Fig 1.
General workflow for the construction of an individual morphological network using gray matter measurements from MRI.
(1) The estimation of gray matter volume as a morphological measure using a routine VBM procedure. (2) Brain parcellation with the AAL atlas and the estimation of morphological distribution for each region. (3) Repeated quantification of the similarity between morphological distributions for pairs of regions and formation of the similarity matrix by filling in corresponding similarity values. (4) Extract the binarized matrix at a specific sparsity threshold. (5) Represent individual brain network as a graph. (6) Calculate the network metrics (e.g., γ, λ, and σ). Note that, in this study steps 4–6 were repeated across a range of different sparsity thresholds, from 10% to 40% with an interval of 1%. HIP: Hippocampus; FFG: Fusiform gyrus; L: left; R: right; KLS: Kullback-Leibler divergence-based similarity; MRI: magnetic resonance imaging; VBM: voxel-based morphometry; AAL: automated anatomical labeling.
Fig 2.
The averaged map of the connectivity matrices.
Red and blue color indicates high and low similarity between regions, respectively. Main diagonal (i.e., self-connection) is indicated in white and excluded from following analyses. L, Left; R, Right.
Fig 3.
Coefficient of variation (CV) map of the connectivity matrices.
Red and blue color indicates high and low dispersion of that connection across participants, respectively. Most of the connections possessed relative low CV and in particular the connections with relative high similarities showed low CV, suggesting relative high consistency across subjects. Main diagonal (i.e., self-connection) is indicated in white and excluded from following analyses. L, Left; R, Right.
Fig 4.
Small-world properties of the morphometric networks as a function of network sparsity thresholds.
The error bar indicates the standard deviation.
Table 1.
Small-world properties in the present study, and for comparison, values from previous morphometric, anatomical, and functional studies.
Fig 5.
Spatial distribution of hubs within the morphometric network.
(A) Three examples of the spatial distributions for betweenness. (B) The similarity (left) and uniqueness (right) of individual spatial distributions. (C) Hubs identified in this study. L: left; R: right. PreCG: precentral gyrus; IFGoper: inferior frontal gyrus opercularis; IFGtri: inferior frontal gyrus triangularis; SMA: supplementary motor area; SMG: supramarginal gyrus; PCUN: precuneus; TPOsup: superior temporal gyrus of temporal pole; MTG: middle temporal gyrus; ITG: inferior temporal gyrus.
Table 2.
Cortical regions identified as hubs in the morphometric network and their properties.
Fig 6.
The Intraclass Correlation Coefficient (ICC) map of the connectivity matrices.
More than 97% of the edges showed excellent reliability (i.e., ICC > 0.75).
Fig 7.
Test-retest reliability with intraclass correlation coefficient (ICC) for each of the network metrics as a function of network sparsity thresholds.
The smallest ICC at various sparsity thresholds for each network metric is listed for the following: Cp: min ICC = 0.605, p = 0.0018; Lp: min ICC = 0.518, p = 0.0060; γ: min ICC = 0.636, p = 0.0020; λ: min ICC = 0.391, p = 0.035; σ: min ICC = 0.611, p = 0.00092; Eloc: min ICC = 0.377, p = 0.047; Eg: min ICC = 0.611, p = 0.0014; meanBet: min ICC = 0.485, p = 0.012.
Table 3.
A summary of test-retest reliability with intraclass correlation coefficient (ICC) for each of the network metrics.
Fig 8.
Age-related changes in each of the network metrics at a predefined sparsity threshold (i.e., 23%).
Fig 9.
Age-related changes in each of the network metrics over the range of sparsity thresholds.
The star markers (correlations falling outside the shaded area) indicate significant correlations (p < 0.05).