Publication bias in neuroimaging research: implications for meta-analyses

Neuroinformatics. 2012 Jan;10(1):67-80. doi: 10.1007/s12021-011-9125-y.


Neuroimaging and the neurosciences have made notable advances in sharing activation results through detailed databases, making meta-analysis of the published research faster and easier. However, the effect of publication bias in these fields has not been previously addressed or accounted for in the developed meta-analytic methods. In this article, we examine publication bias in functional magnetic resonance imaging (fMRI) for tasks involving working memory in the frontal lobes (Brodmann Areas 4, 6, 8, 9, 10, 37, 45, 46, and 47). Seventy-four studies were selected from the literature and the effect of publication bias was examined using a number of regression-based techniques. Pearson's r correlation coefficient and Cohen's d effect size estimates were computed for the activation in each study and compared to the study sample size using Egger's regression, Macaskill's regression, and the 'Trim and Fill' method. Evidence for publication bias was identified in this body of literature (p < 0.01 for each test), generally, though was neither task- nor sub-region-dependent. While we focused our analysis on this subgroup of brain mapping studies, we believe our findings generalize to the brain imaging literature as a whole and databases seeking to curate their collective results. While neuroimaging databases of summary effects are of enormous value to the community, the potential publication bias should be considered when performing meta-analyses based on database contents.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain Mapping / statistics & numerical data*
  • Humans
  • Magnetic Resonance Imaging
  • Meta-Analysis as Topic*
  • Publication Bias / statistics & numerical data*
  • Sample Size