A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps

• Published in: European psychiatry Vol. 27; no. 8; pp. 605 - 611
• Main Authors: Radua, J., Mataix-Cols, D., Phillips, M.L., El-Hage, W., Kronhaus, D.M., Cardoner, N., Surguladze, S.
• Format: Journal Article
• Language: English
• Published: Paris Elsevier SAS 01.11.2012; Elsevier
• Subjects: Adult; Biological and medical sciences; Brain - physiology; Brain - physiopathology; Brain Mapping - methods; Brain Mapping - statistics & numerical data; Facial Expression; Humans; Internal Medicine; Magnetic Resonance Imaging - instrumentation; Magnetic Resonance Imaging - methods; Medical sciences; Meta-analysis; Neuroimaging; Neuroimaging - methods; Neuroimaging - statistics & numerical data; Neuropsychological Tests - statistics & numerical data; Peak coordinate; Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Reproducibility of Results; Signed differential mapping; Statistical parametric map; Neuroimaging; Peak coordinate; Statistical parametric map; Signed differential mapping; Meta-analysis; Parametric method; Cartography; Review; Metaanalysis
• ISSN: 0924-9338; 1778-3585; 1778-3585
• DOI: 10.1016/j.eurpsy.2011.04.001

Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM meta-analysis of studies on the brain response to fearful faces with the results of a pooled analysis of the original individual data. The results showed that ES-SDM is a valid and reliable coordinate-based method, whose performance might be additionally increased by including statistical parametric maps. We anticipate that ES-SDM will be a helpful tool for researchers in the fields of psychiatry, neurology and allied disciplines.