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作者:

Liu, Jinduo (Liu, Jinduo.) | Ji, Junzhong (Ji, Junzhong.) (学者:冀俊忠) | Zhang, Aidong (Zhang, Aidong.) | Liang, Peipeng (Liang, Peipeng.)

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EI Scopus

摘要:

Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization (ACO) with BN method, named as ACOEC. In the proposed algorithm, a brain effective connectivity network is first mapped onto an ant, and then the ant colony optimization by simulating real ants looking for food is employed to construct network structures and finally an ant with the highest score is obtained as the optimal solution. The experimental results on simulated and real fMRI data sets show that the new method can not only accurately identify the connections and directions of the brain networks, but also quantitatively describe the connection strength of the brain networks, which has a good clinical application prospects. © 2016 IEEE.

关键词:

Ant colony optimization Bayesian networks Bioinformatics Functional neuroimaging Learning algorithms Learning systems Magnetic resonance imaging

作者机构:

  • [ 1 ] [Liu, Jinduo]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Ji, Junzhong]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Zhang, Aidong]Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, United States
  • [ 4 ] [Liang, Peipeng]Beijing Key Lab of MRI and Brain Informatics, Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China

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年份: 2016

页码: 360-367

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 10

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