Statistical Science

The Coordinate-Based Meta-Analysis of Neuroimaging Data

Pantelis Samartsidis, Silvia Montagna, Timothy D. Johnson, and Thomas E. Nichols

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text


Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing methodologies, explaining the benefits and drawbacks of each. A demonstration on a real dataset of emotion studies is included. We discuss some still-open problems in the field to highlight the need for future research.

Article information

Statist. Sci., Volume 32, Number 4 (2017), 580-599.

First available in Project Euclid: 28 November 2017

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Meta-analysis neuroimaging functional magnetic resonance imaging


Samartsidis, Pantelis; Montagna, Silvia; Johnson, Timothy D.; Nichols, Thomas E. The Coordinate-Based Meta-Analysis of Neuroimaging Data. Statist. Sci. 32 (2017), no. 4, 580--599. doi:10.1214/17-STS624.

Export citation


  • Arminger, G. and Muthén, B. O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hastings algorithm. Psychometrika 63 271–300.
  • Bailey, D. L., Townsend, D. W., Valk, P. E. and Maisey, M. N., eds. (2006). Positron Emission Tomography: Basic Sciences. Springer, Berlin.
  • Bartels, A. and Zeki, S. (2004). The neural correlates of maternal and romantic love. NeuroImage 21 1155–1166.
  • Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. 57 289–300.
  • Button, K. S., Ioannidis, J. P. a., Mokrysz, C., Nosek, B. a., Flint, J., Robinson, E. S. J. and Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nat. Rev., Neurosci. 14 365–76.
  • Calhoun, V. D. and Pearlson, G. D. (2012). A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directions. NeuroImage 59 25–35.
  • Carp, J. (2012). The secret lives of experiments: Methods reporting in the fMRI literature. NeuroImage 63 289–300.
  • Caspers, J., Zilles, K., Beierle, C., Rottschy, C. and Eickhoff, S. B. (2014). A novel meta-analytic approach: Mining frequent co-activation patterns in neuroimaging databases. Neuroimage 90 390–402.
  • Cole, D. M., Smith, S. M. and Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience 4 8.
  • Costafreda, S. G., David, A. S. and Brammer, M. J. (2009). A parametric approach to voxel-based meta-analysis. NeuroImage 46 115–122.
  • Costafreda, S., Brammer, M., David, A. and Fu, C. (2008). Predictors of amygdala activation during the processing of emotional stimuli. Brains Res. Rev. 58 57–70.
  • David, S. P., Ware, J. J., Chu, I. M., Loftus, P. D., Fusar-Poli, P., Radua, J., Munafò, M. R. and Ioannidis, J. P. A. (2013). Potential reporting bias in fMRI studies of the brain. PLoS ONE 8.
  • Delvecchio, G., Fossati, P., Boyer, P., Brambilla, P., Falkai, P., Gruber, O., Hietala, J., Lawrie, S. M., Martinot, J.-L., McIntosh, A. M., Meisenzahl, E. and Frangou, S. (2012). Common and distinct neural correlates of emotional processing in bipolar disorder and major depressive disorder: A voxel-based meta-analysis of functional magnetic resonance imaging studies. Eur. Neuropsychopharmacol. 22 100–113.
  • Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K. and Fox, P. T. (2009). Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty. Hum. Brain Mapp. 30 2907–2926.
  • Eickhoff, S. B., Bzdok, D., Laird, A. R., Kurth, F. and Fox, P. T. (2012). Activation likelihood estimation meta-analysis revisited. NeuroImage 59 2349–2361.
  • Eickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K., Fox, P. T., Bzdok, D. and Eickhoff, C. R. (2016). Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation. Neuroimage 137 70–85.
  • Eickhoff, S. B., Laird, A. R., Fox, P. M., Lancaster, J. L. and Fox, P. T. (2017). Implementation errors in the GingerALE software: Description and recommendations. Hum. Brain Mapp. 38 7–11.
  • Etkin, A. and Wager, T. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. Am. J. Psychiatry 164 1476–1488.
  • Farah, M. J. (2014). Brain images, babies, and bathwater: Critiquing critiques of functional neuroimaging. Hastings Cent. Rep. 44 S19–30.
  • Fox, P. T., Parsons, L. M. and Lancaster, J. L. (1998). Beyond the single study: Function/location metanalysis in cognitive neuroimaging. Curr. Opin. Neurobiol. 8 178–187.
  • Fox, P. T., Lancaster, J. L., Parsons, L. M., Xiong, J. H. and Zamarripa, F. (1997). Functional volumes modeling: Theory and preliminary assessment. Hum. Brain Mapp. 5 306–311.
  • Friston, K. J., Penny, W., Phillips, C., Kiebel, S., Hinton, G. and Ashburner, J. (2002). Classical and Bayesian inference in neuroimaging: Theory. NeuroImage 16 465–483.
  • Fusar-Poli, P. (2012). Voxel-wise meta-analysis of fMRI studies in patients at clinical high risk for psychosis. J. Psychiatry Neurosci. 37 106–112.
  • Genovese, C., Lazar, N. and Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage 15 870–878.
  • Gorgolewski, K. J., Varoquaux, G., Rivera, G., Schwarz, Y., Ghosh, S. S., Maumet, C., Sochat, V. V., Nichols, T. E., Poldrack, R. A., Poline, J.-B., Yarkoni, T. and Margulies, D. S. (2015). A web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front. Neuroinform 9 8.
  • Greenland, S. (1994). Invited commentary: A critical look at some popular meta-analytic methods. Am. J. Epidemiol. 140 290–296.
  • Hartung, J., Knapp, G. and Sinha, B. K. (2008). Statistical Meta-Analysis with Applications. Wiley, Hoboken, NJ.
  • Hedges, L. V. and Olkin, I. (1985). Statistical Methods for Meta-Analysis. Academic Press, Inc., Orlando, FL.
  • Huettel, S. A., Song, A. W. and McCarthy, G. (2009). Functional Magnetic Resonance Imaging Second Edition. Sinauer Associates, Inc, Sunderland, MA.
  • Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Wiley, Chichester.
  • Kang, J., Johnson, T. D., Nichols, T. E. and Wager, T. D. (2011). Meta analysis of functional neuroimaging data via Bayesian spatial point processes. J. Amer. Statist. Assoc. 106 124–134.
  • Kang, J., Nichols, T. E., Wager, T. D. and Johnson, T. D. (2014). A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis. Ann. Appl. Stat. 8 1800–1824.
  • Kim, S.-G. and Ogawa, S. (2012). Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J. Cereb. Blood Flow Metab. 32 1188–1206.
  • Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K. and Wager, T. D. (2008). Functional grouping and cortical and subcortical interactions in emotion: A meta-analysis of neuroimaging studies. NeuroImage 42 998–1031.
  • Konova, A. B., Moeller, S. J. and Goldstein, R. Z. (2013). Common and distinct neural targets of treatment: Changing brain function in substance addiction. Neurosci. Biobehav. Rev. 37 2806–2817.
  • Laird, A. R., Lancaster, J. J. and Fox, P. T. (2005). BrainMap: The social evolution of a human brain mapping database. Neuroinformatics 3 65–77.
  • Laird, A. R., Fox, P. M., Price, C. J., Glahn, D. C., Uecker, A. M., Lancaster, J. L., Turkeltaub, P. E., Kochunov, P. and Fox, P. T. (2005). ALE meta-analysis: Controlling the false discovery rate and performing statistical contrasts. Hum. Brain Mapp. 25 155–164.
  • Lazar, N. A., Luna, B., Sweeney, J. A. and Eddy, W. F. (2002). Combining brains: A survey of methods for statistical pooling of information. NeuroImage 16 538–550.
  • Lindquist, M. A. (2008). The statistical analysis of fMRI data. Statist. Sci. 23 439–464.
  • Møller, J. and Waagepetersen, R. P. (2004). Statistical Inference and Simulation for Spatial Point Processes. 100. Chapman & Hall/CRC, Boca Raton, FL.
  • Montagna, S., Tokdar, S. T., Neelon, B. and Dunson, D. B. (2012). Bayesian latent factor regression for functional and longitudinal data. Biometrics 68 1064–1073.
  • Montagna, S., Wager, T. D., Barrett, L. F., Johnson, T. D. and Nichols, T. E. (2017). Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data. Biometrics. To appear.
  • Mumford, J. A. and Nichols, T. (2006). Modeling and inference of multisubject fMRI data. IEEE Eng. Med. Biol. Mag. 25 42–51.
  • Mumford, J. A. and Nichols, T. (2009). Simple group fMRI modeling and inference. NeuroImage 47 1469–1475.
  • Nichols, T. and Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. Stat. Methods Med. Res. 12 419–446.
  • Nichols, T. E. and Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum. Brain Mapp. 15 1–25.
  • Nielsen, F. Å. and Hansen, L. K. (2002). Modeling of activation data in the BrainMap™ database: Detection of outliers. Hum. Brain Mapp. 15 146–156.
  • Phelps, E. A. and LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron 48 175–187.
  • Poldrack, R. (2011). Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding. Neuron 72 692–697.
  • Poldrack, R. A., Mumford, J. A. and Nichols, T. E. (2011). Handbook of Functional MRI Data Analysis. Cambridge Univ. Press, Cambridge.
  • Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, O. and Milham, M. P. (2013). Toward open sharing of task-based fMRI data: The OpenfMRI project. Front. Neuroinform 7 12.
  • Poline, J.-B., Breeze, J. L., Ghosh, S., Gorgolewski, K., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., Poldrack, R. A., Schwartz, Y., Ashburner, J. and Kennedy, D. N. (2012). Data sharing in neuroimaging research. Front. Neuroinform 6 9.
  • Radua, J. and Mataix-Cols, D. (2009). Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br. J. Psychiatry 195 393–402.
  • Radua, J. and Mataix-Cols, D. (2012). Meta-analytic methods for neuroimaging data explained. Biol. Mood Anxiety Disord. 2 6.
  • Radua, J., Mataix-Cols, D., Phillips, M. L., El-Hage, W., Kronhaus, D. M., Cardoner, N. and Surguladze, S. (2012). A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. Eur. Psychiatry 27 605–611.
  • Radua, J., Rubia, K., Canales-Rodríguez, E. J., Pomarol-Clotet, E., Fusar-Poli, P. and Mataix-Cols, D. (2014). Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Front. Psychiatry 5 13.
  • Raemaekers, M., Vink, M., Zandbelt, B., van Wezel, R. J. A., Kahn, R. S. and Ramsey, N. F. (2007). Test-retest reliability of fMRI activation during prosaccades and antisaccades. NeuroImage 36 532–542.
  • Richlan, F., Kronbichler, M. and Wimmer, H. (2011). Meta-analyzing brain dysfunctions in dyslexic children and adults. NeuroImage 56 1735–1742.
  • Salimi-Khorshidi, G., Smith, S. M., Keltner, J. R., Wager, T. D. and Nichols, T. E. (2009). Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies. NeuroImage 45 810–823.
  • Samartsidis, P., Montagna, S., Nichols, T. E. and Johnson, T. D. (2017). Supplement to “The coordinate-based meta-analysis of neuroimaging data.” DOI:10.1214/17-STS624SUPP.
  • Shermer, M. (2008). Why you should be skeptical of brain scans. Sci. Am. Mind 19 66–71.
  • Talairach, J. and Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the Human Brain. Thieme, Stuttgart.
  • Thirion, B., Pinel, P., Meriaux, S., Roche, A., Dehaene, S. and Poline, J.-B. (2007). Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses. NeuroImage 35 105–120.
  • Turkeltaub, P. E., Eden, G. F., Jones, K. M. and Zeffiro, T. A. (2002). Meta-analysis of the functional neuroanatomy of single-word reading: Method and validation. NeuroImage 16 765–780.
  • Turkeltaub, P. E., Eickhoff, S. B., Laird, A. R., Fox, M., Wiener, M. and Fox, P. (2012). Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Hum. Brain Mapp. 33 1–13.
  • Vul, E., Harris, C., Winkielman, P. and Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect. Psychol. Sci. 4 274–290.
  • Wager, T. D., Jonides, J. and Reading, S. (2004). Neuroimaging studies of shifting attention: A meta-analysis. NeuroImage 22 1679–1693.
  • Wager, T. D., Lindquist, M. and Kaplan, L. (2007). Meta-analysis of functional neuroimaging data: Current and future directions. Soc. Cogn. Affect Neurosci. 2 150–158.
  • Wager, T. D., Phan, K. L., Liberzon, I. and Taylor, S. F. (2003). Valence, gender, and lateralization of functional brain anatomy in emotion: A meta-analysis of findings from neuroimaging. NeuroImage 19 513–531.
  • Wager, T. D., Barrett, L. F., Bliss-Moreau, E., Lindquist, K., Duncan, S., Kober, H., Joseph, J., Davidson, M. and Mize, J. (2008). The neuroimaging of emotion. In Handbook of Emotions, 3rd ed. (M. Lewis, J. M. Haviland-Jones and L. F. Barrett, eds.) 249–271. Guilford Press, New York, NY.
  • Wolpert, R. L. and Ickstadt, K. (1998). Poisson/gamma random field models for spatial statistics. Biometrika 85 251–267.
  • Xue, W., Kang, J., Bowman, F. D., Wager, T. D. and Guo, J. (2014). Identifying functional co-activation patterns in neuroimaging studies via Poisson graphical models. Biometrics 70 812–822.
  • Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. and Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8 665–670.
  • Yue, Y. R., Lindquist, M. A. and Loh, J. M. (2012). Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression. Ann. Appl. Stat. 6 697–718.
  • Yue, Y. and Speckman, P. L. (2010). Nonstationary spatial Gaussian Markov random fields. J. Comput. Graph. Statist. 19 96–116.

Supplemental materials

  • Supplement to “The coordinate-based meta- analysis of neuroimaging data”. The supplementary material includes the results of the simulation study of Section 4.1 using the MKDA and SDM kernels. MCMC convergence diagnostics for the analysis of the emotions data in Section 4.2 with the BHICP are also presented.