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Dynamic functional connectivity correlates of mental workload

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Abstract

Tasks with high mental workload often involve higher cognitive functions of the human brain and complex information flow involving multiple brain regions. However, the dynamics of functional connectivity between brain regions during high mental workload have not been well-studied. We use an analysis approach designed to find repeating network states from gamma-band phase locking value networks built from electroencephalograph data collected while participants engaged in tasks with different levels of mental workload. First, we define network states as results of clustering based on the closeness centrality node-level network metric. Second, we found that the transition between network states is not completely random. And, we found significant differences in network state statistics between low and high mental workload. Third, we found significant correlation between features calculated from the network state sequence and behavioral performance. Finally, we use dynamic network features as input to a support vector machine classifier and obtain cross-participant average decoding accuracy of 69.6%. Our methods provide a new perspective for analyzing the dynamics of electroencephalograph signals and have potential application to the decoding of mental workload level.

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Acknowledgements

The authors would like to thank Yiming Ding and Yuanxiang Jiang for their useful discussions and the reviewers for their constructive feedback.

Funding

This work was supported in part by the STI 2030-Major Projects of the Ministry of Science and Technology of China (2021ZD0200407), the National Key Research and Development Program of China (2020YFC0832402), and the Innovation Team Project of Guangdong Provincial Department of Education (2021KCXTD014).

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Authors and Affiliations

  1. International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China

    Zhongming Xu & Zengru Di

  2. Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China

    Zhongming Xu & Zheng Li

  3. School of Systems Science, Beijing Normal University, Beijing, 100875, China

    Zhongming Xu

  4. Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China

    Jing Huang

  5. Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China

    Jing Huang

  6. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China

    Chuancai Liu, Qiankun Zhang, Heng Gu & Zheng Li

  7. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Xiaoli Li

  8. Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China

    Xiaoli Li

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  1. Zhongming Xu
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Correspondence to Zheng Li.

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The studies involving human participants were reviewed and approved by the institutional human research ethics review committee of the State Key Laboratory of Neuroscience and Learning at Beijing Normal University (ID number: CNL_A_0010_002, approval date November, 2019). The participants provided their written informed consent to participate in this study.

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Xu, Z., Huang, J., Liu, C. et al. Dynamic functional connectivity correlates of mental workload. Cogn Neurodyn (2024). https://doi.org/10.1007/s11571-024-10101-4

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