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ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations.

Lennart Frahm1, Theodore D Satterthwaite2, Peter T Fox3

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Activation likelihood estimation (ALE) meta-analysis performs similarly for structural neuroimaging as for functional data. For voxel-based morphometry (VBM) studies, increase the minimum experiment count to 23 for robust results.

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Area of Science:

  • Neuroimaging analysis
  • Neuroscience meta-analysis

Background:

  • Activation Likelihood Estimation (ALE) meta-analysis is widely used for structural neuroimaging.
  • Previous assessments of ALE's performance relied on functional neuroimaging data simulations.

Approach:

  • This study simulated 365,000 ALE analyses using parameters from the BrainMap VBM experiment database.
  • Evaluated ALE's sensitivity, susceptibility to spurious convergence, and individual experiment influence in VBM studies.

Key Points:

  • ALE algorithm performance is comparable across structural and functional neuroimaging modalities.
  • Structural VBM experiments, with fewer foci and more participants, showed higher individual study impact.
  • Recommended minimum experiments for VBM ALE datasets increased from 17 to 23 to mitigate single-study bias.

Conclusions:

  • Existing ALE evaluation guidelines are generalizable to VBM meta-analyses.
  • Increased sample size in VBM studies necessitates a higher minimum experiment count for reliable ALE results.
  • Caution and diligent reporting are advised for ALE analyses with fewer than 23 experiments.