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| 001 | 907807 | ||
| 005 | 20230223091802.0 | ||
| 024 | 7 | _ | |a 10.1002/hbm.25898 |2 doi |
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| 037 | _ | _ | |a FZJ-2022-02226 |
| 041 | _ | _ | |a English |
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| 100 | 1 | _ | |a Frahm, Lennart |0 P:(DE-Juel1)185961 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations |
| 260 | _ | _ | |a New York, NY |c 2022 |b Wiley-Liss |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Cieslik, Edna C. |0 P:(DE-Juel1)131855 |b 1 |u fzj |
| 700 | 1 | _ | |a Hoffstaedter, Felix |0 P:(DE-Juel1)131684 |b 2 |u fzj |
| 700 | 1 | _ | |a Satterthwaite, Theodore D. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Fox, Peter T. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Langner, Robert |0 P:(DE-Juel1)131693 |b 5 |u fzj |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 6 |u fzj |
| 773 | _ | _ | |a 10.1002/hbm.25898 |g p. hbm.25898 |0 PERI:(DE-600)1492703-2 |n 13 |p 3987-3997 |t Human brain mapping |v 43 |y 2022 |x 1065-9471 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/907807/files/Human%20Brain%20Mapping%20-%202022%20-%20Frahm%20-%20Evaluation%20of%20thresholding%20methods%20for%20activation%20likelihood%20estimation%20meta%E2%80%90analysis.pdf |y OpenAccess |
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