A Minimum Bayes Factor Based Threshold for Activation Likelihood Estimation
-
Published:2023-03-28
Issue:2
Volume:21
Page:365-374
-
ISSN:1539-2791
-
Container-title:Neuroinformatics
-
language:en
-
Short-container-title:Neuroinform
Author:
Costa Tommaso,Liloia Donato,Cauda Franco,Fox Peter T.,Mutta Francesca Dalla,Duca Sergio,Manuello Jordi
Abstract
AbstractActivation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical p-value selected. However, this is not informative in terms of probabilities of the validity of the hypotheses. Here, we describe an innovative thresholding procedure based on the concept of minimum Bayes factor (mBF). The use of the Bayesian framework allows to consider different levels of probability, each of these being equally significant. In order to simplify the translation between the common ALE practice and the proposed approach, we analised six task-fMRI/VBM datasets and determined the mBF values equivalent to the currently recommended frequentist thresholds based on Family Wise Error (FWE). Sensitivity and robustness toward spurious findings were also analyzed. Results showed that the cutoff log10(mBF) = 5 is equivalent to the FWE threshold, often referred as voxel-level threshold, while the cutoff log10(mBF) = 2 is equivalent to the cluster-level FWE (c-FWE) threshold. However, only in the latter case voxels spatially far from the blobs of effect in the c-FWE ALE map survived. Therefore, when using the Bayesian thresholding the cutoff log10(mBF) = 5 should be preferred. However, being in the Bayesian framework, lower values are all equally significant, while suggesting weaker level of force for that hypothesis. Hence, results obtained through less conservative thresholds can be legitimately discussed without losing statistical rigor. The proposed technique adds therefore a powerful tool to the human-brain-mapping field.
Funder
National Institutes of Health Università degli Studi di Torino
Publisher
Springer Science and Business Media LLC
Subject
Information Systems,General Neuroscience,Software
Reference25 articles.
1. Acar, F., Seurinck, R., Eickhoff, S. B., & Moerkerke, B. (2018). Assessing robustness against potential publication bias in activation likelihood estimation (ALE) meta-analyses for fMRI. PLoS ONE, 13(11), e0208177. https://doi.org/10.1371/journal.pone.0208177 2. Bowring, A., Maumet, C., & Nichols, T. E. (2019). Exploring the impact of analysis software on task fMRI results. Human Brain Mapping, 40(11), 3362–3384. https://doi.org/10.1002/hbm.24603 3. Cauda, F., Nani, A., Liloia, D., Manuello, J., Premi, E., Duca, S., & Costa, T. (2020). Finding specificity in structural brain alterations through Bayesian reverse inference. Hum Brain Mapp, n/a(n/a). https://doi.org/10.1002/hbm.25105 4. Cody, W. J. (1969). Rational Chebyshev approximations for the error function. Mathematics of Computation, 23(107), 631–637. 5. Costa, T., Manuello, J., Ferraro, M., Liloia, D., Nani, A., Fox, P. T., & Cauda, F. (2021). BACON: A tool for reverse inference in brain activation and alteration. Human Brain Mapping, 42(11), 3343–3351. https://doi.org/10.1002/hbm.25452
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|