Spatial Bayesian latent factor regression modeling of coordinate‐based meta‐analysis data. Issue 1 (12th May 2017)

• Record Type: Journal Article
• Authors: Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.
• Is Part Of: Biometrics. Volume 74:Issue 1(2018)
• Page Start: 342
• Page End: 353
• Publication Date: 2017-05-12
• Subjects: Bayesian modeling -- Factor analysis -- Functional principal component analysis -- Meta‐analysis -- Reverse inference -- Spatial point pattern data; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12713
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: 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.