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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
Published on: November 27, 2019
A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical
J Radua1, D Mataix-Cols, M L Phillips
1Department of psychosis Studies, institute of psychiatry, King's College London, P.O. 69, London, SE5 8AF, UK. Joaquim.Radua@iop.kcl.ac.uk
Summary
Effect Size SDM (ES-SDM) is a new meta-analysis tool combining statistical maps and coordinates for neuroimaging research. This validated method enhances the reliability of synthesizing findings in psychiatry and neurology.
Area of Science:
- Neuroimaging
- Psychiatry
- Neurology
Background:
- Meta-analyses are crucial for synthesizing neuroimaging research in psychiatry and neurology.
- Existing methods like activation likelihood estimation (ALE) and multilevel kernel density analysis (MKDA) rely on peak coordinates, but images are often unavailable.
- Signed differential mapping (SDM) improved upon peak-probability methods by enabling patient-control comparisons.
Purpose of the Study:
- To introduce Effect Size SDM (ES-SDM), a novel meta-analysis technique.
- To enable the combination of statistical parametric maps and peak coordinates within a unified framework.
- To validate ES-SDM's reliability and performance in neuroimaging meta-analyses.
Main Methods:
- Developed Effect Size SDM (ES-SDM), integrating statistical parametric maps and peak coordinates.
- Utilized well-established statistical methods for meta-analysis.
- Validated ES-SDM by comparing its results with a pooled analysis of individual data from studies on brain response to fearful faces.
Main Results:
- ES-SDM demonstrated validity and reliability as a coordinate-based meta-analysis method.
- The inclusion of statistical parametric maps potentially enhances ES-SDM's performance.
- Results from ES-SDM meta-analysis aligned with pooled individual data analysis.
Conclusions:
- ES-SDM offers a valuable and reliable tool for neuroimaging meta-analyses.
- The method effectively combines diverse data types (statistical maps and coordinates).
- ES-SDM is anticipated to be beneficial for researchers in psychiatry, neurology, and allied fields.
