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| 001 | 1015277 | ||
| 005 | 20231116095326.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2023.120383 |2 doi |
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| 100 | 1 | _ | |a Frahm, Lennart |0 P:(DE-Juel1)185961 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations |
| 260 | _ | _ | |a Orlando, Fla. |c 2023 |b Academic Press |
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| 500 | _ | _ | |a This study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/11-1 and International Research Training Group 2150, 269953372/GRK2150), the National Institute of Mental Health (R01-MH074457), the National Institute of Aging (P30-AG066546), and the Jülich-Aachen Research Alliance (JARA) granting computation time on the supercomputer JURECA (Jülich Supercomputing Centre, 2018) at Forschungszentrum Jülich. Open access funding enabled and organized by Projekt DEAL. |
| 520 | _ | _ | |a Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters. |
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| 773 | _ | _ | |a 10.1016/j.neuroimage.2023.120383 |g Vol. 281, p. 120383 - |0 PERI:(DE-600)1471418-8 |p 120383 - |t NeuroImage |v 281 |y 2023 |x 1053-8119 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1015277/files/1-s2.0-S1053811923005347-main.pdf |y OpenAccess |
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