Hub recognition for brain functional networks by using multiple-feature combination

• Published in: Computers & electrical engineering Vol. 69; pp. 740 - 752
• Main Authors: Jiao, Zhuqing, Xia, Zhengwang, Cai, Min, Zou, Ling, Xiang, Jianbo, Wang, Shuihua
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.07.2018; Elsevier BV
• Subjects: Brain; Brain functional networks; Brain research; Data acquisition systems; Design of experiments; Feature recognition; Functional Magnetic Resonance Imaging (fMRI); Hub recognition; Hubs; Magnetic resonance imaging; Mathematical analysis; Multiple-feature combination; Network hubs; Networks; Neural networks; NMR; Nodes; Nuclear magnetic resonance; Shortest-path problems; Functional Magnetic Resonance Imaging (fMRI); Hub recognition; Multiple-feature combination; Brain functional networks
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2018.01.010

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.