10.1371/journal.pone.0124681.g003
Long Qian
Long
Qian
Yi Zhang
Yi
Zhang
Li Zheng
Li
Zheng
Yuqing Shang
Yuqing
Shang
Jia-Hong Gao
Jia-Hong
Gao
Yijun Liu
Yijun
Liu
Small world properties in the frequency specific FCNs.
Public Library of Science
2015
CEEMD
brain BOLD signals
fMRI data analysis
emd
oscillation rhythms
mode decomposition method
frequency division method
frequency bands
frequency Dependent Topological Patterns
brain networks
topological patterns
2015-04-30 03:32:30
Figure
https://plos.figshare.com/articles/figure/_Small_world_properties_in_the_frequency_specific_FCNs_/1399747
<p>From Fig 3a to Fig 3e, each figure shows the plot of global topological patterns of distinct frequency intervals (y-axis) versus sparsity (x-axis), including the weighted degree, local network efficiency (locE), global network efficiency (gE), mean clustering coefficient (Cp) and shortest path length (Lp) respectively. The ratio Gamma (Fig 3f) and Lambda (Fig 3g) of five frequency specific FCNs showed a much higher Cp and identical Lp value, compared with closely matched random networks across much sparsity. The saliency of small-worldness, Sigma, dynamically covered different frequency bands at various density intervals. Specifically, small world architecture is prominent in the IMF1, IMF3 and IMF5 component at a range of density threshold from 0.05 to 0.12, 0.12 to 0.18, and 0.24 to 0.4, respectively.</p>