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| Hauptseite > Publikationsdatenbank > Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies > print |
| Hauptseite > Publikationsdatenbank > Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies > print |
| 001 | 849919 | ||
| 005 | 20210129234337.0 | ||
| 024 | 7 | _ | |a 10.1111/rssc.12295 |2 doi |
| 024 | 7 | _ | |a 0035-9254 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2018-04015 |
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| 100 | 1 | _ | |a Samartsidis, Pantelis |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies |
| 260 | _ | _ | |a Oxford |c 2019 |b Wiley-Blackwell |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1545299492_3475 |2 PUB:(DE-HGF) |
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| 500 | _ | _ | |a 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 |
| 520 | _ | _ | |a 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 |
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| 773 | _ | _ | |a 10.1111/rssc.12295 |0 PERI:(DE-600)1482300-7 |n 1 |p 217-234 |t Journal of the Royal Statistical Society / C |v 68 |y - |x 0035-9254 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/849919/files/Samartsidis_et_al-2019-Journal_of_the_Royal_Statistical_Society__Series_C_%28Applied_Statistics%29.pdf |
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