The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Co-Planar Stereotaxic Atlas of the Human Brain
Talairach, J., & Tournoux, P. (1988) · Medical Entomology and Zoology · Cited by 11,798
Co-planar stereotaxic atlas of the human brain
Laitinen, L. (1989) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(89)90128-5 · Cited by 7,565
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Thresholding of statistical maps in functional neuroimaging using the false discovery rate
Genovese, C. R., Lazar, N. A., & Nichols, T. (2002) · NeuroImage · doi:10.1006/nimg.2001.1037 · Cited by 4,923
N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies.
Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005) · Human Brain Mapping · doi:10.1002/hbm.20131 · Cited by 3,560
Self-citationAutomatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431 · cited 1×
“These coordinates are generally published relative to Talairach [Talairach and Tournoux, 1988] or Montreal Neurological Institute (MNI) space [Collins et al., 1994] and must be spatially renormalized to a single template.”
BackgroundMeta-analysis of the functional neuroanatomy of single-word reading: method and validation
Turkeltaub, P. E., Eden, G. F., Jones, K. M., & Zeffiro, T. A. (2002) · NeuroImage · doi:10.1006/nimg.2002.1131 · Cited by 1,604
Self-citationPermutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses
Niknian, M., & Good, P. (1995) · Technometrics · doi:10.2307/1269919 · Cited by 1,463
Nonparametric Analysis of Statistic Images from Functional Mapping Experiments
Holmes, A. P., Blair, R. C., Watson, J. D. G., & Ford, I. (1996) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-199601000-00002 · Cited by 894
Flagged first, then by citations
Research works printed but not linked: 2
- BrettM(1999): The MNI brain and the Talairach atlas Cambridge Imagers. Online athttp://www.mrc‐cbu.cam.ac.uk/Imaging/mnispace.html.
- 10.1006/nimg.2002.1084 · DOI
Cited by
Sorted by citations
Items 101–150 of 150
Pain and post traumatic stress disorder - review of clinical and experimental evidence
Moeller-Bertram, T., Keltner, J., & Strigo, I. A. (2012) · Neuropharmacology · doi:10.1016/j.neuropharm.2011.04.028 · Cited by 163
The neural crossroads of psychiatric illness: an emerging target for brain stimulation
Downar, J., Blumberger, D. M., & Daskalakis, Z. J. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.10.007 · Cited by 166
Song, S., Zilverstand, A., Song, H., d’Oleire Uquillas, F., Wang, Y., Xie, C., Cheng, L., & Zou, Z. (2017) · Scientific Reports · doi:10.1038/s41598-017-02266-2 · Cited by 160
Mapping anhedonia-specific dysfunction in a transdiagnostic approach: an ALE meta-analysis
Zhang, B., Lin, P., Shi, H., Öngür, D., Auerbach, R. P., Wang, X., Yao, S., & Wang, X. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9457-6 · Cited by 152
Tomasino, B., Fregona, S., Skrap, M., & Fabbro, F. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00346 · Cited by 145
Eng, G. K., Sim, K., & Chen, S.-H. A. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.03.002 · Cited by 148
The neural correlates of visual imagery: A co-ordinate-based meta-analysis
Winlove, C. I. P., Milton, F., Ranson, J., Fulford, J., MacKisack, M., Macpherson, F., & Zeman, A. (2018) · Cortex · doi:10.1016/j.cortex.2017.12.014 · Cited by 150
Meta-analysis of functional brain imaging in specific phobia
Ipser, J. C., Singh, L., & Stein, D. J. (2013) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12055 · Cited by 142
Alcaro, A., Panksepp, J., Witczak, J., Hayes, D. J., & Northoff, G. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2009.11.023 · Cited by 144
Peng, W., Chen, Z., Yin, L., Jia, Z., & Gong, Q. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.04.001 · Cited by 136
Hattingh, C. J., Ipser, J., Tromp, S. A., Syal, S., Lochner, C., Brooks, S. J., & Stein, D. J. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00347 · Cited by 129
Endophenotypes of obsessive-compulsive disorder: rationale, evidence and future potential
Chamberlain, S. R., & Menzies, L. (2009) · Expert Review of Neurotherapeutics · doi:10.1586/ern.09.36 · Cited by 123
Functional neuroimaging of intertemporal choice models: a review
Carter, R. M., Meyer, J. R., & Huettel, S. A. (2010) · Journal of Neuroscience Psychology and Economics · doi:10.1037/a0018046 · Cited by 124
Grace, S. A., Rossell, S. L., Heinrichs, M., Kordsachia, C., & Labuschagne, I. (2018) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2018.05.031 · Cited by 120
Cerebellar contributions to visuomotor adaptation and motor sequence learning: an ALE meta-analysis
Bernard, J. A., & Seidler, R. D. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00027 · Cited by 122
Budde, K. S., Barron, D. S., & Fox, P. T. (2014) · Brain and Language · doi:10.1016/j.bandl.2014.10.002 · Cited by 114
Self-citationSutherland, M. T., Riedel, M. C., Flannery, J. S., Yanes, J. A., Fox, P. T., Stein, E. A., & Laird, A. R. (2016) · Behavioral and Brain Functions · doi:10.1186/s12993-016-0100-5 · Cited by 114
Self-citationNeural circuitry of PTSD with or without mild traumatic brain injury: a meta-analysis
Simmons, A. N., & Matthews, S. C. (2012) · Neuropharmacology · doi:10.1016/j.neuropharm.2011.03.016 · Cited by 105
Andrzejewski, J. A., Greenberg, T., & Carlson, J. M. (2019) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-019-00747-7 · Cited by 100
Sundermann, B., & Pfleiderer, B. (2012) · BMC Neuroscience · doi:10.1186/1471-2202-13-119 · Cited by 98
Hobeika, L., Diard‐Detoeuf, C., Garcin, B., Levy, R., & Volle, E. (2016) · Human Brain Mapping · doi:10.1002/hbm.23149 · Cited by 94
Structural basis of empathy and the domain general region in the anterior insular cortex
Mutschler, I., Reinbold, C., Wankerl, J., Seifritz, E., & Ball, T. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00177 · Cited by 92
Disentangling the neural mechanisms involved in Hinduism- and Buddhism-related meditations
Tomasino, B., Chiesa, A., & Fabbro, F. (2014) · Brain and Cognition · doi:10.1016/j.bandc.2014.03.013 · Cited by 94
Functional MRI reveals different response inhibition between adults and children with ADHD
Lei, D., Du, M., Wu, M., Chen, T., Huang, X., Du, X., Bi, F., Kemp, G. J., & Gong, Q. (2015) · Neuropsychology · doi:10.1037/neu0000200 · Cited by 91
Wang, D., Yan, X., Li, M., & Ma, Y. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx085 · Cited by 86
Castellanos, F. X., Kelly, C., & Milham, M. P. (2009) · The Canadian Journal of Psychiatry · doi:10.1177/070674370905401003 · Cited by 88
A neurocognitive model of meditation based on activation likelihood estimation (ALE) meta-analysis
Sperduti, M., Martinelli, P., & Piolino, P. (2012) · Consciousness and Cognition · doi:10.1016/j.concog.2011.09.019 · Cited by 87
Brem, S., Hauser, T. U., Iannaccone, R., Brandeis, D., Drechsler, R., & Walitza, S. (2012) · Journal of Neural Transmission · doi:10.1007/s00702-012-0813-z · Cited by 79
Fathy, Y. Y., Hoogers, S. E., Berendse, H. W., van der Werf, Y. D., Visser, P. J., de Jong, F. J., & van de Berg, W. D. J. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00099-3 · Cited by 82
Meta-analysis of neuroimaging data
Kober, H., & Wager, T. D. (2010) · WIREs Cognitive Science · doi:10.1002/wcs.41 · Cited by 74
Rasgon, A., Lee, W. H., Leibu, E., Laird, A., Glahn, D., Goodman, W., & Frangou, S. (2017) · European Psychiatry · doi:10.1016/j.eurpsy.2017.08.001 · Cited by 73
Self-citationChapleau, M., Aldebert, J., Montembeault, M., & Brambati, S. M. (2016) · Journal of Alzheimer's Disease · doi:10.3233/jad-160382 · Cited by 73
Rotge, J.-Y., Langbour, N., Jaafari, N., Guehl, D., Bioulac, B., Aouizerate, B., Allard, M., & Burbaud, P. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.10.007 · Cited by 70
Del Casale, A., Rapinesi, C., Kotzalidis, G. D., De Rossi, P., Curto, M., Janiri, D., Criscuolo, S., Alessi, M. C., Ferri, V. R., De Giorgi, R., et al. (2016) · The World Journal of Biological Psychiatry · doi:10.3109/15622975.2015.1102323 · Cited by 63
Feng, C., Becker, B., Huang, W., Wu, X., Eickhoff, S. B., & Chen, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.023 · Cited by 58
Janssen, T. W. P., Bink, M., Geladé, K., van Mourik, R., Maras, A., & Oosterlaan, J. (2016) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2015.0144 · Cited by 58
Model-based clustering of meta-analytic functional imaging data
Neumann, J., Cramon, D. Y. V., & Lohmann, G. (2008) · Human Brain Mapping · doi:10.1002/hbm.20380 · Cited by 57
Balsters, J. H., Mantini, D., Apps, M. A. J., Eickhoff, S. B., & Wenderoth, N. (2016) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.03.016 · Cited by 50
Fusar-Poli, P., Radua, J., Frascarelli, M., Mechelli, A., Borgwardt, S., Di Fabio, F., Biondi, M., Ioannidis, J. P. A., & David, S. P. (2014) · Human Brain Mapping · doi:10.1002/hbm.22384 · Cited by 56
“This was achieved by focusing on available voxel-based meta-analyses of VBM studies, such those performed with Activation Likelihood Estimate [Laird et al., 2005] or Signed Differential Mapping [Radua and Mataix-Cols, 2009, 2012; Radua et al.”
MethodConca, F., Esposito, V., Giusto, G., Cappa, S. F., & Catricalà, E. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101760 · Cited by 54
Should Broca’s area include Brodmann area 47?
Ardila, A., Bernal, B., & Rosselli, M. (2017) · Psicothema · doi:10.7334/psicothema2016.11 · Cited by 39
Brain imaging findings in children and adolescents with mental disorders: a cross-sectional review
Mana, S., Paillère Martinot, M.-L., & Martinot, J.-L. (2010) · European Psychiatry · doi:10.1016/j.eurpsy.2010.04.010 · Cited by 40
Zevin, J. D., Yang, J., Skipper, J. I., & McCandliss, B. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.4599-09.2010 · Cited by 36
Shih, H.-C., Kuo, M.-E., Wu, C., Chao, Y.-P., Huang, H.-W., & Huang, C.-M. (2022) · Brain Sciences · doi:10.3390/brainsci12070830 · Cited by 32
Funch Uhre, V., Melissa Larsen, K., Marc Herz, D., Baaré, W., Katrine Pagsberg, A., & Roman Siebner, H. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103268 · Cited by 18
Mahmoodi, A., Luo, S., Harbison, C., Piray, P., & Rushworth, M. F. S. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.001 · Cited by 17
Vartanian, O., Replete, V., Saint, S. A., Lam, Q., Forbes, S., Beaudoin, M. E., Brunyé, T. T., Bryant, D. J., Feltman, K. A., Heaton, K. J., et al. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.868001 · Cited by 15
McKenna, R., Rushe, T., & Woodcock, K. A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00154 · Cited by 13
Radua, J., & Mataix-Cols, D. (2010) · British Journal of Psychiatry · doi:10.1192/bjp.197.1.77 · Cited by 11
Sulpizio, V., Teghil, A., Pitzalis, S., & Boccia, M. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02790-8 · Cited by 7
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
