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A unified framework for association and prediction from vertex-wise grey-matter structure.

ABSTRACT: The recent availability of large-scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex-wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N =?9,497 for discovery, N =?4,323 for replication) and the Human Connectome Project (N =?1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey-matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case-control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex-wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex-wise complexity; (b) comparison of the information retained by different MRI processings.

SUBMITTER: Couvy-Duchesne B

PROVIDER: S-EPMC7469763 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A unified framework for association and prediction from vertex-wise grey-matter structure.

Couvy-Duchesne Baptiste B Strike Lachlan T LT Zhang Futao F Holtz Yan Y Zheng Zhili Z Kemper Kathryn E KE Yengo Loic L Colliot Olivier O Wright Margaret J MJ Wray Naomi R NR Yang Jian J Visscher Peter M PM

Human brain mapping 20200720 14

The recent availability of large-scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex-wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a li ...[more]

PMID: 32687259

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Project description:IntroductionAlzheimer's disease (AD) is a neurodegenerative disorder characterized by changes in beta amyloid (Aß) and tau as well as changes in cerebral glucose metabolism and gray matter volume. This has been categorized as three distinct stages of amyloid, tau, and neurodegeneration. Past studies have shown asymmetric Aβ accumulation and its association with asymmetric cerebral metabolism in preclinical AD. We analyzed data to replicate these findings and extend them to associations with gray matter volume and cognitive function.MethodsWe recruited 93 (mean age = 76.4±6.1 years) cognitively normal adults who underwent magnetic resonance imaging (MRI) and positron emission tomography (PET) with Pittsburgh compound B (PiB) and Fluorodeoxyglucose (FDG) tracers (to estimate Aβ and glucose metabolism, respectively). We conducted voxel-wise paired t-test on PiB (left vs. right hemispheres) to identify regions that differ in Aβ between the left and right cortex. We identified whether these regions showed asymmetry in FDG and gray matter volume using paired t-tests on each region. We then conducted correlations between asymmetry indices for each region that had significant asymmetry in PiB, FDG, and gray matter volume. We ran a group regression analysis on cognitive functions.ResultsWe found 26 regions that had significant rightward asymmetry in PiB including prefrontal cortex, temporal cortex, insula, parahippocampus, caudate, and putamen. All these regions showed significant gray matter rightward asymmetry, and most of these regions showed significant FDG asymmetry except the caudate, orbital cortex, medial frontal gyrus, and superior temporal gyrus. Only in the superior frontal gyrus, we found that greater rightward asymmetry in PiB was associated with greater rightward asymmetry in FDG, r(82) =0.38, p<0.005 (FDR corrected) - no other regions showed significant Aß asymmetry correlation with either FDG or gray matter volume asymmetry. We found that greater rightward FDG asymmetry in the superior frontal gyrus was associated with greater visuospatial processing scores in our cognitive domain group regression analysis.DiscussionAD has previously been modeled in three-stages: however, our results indicate that cerebral glucose metabolism may be dynamic throughout the disease progression and may serve as a compensatory pathway for maintaining cognitive functioning.

Dataset Information

A unified framework for association and prediction from vertex-wise grey-matter structure.

Publications

A unified framework for association and prediction from vertex-wise grey-matter structure.

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