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In regression-based analyses of group-level neuroimage data researcherstypically fit a series of marginal general linear models to image outcomes ateach spatially-referenced pixel. Spatial regularization of effects of interestis usually induced indirectly by applying spatial smoothing to the data duringpreprocessing. While this procedure often works well, resulting inference canbe poorly calibrated. Spatial modeling of effects of interest leads to morepowerful analyses, however the number of locations in a typical neuroimage canpreclude standard computation with explicitly spatial models. Here wecontribute a Bayesian spatial regression model for group-level neuroimaginganalyses. We induce regularization of spatially varying regression coefficientfunctions through Gaussian process priors. When combined with a simplenonstationary model for the error process, our prior hierarchy can lead to moredata-adaptive smoothing than standard methods. We achieve computationaltractability through Vecchia approximation of our prior which, critically, canbe constructed for a wide class of spatial correlation functions and results inprior models that retain full spatial rank. We outline several ways to workwith our model in practice and compare performance against standard vertex-wiseanalyses. Finally we illustrate our method in an analysis of cortical surfacefMRI task contrast data from a large cohort of children enrolled in theAdolescent Brain Cognitive Development study.

Bayesian inference for group-level cortical surface image-on-scalar-regression with Gaussian process priors