Literature DB >> 28113875

The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue.

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Abstract

The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

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Year:  2016        PMID: 28113875     DOI: 10.1109/JBHI.2016.2544061

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  19 in total

1.  Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis.

Authors:  Beige Ye; Taorong Qiu; Xiaoming Bai; Ping Liu
Journal:  Entropy (Basel)       Date:  2018-09-13       Impact factor: 2.524

2.  Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness.

Authors:  Jichi Chen; Hong Wang; Chengcheng Hua; Qiaoxiu Wang; Chong Liu
Journal:  Cogn Neurodyn       Date:  2018-07-14       Impact factor: 5.082

3.  A novel real-time driving fatigue detection system based on wireless dry EEG.

Authors:  Hongtao Wang; Andrei Dragomir; Nida Itrat Abbasi; Junhua Li; Nitish V Thakor; Anastasios Bezerianos
Journal:  Cogn Neurodyn       Date:  2018-02-21       Impact factor: 5.082

4.  Sleepiness and Driving: Multidimensional Legal, Social, Technological, and Biological Challenges.

Authors:  Robert Joseph Thomas
Journal:  Sleep       Date:  2016-05-01       Impact factor: 5.849

Review 5.  Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity.

Authors:  Danielle S Bassett; Ankit N Khambhati; Scott T Grafton
Journal:  Annu Rev Biomed Eng       Date:  2017-03-27       Impact factor: 9.590

6.  EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition.

Authors:  Ronglin Zheng; Zhongmin Wang; Yan He; Jie Zhang
Journal:  Cogn Neurodyn       Date:  2021-09-13       Impact factor: 5.082

7.  Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection.

Authors:  Quan Liu; Yang Liu; Kun Chen; Lei Wang; Zhilei Li; Qingsong Ai; Li Ma
Journal:  Entropy (Basel)       Date:  2021-04-13       Impact factor: 2.524

8.  Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials.

Authors:  Jun-Feng Gao; Yong Yang; Wen-Tao Huang; Pan Lin; Sheng Ge; Hong-Mei Zheng; Ling-Yun Gu; Hui Zhou; Chen-Hong Li; Ni-Ni Rao
Journal:  Sci Rep       Date:  2016-11-11       Impact factor: 4.379

9.  Positive affect, surprise, and fatigue are correlates of network flexibility.

Authors:  Richard F Betzel; Theodore D Satterthwaite; Joshua I Gold; Danielle S Bassett
Journal:  Sci Rep       Date:  2017-03-31       Impact factor: 4.379

10.  Dynamic Default Mode Network across Different Brain States.

Authors:  Pan Lin; Yong Yang; Junfeng Gao; Nicola De Pisapia; Sheng Ge; Xiang Wang; Chun S Zuo; James Jonathan Levitt; Chen Niu
Journal:  Sci Rep       Date:  2017-04-06       Impact factor: 4.379

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