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@ARTICLE{Samartsidis:849919,
      author       = {Samartsidis, Pantelis and Eickhoff, Claudia and Eickhoff,
                      Simon and Wager, Tor D. and Barrett, Lisa Feldman and Atzil,
                      Shir and Johnson, Timothy D. and Nichols, Thomas E.},
      title        = {{B}ayesian log-{G}aussian {C}ox process regression:
                      applications to meta-analysis of neuroimaging working memory
                      studies},
      journal      = {Journal of the Royal Statistical Society / C},
      volume       = {68},
      number       = {1},
      issn         = {0035-9254},
      address      = {Oxford},
      publisher    = {Wiley-Blackwell},
      reportid     = {FZJ-2018-04015},
      pages        = {217-234},
      year         = {2019},
      note         = {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},
      abstract     = {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},
      cin          = {INM-7 / INM-1},
      ddc          = {510},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000453687800011},
      pubmed       = {pmid:30906075},
      doi          = {10.1111/rssc.12295},
      url          = {https://juser.fz-juelich.de/record/849919},
}