TY - JOUR
T1 - Neural Mechanisms of Mental Fatigue Revisited
T2 - New Insights from the Brain Connectome
AU - Qi, Peng
AU - Ru, Hua
AU - Gao, Lingyun
AU - Zhang, Xiaobing
AU - Zhou, Tianshu
AU - Tian, Yu
AU - Thakor, Nitish
AU - Bezerianos, Anastasios
AU - Li, Jinsong
AU - Sun, Yu
N1 - Funding Information:
This work was supported by the “Hundred Talents Program” of Zhejiang University (awarded to Y. Sun), by the Fundamental Research Funds for the Central Universities ( 2018QNA5017 , awarded to Y. Sun), and by the National Natural Science Foundation of China ( 81801785 , awarded to Y. Sun). The authors would also like to thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology ( R-719-001-102-232 , awarded to N. Thakor). This work was supported in part by the Ministry of Education of Singapore ( MOE2014-T2-1-115 , awarded to A. Bezerianos) and the Shanghai Sailing Program (17YF1420400, awarded to P. Qi).
Publisher Copyright:
© 2019 Chinese Academy of Engineering
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - Brain network
KW - Functional connectivity
KW - Graph theoretical analysis
KW - Mental fatigue
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U2 - 10.1016/j.eng.2018.11.025
DO - 10.1016/j.eng.2018.11.025
M3 - Review article
AN - SCOPUS:85062678782
SN - 2095-8099
VL - 5
SP - 276
EP - 286
JO - Engineering
JF - Engineering
IS - 2
ER -