Finding Community Modules of Brain Networks Based on PSO with Uniform Design

被引:5
|
作者
Zhang, Jie [1 ,2 ,3 ]
Tang, Lingkai [2 ,3 ]
Liao, Bo [2 ,3 ]
Zhu, Xiaoshu [1 ]
Wu, Fang-Xiang [2 ,3 ]
机构
[1] Yulin Normal Univ, Guangxi Coll & Univ Key Lab Complex Syst Optimiza, Sch Comp Sci & Engn, Yulin 537000, Guangxi, Peoples R China
[2] Univ Saskatchewan, Div Biomed Engn, Sakatoon, SK S7N 5A9, Canada
[3] Univ Saskatchewan, Dept Mech Engn, Sakatoon, SK S7N 5A9, Canada
基金
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词
PARTICLE SWARM; EVOLUTIONARY ALGORITHM; COMPLEX NETWORKS; OPTIMIZATION; ORGANIZATION; CONNECTIVITY; MODULARITY;
D O I
10.1155/2019/4979582
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The brain has the most complex structures and functions in living organisms, and brain networks can provide us an effective way for brain function analysis and brain disease detection. In brain networks, there exist some important neural unit modules, which contain many meaningful biological insights. It is appealing to find the neural unit modules and obtain their affiliations. In this study, we present a novel method by integrating the uniform design into the particle swarm optimization to find community modules of brain networks, abbreviated as UPSO. The difference between UPSO and the existing ones lies in that UPSO is presented first for detecting community modules. Several brain networks generated from functional MRI for studying autism are used to verify the proposed algorithm. Experimental results obtained on these brain networks demonstrate that UPSO can find community modules efficiently and outperforms the other competing methods in terms of modularity and conductance. Additionally, the comparison of UPSO and PSO also shows that the uniform design plays an important role in improving the performance of UPSO.
引用
收藏
页数:14
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