Title
Heritability of the network architecture of intrinsic brain functional connectivity.
Abstract
The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and “rich-club” properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5–25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23±2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h2 (γ=47–59%, Q=38–59%, ϕnorm=0–29%, λ=52–64%, σ=51–59%) at lower connection densities (≤15%), and when global signal regression was implemented, heritability estimates decreased substantially h2 (γ=0–26%, Q=0–28%, ϕnorm=0%, λ=23–30%, σ=0–27%). Distinct network features were phenotypically correlated (|r|=0.15–0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.
Year
DOI
Venue
2015
10.1016/j.neuroimage.2015.07.048
NeuroImage
Keywords
Field
DocType
Resting state,Graph theory,Genetics,Heritability,Functional connectivity
Graph theory,Heritability,Regression,Endophenotype,Resting state fMRI,Network architecture,Genetic association,Statistics,Clustering coefficient,Mathematics
Journal
Volume
ISSN
Citations 
121
1053-8119
8
PageRank 
References 
Authors
0.57
8
9