Cited by 684Functional Brain Connectivity StudiesMeta-analysis and systematic reviewsAdvanced Neuroimaging Techniques and Applications

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation

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Eickhoff, Simon B., Nichols, Thomas E., Laird, Angela R., Hoffstaedter, Felix, Amunts, Katrin, Fox, Peter T., Bzdok, Danilo, Eickhoff, Claudia R.·2016·NeuroImage

Abstract

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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  1. Eickhoff SB Laird AR Grefkes C Wang LE Zilles K Fox PT Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty Hum Brain Mapp 2009b 30 2907 2926 19172646 PMC2872071

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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.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2016-08
Volume
137
Pages
70-85
ISSN
1053-8119 (ISSN-L)
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 · 2015: 1
FWCI
32.88
Type
journal article
Language
en
Open access
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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