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Brain Science Advances 2020, 6(2): 120-131 https://doi.org/10.26599/BSA.2020.9050008
Research Article | Open Access | Issue | Published: 31 August 2020

The effect of fatigue on brain connectivity networks

Show Author's Information Shangen Zhang1,  Jingnan Sun2,  Xiaorong Gao2( )
Department of School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
Keywords:
fatigue, brain connection analysis, steady-state visual evoked potential (SSVEP), noise, rapid serial visual presentation
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Zhang S, Sun J, Gao X. The effect of fatigue on brain connectivity networks. Brain Science Advances, 2020, 6(2): 120-131. https://doi.org/10.26599/BSA.2020.9050008
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Abstract Full text About this article

In the fatigue state, the neural response characteristics of the brain might be different from those in the normal state. Brain functional connectivity analysis is an effective tool for distinguishing between different brain states. For example, comparative studies on the brain functional connectivity have the potential to reveal the functional differences in different mental states. The purpose of this study was to explore the relationship between human mental states and brain control abilities by analyzing the effect of fatigue on the brain response connectivity. In particular, the phase-scrambling method was used to generate images with two noise levels, while the N-back working memory task was used to induce the fatigue state in subjects. The paradigm of rapid serial visual presentation (RSVP) was used to present visual stimuli. The analysis of brain connections in the normal and fatigue states was conducted using the open-source eConnectome toolbox. The results demonstrated that the control areas of neural responses were mainly distributed in the parietal region in both the normal and fatigue states. Compared to the normal state, the brain connectivity power in the parietal region was significantly weakened under the fatigue state, which indicates that the control ability of the brain is reduced in the fatigue state.


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About this article

The effect of fatigue on brain connectivity networks

Show Author's information Shangen Zhang1, Jingnan Sun2, Xiaorong Gao2( )
Department of School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China

Abstract

In the fatigue state, the neural response characteristics of the brain might be different from those in the normal state. Brain functional connectivity analysis is an effective tool for distinguishing between different brain states. For example, comparative studies on the brain functional connectivity have the potential to reveal the functional differences in different mental states. The purpose of this study was to explore the relationship between human mental states and brain control abilities by analyzing the effect of fatigue on the brain response connectivity. In particular, the phase-scrambling method was used to generate images with two noise levels, while the N-back working memory task was used to induce the fatigue state in subjects. The paradigm of rapid serial visual presentation (RSVP) was used to present visual stimuli. The analysis of brain connections in the normal and fatigue states was conducted using the open-source eConnectome toolbox. The results demonstrated that the control areas of neural responses were mainly distributed in the parietal region in both the normal and fatigue states. Compared to the normal state, the brain connectivity power in the parietal region was significantly weakened under the fatigue state, which indicates that the control ability of the brain is reduced in the fatigue state.

Keywords: fatigue, brain connection analysis, steady-state visual evoked potential (SSVEP), noise, rapid serial visual presentation

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Publication history

Received: 24 February 2020
Revised: 23 March 2020
Accepted: 24 March 2020
Published: 31 August 2020
Issue date: June 2020

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© The authors 2020

Acknowledgements

This work was supported by the Key R&D Program of Guangdong Province, China (No. 2018B030339001); National Key R&D Program of China (No. 2017YFB1002505); and National Natural Science Foundation of China (No. 61431007).

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