A guided multiverse study of neuroimaging analyses

Abstract

Most neuroimaging studies are associated with a broad range analytic and methodological choices that the researcher needs to make, but every choice might lead to different answers, and evaluating all possible analytic choices is computationally challenging. Here, authors present a framework that maps the space of analysis by creating a low-dimensional space and using a Bayesian optimization to navigate it.