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01-04-2024 | Research Article

Dynamic functional connectivity correlates of mental workload

Authors: Zhongming Xu, Jing Huang, Chuancai Liu, Qiankun Zhang, Heng Gu, Xiaoli Li, Zengru Di, Zheng Li

Published in: Cognitive Neurodynamics

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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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Appendix
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Metadata
Title
Dynamic functional connectivity correlates of mental workload
Authors
Zhongming Xu
Jing Huang
Chuancai Liu
Qiankun Zhang
Heng Gu
Xiaoli Li
Zengru Di
Zheng Li
Publication date
01-04-2024
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-024-10101-4