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Neuroimage. 2016 Aug 15;137:70-85. doi: 10.1016/j.neuroimage.2016.04.072. Epub 2016 May 11.

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation.

Author information

Institute for Neuroscience and Medicine (INM-1), Research Center Jülich, Germany.
Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Germany.
Department of Statistics and Warwick Manufacturing Group, University of Warwick, Coventry, UK.
Department of Physics, Florida International University, USA.
C & O Vogt Institute for Brain Research, Heinrich-Heine University Düsseldorf, Germany.
Research Imaging Institute, University of Texas Health Science Center at San Antonio, USA.
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Hospital, Germany.
JARA, Translational Brain Medicine, Aachen.
Parietal team, INRIA, Neurospin, bat 145, CEA Saclay, 91191 Gif-sur-Yvette, France.
Contributed equally


Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis.

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