TY - JOUR
T1 - Bayesian log-Gaussian Cox process regression
T2 - applications to meta-analysis of neuroimaging working memory studies
AU - Samartsidis, Pantelis
AU - Eickhoff, Claudia R.
AU - Eickhoff, Simon B.
AU - Wager, Tor D.
AU - Barrett, Lisa Feldman
AU - Atzil, Shir
AU - Johnson, Timothy D.
AU - Nichols, Thomas E.
N1 - 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.
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
KW - Functional magnetic resonance imaging
KW - Metaregression
KW - Random-effects meta-analysis
KW - Working memory
UR - http://www.scopus.com/inward/record.url?scp=85058812946&partnerID=8YFLogxK
U2 - 10.1111/rssc.12295
DO - 10.1111/rssc.12295
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AN - SCOPUS:85058812946
SN - 0035-9254
VL - 68
SP - 217
EP - 234
JO - Journal of the Royal Statistical Society. Series C: Applied Statistics
JF - Journal of the Royal Statistical Society. Series C: Applied Statistics
IS - 1
ER -