Abstract
Brain functional network has been widely applied to investigate brain function changes among different conditions and proved to be a small-world-like network. But seldom researches explore the effects of mental fatigue on the small-world brain functional network organization. In the present study, 20 healthy individuals were included to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) signals were recorded before and after the task. Correlations between all pairs of EEG channels were determined by mutual information (MI). The resulting adjacency matrices were converted into brain functional networks by applying a threshold, and then, the clustering coefficient (C), characteristic path length (L), and corresponding small-world feature were calculated. Through performing analysis of variance (ANOVA) on the mean MI for every EEG rhythm, only the data of α1 rhythm during the task state were emerged for the further explorations of mental fatigue. For a wide range of thresholds, C increased and L and small-world feature decreased with the deepening mental fatigue. The pattern of the small-world characteristic still existed when computed with a constant degree. Our present findings indicated that more functional connectivities were activated at the mental fatigue stage for efficient information transmission and processing, and mental fatigue can be characterized by a reduced small-world network characteristic. Our results provide a new perspective to understand the neural mechanisms of mental fatigue based on complex network theories.
Highlights
Mental fatigue refers to a status that decreased mental alertness and focus and worsening performances [1, 2]
The results showed that significant differences of the mean mutual information (MI) among T0, T1, T2, T3, and T4 were gained only in α1 (8-10 Hz) rhythm during the task state, which can reveal that α1 is the most sensitive rhythm in response to mental fatigue
Mental fatigue was induced by the mental arithmetic math task and validated by the algorithm of ðθ + α1 + α2Þ/β
Summary
Mental fatigue refers to a status that decreased mental alertness and focus and worsening performances [1, 2] It is often caused by prolonged periods of cognitive activities. As one type of the complex networks in statistical physics, is a demonstration of the temporal correlations among the different brain regions in the course of nervous activities [6]. It has become one of the most widely used methods to investigate neurodynamics of cognitive functions [7,8,9], which are especially sensitive to mental fatigue [10, 11]. Explored neuroimaging techniques of brain functional networks are mainly on the basis of electroencephalogram (EEG) data [12], because EEG has the advantages of high temporal resolution, low costs, and easy operation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
