Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology 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.