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Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets.

Original publication

DOI

10.1111/biom.12713

Type

Journal article

Journal

Biometrics

Publication Date

03/2018

Volume

74

Pages

342 - 353

Keywords

Bayesian modeling, Factor analysis, Functional principal component analysis, Meta-analysis, Reverse inference, Spatial point pattern data, Bayes Theorem, Humans, Latent Class Analysis, Magnetic Resonance Imaging, Meta-Analysis as Topic, Models, Statistical, Neuroimaging, Principal Component Analysis, Spatial Regression, Stochastic Processes