ArticleBiometrics2018
Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Effects of acupuncture on brain function in patients with depression: A meta-analysis of functional magnetic resonance imaging studies.Medicine · 2026Pooled it
- Meta-analysis of neural correlates of working memory, reward, and emotion processing in major depressive disorder using AES-SDM.BMC psychiatry · 2026Pooled it
- A Cortical Surface-Based Meta-Analysis of Human Reasoning.Cerebral cortex (New York, N.Y. : 1991) · 2021Pooled it
- Estimating the prevalence of missing experiments in a neuroimaging meta-analysis.Research synthesis methods · 2020Pooled it
- CBMR: Coordinate-based meta-regression for group and covariate inference.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.Biostatistics (Oxford, England) · 2024Article
- Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.Journal of the American Statistical Association · 2024Article
- Article
- A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2021Article
- Finding specificity in structural brain alterations through Bayesian reverse inference.Human brain mapping · 2020Article
- Bayesian log-Gaussian Cox process regression: with applications to meta-analysis of neuroimaging working memory studies.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
- A Bayesian Approach for the Use of Athlete Performance Data Within Anti-doping.Frontiers in physiology · 2018Article
- The coordinate-based meta-analysis of neuroimaging data.Statistical science : a review journal of the Institute of Mathematical StatisticsArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
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 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.
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