Bootstrapping GEE models for fMRI regional connectivity

Abstract

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations during rest between groups. We apply generalized estimating equation (GEE) models to test for differences in regional correlations across groups. Both the GEE marginal model and GEE transition model are evaluated and compared to the standard pooling Fisher-z approach using simulation studies. Standard errors of all methods are estimated both theoretically (model-based) and empirically (bootstrap). Of all the methods, we find that the transition models have the best statistical properties. Overall, the model-based standard errors and bootstrap standard errors perform about the same. We also demonstrate the methods with a functional connectivity study in a healthy cognitively normal population of ApoE4+ participants and ApoE4- participants who are recruited from the Adult Children's Study conducted at the Washington University Knight Alzheimer's Disease Research Center.