In this work we perform a meta-analysis of neuroimaging data,
consisting of locations of peak activations identified in 162
separate studies on emotion. Neuroimaging meta-analyses are
typically performed using kernel-based methods. However, these
methods require the width of the kernel to be set a priori and
to be constant across the brain. To address these issues, we
propose a fully Bayesian nonparametric binary regression method
to perform neuroimaging meta-analyses. In our method, each
location (or voxel) has a probability of being a peak
activation, and the corresponding probability function is based
on a spatially adaptive Gaussian Markov random field (GMRF). We
also include parameters in the model to robustify the procedure
against miscoding of the voxel response. Posterior inference is
implemented using efficient MCMC algorithms extended from those
introduced in Holmes and Held [Bayesian Anal.
1 (2006) 145–168]. Our method allows the probability
function to be locally adaptive with respect to the covariates,
that is, to be smooth in one region of the covariate space and
wiggly or even discontinuous in another. Posterior miscoding
probabilities for each of the identified voxels can also be
obtained, identifying voxels that may have been falsely
classified as being activated. Simulation studies and
application to the emotion neuroimaging data indicate that our
method is superior to standard kernel-based methods.
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