Open AccessHeritability of structural brain network topology: A DTI study of 156 twins
Author(s) -
Bohlken Marc M.,
Mandl René C.W.,
Brouwer Rachel M.,
den Heuvel Martijn P.,
Hedman Anna M.,
Kahn René S.,
Hulshoff Pol Hilleke E.
Publication year - 2014
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22550
Subject(s) - heritability , endophenotype , diffusion mri , white matter , twin study , connectome , network topology , brain size , missing heritability problem , additive genetic effects , topology (electrical circuits) , biology , mathematics , computer science , genetics , neuroscience , medicine , magnetic resonance imaging , cognition , radiology , combinatorics , functional connectivity , genotype , single nucleotide polymorphism , gene , operating system
Individual variation in structural brain network topology has been associated with heritable behavioral phenotypes such as intelligence and schizophrenia, making it a candidate endophenotype. However, little is known about the genetic influences on individual variation in structural brain network topology. Moreover, the extent to which structural brain network topology overlaps with heritability for integrity and volume of white matter remains unknown. In this study, structural network topology was examined using diffusion tensor imaging at 3T. Binary connections between 82 structurally defined brain regions per subject were traced, allowing for estimation of individual topological network properties. Heritability of normalized characteristic path length ( λ ), normalized clustering coefficient ( γ ), microstructural integrity (FA), and volume of the white matter were estimated using a twin design, including 156 adult twins from the newly acquired U‐TWIN cohort. Both γ and λ were estimated to be under substantial genetic influence. The heritability of γ was estimated to be 68%, the heritability estimate for λ was estimated to be 57%. Genetic influences on network measures were found to be partly overlapping with volumetric and microstructural properties of white matter, but the largest component of genetic variance was unique to both network traits. Normalized clustering coefficient and normalized characteristic path length are substantially heritable, and influenced by independent genetic factors that are largely unique to network measures, but partly also implicated in white matter directionality and volume. Thus, network measures provide information about genetic influence on brain structure, independent of global white matter characteristics such as volume and microstructural directionality. Hum Brain Mapp 35:5295–5305, 2014 . © 2014 Wiley Periodicals, Inc.