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Open AccessArticle

Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional Networks of the Brain

by Yanbing Jia 1 and Huaguang Gu 2,*
1
School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China
2
School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1156; https://doi.org/10.3390/e21121156
Received: 30 September 2019 / Revised: 22 November 2019 / Accepted: 22 November 2019 / Published: 26 November 2019
Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain. View Full-Text
Keywords: sample entropy; brain functional networks; complexity; dynamic functional connectivity; static functional connectivity; K-means clustering algorithm sample entropy; brain functional networks; complexity; dynamic functional connectivity; static functional connectivity; K-means clustering algorithm
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Jia, Y.; Gu, H. Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional Networks of the Brain. Entropy 2019, 21, 1156.

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