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@ARTICLE{Eickhoff:809682,
      author       = {Eickhoff, Simon and Nichols, Thomas E. and Laird, Angela R.
                      and Hoffstaedter, Felix and Amunts, Katrin and Fox, Peter T.
                      and Bzdok, Danilo and Eickhoff, Claudia R.},
      title        = {{B}ehavior, sensitivity, and power of activation likelihood
                      estimation characterized by massive empirical simulation},
      journal      = {NeuroImage},
      volume       = {137},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2016-02616},
      pages        = {70-85},
      year         = {2016},
      abstract     = {Given the increasing number of neuroimaging publications,
                      the automated knowledge extraction on brain-behavior
                      associations by quantitative meta-analyses has become a
                      highly important and rapidly growing field of research.
                      Among several methods to perform coordinate-based
                      neuroimaging meta-analyses, Activation Likelihood Estimation
                      (ALE) has been widely adopted. In this paper, we addressed
                      two pressing questions related to ALE meta-analysis: i)
                      Which thresholding method is most appropriate to perform
                      statistical inference? ii) Which sample size, i.e., number
                      of experiments, is needed to perform robust meta-analyses?
                      We provided quantitative answers to these questions by
                      simulating more than 120,000 meta-analysis datasets using
                      empirical parameters (i.e., number of subjects, number of
                      reported foci, distribution of activation foci) derived from
                      the BrainMap database. This allowed to characterize the
                      behavior of ALE analyses, to derive first power estimates
                      for neuroimaging meta-analyses, and to thus formulate
                      recommendations for future ALE studies. We could show as a
                      first consequence that cluster-level family-wise error (FWE)
                      correction represents the most appropriate method for
                      statistical inference, while voxel-level FWE correction is
                      valid but more conservative. In contrast, uncorrected
                      inference and false-discovery rate correction should be
                      avoided. As a second consequence, researchers should aim to
                      include at least 20 experiments into an ALE meta-analysis to
                      achieve sufficient power for moderate effects. We would like
                      to note, though, that these calculations and recommendations
                      are specific to ALE and may not be extrapolated to other
                      approaches for (neuroimaging) meta-analysis.},
      cin          = {INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP - The
                      Human Brain Project (604102) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)604102 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000378048700008},
      pubmed       = {pmid:27179606},
      doi          = {10.1016/j.neuroimage.2016.04.072},
      url          = {https://juser.fz-juelich.de/record/809682},
}