Lara Marise Wierenga

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 mood variability in adolescents, finding gender differences in its peak timing and associations with brain structure and sleep patterns.

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

The paper quantifies individual and sex differences in structural brain development from childhood to young adulthood using MRI data from three longitudinal datasets.

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.

Qoala-T is a supervised-learning tool that predicts the quality of FreeSurfer segmented MRI data with high accuracy, reducing manual quality control time and variability.