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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: Neuroimage. 2015 Mar 19;113:310–319. doi: 10.1016/j.neuroimage.2015.03.021

Table 3.

Weighted network metrics of observed and simulated networks from the aging study data at rest.

Observed (N=39)
 Simulated (N=100)
Condition Metric Mean (SE) Mean (SE)
Rest Clustering coefficient (C) 0.149 (0.001) 0.155 (0.000)
Global Efficiency (Eglob) 0.231 (0.001) 0.214 (0.000)
Characteristic path length (L) 4.677 (0.109) 4.720 (0.004)
Mean Nodal Degree (K) 10.649 (0.055) 12.747 (0.016)
Leverage Centrality (l) 2.678 (0.015) 1.945 (0.003)
Modularity (Q) 0.342 (0.001) 0.136 (0.000)
Visual Clustering coefficient (C) 0.150 (0.001) 0.150 (0.000)
Global Efficiency (Eglob) 0.232 (0.001) 0.205 (0.000)
Characteristic path length (L) 4.553 (0.073) 4.992 (0.008)
Mean Nodal Degree (K) 10.656 (0.056) 12.379 (0.025)
Leverage Centrality (l) 2.671 (0.015) 2.155 (0.010)
Modularity (Q) 0.348 (0.001) 0.136 (0.000)
Multisensory Clustering coefficient (C) 0.140 (0.001) 0.165 (0.000)
Global Efficiency (Eglob) 0.230 (0.001) 0.218 (0.000)
Characteristic path length (L) 4.431 (0.011) 4.700 (0.008)
Mean Nodal Degree (K) 10.547 (0.052) 13.830 (0.027)
Leverage Centrality (l) 2.862 (0.014) 2.054 (0.006)
Modularity (Q) 0.327 (0.001) 0.123 (0.000)