A unified framework for association and prediction from vertex‐wise grey‐matter structure. Issue 14 (20th July 2020) - Couvy‐Duchesne, Baptiste; Strike, Lachlan T.; Zhang, Futao; Holtz, Yan; Zheng, Zhili; Kemper, Kathryn E.; Yengo, Loic; Colliot, Olivier; Wright, Margaret J.; Wray, Naomi R.; Yang, Jian; Visscher, Peter M.
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. Abstract : This manuscript introduces a set of analyses, that rely on linear mixed models to perform association and prediction, while being suited to tackle the challenges of big‐data in neuroimaging. Our framework allows estimating new sample characteristics such as the total association (morphometricity) between a phenotype and vertex‐wise brain data or grey‐matter correlations that quantify how much phenotypes may be similarly associated with grey‐matter. In addition, it offers to build performant brain‐based predictors that do not require hyper‐parameter estimation.