Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Frahm, Lennart, Cieslik, Edna C., Hoffstaedter, Felix, Satterthwaite, Theodore D., Fox, Peter T., Langner, Robert, Eickhoff, Simon B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374884/
https://www.ncbi.nlm.nih.gov/pubmed/35535616
http://dx.doi.org/10.1002/hbm.25898
_version_ 1784767872368640000
author Frahm, Lennart
Cieslik, Edna C.
Hoffstaedter, Felix
Satterthwaite, Theodore D.
Fox, Peter T.
Langner, Robert
Eickhoff, Simon B.
author_facet Frahm, Lennart
Cieslik, Edna C.
Hoffstaedter, Felix
Satterthwaite, Theodore D.
Fox, Peter T.
Langner, Robert
Eickhoff, Simon B.
author_sort Frahm, Lennart
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9374884
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher John Wiley & Sons, Inc.
record_format MEDLINE/PubMed
spelling pubmed-93748842022-08-17 Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations Frahm, Lennart Cieslik, Edna C. Hoffstaedter, Felix Satterthwaite, Theodore D. Fox, Peter T. Langner, Robert Eickhoff, Simon B. Hum Brain Mapp Research Articles 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. John Wiley & Sons, Inc. 2022-05-10 /pmc/articles/PMC9374884/ /pubmed/35535616 http://dx.doi.org/10.1002/hbm.25898 Text en © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research Articles
Frahm, Lennart
Cieslik, Edna C.
Hoffstaedter, Felix
Satterthwaite, Theodore D.
Fox, Peter T.
Langner, Robert
Eickhoff, Simon B.
Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
title Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
title_full Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
title_fullStr Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
title_full_unstemmed Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
title_short Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
title_sort evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374884/
https://www.ncbi.nlm.nih.gov/pubmed/35535616
http://dx.doi.org/10.1002/hbm.25898
work_keys_str_mv AT frahmlennart evaluationofthresholdingmethodsforactivationlikelihoodestimationmetaanalysisvialargescalesimulations
AT cieslikednac evaluationofthresholdingmethodsforactivationlikelihoodestimationmetaanalysisvialargescalesimulations
AT hoffstaedterfelix evaluationofthresholdingmethodsforactivationlikelihoodestimationmetaanalysisvialargescalesimulations
AT satterthwaitetheodored evaluationofthresholdingmethodsforactivationlikelihoodestimationmetaanalysisvialargescalesimulations
AT foxpetert evaluationofthresholdingmethodsforactivationlikelihoodestimationmetaanalysisvialargescalesimulations
AT langnerrobert evaluationofthresholdingmethodsforactivationlikelihoodestimationmetaanalysisvialargescalesimulations
AT eickhoffsimonb evaluationofthresholdingmethodsforactivationlikelihoodestimationmetaanalysisvialargescalesimulations