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

scholarly journals Disrupted Topological Organization in Whole-Brain Functional Networks of Heroin-Dependent Individuals: A Resting-State fMRI Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82715 ◽  
Author(s):  
Guihua Jiang ◽  
Xue Wen ◽  
Yingwei Qiu ◽  
Ruibin Zhang ◽  
Junjing Wang ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks ◽  
Fmri Study ◽  
Topological Organization

scholarly journals Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study

PLoS ONE ◽  
2012 ◽  
Vol 7 (3) ◽  
pp. e33540 ◽  
Author(s):  
Xiaohu Zhao ◽  
Yong Liu ◽  
Xiangbin Wang ◽  
Bing Liu ◽  
Qian Xi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Resting State Fmri ◽  
Fmri Study ◽  
World Brain

scholarly journals Efficiency of a "Small-World" Brain Network Depends on Consciousness Level: A Resting-State fMRI Study

2013 ◽  
Vol 24 (6) ◽  
pp. 1529-1539 ◽  
Author(s):  
T. Uehara ◽  
T. Yamasaki ◽  
T. Okamoto ◽  
T. Koike ◽  
S. Kan ◽  
...  
Keyword(s):  
Resting State ◽  
Small World ◽  
Resting State Fmri ◽  
Brain Network ◽  
Consciousness Level ◽  
Fmri Study ◽  
World Brain

scholarly journals Alteration of Brain Functional Networks in Early-Stage Parkinson’s Disease: A Resting-State fMRI Study

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0141815 ◽  
Author(s):  
Linqiong Sang ◽  
Jiuquan Zhang ◽  
Li Wang ◽  
Jingna Zhang ◽  
Ye Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Resting State ◽  
Early Stage ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Brain Functional Networks ◽  
Fmri Study

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 Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder

2008 ◽  
Vol 30 (2) ◽  
pp. 638-649 ◽  
Author(s):  
Liang Wang ◽  
Chaozhe Zhu ◽  
Yong He ◽  
Yufeng Zang ◽  
QingJiu Cao ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Small World ◽  
Functional Networks ◽  
Hyperactivity Disorder ◽  
Brain Functional Networks ◽  
World Brain
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