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

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.

Mental Fatigue Has Great Impact on the Fractal Dimension of Brain Functional Network

“…That is to say, the coexistence of small-world feature and fractal feature in the brain functional networks may be caused by neither complete attraction nor complete exclusion between central nodes. Previous researchers have reported that the small-world feature still exists in the brain functional networks when the brain is at fatigue state [ 3 , 10 , 38 ]. Taking together with the results of this study, we can infer that the small-world feature and fractal feature of the brain functional networks can still exist simultaneously during the formation of mental fatigue.…”

“…Mental fatigue has become a widespread subhealthy condition in nowadays society [ 1 ]. Many researchers are dedicated to the studies of detection methods [ 2 ], neural mechanisms [ 3 ], and mitigation methods [ 4 ] of the mental fatigue, owing to the negative effects of mental fatigue on the human cognitive functions [ 5 ], especially serious in driving fatigue [ 6 ]. Recently, mental fatigue has been prevalently explored by the means of brain functional network with the neuroimaging technique of electroencephalogram (EEG) [ 7 – 9 ].…”

“…Recently, mental fatigue has been prevalently explored by the means of brain functional network with the neuroimaging technique of electroencephalogram (EEG) [ 7 – 9 ]. It has been widely proved that mental fatigue can lead to distinct changes in brain functional network structures, for example, the changes of small-world property [ 3 , 10 ]. However, to our knowledge, no researcher has studied the effects of mental fatigue on the fractal feature of brain functional network.…”

“…Brain functional network, as one type of the complex networks in statistical physics, is a demonstration of the temporal correlations between different brain areas in the processes of neural physiological events [ 16 ]. It has become one of the most widely used techniques to investigate the neurodynamics of cognitive functions [ 17 – 20 ], which are especially sensitive to mental fatigue [ 3 , 9 ]. Since the small-world characteristic [ 21 ] and scale-free property [ 22 ] of complex networks were proposed, the studies of complex network topology have entered a high-speed developing period [ 23 ].…”

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

“…14 Furthermore, a large number of electrodes increases the accuracy of electrical source estimation [167] and signal preprocessing [47]. In contrast, García-Prieto et al [168], Li et al [169], and Wang et al [170] recommend fewer than 32 electrodes, demonstrating that a small number is adequate to cover the ROI and obtain reliable information. A small number is also suggested by Luck [33], indicating that the use of 16-32 active electrodes is better for monitoring brain activity.…”