Heritability Estimation of Reliable Connectomic Features

Abstract

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.

Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment

“…A range of papers has investigated genetic determination of morphology 48,49 as well as structural 16,17 and functional features of the brain 50,15,14,45 . In a study of White et al 48 cerebral brain volumes of twins, including whole brain volume as well as divisions into lobes and tissue types, showed correlations above 0.90, except the frontal white matter and occipital gray matter volume.…”

“…Useful regions for pair classification belonged to higher order systems, such as the fronto-parietal, dorsal attention and default mode network. Measures of structural connectivity were found to be especially 20 heritable for connections within the default mode network, visual circuits and connections between default mode and fronto-parietal or ventral attention network 16,17 .…”

“…Inter-subject variability in preterm neonates and healthy adults exhibit overall similar patterns 13 , but variability increases for parts of the fronto-parietal and dorsal attention network during maturation. At the same time, genes contribute significantly to the variability of functional 14,15 , and structural 16,17 cortical networks. Despite the evidence for both genetic and environmental influences on brain variability, their specific contributions to the two key features of functional organization -strength and topography -remain poorly understood.…”

Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

“…Estimating interregional neural connectivity, reconstructing geometric structure of fiber pathways, and mapping the network connectivity to corresponding inter-individual variabilities provide fundamental insights in understanding detailed brain topological architecture, function and dysfunction. A large body of research has been devoted to extracting and investigating macro-scale brain networks from diffusion-weighted imaging (DWI) data (Bertolero et al, 2019;Elsheikh et al, 2020;van den Heuvel et al, 2019;Jiang et al, 2019;Xie et al, 2018), and various behavioural, neurological and neuropsychiatric disorders have been linked to the disrupted brain connectivity (van den Heuvel et al, 2019;Jiang et al, 2019). As structural changes of brain connectivity are phenotypically associated with massive complex traits across different categories, the brain-wide connectome has been extensively studied.…”

“…Among a large amount of complex-network attributes, it has been well documented in recent literatures (Cohen and D'Esposito, 2016;Xie et al, 2018) that segregation of neural information such as modularity, transitivity, clustering coefficients and local efficiency represent the connectivity of local network communities that are intrinsically densely connected and strongly coupled.…”