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Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes

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Abstract

The human brain is a complex network, in which some brain regions, denoted as ‘hub’ regions, play critically important roles. Some of these hubs are highly interconnected forming a rich-club organization, which has been identified based on the degree metric from structural connectomes constructed using diffusion tensor imaging (DTI)-based fiber tractography. However, given the limitations of DTI, the yielded structural connectomes are largely compromised, possibly affecting the characterization of rich-club organizations. Recent progress in diffusion MRI and fiber tractography now enable more reliable but also very dense structural connectomes to be achieved. However, while the existing rich-club analysis method is based on weighted networks, it is essentially built upon degree metric and, therefore, not suitable for identifying rich-club organizations from such dense networks, as it yields nodes with indistinguishably high degrees. Therefore, we propose a novel method, i.e. Rich-club organization Identification using Combined H-degree and Effective strength to h-degree Ratio (RICHER), to identify rich-club organizations from dense weighted networks. Overall, it is shown that more robust rich-club organizations can be achieved using our proposed framework (i.e., state-of-the-art fiber tractography approaches and our proposed RICHER method) in comparison to the previous method focusing on weighted networks based on degree, i.e., RC-degree. Furthermore, by simulating network attacks in 3 ways, i.e., attack to non-rich-club/non-rich-club edges (NRC2NRC), rich-club/non-rich-club edges (RC2NRC), and rich-club/rich-club edges (RC2RC), brain network damage consequences have been evaluated in terms of global efficiency (GE) reductions. As expected, significant GE reductions have been detected using our proposed framework among conditions, i.e., NRC2NRC < RC2NRC, NRC2NRC < RC2RC and RC2NRC < RC2RC, which however have not been detected otherwise.

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Notes

  1. Although rich-club formation could alternatively be present as a disassortative network, i.e., hubs tend to be more likely to be connected to low-degree nodes (Colizza et al. 2006), this is unlikely the case in the human brain network which has been proposed to be an assortative network (van den Heuvel and Sporns 2011) and therefore will not be considered in this study.

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Acknowledgements

We thank Dr. Robert Elton Smith (Florey Institute of Neuroscience and Mental Health) for very helpful discussions and advice. We are grateful to the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government’s Operational Infrastructure Support Program for their support. The authors also acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Florey Node.

Funding

Funding was provided by National Health and Medical Research Council (Grant Nos. 1091593 and APP1117724).

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Author notes

  1. Fernando Calamante

    Present address: Sydney Imaging and School of Aerospace, Mechanical and Mechatronic Engineering (Faculty of Engineering & Information Technologies), University of Sydney, Sydney, Australia

  2. Xiaoyun Liang and Chun-Hung Yeh have contributed equally to this work.

Authors and Affiliations

  1. The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia

    Xiaoyun Liang, Chun-Hung Yeh, Alan Connelly & Fernando Calamante

  2. The Florey Department of Neuroscience and Mental Health Medicine, University of Melbourne, Melbourne, VIC, Australia

    Alan Connelly & Fernando Calamante

  3. Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, VIC, Australia

    Alan Connelly & Fernando Calamante

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  1. Xiaoyun Liang
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  2. Chun-Hung Yeh
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Correspondence to Xiaoyun Liang.

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Handling Editor: Fabrizio De Vico Fallani.

Appendix: Labelled Cortical and Subcortical Regions of Interest

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Liang, X., Yeh, CH., Connelly, A. et al. Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes. Brain Topogr 32, 1–16 (2019). https://doi.org/10.1007/s10548-018-0661-8

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