Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eng.2018.11.025
Title: Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome
Authors: Qi, P.
Ru, H.
Gao, L.
Zhang, X.
Zhou, T.
Tian, Y.
Thakor, N. 
Bezerianos, A. 
Li, J.
Sun, Y. 
Keywords: Brain network
Functional connectivity
Graph theoretical analysis
Mental fatigue
Issue Date: 2019
Publisher: Elsevier Ltd
Citation: Qi, P., Ru, H., Gao, L., Zhang, X., Zhou, T., Tian, Y., Thakor, N., Bezerianos, A., Li, J., Sun, Y. (2019). Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome. Engineering 5 (2) : 276-286. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eng.2018.11.025
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand; it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future. © 2019 Chinese Academy of Engineering
Source Title: Engineering
URI: https://scholarbank.nus.edu.sg/handle/10635/206346
ISSN: 2095-8099
DOI: 10.1016/j.eng.2018.11.025
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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