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Abstract #1801

Robust identification of rich-club organization in weighted and dense structural connectomes

Xiaoyun Liang1, Chun-Hung Yeh1, Robert Elton Smith1, Alan Connelly1, and Fernando Calamante1

1Florey Institute of Neuroscience and Mental Health, Melbourne, Australia

Rich-club organizations, characterizing the higher-level topology of the brain network, has been commonly identified from structural connectomes constructed using DTI based on network degrees. This analysis can however be compromised by the following issues: (i) DTI limitations in resolving crossing-fibers; and (ii) the original degree-metric based approach is unsuitable for highly-connected connectomes, because it leads to nodes with indistinguishably high degrees. Importantly, increasing evidence suggests that brain connectomes could be very dense. To address these issues, we propose a robust framework by: (i) applying advanced-tractography to construct connectomes; and (ii) developing a h-degree based method, RICHER, to identify rich-club organization.

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