Brain Topography

, Volume 32, Issue 1, pp 1–16 | Cite as

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

  • Xiaoyun LiangEmail author
  • Chun-Hung Yeh
  • Alan Connelly
  • Fernando Calamante
Original Paper


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.


Rich club h-Degree Diffusion MRI Fiber tractography Structural connectome 



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

Supplementary material

10548_2018_661_MOESM1_ESM.docx (2.3 mb)
Supplementary material 1 (DOCX 2370 KB)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Florey Institute of Neuroscience and Mental HealthHeidelbergAustralia
  2. 2.The Florey Department of Neuroscience and Mental Health MedicineUniversity of MelbourneMelbourneAustralia
  3. 3.Department of Medicine, Austin Health and Northern HealthUniversity of MelbourneMelbourneAustralia
  4. 4.Sydney Imaging and School of Aerospace, Mechanical and Mechatronic Engineering (Faculty of Engineering & Information Technologies)University of SydneySydneyAustralia

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