TY  - JOUR
AU  - Samartsidis, Pantelis
AU  - Eickhoff, Claudia
AU  - Eickhoff, Simon
AU  - Wager, Tor D.
AU  - Barrett, Lisa Feldman
AU  - Atzil, Shir
AU  - Johnson, Timothy D.
AU  - Nichols, Thomas E.
TI  - Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies
JO  - Journal of the Royal Statistical Society / C
VL  - 68
IS  - 1
SN  - 0035-9254
CY  - Oxford
PB  - Wiley-Blackwell
M1  - FZJ-2018-04015
SP  - 217-234
PY  - 2019
N1  - This work was largely completed while PS and TEN were at the University of Warwick, De-partment of Statistics. PS, TDJ and TEN were supported by National Institutes of Health grant5-R01-NS-075066; TEN was supported by Wellcome Trust fellowship 100309/Z/12/Z and Na-tional Institutes of Health grant R01 2R01EB015611-04. The work that is presented in thispaper represents the views of the authors and not necessarily those of the National Institutesof Health or the Wellcome Trust Foundation
AB  - Working memory (WM) was one of the first cognitive processes studied with func-tional magnetic resonance imaging. With now over 20 years of studies on WM, each study withtiny sample sizes, there is a need for meta-analysis to identify the brain regions that are con-sistently 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 neuroimagingmeta-analysis data or the heterogeneity observed among WM studies. In this work, we proposea 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 MonteCarlo scheme for posterior simulations is presented which makes use of some recent advancesin parallel computing using graphics processing units. Application of the proposed model to areal data set provides valuable insights regarding the function of the WM.Keywords:   Functional magnetic resonance imaging; Metaregression; Random-effectsmeta-analysis; Working memory
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000453687800011
C6  - pmid:30906075
DO  - DOI:10.1111/rssc.12295
UR  - https://juser.fz-juelich.de/record/849919
ER  -