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Impacts of Working Memory Training on Brain Network Topology

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

A variety of network analysis methods that can reveal the neural mechanism underlying the course of dealing information in the brain by characterizing the topology and properties of brain networks have been applied to investigate the complexity of brain activities.. Working memory refers to the maintaining and handling of information in high-level cognition. It has been demonstrated that working memory performances can be enhanced by training. However, how working memory training affects the brain network topology and behavioral performance remains unclear. In this study, independent component analysis and graph theory were applied to the study of brain networks during real time fMRI based working memory training. The results showed that the training not only recruited the central execution network, the default-mode network, and the salience network, but also exerted lasting effects on the brain minimum spanning tree structure. These results demonstrated that the organization and working pattern of brain networks were altered by the training and provide new insights into the neural mechanisms underlying working memory training.

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Acknowledgments

This study is supported by the Funds of National Natural Science Foundation of China (grant number 61473044), and International Cooperation and Exchange of the National Natural Science Foundation of China (grant number 61210001).

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Authors and Affiliations

  1. School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Dongping Zhao

  2. College of Information Science and Technology, Beijing Normal University, Beijing, China

    Qiushi Zhang, Li Yao & Xiaojie Zhao

  3. Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA

    Qiushi Zhang

Authors
  1. Dongping Zhao
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  2. Qiushi Zhang
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  3. Li Yao
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  4. Xiaojie Zhao
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Corresponding author

Correspondence to Xiaojie Zhao .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Zhao, D., Zhang, Q., Yao, L., Zhao, X. (2017). Impacts of Working Memory Training on Brain Network Topology. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_67

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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