In this talk, we focus on some new spatial point process models with their applications to meta analysis of functional neuroimaging data. We propose a Bayesian spatial hierarchical model using a marked independent cluster process for functional neuroimaging meta analysis. In contrast to the current approaches, our hierarchical model accounts for intra-study variation in location (if any), inter-study variation, and idiosyncratic foci that do not cluster between studies. A defining feature of our model is its ability to dissociate inter-study spread of foci from the spatial uncertainty in population centers. Our model is illustrated on a meta analysis consisting of 437 studies from 164 publications. Another interesting topic is “reverse inference” on psychological states given functional neuroimaging meta analysis data. Given type labels that classify each study, we construct a Bayesian spatial point process classifier based on the posterior predictive probability of class membership. We measure performance via leave-one-out cross validation using an importance sampling approach that avoids multiple posterior simulations. We demonstrate our method on the meta analysis of emotions, classifying different sub-types of emotions. Our method attains a much higher prediction accuracy compared with a comparable naive Bayesian classifier.