Journal
NEUROIMAGE
Volume 176, Issue -, Pages 550-553Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2018.04.065
Keywords
Coordinate-based meta-analysis; Tests for spatial convergence; Familywise error rate; Activation likelihood estimation; Seed-based d mapping; Signed differential mapping
Funding
- Miguel Servet Research from the Plan Nacional de I+D+i [MS14/00041]
- Plan Nacional de I+D+i [PI14/00292]
- Instituto de Salud Carlos III-Subdireccion General de Evaluacion y Fomento de la Investigacion
- European Regional Development Fund
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Coordinate-based meta-analyses (CBMA) methods, such as Activation Likelihood Estimation (ALE) and Seed-based d Mapping (SDM), have become an invaluable tool for summarizing the findings of voxel-based neuroimaging studies. However, the progressive sophistication of these methods may have concealed two particularities of their statistical tests. Common univariate voxelwise tests (such as the t/z-tests used in SPM and FSL) detect voxels that activate, or voxels that show differences between groups. Conversely, the tests conducted in CBMA test for spatial convergence of findings, i.e., they detect regions where studies report more peaks than in most regions, regions that activate more than most regions do, or regions that show larger differences between groups than most regions do. The first particularity is that these tests rely on two spatial assumptions (voxels are independent and have the same probability to have a false peak), whose violation may make their results either conservative or liberal, though fortunately current versions of ALE, SDM and some other methods consider these assumptions. The second particularity is that the use of these tests involves an important paradox: the statistical power to detect a given effect is higher if there are no other effects in the brain, whereas lower in presence of multiple effects.
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