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

Radua, J., Mataix-Cols, D., Phillips, Mary L., El-Hage, W., Kronhaus, D.M., Cardoner, N. and Surguladze, S. 2012. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry 27 (8) , pp. 605-611. 10.1016/j.eurpsy.2011.04.001

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Medicine
Subjects:	R Medicine > R Medicine (General)
Publisher:	Elsevier
ISSN:	0924-9338
Last Modified:	04 Nov 2015 10:17
URI:	https://orca.cardiff.ac.uk/id/eprint/80439

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