Ingrid Agartz

This study uses free-water imaging to find microstructural white matter differences in chronic schizophrenia patients compared to controls, with associations to subcortical structures and clinical symptoms.

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

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 identifies hippocampal subfield volume differences in PTSD and depression across 31 cohorts, finding depression has a stronger effect than PTSD, especially in CA1, with comorbid cases showing more pronounced changes.

The paper identifies 161 genome-wide significant genetic associations for cortical structure traits across 34 brain regions, highlighting key biological pathways and gene functions.

The paper identifies genetic associations with cortical surface area using PCA and GWAS, linking DAAM1 to visual cortex development through mitochondrial pathways and thalamocortical input.

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 conducts a GWAS on suicide attempt across psychiatric disorders, identifying genetic loci and showing polygenic risk for major depression predicts suicide attempt in multiple disorders.

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 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 finds a genetic association between schizophrenia risk and age at first birth in women, with different genetic correlations in younger and older age groups.

This study identifies 114 genome-wide significant loci for schizophrenia and bipolar disorder, with specific subphenotype associations and four genes distinguishing the disorders.

This study compares LDSC and GREML methods for estimating genetic correlation, showing GREML provides higher accuracy, especially in the presence of genetic heterogeneity.

The paper identifies 25 genetic loci associated with subcortical brain structure volumes in over 40,000 individuals, revealing novel biology and potential drug targets.

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.

This study identifies eight genome-wide significant CNV loci associated with schizophrenia using a large cohort of 41,321 subjects.