Tobias Kaufmann

This study examines how sex-hormone exposure, pregnancies, and genetic risk for Alzheimer's disease influence brain aging in women using UK Biobank data.

The paper predicts brain age using multimodal MRI data from the Whitehall II cohort and links brain aging to cardiovascular risk factors.

The paper identifies genetic loci associated with brainstem volumes and finds genetic overlaps with psychiatric and neurological disorders.

This study examines age-related changes in the T1w/T2w ratio across the cortical surface in children and adolescents, linking it to cognitive development and sex differences.

The paper examines associations between multimodal brain structural MRI measures and age, sex, and polygenic scores for neuroticism in children and adolescents, finding age and sex effects but weak polygenic associations.

The paper introduces MOSTest, a multivariate statistical method for GWAS, identifying 347 genetic loci associated with brain morphology in UK Biobank data.

This study uses diffusion tensor imaging and machine learning to predict brain age in schizophrenia and bipolar disorder patients, finding age overestimation in both groups compared to controls.

The paper uses dynamic graphical models to analyze directed functional brain connectivity in rsfMRI data from HCP and UK Biobank, finding associations with age, sex, and mental health.

The paper quantifies genetic overlap between cortical thickness and surface area using UK Biobank data, finding shared causal variants and suggesting more discoverable parcellation schemes.

This study links a history of childbirths to region-specific brain aging patterns in middle and older-aged women using brain-age prediction methods.

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

The paper identifies genetic loci associated with variability in brain volumes and thickness, showing they are distinct from loci affecting mean volumes.

The paper identifies three modes of brain co-variation in children linked to perinatal risk, socio-cognitive stratification, and urbanicity factors using brain imaging and socio-economic data.

This study tests if brain age prediction can predict cognitive outcomes and response to training in stroke patients, but finds no significant associations.

This study uses machine learning on brain connectomics data to predict cognitive and mental health traits and their polygenic scores in a large population sample.

This study shows that the brain's functional connectome remains stable over 2-3 years in middle and old age, and its stability relates to episodic memory performance.

The paper uses neuroimaging and machine learning to show that more childbirths are associated with a younger-looking brain in women, independent of genetics.

This study fuses structural and functional brain imaging data in depression using linked independent component analysis but finds limited associations with case-control status and symptom loads.

The paper estimates brain age gaps using MRI and machine learning in individuals with and without brain disorders, finding heritability and genetic overlap with common disorders.

This study shows that brain age gap from MRI correlates with processing speed in early multiple sclerosis.

This study finds that multiple sclerosis patients have higher brain age gaps and accelerated brain aging rates compared to healthy controls using MRI and machine learning.

This study maps oxytocin pathway gene expression in the human brain and links it to brain function using fMRI, showing enrichment in specific regions and interactions with dopamine and acetylcholine systems.

This study uses machine learning to show cerebellar morphology predicts cognitive function and psychopathology in adolescents, with stronger links to prodromal psychotic symptoms.

The paper maps genetic architectures of cognitive traits and psychiatric disorders onto the brain and shows their association with normative brain maturity patterns in youths, while suggesting environmental factors may influence this relationship.

The paper develops tissue-specific brain age prediction models using diffusion tensor imaging and morphometry to assess cognitive aging patterns and their deviations from normal trajectories.

The paper examines age-related changes in gray/white matter contrast and links them to psychopathology and cognitive ability in youth using MRI data.

This study identifies 15 genome-wide significant loci associated with hippocampal subfield volumes in 21,297 individuals, revealing distinct genetic determinants for different subfields.

The paper uses diffusion tensor imaging to identify white matter abnormalities in schizophrenia and bipolar disorder, finding distinct patterns between groups and age-related changes in schizophrenia.

This paper investigates brain structural heterogeneity in schizophrenia and finds that polygenic risk is associated with thinner fronto-temporal cortices and smaller hippocampal subfields, but not with increased variability.

The paper identifies symptom-based subgroups in depression and anxiety using clustering and finds distinct static brain connectivity patterns associated with these subgroups.