Semiparametric confidence sets for arbitrary effect sizes in longitudinal neuroimaging

Oregon Convention Center 

Longitudinal data are increasingly prevalent in psychiatric neuroimaging as investigators aim to explore the relationships between biological factors and symptom variations on an individual level. This study addresses the complexity of longitudinal neuroimaging data to construct spatial confidence sets using a flexible semiparametric bootstrap joint (sPBJ) spatial extent inference (SEI) method. Our method involves robust estimation of the spatial covariance function based on the generalized estimating equation. We obtain more efficient effect size estimates by concurrently estimating the exchangeable working covariance and using the sPBJ bootstrap to determine the joint distribution of effect size across voxels. The bootstrap procedure is used to construct confidence sets for the effect size parameter. These confidence sets can identify the target and null regions of the image where the effect size is above or below given thresholds, respectively, with high probability. We evaluate the coverage of the proposed procedures using realistic simulations. This comprehensive approach, integrated into the pbj R package, offers a robust tool for analyzing repeated neuroimaging measurements.

Effect size

Confidence sets

Longitudinal neuroimaging data 

Section on Statistics in Imaging