Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Pantelis Samartsidis*, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects metaregression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient Markov chain Monte Carlo scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units. Application of the proposed model to a real data set provides valuable insights regarding the function of the WM.

Original languageAmerican English
Pages (from-to)217-234
Number of pages18
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number1
StatePublished - Jan 2019

Bibliographical note

Publisher Copyright:
© 2018 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.


  • Functional magnetic resonance imaging
  • Metaregression
  • Random-effects meta-analysis
  • Working memory


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