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The Influence of Study-Level Inference Models and Study Set Size on Coordinate-Based fMRI Meta-Analyses
Han Bossier1, Ruth Seurinck1, Simone Kühn2
1Department of Data Analysis, Ghent University, Ghent, Belgium.
Frontiers in Neuroscience
|February 7, 2018
Summary
This study evaluates coordinate-based meta-analysis methods for neuroimaging. Mixed-effects models and random-effects meta-analysis offer the best performance, especially with more studies included.
Area of Science:
- Neuroimaging analysis
- Statistical methodology
Background:
- Neuroimaging studies are increasing, necessitating robust methods for summarizing results.
- Coordinate-based meta-analyses aggregate findings using peak activation locations.
Purpose of the Study:
- To investigate how different meta-analysis characteristics influence false/true positives and activation reliability.
- To compare group-level models, meta-analysis types, and study numbers.
Main Methods:
- A resampling scheme was applied to a large dataset (N=1,400) to create test and evaluation conditions.
- Test condition involved subsampling participants and applying meta-analyses.
- Evaluation condition used high-powered group analysis for comparison.
Main Results:
- Mixed-effects models at the study level combined with random-effects meta-analysis yielded optimal performance.
- Performance improved with an increased number of studies.
- Activation Likelihood Estimation (ALE) is a viable alternative for balancing errors but requires more studies for reliability.
Conclusions:
- The combination of mixed-effects models and random-effects meta-analysis is recommended for neuroimaging meta-analysis.
- Increasing the number of studies enhances the reliability of meta-analysis outcomes.
- ALE offers a good balance of statistical errors but may reduce activation reliability.
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