Maureen McHugo

This paper develops a method for constructing confidence sets for arbitrary effect sizes in longitudinal neuroimaging data, enabling more accurate inference of brain changes over time.

The paper evaluates resampling methods for neuroimage topological features, finding that wild bootstrap and permutation perform well in larger samples, while GRF-based methods have inflated error rates.