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Neuroimage. 2015 Nov 1;121:243-52. doi: 10.1016/j.neuroimage.2015.07.048. Epub 2015 Jul 28.

Heritability of the network architecture of intrinsic brain functional connectivity.

Author information

1
Centre for Advanced Imaging, University of Queensland, Brisbane, QLD 4072, Australia; School of Psychology, University of Queensland, Brisbane, QLD 4072 Australia; QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia. Electronic address: b.sinclair@uq.edu.au.
2
QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia. Electronic address: Narelle.Hansell@qimrberghofer.edu.au.
3
QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia. Electronic address: gabriellablokland@gmail.com.
4
QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia. Electronic address: Nick.Martin@qimrberghofer.edu.au.
5
Imaging Genetics Center, Dept. of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90095, USA. Electronic address: pthomp@usc.edu.
6
QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia. Electronic address: mjbreaks@gmail.com.
7
School of Psychology, University of Queensland, Brisbane, QLD 4072 Australia. Electronic address: Greig.dezubicaray@uq.edu.au.
8
QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia. Electronic address: Margie.Wright@qimrberghofer.edu.au.
9
Centre for Advanced Imaging, University of Queensland, Brisbane, QLD 4072, Australia. Electronic address: Katie.McMahon@uq.edu.au.

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 h(2) (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 h(2) (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.

KEYWORDS:

Functional connectivity; Genetics; Graph theory; Heritability; Resting state

PMID:
26226088
PMCID:
PMC4837693
DOI:
10.1016/j.neuroimage.2015.07.048
[Indexed for MEDLINE]
Free PMC Article

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