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>