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@ARTICLE{Frahm:907807,
      author       = {Frahm, Lennart and Cieslik, Edna C. and Hoffstaedter, Felix
                      and Satterthwaite, Theodore D. and Fox, Peter T. and
                      Langner, Robert and Eickhoff, Simon B.},
      title        = {{E}valuation of thresholding methods for activation
                      likelihood estimation meta‐analysis via large‐scale
                      simulations},
      journal      = {Human brain mapping},
      volume       = {43},
      number       = {13},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2022-02226},
      pages        = {3987-3997},
      year         = {2022},
      abstract     = {In recent neuroimaging studies, threshold-free cluster
                      enhancement (TFCE) gained popularity as a sophisticated
                      thresholding method for statistical inference. It was shown
                      to feature higher sensitivity than the frequently used
                      approach of controlling the cluster-level family-wise error
                      (cFWE) and it does not require setting a cluster-forming
                      threshold at voxel level. Here, we examined the
                      applicability of TFCE to a widely used method for
                      coordinate-based neuroimaging meta-analysis, Activation
                      Likelihood Estimation (ALE), by means of large-scale
                      simulations. We created over 200,000 artificial
                      meta-analysis datasets by independently varying the total
                      number of experiments included and the amount of spatial
                      convergence across experiments. Next, we applied ALE to all
                      datasets and compared the performance of TFCE to both
                      voxel-level and cluster-level FWE correction approaches. All
                      three multiple-comparison correction methods yielded valid
                      results, with only about $5\%$ of the significant clusters
                      being based on spurious convergence, which corresponds to
                      the nominal level the methods were controlling for. On
                      average, TFCE's sensitivity was comparable to that of cFWE
                      correction, but it was slightly worse for a subset of
                      parameter combinations, even after TFCE parameter
                      optimization. cFWE yielded the largest significant clusters,
                      closely followed by TFCE, while voxel-level FWE correction
                      yielded substantially smaller clusters, showcasing its high
                      spatial specificity. Given that TFCE does not outperform the
                      standard cFWE correction but is computationally much more
                      expensive, we conclude that employing TFCE for ALE cannot be
                      recommended to the general user.},
      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       = {35535616},
      UT           = {WOS:000792603500001},
      doi          = {10.1002/hbm.25898},
      url          = {https://juser.fz-juelich.de/record/907807},
}