  Working memory (WM) was one of the first cognitive processes studied with
functional magnetic resonance imaging. With now over 20 years of studies on WM,
each study with tiny sample sizes, there is a need for meta-analysis to
identify the brain regions that are consistently activated by WM tasks, and to
understand the interstudy variation in those activations. However, current
methods in the field cannot fully account for the spatial nature of
neuroimaging meta-analysis data or the heterogeneity observed among WM studies.
In this work, we propose a fully Bayesian random-effects metaregression model
based on log-Gaussian Cox processes, which can be used for meta-analysis of
neuroimaging studies. An efficient Markov chain Monte Carlo scheme for
posterior simulations is presented which makes use of some recent advances in
parallel computing using graphics processing units. Application of the proposed
model to a real data set provides valuable insights regarding the function of
the WM.
