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@ARTICLE{Acar:858068,
      author       = {Acar, Freya and Seurinck, Ruth and Eickhoff, Simon and
                      Moerkerke, Beatrijs},
      title        = {{A}ssessing robustness against potential publication bias
                      in {A}ctivation {L}ikelihood {E}stimation ({ALE})
                      meta-analyses for f{MRI}},
      journal      = {PLOS ONE},
      volume       = {13},
      number       = {11},
      issn         = {1932-6203},
      address      = {San Francisco, California, US},
      publisher    = {PLOS},
      reportid     = {FZJ-2018-06986},
      pages        = {-},
      year         = {2018},
      note         = {FA, RS and BM would like to acknowledge the Research
                      Foundation Flanders (FWO) for financial support (Grant
                      G.0149.14N).SBE was supported by the National Institute of
                      Mental Health (R01-MH074457), the Helmholtz Portfolio Theme
                      "Supercomputing and Modeling for the Human Brain" and the
                      European Union’s Horizon 2020 Research and Innovation
                      Programme under Grant Agreement No. 7202070 (HBP SGA1).},
      abstract     = {The importance of integrating research findings is
                      incontrovertible and procedures for coordinate-based
                      meta-analysis (CBMA) such as Activation Likelihood
                      Estimation (ALE) have become a popular approach to combine
                      results of fMRI studies when only peaks of activation are
                      reported. As meta-analytical findings help building
                      cumulative knowledge and guide future research, not only the
                      quality of such analyses but also the way conclusions are
                      drawn is extremely important. Like classical meta-analyses,
                      coordinate-based meta-analyses can be subject to different
                      forms of publication bias which may impact results and
                      invalidate findings. The file drawer problem refers to the
                      problem where studies fail to get published because they do
                      not obtain anticipated results (e.g. due to lack of
                      statistical significance). To enable assessing the stability
                      of meta-analytical results and determine their robustness
                      against the potential presence of the file drawer problem,
                      we present an algorithm to determine the number of noise
                      studies that can be added to an existing ALE fMRI
                      meta-analysis before spatial convergence of reported
                      activation peaks over studies in specific regions is no
                      longer statistically significant. While methods to gain
                      insight into the validity and limitations of results exist
                      for other coordinate-based meta-analysis toolboxes, such as
                      Galbraith plots for Multilevel Kernel Density Analysis
                      (MKDA) and funnel plots and egger tests for seed-based d
                      mapping, this procedure is the first to assess robustness
                      against potential publication bias for the ALE algorithm.
                      The method assists in interpreting meta-analytical results
                      with the appropriate caution by looking how stable results
                      remain in the presence of unreported information that may
                      differ systematically from the information that is included.
                      At the same time, the procedure provides further insight
                      into the number of studies that drive the meta-analytical
                      results. We illustrate the procedure through an example and
                      test the effect of several parameters through extensive
                      simulations. Code to generate noise studies is made freely
                      available which enables users to easily use the algorithm
                      when interpreting their results},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain Project
                      Specific Grant Agreement 1 (720270) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)720270 / G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30500854},
      UT           = {WOS:000451883700027},
      doi          = {10.1371/journal.pone.0208177},
      url          = {https://juser.fz-juelich.de/record/858068},
}