Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised withmeta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in thearticle are available for Coordinate-Based Meta-Ana lysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas ofconsistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). Tosimultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci fromeach study as a doubly stochastic Poisson process, where the study-speci?c log intensity function is characterized as a linearcombination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factormodeling of the basis coe?cients. Within our framework, it is also possible to account for the e?ect of study-level covariates(meta-regression), signi?cantly expanding the capabilities of the current neuroimaging meta-analysis methods available. Weapply our methodology to synthetic data and neuroimaging meta-analysis datasets.