scholarly journals Brain Functional Network Improved by Magnetic Stimulation at Acupoints during Mental Fatigue

2016 ◽  
Vol 09 (10) ◽  
pp. 65-70
Author(s):  
Shuo Yang ◽  
Na Ai ◽  
Yanyun Qiao ◽  
Lei Wang ◽  
Hongli Yu ◽  
...  
Keyword(s):  
Magnetic Stimulation ◽  
Mental Fatigue ◽  
Functional Network ◽  
Brain Functional Network

scholarly journals The Maximum Eigenvalue of the Brain Functional Network Adjacency Matrix: Meaning and Application in Mental Fatigue Evaluation

2020 ◽  
Vol 10 (2) ◽  
pp. 92 ◽  
Author(s):  
Gang Li ◽  
Yonghua Jiang ◽  
Weidong Jiao ◽  
Wanxiu Xu ◽  
Shan Huang ◽  
...  
Keyword(s):  
Adjacency Matrix ◽  
Mental Fatigue ◽  
Functional Network ◽  
Maximum Eigenvalue ◽  
Characteristic Path Length ◽  
Brain Functional Network ◽  
Large Maximum ◽  
Fatigue Estimation ◽  
Fatigue Evaluation ◽  
High Degree

The maximum eigenvalue of the adjacency matrix (AM) has been supposed to contain rich information about the corresponding network. An experimental study focused on revealing the meaning and application of the maximum eigenvalue is missing. To this end, AM was constructed using mutual information (MI) to determine the functional connectivity with electroencephalogram (EEG) data recorded with a mental fatigue model, and then was converted into both binary and weighted brain functional network (BFN) and corresponding random networks (RNs). Both maximum eigenvalue and corresponding network characters in BFNs and RNs were considered to explore the changes during the formation of mental fatigue. The results indicated that large maximum eigenvalue means more edges in the corresponding network, along with a high degree and a short characteristic path length both in weighted and binary BFNs. Interestingly, the maximum eigenvalue of AM was always a little larger than that of the corresponding random matrix (RM), and had an obvious linearity with the sum of the AM elements, indicating that the maximum eigenvalue can be able to distinguish the network structures which have the same mean degree. What is more, the maximum eigenvalue, which increased with the deepening of mental fatigue, can become a good indicator for mental fatigue estimation.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Gang Li ◽  
Yanting Xu ◽  
Yonghua Jiang ◽  
Weidong Jiao ◽  
Wanxiu Xu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Mental Fatigue ◽  
Functional Network ◽  
Functional Networks ◽  
Fractal Structures ◽  
Brain Functional Network ◽  
Brain Functional Networks ◽  
Negative Impacts ◽  
Binary Network ◽  
The Brain

Mental fatigue has serious negative impacts on the brain cognitive functions and has been widely explored by the means of brain functional networks with the neuroimaging technique of electroencephalogram (EEG). Recently, several researchers reported that brain functional network constructed from EEG signals has fractal feature, raising an important question: what are the effects of mental fatigue on the fractal dimension of brain functional network? In the present study, the EEG data of alpha1 rhythm (8-10 Hz) at task state obtained by a mental fatigue model were chosen to construct brain functional networks. A modified greedy colouring algorithm was proposed for fractal dimension calculation in both binary and weighted brain functional networks. The results indicate that brain functional networks still maintain fractal structures even when the brain is at fatigue state; fractal dimension presented an increasing trend along with the deepening of mental fatigue fractal dimension of the weighted network was more sensitive to mental fatigue than that of binary network. Our current results suggested that mental fatigue has great regular impacts on the fractal dimension in both binary and weighted brain functional networks.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Gang Li ◽  
Youdong Luo ◽  
Zhengru Zhang ◽  
Yanting Xu ◽  
Weidong Jiao ◽  
...  
Keyword(s):  
Mental Arithmetic ◽  
Small World ◽  
Mental Fatigue ◽  
Clustering Coefficient ◽  
Functional Network ◽  
Network Organization ◽  
Characteristic Path Length ◽  
Arithmetic Task ◽  
Brain Functional Network ◽  
Wide Range

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.

Tensor-based dynamic brain functional network for motor imagery classification

2021 ◽  
Vol 69 ◽  
pp. 102940
Author(s):  
Qizhong Zhang ◽  
Bin Guo ◽  
Wanzeng Kong ◽  
Xugang Xi ◽  
Yizhi Zhou ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Functional Network ◽  
Brain Functional Network
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