Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations.

Autor: Frahm L; Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, Aachen, Germany.; Institute of Neuroscience and Medicine (INM7: Brain and Behavior), Research Centre Jülich, Jülich, Germany., Cieslik EC; Institute of Neuroscience and Medicine (INM7: Brain and Behavior), Research Centre Jülich, Jülich, Germany.; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany., Hoffstaedter F; Institute of Neuroscience and Medicine (INM7: Brain and Behavior), Research Centre Jülich, Jülich, Germany.; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany., Satterthwaite TD; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.; Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA., Fox PT; Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA., Langner R; Institute of Neuroscience and Medicine (INM7: Brain and Behavior), Research Centre Jülich, Jülich, Germany.; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany., Eickhoff SB; Institute of Neuroscience and Medicine (INM7: Brain and Behavior), Research Centre Jülich, Jülich, Germany.; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Jazyk: angličtina
Zdroj: Human brain mapping [Hum Brain Mapp] 2022 Sep; Vol. 43 (13), pp. 3987-3997. Date of Electronic Publication: 2022 May 10.
DOI: 10.1002/hbm.25898
Abstrakt: 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.
(© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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