate that the distribution of Rich Clubs in the dynamic brain functional network is consistent with that from the entire fMRI time series, while the composition and functional connectivity of Rich Club dynamically change across time. Moreover, Rich connection and Local connection in the brain functional networks show a significant correlation with the efficiency of Rich Club, and the local and the global efficiency of Rich Clubs are greater than that of the global network. These results further illustrate the viewpoint that Rich Clubs have significant influence on the functional characteristics of global brain functional networks.

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Acknowledgements

The authors would like to thank the reviewers and the editors for their valuable comments and suggestions on improving this paper. This work is supported by the National Natural Science Foundation of China (No. 51307010), the Natural Science Foundation of Jiangsu Province and the University Natural Science Research Program of Jiangsu Province (No. 17KJB510003).

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Authors and Affiliations

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

    Zhuqing Jiao, Huan Wang, Min Cai & Ling Zou

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

    Yin Cao

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

    Shuihua Wang

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  1. Zhuqing Jiao
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Contributions

ZJ and HW conceived of the study and contributed to the manuscript. SW contributed to the resting-state functional networks analysis. YC and LZ contributed to the fMRI experimental design and analysis. All authors read and approved the final manuscript.

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Correspondence to Ling Zou or Shuihua Wang.

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Jiao, Z., Wang, H., Cai, M. et al. Rich club characteristics of dynamic brain functional networks in resting state. Multimed Tools Appl 79, 15075–15093 (2020). https://doi.org/10.1007/s11042-018-6424-4

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