Abstract
AbstractTo facilitate pre-symptomatic diagnosis of late-onset Alzheimer’s disease, non-invasive imaging biomarkers could be combined with genetic risk information. In this work, we investigated the structural brain networks of young adults in relation to polygenic risk for Alzheimer’s disease, using magnetic resonance imaging (MRI) and genotype data for 564 19-year-old participants from the Avon Longitudinal Study of Parents and Children. Diffusion MRI data were acquired on a 3T scanner, and the data were used to perform whole-brain tractography. The resulting tractograms were used to generate structural brain networks, using the number of streamlines and the diffusion tensor fractional anisotropy as edge weights. This was done for the wholebrain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The hubs of the networks were also identified, and the connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating the burden of genetic risk carried by an individual, were calculated both at genome-wide level and for nine specific disease pathways. The correlation coefficients were calculated between the PRS and a) the graph theoretical metrics of the structural networks and b) the rich-club, feeder and local connectivity of the whole-brain networks.In the visual subnetwork, the mean nodal strength was negatively correlated with the genomewide PRS including the APOE locus (r = −0.132, p = 0.0016), while the mean betweenness centrality was positively correlated with the pathway-specific PRS for plasma lipoprotein particle assembly including the APOE locus (r = 0.134, p = 0.0014). The rich-club connectivity was negatively correlated with the genome-wide PRS including the APOE locus (r = −0.149, p = 0.0004). Our results indicate small changes in the brain connectome of young adults at risk of developing Alzheimer’s disease in later life.
Publisher
Cold Spring Harbor Laboratory
Reference98 articles.
1. Computing the orientational-average of diffusion-weighted MRI signals: a comparison of different techniques;Scientific Reports,2021
2. Reduced Hippocampal Functional Connectivity in Alzheimer Disease
3. A comprehensive analysis of methods for assessing polygenic burden in Alzheimer’s disease pathology and risk beyond APOE;Brain Commun,2020
4. A. Arnatkeviciute , B.D. Fulcher , S. Oldham , J. Tiego , C. Paquola , Z. Gerring , K. Aquino , Z. Hawi , B. Johnson , G. Ball , M. Klein , G. Deco , B. Franke , M. Bellgrove , A. Fornito . “Genetic influences on hub connectivity of the human connectome.” BioRxiv, 2020.06.21.163915. doi.org/10.1101/2020.06.21.163915
5. Resting-state network dysfunction in Alzheimer’s disease: A systematic review and meta-analysis;Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring,2017
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献