A new method for automatically modelling brain functional networks

Li, Gang; Li, Bo; Jiang, Yonghua; Jiao, Weidong; Hu, Lan; Zhu, Chungeng

doi:10.1016/j.bspc.2018.05.024

Cited by 17 publications

(12 citation statements)

References 30 publications

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“…Ten pieces of five seconds of continuous EEG data for each state were singled out for functional connectivity computation. Here, only alpha1 rhythm (8-10 Hz) at task state, which had significant statistical difference for mutual information in mental fatigue detection, was further analyzed according to our previous study [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

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

Jiang

et al. 2020

Neural Plasticity

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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.

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Section: Methodsmentioning

confidence: 99%

“…Calculate the shortest path length l ij (see reference [ 30 ] for detailed description and definition) between all nodes in network G .…”

Section: Methodsmentioning

confidence: 99%

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

Jiang

et al. 2020

Neural Plasticity

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“…Brain functional network, 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 [7–9], which are especially sensitive to mental fatigue [10, 11]. Commonly 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.…”

Section: Introductionmentioning

confidence: 99%

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

Luo

Zhang

et al. 2019

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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 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.

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“…These changes in EEG can be used to detect mental fatigue [10,15], which is especially important and meaningful for driving fatigue estimation [7,11]. From the above, we can conclude that EEG has become the most effective technical means for exploring the neuromechanism and detection of mental fatigue [18,19].…”

Section: Introductionmentioning

confidence: 89%

The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

Luo

Jiao

et al. 2019

Preprint

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Background: Mental fatigue is usually caused by long-term cognitive activities, mainly manifested as drowsiness, difficulty in concentrating, decreased alertness, disordered thinking, slow reaction, lethargy, reduced work efficiency, error-prone and so on. Mental fatigue has become a widespread sub-health condition, and has a serious impact on the cognitive function of the brain. However, seldom researches explore the differences of mental fatigue on electrophysiological activity between resting state and task state. In the present study, 20 healthy male individuals were recruited to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) data were collected before and after the task. The power and relative power of five EEG rhythms both in resting state and task state were analyzed statistically. Results: The results of brain topographies and statistical analysis indicated that mental arithmetic task can successfully induce mental fatigue in the enrolled subjects. The relative power index was more sensitive than the power index in response to mental fatigue, and the relative power for assessing mental fatigue was better in resting state than in task state. Furthermore, we found that it is of great physiological significance to divide alpha frequency band into alpha1 band and alpha2 band in fatigue related studies, and at the same time improve the statistical differences of sub-bands. Conclusions: Our current results suggested that the brain activity in mental fatigue state has great differences between resting state and task state, and it is imperative to select the appropriate state in EEG data acquisition and divide alpha band into alpha1 and alpha2 bands in mental fatigue related researches.

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A new method for automatically modelling brain functional networks

Cited by 17 publications

References 30 publications

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

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

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

The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

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