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Table 2 Comparison of the main meta-analytic methods for neuroimaging studies comparing patients and controls

From: Meta-analytic methods for neuroimaging data explained

 

ROI-based meta-analyses

Voxel-based meta-analyses

  

KDA/old ALE

Multilevel KDA/new ALE

SDM

ES-SDM

Selection of studies

     

   Exhaustive inclusion of studies

Limited, as information for a given brain region is present in few or no studies

Probable, as far as the included studies investigate the whole brain and not only some ROIs (in which case should be discarded)

More probable, because statistical parametric maps can also be included

   Unbiased inclusion of studies

Limited, as information is only available for regions hypothesized a priori, ignoring the rest of the brain

Probable, as far as the included studies do not use different statistical thresholds for different parts of the brain (this is a strict inclusion criterion in SDM and ES-SDM)

Statistical analyses

     

   Weighting of the studies

Complete (sample size and study precision)

None

Partial (only sample size)

Complete (sample size and study precision)

   Control of the heterogeneity

Residual heterogeneity is correctly included in the analyses

Residual heterogeneity is not controlled, and increases and decreases are not counteracted, potentially leading to voxels being detected as increased and decreased at the same time

Residual heterogeneity is not included in the weightings, but increases and decreases are counteracted

Residual heterogeneity is correctly included in the weightings

   Study of the heterogeneity

Possible, by means of meta-regressions and subgroup analyses

Limited to subgroup analyses

Possible, by means of meta-regressions and subgroup analyses

   Correction for multiple comparisons

Possible

Not possible, questionable or limited to conventional voxel-thresholded cluster-based methods

   Description of the effect sizes

Possible

Not possible

Possible though limited to pseudo-effect sizes based on the proportion of studies reporting significant findings

Possible

   Description of relevant non-significant trends

Possible, as the number of ROIs is manageable

Not possible, or limited to the visual inspection of liberally thresholded maps, as the number of voxels is too massive for a more accurate individual inspection

  1. Please see text for further details. ALE: activation likelihood estimation; ES: effect size; KDA: kernel density analysis; ROI: region of interest; SDM: signed differential mapping.