Please use this identifier to cite or link to this item:
|Title:||Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes.|
|Authors:||Liang, Xiaoyun;Yeh, Chun-Hung;Connelly, Alan;Calamante, Fernando|
|Affiliation:||The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. firstname.lastname@example.org..|
The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
The Florey Department of Neuroscience and Mental Health Medicine, University of Melbourne, Melbourne, VIC, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, VIC, Australia
Sydney Imaging and School of Aerospace, Mechanical and Mechatronic Engineering (Faculty of Engineering & Information Technologies), University of Sydney, Sydney, Australia
|Citation:||Brain topography 2018; online first: 3 July|
|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.|
|Appears in Collections:||Journal articles|
Files in This Item:
There are no files associated with this item.
Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.