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Neuroimaging meta-analysis is an important tool for finding consistenteffects over studies that each usually have 20 or fewer subjects. Interest inmeta-analysis in brain mapping is also driven by a recent focus on so-called"reverse inference": where as traditional "forward inference" identifies theregions of the brain involved in a task, a reverse inference identifies thecognitive processes that a task engages. Such reverse inferences, however,require a set of meta-analysis, one for each possible cognitive domain.However, existing methods for neuroimaging meta-analysis have significantlimitations. Commonly used methods for neuroimaging meta-analysis are not modelbased, do not provide interpretable parameter estimates, and only produce nullhypothesis inferences; further, they are generally designed for a single groupof studies and cannot produce reverse inferences. In this work we address theselimitations by adopting a nonparametric Bayesian approach for meta-analysisdata from multiple classes or types of studies. In particular, foci from eachtype of study are modeled as a cluster process driven by a random intensityfunction that is modeled as a kernel convolution of a gamma random field. Thetype-specific gamma random fields are linked and modeled as a realization of acommon gamma random field, shared by all types, that induces correlationbetween study types and mimics the behavior of a univariate mixed effectsmodel. We illustrate our model on simulation studies and a meta-analysis offive emotions from 219 studies and check model fit by a posterior predictiveassessment. In addition, we implement reverse inference by using the model topredict study type from a newly presented study. We evaluate this predictiveperformance via leave-one-out cross-validation that is efficiently implementedusing importance sampling techniques.

A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis