Oliver Gruber

This study identifies a shared genetic factor underlying brain age gaps across 30 cohorts, finding 19 SNPs and associations with health traits.

The paper investigates how variations in the LC3 conjugation system affect reward system reactivity in humans using fMRI and genetic analysis.

This study identifies lifestyle, early-life, and genetic factors associated with brain age gaps in individuals with major depressive disorder compared to controls.

This study uses normative modeling to analyze cortical thickness deviations in MDD patients and healthy controls, finding large overlaps and significant heterogeneity in extreme deviations across individuals.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

This study identifies a genetic variant in DNAJC13 that influences neural activation in the reward system in response to conditioned stimuli using GWAS and fMRI.

The paper evaluates factors affecting brain-age prediction models using structural neuroimaging data from 35,683 individuals, finding optimal model performance with no site harmonization, age-binning, and a sample size of over 1,600.

This study examines how genetic variation in ULK4 affects brain activation in the reward system during a conditioned reward task, finding increased activation in specific brain regions for a particular variant.

The paper investigates how genetic variations affecting endosomal sorting processes influence neural responses in the reward system using fMRI and gene set analysis.

The paper benchmarks and optimizes a normative modeling framework for brain morphometry across the lifespan using MFPR, showing excellent accuracy and stability.

The paper examines age-related variations in cortical thickness across different life stages using gene expression data from a large neuroimaging dataset.

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

The paper analyzes sex differences in brain structure variability across the lifespan using a large dataset, finding greater male variability in subcortical volumes, cortical surface area, and thickness in most regions.

The paper predicts individual schizophrenia symptom dimensions using functional brain networks and links these networks to molecular features like dopamine and serotonin systems.

This paper examines brain aging in major depressive disorder (MDD) using a large dataset, finding that MDD patients show increased brain age differences compared to controls.

This study analyzes subcortical volume changes across the lifespan using MRI data from 18,605 individuals, finding age-related declines in some structures and increases in others.

The paper uses machine learning to rank cognitive domains based on brain data to predict schizophrenia in a multi-site dataset.