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

Baptiste Couvy‐duchesne (1, 2) , Lachlan T Strike (3) , Futao Zhang (1) , Yan Holtz (1) , Zhili Zheng (1) , Kathryn E Kemper (1) , Loïc Yengo (1) , Olivier Colliot (2, 4, 5, 6) , Margaret Wright (7) , Naomi Wray (1) , Jian Yang (8, 1) , Peter M. Visscher (1)

1 Institute for Molecular Bioscience
2 ICM - Institut du Cerveau = Paris Brain Institute
3 Queensland Brain Institute
4 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
5 INSERM - Institut National de la Santé et de la Recherche Médicale
6 CNRS-L - National Council for Scientific Research = Conseil national de la recherche scientifique du Liban [Lebanon]
7 UQ [All campuses : Brisbane, Dutton Park Gatton, Herston, St Lucia and other locations] - The University of Queensland
8 IFAR - Institute for Advanced Research

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 (i) Region Of Interest (ROI) analysis that retain the vertex-wise complexity; (ii) comparison of the information retained by different MRI processings.

Mots clés: Grey-matter correlation; Prediction; Brain MRI; Morphometricity; Mixed models; Association

Domaines: Sciences du Vivant [q-bio]; Neurosciences [q-bio.NC]; Statistiques [stat]; Méthodologie [stat.ME]; Applications [stat.AP]; Machine Learning [stat.ML]

Baptiste Couvy‐duchesne, Lachlan T Strike, Futao Zhang, Yan Holtz, Zhili Zheng, et al.. A unified framework for association and prediction from vertex-wise grey-matter structure. Human Brain Mapping, 2020, ⟨10.1002/hbm.25109⟩. ⟨hal-02904458⟩