Bayesian meta-analysis of fMRI image data

References

• Berger, J. (2006). The case for objective Bayesian analysis. Bayesian Analysis, 1(3), 385–402.
   Web of Science ®Google Scholar
• Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1(2), 97–111.
   PubMed Web of Science ®Google Scholar
• Cowles, M. K., & Carlin, B. P. (1996). Markov Chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91(434), 883–904.
   Web of Science ®Google Scholar
• Cui, X., Li, J., & Song, X. (2015, June 28). Xjview. Retrieved from http://www.alivelearn.net/xjview
   Google Scholar
• Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the United States of America, 113(28), 7900–7905.
   PubMed Web of Science ®Google Scholar
• Gelman, A., Hill, J., & Yajima, M. (2012). Why we (usually) don’t have to worry about multiple comparisons. Journal of Research on Educational Effectiveness, 5(2), 189–211.
   Web of Science ®Google Scholar
• Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606.
   Google Scholar
• Goldstein, M. (2006). Subjective Bayesian analysis: Principles and practice. Bayesian Analysis. doi:10.1214/06-BA116
   Web of Science ®Google Scholar
• Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E.-J. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power. Comprehensive Results in Social Psychology, 2(1), 123–138.
   Google Scholar
• Han, H., & Glenn, A. L. (2018). Evaluating methods of correcting for multiple comparisons implemented in SPM12 in social neuroscience fMRI studies: An example from moral psychology. Society for Neuroscience, 13(3), 257–267.
   Google Scholar
• Han, H., & Park, J. (2018). Using SPM 12’s second-level bayesian inference procedure for fMRI analysis: Practical guidelines for end users. Frontiers in Neuroinformatics, 12. doi:10.3389/fninf.2018.00001
   Web of Science ®Google Scholar
• Heck, D. W., Gronau, Q. F., & Wagenmakers, E.-J. (2017). metaBMA: Bayesian model averaging for random and fixed effects meta-analysis. R package version 0.3.9. Retrieved from https://cran.r-project.org/package=metaBMA
   Google Scholar
• Jones, H. E., Ades, A. E., Sutton, A. J., & Welton, N. J. (2018). Use of a random effects meta-analysis in the design and analysis of a new clinical trial. Statistics in Medicine. doi:10.1002/sim.7948
   Web of Science ®Google Scholar
• Kang, J., Johnson, T. D., Nichols, T. E., & Wager, T. D. (2011). Meta analysis of functional neuroimaging data via Bayesian spatial point processes. Journal of the American Statistical Association, 106(493), 124–134.
   PubMed Web of Science ®Google Scholar
• Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. arXiv: 1099-1255.
   Web of Science ®Google Scholar
• Lieberman, M. D., & Cunningham, W. A. (2009). Type I and type II error concerns in fMRI research: Re-balancing the scale. Social, Cognitive and Affective Neuroscience, 4, 423–428.
   PubMed Web of Science ®Google Scholar
• Quintana, D. S., & Williams, D. R. (2018). Bayesian alternatives for common null-hypothesis significance tests in psychiatry: A non-technical guide using JASP. BMC Psychiatry, 18(1), 178.
   PubMed Web of Science ®Google Scholar
• Rottschy, C., Langner, R., Dogan, I., Reetz, K., Laird, A., Schulz, J., … Eickhoff, S. (2012). Modelling neural correlates of working memory: A coordinate-based meta-analysis. NeuroImage, 60(1), 830–846.
   PubMed Web of Science ®Google Scholar
• Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.
   PubMed Web of Science ®Google Scholar
• Salimi-Khorshidi, G., Smith, S. M., Keltner, J. R., Wager, T. D., & Nichols, T. E. (2009). Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies. Neuroimage, 45(3), 810–823.
   PubMed Web of Science ®Google Scholar
• Samartsidis, P., Eickhoff, C. R., Eickhoff, S. B., Wager, T. D., Barrett, L. F., Atzil, S., … Nichols, T. E. (2018). Bayesian log-Gaussian Cox process regression: Applications to meta-analysis of neuroimaging working memory studies. The Journal of the Royal Statistical Society, Series C (Applied Statistics). doi:10.1111/rssc.12295
   Web of Science ®Google Scholar
• Scott, J. G., & Berger, J. O. (2006). An exploration of aspects of bayesian multiple testing. Journal of Statistical Planning and Inference, 136(7), 2144–2162.
   Web of Science ®Google Scholar
• Sinharay, S., & Stern, H. S. (2002). On the sensitivity of Bayes factors to the prior distributions. The American Statistician, 56(3), 196–201.
   Web of Science ®Google Scholar
• Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36, 3.
   Web of Science ®Google Scholar
• Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems ofp values. Psychonomic Bulletin & Review, 14(5), 779–804.
   PubMed Web of Science ®Google Scholar
• Westfall, P. H., Johnson, W. O., & Utts, J. M. (1997). A bayesian perspective on the bonferroni adjustment. Biometrika, 84(2), 419–427.
   Web of Science ®Google Scholar
• Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670.
   PubMed Web of Science ®Google Scholar
• Yue, Y. R., Lindquist, M. A., & Loh, J. M. (2012). Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression. The Annals of Applied Statistics, 6(2), 697–718.
   Web of Science ®Google Scholar