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Brain Connectivity: Exploring from a High-Level Topological Perspective

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12889))

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Abstract

How invariant structural architecture of brain coupling with variant functionality is still unclear in neuroscience. The previous exploration of relationships between large-scale structural and functional brain networks mainly focused on whole or partial statistical correlation, ignoring network context information, such as network topology structure. Here we applied a network representation learning approach to create high-order representations of structural or functional networks while preserving network context information for studying the function-structure coupling of the brain at topological subnetwork levels. We found that the structural and functional network obtained from the network representation learning method was more stable and more tightly coupled than those from the conventional correlation method, primarily distributed in high-order cognitive networks. Application on schizophrenia patients showed decoupling on the default-mode network, dorsal attention network, executive control network, and salience network, as well as the over-coupling on the sensorimotor network, compared with healthy controls. Overall, network representation learning can more effectively capture the higher-order coupling between brain structure and function and provides a good technical means for us to study mental illness.

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Acknowledgments

This study was supported by the Key Project of Research and Development of Ministry of Science and Technology (2018AAA0100705), and the Natural Science Foundation of China (61533006, U1808204, 62036003, 81771919).

Author information

Authors and Affiliations

  1. The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    Wei Sheng, Liang Li, Shaoqiang Han, Yunshuang Fan, Chong Wang, Qin Tang, Yuyan Chen & Huafu Chen

  2. MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China

    Wei Sheng, Liang Li, Shaoqiang Han, Yunshuang Fan, Chong Wang, Qin Tang, Yuyan Chen, Qian Cui & Huafu Chen

Authors
  1. Wei Sheng
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  2. Liang Li
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  9. Huafu Chen
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Corresponding author

Correspondence to Huafu Chen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Sheng, W. et al. (2021). Brain Connectivity: Exploring from a High-Level Topological Perspective. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-87358-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87357-8

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