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

Montagna, Silvia, Wager, Tor, Feldman Barrett, Lisa, Johnson, Timothy D., Nichols, Thomas E. (2017) Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data. Biometrics, . ISSN 0006-341X. E-ISSN 1541-0420. (doi:10.1111/biom.12713) (KAR id:61627)

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Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised withmeta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in thearticle are available for Coordinate-Based Meta-Ana lysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas ofconsistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). Tosimultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci fromeach study as a doubly stochastic Poisson process, where the study-speci?c log intensity function is characterized as a linearcombination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factormodeling of the basis coe?cients. Within our framework, it is also possible to account for the e?ect of study-level covariates(meta-regression), signi?cantly expanding the capabilities of the current neuroimaging meta-analysis methods available. Weapply our methodology to synthetic data and neuroimaging meta-analysis datasets.

Item Type: Article
DOI/Identification number: 10.1111/biom.12713
Uncontrolled keywords: Bayesian modeling; Factor analysis; Functional principal component analysis; Meta-analysis; Spatial point pattern data; Reverse inference
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Silvia Montagna
Date Deposited: 08 May 2017 10:33 UTC
Last Modified: 21 Jan 2020 09:49 UTC
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