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@ARTICLE{Frahm:1015277,
      author       = {Frahm, Lennart and Satterthwaite, Theodore D. and Fox,
                      Peter T. and Langner, Robert and Eickhoff, Simon B.},
      title        = {{ALE} meta-analyses of voxel-based morphometry studies:
                      {P}arameter validation via large-scale simulations},
      journal      = {NeuroImage},
      volume       = {281},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-03632},
      pages        = {120383 -},
      year         = {2023},
      note         = {This study was supported by the Deutsche
                      Forschungsgemeinschaft (DFG, EI 816/11-1 and International
                      Research Training Group 2150, 269953372/GRK2150), the
                      National Institute of Mental Health (R01-MH074457), the
                      National Institute of Aging (P30-AG066546), and the
                      Jülich-Aachen Research Alliance (JARA) granting computation
                      time on the supercomputer JURECA (Jülich Supercomputing
                      Centre, 2018) at Forschungszentrum Jülich. Open access
                      funding enabled and organized by Projekt DEAL.},
      abstract     = {Activation likelihood estimation (ALE) meta-analysis has
                      been applied to structural neuroimaging data since long, but
                      up to now, any systematic assessment of the algorithm's
                      behavior, power and sensitivity has been based on
                      simulations using functional neuroimaging databases as their
                      foundation. Here, we aimed to determine whether the
                      guidelines offered by previous evaluations can be
                      generalized to ALE meta-analyses of voxel-based morphometry
                      (VBM) studies. We ran 365000 distinct ALE analyses filled
                      with simulated experiments, randomly sampling parameters
                      from BrainMap's VBM experiment database. We then examined
                      the algorithm's sensitivity, its susceptibility to spurious
                      convergence, and its susceptibility to excessive
                      contributions by individual experiments. In general, the
                      performance of the ALE algorithm was highly comparable
                      between imaging modalities, with the algorithm's sensitivity
                      and specificity reaching similar levels with structural data
                      as previously observed with functional data. Because of the
                      lower number of foci reported and the higher number of
                      participants usually included in structural experiments,
                      individual studies had, on average, a higher impact towards
                      significant clusters. To prevent significant clusters from
                      being driven by single experiments, we recommend that
                      researchers include at least 23 experiments in a VBM ALE
                      dataset, instead of the previously recommended minimum of n
                      = 17. While these recommendations do not constitute hard
                      borders, running ALE analyses on smaller datasets would
                      require special diligence in assessing and reporting the
                      contributions of experiments to individual clusters.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37734477},
      UT           = {WOS:001083784800001},
      doi          = {10.1016/j.neuroimage.2023.120383},
      url          = {https://juser.fz-juelich.de/record/1015277},
}