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ALE meta-analysis: controlling the false discovery rate and performing statistical contrasts
Open accessAbstract
Activation likelihood estimation (ALE) has greatly advanced voxel‐based meta‐analysis research in the field of functional neuroimaging. We present two improvements to the ALE method. First, we evaluate the feasibility of two techniques for correcting for multiple comparisons: the single threshold test and a procedure that controls the false discovery rate (FDR). To test these techniques, foci from four different topics within the literature were analyzed: overt speech in stuttering subjects, the color‐word Stroop task, picture‐naming tasks, and painful stimulation. In addition, the performance of each thresholding method was tested on randomly generated foci. We found that the FDR method more effectively controls the rate of false positives in meta‐analyses of small or large numbers of foci. Second, we propose a technique for making statistical comparisons of ALE meta‐analyses and investigate its efficacy on different groups of foci divided by task or response type and random groups of similarly obtained foci. We then give an example of how comparisons of this sort may lead to advanced designs in future meta‐analytic research. Hum Brain Mapp 25:155–164, 2005. © 2005 Wiley‐Liss, Inc.
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“Additionally, Brem et al. (2012) and Menzies et al. (2008) used the false-discovery rate correction for multiple comparisons (Laird et al., 2005)”
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 Laird et al., 2005
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“This procedure should effectively balance inference on reliable areas of activation without generating a bias toward false positives (Laird et al., 2005;Eickhoff et al., 2011).”
From Informatic parcellation of the network involved in the computation of subjective value (2014) · cites this paper as Laird et al., 2005
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Brown, S., Laird, A. R., Pfordresher, P. Q., Thelen, S. M., Turkeltaub, P., & Liotti, M. (2009) · Brain and Cognition · doi:10.1016/j.bandc.2008.12.006 · Cited by 210
Self-citationAmanzio, M., Benedetti, F., Porro, C. A., Palermo, S., & Cauda, F. (2013) · Human Brain Mapping · doi:10.1002/hbm.21471 · Cited by 202
Remember the future II: Meta-analyses and functional overlap of working memory and delay discounting
Wesley, M. J., & Bickel, W. K. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.08.008 · Cited by 205
Fouragnan, E., Retzler, C., & Philiastides, M. G. (2018) · Human Brain Mapping · doi:10.1002/hbm.24047 · Cited by 176
Gender differences in working memory networks: a BrainMap meta-analysis
Hill, A. C., Laird, A. R., & Robinson, J. L. (2014) · Biological Psychology · doi:10.1016/j.biopsycho.2014.06.008 · Cited by 182
Self-citationALE meta-analysis on facial judgments of trustworthiness and attractiveness
Bzdok, D., Langner, R., Caspers, S., Kurth, F., Habel, U., Zilles, K., Laird, A., & Eickhoff, S. B. (2011) · Brain Structure and Function · doi:10.1007/s00429-010-0287-4 · Cited by 183
Self-citationNetworks of task co-activations
Laird, A. R., Eickhoff, S. B., Rottschy, C., Bzdok, D., Ray, K. L., & Fox, P. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.073 · Cited by 168
Self-citationNeural correlates of psychotherapy in anxiety and depression: a meta-analysis
Messina, I., Sambin, M., Palmieri, A., & Viviani, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074657 · Cited by 176
Metadata
- Authors
- Laird, A. R., Fox, P. M., Price, C. J., Glahn, D. C., Uecker, A. M., Lancaster, J. L., Turkeltaub, P. E., Kochunov, P., & Fox, P. T.
- Affiliations
- The University of Texas at San Antonio Health Science Center (US)
- UCL Queen Square Institute of Neurology (GB)
- Georgetown University (US)
- ORCID
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2005-04-21
- Volume
- 25
- Issue
- 1
- Pages
- 155-164
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6871747
- OpenAlex
- W2060259639
- Cited by
- 868
- References
- 22
- Citations by year
- 2026: 23 · 2025: 25 · 2024: 26 · 2023: 32 · 2022: 45 · 2021: 31 · 2020: 39 · 2019: 44 · 2018: 48 · 2017: 38 · 2016: 62 · 2015: 53 · 2014: 55 · 2013: 58 · 2012: 66
- FWCI
- 8.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Vanderbilt University
- University of Wisconsin-Madison
- Wellcome Trust (051067)
- National Institutes of Health (5r01lm006858-03)
- U.S. National Library of Medicine (LM6858)
- 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, false discovery rate, multiple testing correction, false positives, stuttering, Stroop task, picture naming, pain
- MeSH terms
- Brain, Brain Mapping, Humans, Mathematical Computing, Pain Measurement, Psychomotor Performance, Stuttering, Meta-Analysis as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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