Topological Properties of Large-Scale Cortical Networks Based on Multiple Morphological Features in Amnestic Mild Cognitive Impairment

Figure 2

Small-world properties of volumetric measures networks and geometric measures networks. The graph shows the normalized characteristic path length (lambda, and clustering coefficients (gamma, ) over a range of sparsity values (). All the networks have (green lines) and (red lines), which imply small-world properties. (a) The values of gamma and lambda in NC and aMCI of cortical thickness networks. (b) The values of gamma and lambda in NC and aMCI of GM volume networks. (c) The values of gamma and lambda in NC and aMCI of surface area networks. (d) The values of gamma and lambda in NC and aMCI of mean curvature networks. (e) The values of gamma and lambda in NC and aMCI of metric distortion (Jacobian) networks. (f) The values of gamma and lambda in NC and aMCI of sulcal depth networks. Thickness, cortical thickness. Volume, gray matter volume. Area, surface area. Curv, mean curvature. Sulc, sulcal depth. NC, normal controls.

(a) Thickness

(b) Volume

(c) Area

(d) Curv

(e) Jacobian

(f) Sulc