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Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study

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Li, F and Jiang, L and Liao, Y and Si, Y and Yi, C and Zhang, Y and Zhu, X and Yang, Z and Yao, D and Cao, Z and Xu, P, Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study, Journal of Neural Engineering, 18, (4) Article 046097. ISSN 1741-2552 (2021) [Refereed Article]


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Copyright Statement

This is the version of the article before peer review or editing, as submitted by an author to Journal of neeral Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it.  The Version of Record is available online at https://iopscience.iop.org/article/10.1088/1741-2552/ac0d41

DOI: doi:10.1088/1741-2552/ac0d41

Abstract

Objective: Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.

Approach: In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).

Main results: The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.

Significance: This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.

Item Details

Item Type:Refereed Article
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Fuzzy computation
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Applied computing
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:145189
Year Published:2021
Deposited By:Information and Communication Technology
Deposited On:2021-07-08
Last Modified:2021-09-06
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