Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

TL;DR

This paper introduces a Bayesian hierarchical model for coordinate-based meta-analysis of neuroimaging data, enabling identification of consistent brain activation and prediction of cognitive tasks while incorporating study-level covariates.

Contribution

It proposes a novel Bayesian point process hierarchical model that captures study heterogeneity and covariate effects in neuroimaging meta-analysis, with sparse latent factor representation.

Findings

Successfully applied to synthetic data demonstrating model validity.

Effectively analyzed real neuroimaging meta-analysis dataset.

Enhanced meta-analytic inference with covariate adjustment.

Abstract

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 paper are available for Coordinate-based Meta-analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) 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 characterised 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 a neuroimaging meta-analysis dataset.