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@ARTICLE{Yeung:1005429,
      author       = {Yeung, Andy Wai Kan and Robertson, Michaela and Uecker,
                      Angela and Fox, Peter T. and Eickhoff, Simon B.},
      title        = {{T}rends in the sample size, statistics, and contributions
                      to the {B}rain{M}ap database of activation likelihood
                      estimation meta‐analyses: {A}n empirical study of
                      10‐year data},
      journal      = {Human brain mapping},
      volume       = {44},
      number       = {5},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2023-01466},
      pages        = {1876 - 1887},
      year         = {2023},
      abstract     = {The literature of neuroimaging meta-analysis has been
                      thriving for over a decade. A majority of them were
                      coordinate-based meta-analyses, particularly the activation
                      likelihood estimation (ALE) approach. A meta-evaluation of
                      these meta-analyses was performed to qualitatively evaluate
                      their design and reporting standards. The publications
                      listed from the BrainMap website were screened. Six hundred
                      and three ALE papers published during 2010–2019 were
                      included and analysed. For reporting standards, most of the
                      ALE papers reported their total number of Papers involved
                      and mentioned the inclusion/exclusion criteria on Paper
                      selection. However, most papers did not describe how data
                      redundancy was avoided when multiple related Experiments
                      were reported within one paper. The most prevalent
                      repeated-measures correction methods were voxel-level FDR
                      $(54.4\%)$ and cluster-level FWE $(33.8\%),$ with the latter
                      quickly replacing the former since 2016. For study
                      characteristics, sample size in terms of number of Papers
                      included per ALE paper and number of Experiments per
                      analysis seemed to be stable over the decade. One-fifth of
                      the surveyed ALE papers failed to meet the recommendation of
                      having >17 Experiments per analysis. For data sharing, most
                      of them did not provide input and output data. In
                      conclusion, the field has matured well in terms of rising
                      dominance of cluster-level FWE correction, and slightly
                      improved reporting on elimination of data redundancy and
                      providing input data. The provision of Data and Code
                      availability statements and flow chart of literature
                      screening process, as well as data submission to BrainMap,
                      should be more encouraged.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / 5254 -
                      Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36479854},
      UT           = {WOS:000894154500001},
      doi          = {10.1002/hbm.26177},
      url          = {https://juser.fz-juelich.de/record/1005429},
}