Stephan Lau

This paper introduces a deep-learning method for creating age-dependent brain templates using diffeomorphic transformations, showing it produces high-fidelity spatial templates but struggles to capture all age-related morphological changes.

This study evaluates normative models for neuroimaging data, showing that larger sample sizes reduce variance but not bias, and flexible models perform better for nonlinear data.