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A Minimum Bayes Factor Based Threshold for Activation Likelihood Estimation.

Tommaso Costa1,2,3, Donato Liloia4,5, Franco Cauda1,2,3

  • 1GCS-fMRI Group, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.

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Summary
This summary is machine-generated.

This study introduces a novel Bayesian thresholding method for neuroimaging meta-analysis, offering more informative probability assessments than traditional frequentist approaches. The minimum Bayes factor (mBF) provides a flexible and statistically rigorous alternative for analyzing brain imaging data.

Keywords:
Activation likelihood estimationBrainMapCoordinate-based meta-analysisGingerALEMinimum Bayes Factor

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Modeling

Background:

  • Activation Likelihood Estimation (ALE) is a standard neuroimaging meta-analysis algorithm.
  • Existing frequentist thresholding methods provide p-values but lack direct probability interpretation.
  • There is a need for more informative statistical frameworks in neuroimaging meta-analysis.

Approach:

  • Developed a novel thresholding procedure for ALE based on the minimum Bayes factor (mBF) within a Bayesian framework.
  • Analyzed six task-fMRI/VBM datasets to establish equivalencies between mBF and frequentist thresholds (FWE and cluster-FWE).
  • Evaluated the sensitivity and robustness of the mBF approach against spurious findings.

Key Points:

  • A cutoff of log10(mBF) = 5 corresponds to the standard voxel-level Family Wise Error (FWE) threshold.
  • A cutoff of log10(mBF) = 2 aligns with the cluster-level FWE (c-FWE) threshold, though it may include distant voxels.
  • The Bayesian framework allows for nuanced interpretation of results, with lower mBF values indicating weaker evidence rather than outright rejection.

Conclusions:

  • The minimum Bayes factor (mBF) offers a powerful and flexible alternative to frequentist thresholding in neuroimaging meta-analysis.
  • A cutoff of log10(mBF) = 5 is recommended for Bayesian ALE thresholding to maintain rigor comparable to FWE.
  • The Bayesian approach permits legitimate discussion of results with less conservative thresholds, enhancing statistical rigor in human brain mapping.