Marc-André Schulz

This study shows simpler brain-age models with lower age prediction accuracy are more sensitive to disease-related changes, suggesting they may be better biomarkers for detecting neurological and psychiatric disorders.

This study compares transformers and CNNs in predicting brain age using MRI data, finding they perform similarly and represent different concepts of brain aging.

This study shows that prediction performance for brain-imaging-based phenotypes can improve significantly with larger sample sizes and multimodal data.

The paper investigates neural pathways linking psychological stress and brain health in healthy individuals and multiple sclerosis patients, finding similar functional connectivity patterns.

The paper compares linear and nonlinear model performance on brain imaging data and standard datasets, finding linear models outperform nonlinear ones in brain scans despite larger sample sizes.