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@ARTICLE{Eickhoff:5635,
      author       = {Eickhoff, S. B. and Laird, A.R. and Grefkes, C. and Wang,
                      L.E. and Zilles, K. and Fox, P.T.},
      title        = {{C}oordinate-{B}ased {A}ctivaton {L}ikelihood {E}stimation
                      {M}eta-{A}nalysis of {N}euroimaging {D}ata: {A}
                      {R}andom-{E}ffects {A}pproach {B}ased on {E}mpirical
                      {E}stimates of {S}patial {U}ncertainty},
      journal      = {Human brain mapping},
      volume       = {30},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {PreJuSER-5635},
      pages        = {2907 - 2926},
      year         = {2009},
      note         = {National Institute of Biomedical Imaging and
                      Bioengineering, National Institute of Neurological Disorders
                      and Stroke, National Institute of Mental Health.},
      abstract     = {A widely used technique for coordinate-based meta-analyses
                      of neuroimaging data is activation likelihood estimation
                      (ALE). ALE assesses the overlap between foci based on
                      modeling them as probability distributions centered at the
                      respective coordinates. In this Human Brain
                      Project/Neuroinformatics research, the authors present a
                      revised ALE algorithm addressing drawbacks associated with
                      former implementations. The first change pertains to the
                      size of the probability distributions, which had to be
                      specified by the used. To provide a more principled
                      solution, the authors analyzed fMRI data of 21 subjects,
                      each normalized into MNI space using nine different
                      approaches. This analysis provided quantitative estimates of
                      between-subject and between-template variability for 16
                      functionally defined regions, which were then used to
                      explicitly model the spatial uncertainty associated with
                      each reported coordinate. Secondly, instead of testing for
                      an above-chance clustering between foci, the revised
                      algorithm assesses above-chance clustering between
                      experiments. The spatial relationship between foci in a
                      given experiment is now assumed to be fixed and ALE results
                      are assessed against a null-distribution of random spatial
                      association between experiments. Critically, this
                      modification entails a change from fixed- to random-effects
                      inference in ALE analysis allowing generalization of the
                      results to the entire population of studies analyzed. By
                      comparative analysis of real and simulated data, the authors
                      showed that the revised ALE-algorithm overcomes conceptual
                      problems of former meta-analyses and increases the
                      specificity of the ensuing results without loosing the
                      sensitivity of the original approach. It may thus provide a
                      methodologically improved tool for coordinate-based
                      meta-analyses on functional imaging data.},
      keywords     = {Adult / Algorithms / Brain: anatomy $\&$ histology / Brain:
                      physiology / Brain: radionuclide imaging / Brain Mapping:
                      methods / Computational Biology: methods / Computer
                      Simulation / Data Interpretation, Statistical / Female /
                      Humans / Image Processing, Computer-Assisted: methods /
                      Magnetic Resonance Imaging: methods / Male / Meta-Analysis
                      as Topic / Middle Aged / Models, Neurological /
                      Positron-Emission Tomography: methods / Probability /
                      Psychomotor Performance: physiology / Uncertainty / J
                      (WoSType)},
      cin          = {INM-2 / INM-3 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-2-20090406 / I:(DE-Juel1)INM-3-20090406 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {Funktion und Dysfunktion des Nervensystems},
      pid          = {G:(DE-Juel1)FUEK409},
      shelfmark    = {Neurosciences / Neuroimaging / Radiology, Nuclear Medicine
                      $\&$ Medical Imaging},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:19172646},
      pmc          = {pmc:PMC2872071},
      UT           = {WOS:000269510200018},
      doi          = {10.1002/hbm.20718},
      url          = {https://juser.fz-juelich.de/record/5635},
}