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This study transforms p-values into Bayes factor bounds, offering a more robust measure of evidence in neuroimaging meta-analyses. This Bayesian-inspired approach enhances the reliability of conclusions by revealing the true weight of evidence beyond traditional significance thresholds.

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Area of Science:

  • Neuroimaging
  • Statistical Inference
  • Bayesian Analysis

Background:

  • P-values are widely used but fail to quantify evidence strength for hypotheses.
  • This limitation is critical in neuroimaging meta-analyses for assessing brain activation patterns.
  • Current methods lack a precise measure of evidence supporting specific findings.

Purpose of the Study:

  • To present a practical method for converting p-values into upper bounds on Bayes factors.
  • To quantify the maximum plausible evidence for alternative hypotheses in neuroimaging meta-analyses.
  • To improve the interpretability and reliability of neuroimaging meta-analytic conclusions.

Main Methods:

  • Developed an approach to transform p-values into Bayes factor upper bounds.
  • Applied the method within the Activation Likelihood Estimation (ALE) framework.
  • Utilized a reference dataset of 73 finger-tapping experiments for illustration.

Main Results:

  • Statistically significant effects in ALE showed a wide range of evidence strengths (Bayes factor bounds from 46 to 410).
  • Conventional threshold-based inference conceals substantial heterogeneity in the weight of evidence.
  • Demonstrated that p-values do not accurately reflect the strength of evidence.

Conclusions:

  • The proposed method generates voxel-wise maps of evidential strength.
  • It is compatible with existing neuroimaging analysis pipelines.
  • Offers a conservative, Bayesian-inspired complement to standard significance maps, improving interpretability and avoiding p-value misinterpretations.