Dominik Grotegerd

The paper identifies neural correlates of interpersonal resilience, finding larger gray matter volume in the left supramarginal gyrus in individuals with secure attachment despite childhood maltreatment.

This study analyzes structural brain network changes in bipolar disorder, finding widespread connectivity disruptions linked to illness severity and medication use.

The paper examines how childhood maltreatment affects brain structure in 3,711 individuals with MDD and PTSD, finding age- and sex-specific brain morphometric differences.

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

This study investigates the replicability of gray matter correlates of childhood maltreatment across three cohorts and finds no significant replication after controlling for depression.

The paper identifies neural markers of impending initial major depressive episodes by comparing converters, MDD patients, and healthy controls using voxel-wise gray matter volume analysis.

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 investigates how structural brain alterations in schizophrenia relate to network architecture, finding hub susceptibility and shared epicenters across disease stages and disorders.

This paper investigates how cross-validation affects effect size estimation in brain-wide association studies and finds that cross-validation improves generalizability, especially in small samples.

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

The paper demonstrates a data harmonization process for memory testing across 53 studies, reducing inter-site variance while preserving covariate effects.

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

The paper investigates genetic factors influencing a neurobiological substrate for psychiatric disorders and finds that a common genetic variant is associated with the substrate but not directly with disease risk.

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.

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.

This study identifies genetic variants linked to longitudinal brain structure changes across the lifespan using MRI data from 15,640 individuals.

This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.

The paper identifies genetic loci associated with human cortical structure and finds genetic correlations with neuropsychiatric disorders.

This study finds that higher HbA1c levels are associated with worse cognitive performance and reduced white matter integrity in healthy young adults.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.