“Widespread Associations between Grey Matter Structure and the Human Phenome”, 2019-07-09 (; backlinks; similar):
Our linear mixed model approach unifies association and prediction analyses for highly dimensional vertex-wise MRI data
Grey-matter structure is associated with measures of substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains
Body size (height, weight, BMI, waist and hip circumference) is an important source of covariation between the phenome and grey-matter structure
Grey-matter scores quantify grey-matter based risk for the associated traits and allow to study phenotypes not collected
The most general cortical processing (“fsaverage” mesh with no smoothing) maximises the brain-morphometricity for all UKB phenotypes
The recent availability of large-scale neuroimaging cohorts (here the UK Biobank [UKB] and the Human Connectome Project [HCP]) facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. We tested the association between 654,386 vertex-wise measures of cortical and subcortical morphology (from T1w and T2w MRI images) and behavioral, cognitive, psychiatric and lifestyle data. We found a statistically-significant association of grey-matter structure with 58⁄167 UKB phenotypes spanning substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains (UKB discovery sample: n = 9,888). Twenty-three of the 58 associations replicated (UKB replication sample: n = 4,561; HCP, n = 1,110). In addition, differences in body size (height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the association, providing possible insight into previous MRI case-control studies for psychiatric disorders where case status is associated with body mass index. Using the same linear mixed model, we showed that most of the associated characteristics (eg. age, sex, body size, diabetes, being a twin, maternal smoking, body size) could be predicted using all the brain measurements in out-of-sample prediction. Finally, we demonstrated other applications of our approach including a Region Of Interest (ROI) analysis that retain the vertex-wise complexity and ranking of the information contained across MRI processing options.