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Updated: Mar 3, 2026
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
Published on: November 27, 2019
Evaluating methods of correcting for multiple comparisons implemented in SPM12 in social neuroscience fMRI studies:
1a Educational Psychology Program , University of Alabama , Tuscaloosa , AL , United States.
Abstract:
In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of social neuroscience that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial and also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in Statistical Parametric Mapping 12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxelwise thresholding with familywise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods.
Insights
Correcting for multiple comparisons in fMRI is crucial. Voxelwise thresholding with familywise error correction offers the most precise results for social neuroscience studies, balancing sensitivity and accuracy.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Social Neuroscience
Background:
- fMRI research aims to identify true neural activity, balancing false positives and Type II errors.
- Social neuroscience studies, like moral judgment, often have smaller effects and weaker statistical power.
- Choosing appropriate multiple comparison correction methods is vital for reliable fMRI findings.
Purpose of the Study:
- To evaluate four common multiple comparison correction methods in fMRI.
- To determine the most effective method for identifying true brain activity in social neuroscience.
- To assess overlap with meta-analysis and permutation results for validation.
Main Methods:
- Utilized moral judgment fMRI data.
- Compared voxelwise thresholding (Familywise Error Rate - FWER), clusterwise thresholding, Bonferroni correction, and False Discovery Rate (FDR) correction.
- Assessed method precision against meta-analysis and nonparametric permutation results using Statistical Parametric Mapping 12.
Main Results:
- Voxelwise thresholding with FWER correction demonstrated superior precision.
- This method showed better overlap with meta-analysis and permutation results compared to other tested methods.
- Other methods like clusterwise thresholding, Bonferroni, and FDR correction were less precise.
Conclusions:
- Voxelwise thresholding using FWER is recommended for multiple comparison correction in social neuroscience fMRI.
- This approach offers a better balance, minimizing omitted true effects and extraneous false positives.
- The findings aid in improving the reliability and replicability of social neuroscience research.
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