ArticleHuman brain mapping2020
A unified framework for association and prediction from vertex-wise grey-matter structure.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Grey-Matter Structure Markers of Alzheimer's Disease, Alzheimer's Conversion, Functioning and Cognition: A Meta-Analysis Across 11 Cohorts.Human brain mapping · 2025Pooled it
- Illuminating the complex interplay of risk factors for depression symptoms within a large-scale US longitudinal cohort.Social psychiatry and psychiatric epidemiology · 2026Article
- Estimating the fraction of variance of crystallized intelligence explained by cortical surface area in early adolescence.bioRxiv : the preprint server for biology · 2026Article
- Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology.Translational psychiatry · 2026Article
- Choice of Processing Pipelines for T1-Weighted Brain MRI Impacts Association and Prediction Analyses.Human brain mapping · 2025Article
- Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes.Communications biology · 2024Article
- MULTISCALE ESTIMATION OF MORPHOMETRICITY FOR REVEALING NEUROANATOMICAL BASIS OF COGNITIVE TRAITS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions.Developmental cognitive neuroscience · 2024Article
- Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis.Medical image computing and computer assisted intervention - MICCAI 2023 workshops : ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, proceedings · 2024Article
- Parsimonious model for mass-univariate vertexwise analysis.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Are Sex Differences in Human Brain Structure Associated With Sex Differences in Behavior?Psychological science · 2021Article
- A unified framework for association and prediction from vertex-wise grey-matter structure.Human brain mapping · 2020Article
- Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge.Frontiers in psychiatry · 2020Article
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Authors and funding
12 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.