Kathryn I. Alpert

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.

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.

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.

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.