统计参数映射
参数统计
非参数统计
核密度估计
荟萃分析
神经影像学
计算机科学
核(代数)
统计分析
统计
人工智能
机器学习
数据挖掘
心理学
数学
医学
精神科
磁共振成像
放射科
内科学
组合数学
估计员
作者
Joaquim Raduà,David Mataix‐Cols,Mary L. Phillips,Wissam El‐Hage,Dina M Kronhaus,Narcı́s Cardoner,Simon Surguladze
出处
期刊:European Psychiatry
[Cambridge University Press]
日期:2011-06-11
卷期号:27 (8): 605-611
被引量:593
标识
DOI:10.1016/j.eurpsy.2011.04.001
摘要
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.
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