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Complexity
Volume 2017, Article ID 8362741, 27 pages
https://doi.org/10.1155/2017/8362741
Review Article

Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

1School of Information Science and Engineering, Central South University, Changsha 410083, China
2Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
3Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N5A9, Canada

Correspondence should be addressed to Jianxin Wang; nc.ude.usc.liam@gnawxj

Received 26 April 2017; Revised 18 September 2017; Accepted 27 September 2017; Published 22 October 2017

Academic Editor: Manlio De Domenico

Copyright © 2017 Jin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It is well known that most brain disorders are complex diseases, such as Alzheimer’s disease (AD) and schizophrenia (SCZ). In general, brain regions and their interactions can be modeled as complex brain network, which describe highly efficient information transmission in a brain. Therefore, complex brain network analysis plays an important role in the study of complex brain diseases. With the development of noninvasive neuroimaging and electrophysiological techniques, experimental data can be produced for constructing complex brain networks. In recent years, researchers have found that brain networks constructed by using neuroimaging data and electrophysiological data have many important topological properties, such as small-world property, modularity, and rich club. More importantly, many brain disorders have been found to be associated with the abnormal topological structures of brain networks. These findings provide not only a new perspective to explore the pathological mechanisms of brain disorders, but also guidance for early diagnosis and treatment of brain disorders. The purpose of this survey is to provide a comprehensive overview for complex brain network analysis and its applications to brain disorders.