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@ARTICLE{Eickhoff:19632,
      author       = {Eickhoff, S.B. and Bzdok, D. and Laird, A.R. and Kurth, F.
                      and Fox, P.T.},
      title        = {{A}ctivation likelihood estimation meta-analyis revisited},
      journal      = {NeuroImage},
      volume       = {59},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {PreJuSER-19632},
      pages        = {2349- 2361},
      year         = {2012},
      note         = {We acknowledge funding by the Human Brain Project
                      (R01-MH074457-01A1; PTF, ARL, SBE), the DFG (IRTG 1328; SBE,
                      DB) and the Helmholtz Initiative on Systems-Biology "The
                      Human Brain Model" (SBE).},
      abstract     = {A widely used technique for coordinate-based meta-analysis
                      of neuroimaging data is activation likelihood estimation
                      (ALE), which determines the convergence of foci reported
                      from different experiments. ALE analysis involves modelling
                      these foci as probability distributions whose width is based
                      on empirical estimates of the spatial uncertainty due to the
                      between-subject and between-template variability of
                      neuroimaging data. ALE results are assessed against a
                      null-distribution of random spatial association between
                      experiments, resulting in random-effects inference. In the
                      present revision of this algorithm, we address two remaining
                      drawbacks of the previous algorithm. First, the assessment
                      of spatial association between experiments was based on a
                      highly time-consuming permutation test, which nevertheless
                      entailed the danger of underestimating the right tail of the
                      null-distribution. In this report, we outline how this
                      previous approach may be replaced by a faster and more
                      precise analytical method. Second, the previously applied
                      correction procedure, i.e. controlling the false discovery
                      rate (FDR), is supplemented by new approaches for correcting
                      the family-wise error rate and the cluster-level
                      significance. The different alternatives for drawing
                      inference on meta-analytic results are evaluated on an
                      exemplary dataset on face perception as well as discussed
                      with respect to their methodological limitations and
                      advantages. In summary, we thus replaced the previous
                      permutation algorithm with a faster and more rigorous
                      analytical solution for the null-distribution and
                      comprehensively address the issue of multiple-comparison
                      corrections. The proposed revision of the ALE-algorithm
                      should provide an improved tool for conducting
                      coordinate-based meta-analyses on functional imaging data.},
      keywords     = {Algorithms / Brain: anatomy $\&$ histology / Cluster
                      Analysis / Data Interpretation, Statistical / False Positive
                      Reactions / Humans / Image Processing, Computer-Assisted:
                      methods / Image Processing, Computer-Assisted: statistics
                      $\&$ numerical data / Likelihood Functions / Magnetic
                      Resonance Imaging: methods / Magnetic Resonance Imaging:
                      statistics $\&$ numerical data / Meta-Analysis as Topic /
                      Positron-Emission Tomography: methods / Positron-Emission
                      Tomography: statistics $\&$ numerical data / Signal
                      Processing, Computer-Assisted / J (WoSType)},
      cin          = {INM-2 / INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-2-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {Funktion und Dysfunktion des Nervensystems (FUEK409) /
                      89574 - Theory, modelling and simulation (POF2-89574)},
      pid          = {G:(DE-Juel1)FUEK409 / G:(DE-HGF)POF2-89574},
      shelfmark    = {Neurosciences / Neuroimaging / Radiology, Nuclear Medicine
                      $\&$ Medical Imaging},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:21963913},
      pmc          = {pmc:PMC3254820},
      UT           = {WOS:000299494000037},
      doi          = {10.1016/j.neuroimage.2011.09.017},
      url          = {https://juser.fz-juelich.de/record/19632},
}