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Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation
Open accessAbstract
Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis.
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“However, new studies have since been published, and existing ALE meta-analyses do not meet the current standards for type I error control (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Following current best-practice guidelines, we assessed the significance of convergence of activation foci using a voxel-level threshold of and a cluster-level familywise error-corrected threshold of , and performed ALE meta-analyses only when at least 17 experiments reported activation foci for a contrast (Eickhoff et al., 2016, Tahmasian et al., 2019).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Notably, this null-finding occurred in a well-powered meta-analysis: the sample of 35 experiments is considered adequate by present guidelines for neuroimaging meta-analyses (Müller et al., 2018, Tahmasian et al., 2019) and gave us more than 80 % power to detect a convergence of activation differences in areas that were identified in just 7 (20 %) of the experiments (Eickhoff et al., 2016)”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“which has been shown to be inappropriate for neuroimaging data (Chumbley and Friston, 2009) and to entail both low sensitivity and high susceptibility to spurious, false positive findings (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
In the citing paper
“Brem et al. (2012); Eng et al. (2015) and Menzies et al. (2008) performed meta-analyses on activation differences obtained from less than the recommended minimum of 17 fMRI experiments (Eickhoff et al., 2016).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Eickhoff et al., 2016
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“In turn, a higher number of significant voxels at the target location allow for more false positive voxels when the false positive rate is controlled (Genovese et al., 2002).”
Self-citationLarge-scale automated synthesis of human functional neuroimaging data.
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“…topics from cognitive (Chase et al., 2015; Molenberghs et al., 2012), affective (Kohn et al., 2014; Lamm et al., 2011) and motor (King et al., 2014; Yuan and Brown, 2015) neuroscience to the neurobiology of neurological (Herz et al., 2014; Rehme et al., 2012) and psychiatric (Bludau et al., 2015;…”
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“Particularly ALE has been applied to a wide range of different topics from cognitive (Chase et al., 2015; Molenberghs et al., 2012), affective (Kohn et al., 2014; Lamm et al., 2011) and motor (King et al., 2014; Yuan and Brown, 2015) neuroscience to the neurobiology of neurological (Herz et al.,…”
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“More specifically, this is quite likely to happen if one experiment is based on a substantially larger N than the remaining ones and hence features a tighter Gaussian leading to higher voxel-wise probabilities around the reported foci (Eickhoff et al., 2009b).”
InfluentialSelf-citationMeta-analysis of the functional neuroanatomy of single-word reading: method and validation
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InfluentialLogothetis, N. K., & Wandell, B. A. (2004) · Annual Review of Physiology · doi:10.1146/annurev.physiol.66.082602.092845 · Cited by 1,568 · cited 1×
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BackgroundRaikes, A., Garza, M., Murrell, A. N., & Brinton, R. D. (2026) · medRxiv · doi:10.64898/2026.04.07.26350339 · Cited by 1
“Statistically significant clusters were defined using edge-wise connectivity with a voxel-level threshold of p 0.001 from the uncorrected null distribution and family-149 wise error corrected cluster-forming p < 0.05 threshold 42,43 .”
InfluentialFromm, A. E., Abdelmotaleb, M., Olschewski, F., Limanowski, J., Meinzer, M., Flöel, A., & Antonenko, D. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.64898/2026.07.01.735849 · Cited by 1
“Statistical inference followed the recommendations of the large-scale ALE simulation study by Eickhoff et al. (2016) and neuroimaging meta-analysis guidelines (Müller et al., 2018).”
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Self-citationLiloia, D., & Costa, T. (2025) · Psychological applications and trends · doi:10.36315/2025inpact145
“The ALE algorithm, implemented in the GingerALE software (v.3.0.2) (Eickhoff et al., 2016), models the activation foci from each fMRI experiment as three-dimensional p-ISSN: 2184-2205 e-ISSN: 2184-3414 ISBN: 978-989-35728-4-9 © 2025 Gaussian probability distributions centered on the reported…”
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“We conducted a Jackknife sensitivity analysis to evaluate the robustness of the ALE results against potentially spurious effects like publication bias in the literature [52].”
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“…meta-analysis was initially planned to synthesise the results of the review, the final number of included studies was insufficient to meet recommended thresholds for statistical power, in line with best practice guidelines for conducting ALE analyses which recommend a minimum of 17 studies [44].”
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Sala, A., Meys, M., Alnagger, N., Beliy, N., Abagnale, S., Szirmai, D., Ok, B. K., Wang, Z., Bardiau, M., Beaudart, C., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8480413/v1
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“Power recommendations for ALE analyses ( Eickhoff et al., 2016) indicate that at least 17– 20 studies are required to reliably detect moderate effects.”
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“Finally, the thresholded ALE map is obtained using a cluster-form-ing threshold of p 0.001 and a cluster-level threshold of p 0.05 family wise error (FWE)-corrected (Eickhoff et al., 2016).”
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“The resulting thresholded ALE map was then computed using a cluster-forming threshold of p 0.001 and a cluster-level threshold of p 0.05 with a minimum cluster size of suprathreshold voxels exceeding 50 mm 3 (Eickhoff et al., 2016).”
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“This approach of using the mean across a cluster is standard practice, as it provides a robust and representative measure of a region’s structural characteristic 41 .”
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“For areas not listed in this tool, we relied on the Talairach Daemon [37], including Structural Probability Maps, which in turn are based on 50 or more MRI brain volumes that were automatically labeled using the non-linear image-matching ANIMAL algorithm [38].”
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“Foci coordinates ( x , y , z ) from each study are used to centre spatial probabilities (Eickhoff et al. 2012, 2016).”
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“…targeting decimal processing (i.e., six studies in total with only four reporting target contrasts, two of them being derived from the same sample), the number of independent experiments eligible for inclusion was insufficient to conduct a meaningful ALE meta-analysis (Eickhoff et al., 2016).”
A neuroimaging meta-analysis on social impression formation of stable characteristics
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“Overall, the number of included experiments aligns with common recommendations, suggesting a minimum of 17 experiments for sufficient power to detect smaller effects ( Eickhoff et al. 2016; Müller et al. 2018; Yeung et al. 2023).”
Zhou, J.-X., Xu, Y., Li, T., & Yang, X. (2026) · Human Brain Mapping · doi:10.1002/hbm.70594
“Then the standardized coordinates were entered into GingerALE for ALE meta-analysis, and the optimized statistical protocol recommended by Eickhoff et al. (2016) was ap-plied—specifically, we used a random-effects ALE model with non-additive correction and sample- size-dependent FWHM, with a…”
Cognitive Flexibility and Bilingual Language Switching: An fMRI Meta‐Analysis
Chuikova, Z. V., Faber, A., Abutalebi, J., Filatov, A. A., Юрьевич, Ш. Ю., & Myachykov, A. (2026) · European Journal of Neuroscience · doi:10.1111/ejn.70591
“For TSP and WCST, the significance threshold was set to a cluster-level family-wise error (FWE) corrected p - value of 0.05 (with cluster forming p < 0.001), as recommended by Eickhoff and colleagues (Eickhoff et al. 2016).”
InfluentialMcCurry, K. L., Brown, V. M., Bansal, V., Goyal, S., Casas, B. R., & Chiu, P. H. (2026) · Biological Psychiatry · doi:10.1016/j.biopsych.2026.06.027
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Brain Structural Abnormalities in Addiction Disorders: Transdiagnostic Meta‐Analyses
Banaja, D. M. A., Hernandez, E. T., Franklin, C. G., Fox, P. M., Okafor, C. N., Ginsburg, B. C., Salinas, F. S., Moring, J. C., & Fox, P. T. (2026) · Human Brain Mapping · doi:10.1002/hbm.70551
“The GingerALE application (Version 3.0.2, https:// www. brain map. org/ ale/ ) was used for all analyses (Turkeltaub et al. 2002, 2012; Laird et al. 2005; Laird, Eickhoff, Kurth, et al. 2009; Laird, Eickhoff, Li, et al. 2009; Eickhoff et al. 2009, 2011, 2012, 2016; Kuppers et al. 2024).”
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“All Talairach coordinates were converted into MNI space using the Talairach to MNI (SPM) transform for analytic consistency provided by the GingerALE default tool (version 3.0.2; 64, 65, 68).”
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“The choice of the FWE and cluster-forming thresholds aligned with the recommendations of Eickhoff et al. (2016).”
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“(1) Small-sample sensitivity: each ALE analysis was re-run after excluding experiments with a per-experiment sample size below 10 participants, because small samples inflate effective FWHM estimates (Eickhoff et al., 2016).”
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“All ALE maps were thresholded using a cluster-level family-wise error (cFWE) correction at p < 0.05, with a cluster-forming threshold of p < 0.001 and 10,000 permutations to correct for multiple comparisons (Eickhoff et al. 2012 , 2016 ).”
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Metadata
- Authors
- Eickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K., Fox, P. T., Bzdok, D., & Eickhoff, C. R.
- Affiliations
- Forschungszentrum Jülich (DE)
- University of Warwick (GB)
- Florida International University (US)
- The University of Texas at San Antonio Health Science Center (US)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Eickhoff, Simon B.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 137
- Pages
- 70-85
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4981641
- OpenAlex
- W2349406327
- Cited by
- 683
- References
- 76
- Citations by year
- 2026: 61 · 2025: 68 · 2024: 58 · 2023: 83 · 2022: 92 · 2021: 93 · 2020: 72 · 2019: 61 · 2018: 46 · 2017: 35 · 2016: 13
- FWCI
- 32.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (EI 816/4-1, LA 3071/3-1, EI 816/6-1)
- National Institute of Mental Health (R01-MH074457)
- Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union Seventh Framework Program (604102)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- activation likelihood estimation, coordinate-based meta-analysis, statistical thresholding, false discovery rate, sample size, statistical power, brain-behavior relationship, BrainMap
- MeSH terms
- Brain, Data Interpretation, Statistical, Humans, Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Meta-Analysis as Topic, Reproducibility of Results, Likelihood Functions, Neuroimaging
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