Small-world properties of human brain functional networks based on resting-state functional MRI

2011 ◽  
Author(s):  
Wentao Huang ◽  
Youceng Feng
Keyword(s):  
Human Brain ◽  
Functional Mri ◽  
Resting State ◽  
Small World ◽  
Functional Networks ◽  
Brain Functional Networks

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

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1156 ◽  
Author(s):  
Yanbing Jia ◽  
Huaguang Gu
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Clustering Algorithm ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Sample Entropy ◽  
Blood Oxygenation ◽  
Fmri Data ◽  
Functional Networks ◽  
Brain Functional Networks

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.

scholarly journals Adaptive reconfiguration of fractal small-world human brain functional networks

2006 ◽  
Vol 103 (51) ◽  
pp. 19518-19523 ◽  
Author(s):  
D. S. Bassett ◽  
A. Meyer-Lindenberg ◽  
S. Achard ◽  
T. Duke ◽  
E. Bullmore
Keyword(s):  
Human Brain ◽  
Small World ◽  
Functional Networks ◽  
Brain Functional Networks

scholarly journals Altered Small-World Efficiency of Brain Functional Networks in Acupuncture at ST36: A Functional MRI Study

PLoS ONE ◽  
2012 ◽  
Vol 7 (6) ◽  
pp. e39342 ◽  
Author(s):  
Bo Liu ◽  
Jun Chen ◽  
Jinhui Wang ◽  
Xian Liu ◽  
Xiaohui Duan ◽  
...  
Keyword(s):  
Functional Mri ◽  
Small World ◽  
Functional Networks ◽  
Brain Functional Networks ◽  
Mri Study

scholarly journals Parcellation-dependent small-world brain functional networks: A resting-state fMRI study

2009 ◽  
Vol 30 (5) ◽  
pp. 1511-1523 ◽  
Author(s):  
Jinhui Wang ◽  
Liang Wang ◽  
Yufeng Zang ◽  
Hong Yang ◽  
Hehan Tang ◽  
...  
Keyword(s):  
Resting State ◽  
Small World ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Brain Functional Networks ◽  
Fmri Study ◽  
World Brain
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