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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  α 1 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.

Effects of Mental Fatigue on Small-World Brain Functional Network Organization