TY - JOUR
T1 - Hub recognition for brain functional networks by using multiple-feature combination
AU - Jiao, Zhuqing
AU - Xia, Zhengwang
AU - Cai, Min
AU - Zou, Ling
AU - Xiang, Jianbo
AU - Wang, Shuihua
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/7
Y1 - 2018/7
N2 - Hubs in complex networks can greatly influence the integration of network functions, and recognition of hubs helps to better understand the interaction between pairs of network nodes. This paper proposes a new hub recognition method with multiple-feature combination for the brain functional networks constructed by resting-state functional Magnetic Resonance Imaging (fMRI). Three single-feature methods, including degree centrality, betweenness centrality and closeness centrality, are used to calculate hubs of the brain functional network separately. For reordering the nodes, a composite equation is constructed based on the three recognition parameters. Network vulnerability and average shortest path length are used to evaluate the importance of the hubs recognized by above four methods. Experimental result demonstrates that, the hubs recognized by multiple-feature combination have more significant differences from ordinary nodes than those by single-feature methods, and they have an important impact on the global efficiency of brain functional networks.
AB - Hubs in complex networks can greatly influence the integration of network functions, and recognition of hubs helps to better understand the interaction between pairs of network nodes. This paper proposes a new hub recognition method with multiple-feature combination for the brain functional networks constructed by resting-state functional Magnetic Resonance Imaging (fMRI). Three single-feature methods, including degree centrality, betweenness centrality and closeness centrality, are used to calculate hubs of the brain functional network separately. For reordering the nodes, a composite equation is constructed based on the three recognition parameters. Network vulnerability and average shortest path length are used to evaluate the importance of the hubs recognized by above four methods. Experimental result demonstrates that, the hubs recognized by multiple-feature combination have more significant differences from ordinary nodes than those by single-feature methods, and they have an important impact on the global efficiency of brain functional networks.
KW - Brain functional networks
KW - Functional Magnetic Resonance Imaging (fMRI)
KW - Hub recognition
KW - Multiple-feature combination
UR - http://www.scopus.com/inward/record.url?scp=85040667473&partnerID=8YFLogxK
U2 - 10.1016/j.compeleceng.2018.01.010
DO - 10.1016/j.compeleceng.2018.01.010
M3 - Article
AN - SCOPUS:85040667473
SN - 0045-7906
VL - 69
SP - 740
EP - 752
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
ER -