Abstract
Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.
Highlights
A large body of research has been devoted to extracting and investigating macro-scale brain networks from diffusion-weighted imaging (DWI) data (Xie et al, 2018; Jiang et al, 2019; van den Heuvel et al, 2019; Bertolero et al, 2019; Elsheikh et al, 2020), and various behavioral, neurological and neuropsychiatric disorders have been linked to the disrupted brain connectivity (Jiang et al, 2019; van den Heuvel et al, 2019)
2.7.2 Alzheimer’s disease Neuroimaging Initiative (ADNI) Cohort Genetic findings of the segregation measures from Human Connectome Project (HCP) young adult dataset were treated as genotypic candidates and segregation measures at specific ROIs as phenotypic candidates, we further examined in ADNI elderly adult dataset regarding their associations
Given the list of significant findings from the aforementioned genome-wide association studies (GWAS) of HCP segregation measures, we further examined their statistical significance in the ADNI cohort to identify brain network relevant genetic variants which were consistent for brain aging
Summary
Brain structural connectivity is a major organizing principle of the nervous system. Estimating interregional neural connectivity, reconstructing geometric structure of fiber pathways, and mapping the network connectivity to corresponding interindividual variabilities provide fundamental insights in understanding detailed brain topological architecture, function and dysfunction. The underlying neurobiological mechanism from gene to brain connectome, and to cognitive and behavioral outcomes, and whether this mechanism changes over time, remain unclear. To bridge this gap, we perform a genetic study of brain connectome phenotypes on two different age-specific cohorts: one contains healthy young adults (age: 28.7 ± 3.6), and the other contains elderly participants (age: 73.8 ± 7.0). Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. The underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear
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