Robert J. Loughnan

This study examines how BMI relates to brain microstructure across adolescence, finding nonlinear associations that intensify at higher BMI percentiles.

FEMA-Long enables efficient modeling of time-dependent effects in longitudinal data, discovering genetic variants with time-dependent effects on infant growth traits.

The paper uses an extended mixed-effects model to analyze age-related changes in brain structure across a large adolescent cohort, finding distinct developmental patterns in cortical and subcortical regions.

This study uses polygenic scores to predict impulsivity and substance use in adolescents, finding age-related changes in genetic associations and highlighting caffeine as an early risk factor.

The paper introduces a variational Bayes algorithm to estimate variance explained by whole-brain imaging features for zero-inflated outcomes, showing higher variance explained for externalizing behaviors in the ABCD Study.

The paper estimates genetic, environmental, and subject-specific variance components in cortical morphometry using longitudinal data from the ABCD Study.

FEMA enables fast and efficient mixed-effects analyses of large-scale whole-brain imaging data, revealing distinct spatial patterns of brain maturation in early adolescence.

This study identifies genetic overlap between functional brain connectivity and six psychiatric disorders using multivariate genome-wide association analysis in 30,701 UK Biobank individuals.

The paper examines gene-experience correlations in cognitive development using ABCD Study data, finding stronger genetic associations with crystallized intelligence measures and significant mediation by recreational reading.

The paper estimates heritability of cognitive phenotypes in the ABCD Study using mixed models and the FEMA package, finding narrower confidence intervals with SNP-derived genetic relatedness.

The paper evaluates the replication of genome-wide association loci discovered using MOSTest across different cohorts and finds higher replication yields and rates compared to min-P.

This paper evaluates how population stratification affects multimodal neuroimaging measures in a large, diverse sample and shows that structural imaging metrics are more confounded by population stratification than functional ones.

The paper examines genetic and familial risk factors for psychopathology in youth, finding shared and unique associations between polygenic risk scores and behavioral measures.

This study identifies 856 genetic loci associated with sulcal depth through a genome-wide association study using UK Biobank data, showing higher heritability and locus yield compared to cortical thickness and surface area.

The paper uses a multivariate GWAS approach to identify 780 genetic loci associated with cortical morphology, showing a 10-fold increase in loci compared to univariate methods.

This study uses multivariate GWAS on diffusion imaging data from two large datasets to identify 503 genetic loci affecting brain tissue composition and uncover molecular pathways related to axonal growth, synaptogenesis, and neuroinflammation.

The paper uses MOSTest to analyze cortical morphology in 10,145 children, showing that regional cortical patterns predict cognitive performance after accounting for global brain measures.

The paper shows that distinct cortical morphology patterns are associated with different cognitive domains in children, highlighting the importance of sociodemographic factors.