Meta-analysis of functional neuroimaging data: current and future directions
Meta-analysis of functional neuroimaging data: current and future directions
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DOI：
10.1093/scan/nsm015
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发表时间：
2007-06-01
影响因子：
4.2
通讯作者：
Kaplan, Lauren
中科院分区：
文献类型：
--
作者：
Wager, Tor D.;Lindquist, Martin;Kaplan, Lauren
荟萃分析是一种越来越流行和有价值的工具，用于总结许多神经影像学研究的结果。它可以用来建立关于功能区位置的共识，测试从患者和动物研究中得出的假设，并提出关于结构-功能对应关系的新假设。它在神经影像学中特别有价值，因为大多数研究没有充分纠正多重比较；根据所使用的统计阈值，我们估计在已发表的研究中报告的激活中大约有10-20%是假阳性。在本文中，我们简要总结了一些最流行的元分析方法及其局限性，并概述了一种改进的多层次方法，该方法在建立研究一致性方面具有更高的有效性。我们还讨论了多元方法，通过这些方法，元分析可以用来开发和测试关于大脑区域共同活动的假设。最后，我们认为，荟萃分析可以从大脑活动模式预测心理状态做出独特的有价值的贡献，我们简要讨论了一些方法来做出这样的预测。
Meta-analysis is an increasingly popular and valuable tool for summarizing results across many neuroimaging studies. It can be used to establish consensus on the locations of functional regions, test hypotheses developed from patient and animal studies and develop new hypotheses on structure-function correspondence. It is particularly valuable in neuroimaging because most studies do not adequately correct for multiple comparisons; based on statistical thresholds used, we estimate that roughly 10-20% of reported activations in published studies are false positives. In this article, we briefly summarize some of the most popular meta-analytic approaches and their limitations, and we outline a revised multilevel approach with increased validity for establishing consistency across studies. We also discuss multivariate methods by which meta-analysis can be used to develop and test hypotheses about co-activity of brain regions. Finally, we argue that meta-analyses can make a uniquely valuable contribution to predicting psychological states from patterns of brain activity, and we briefly discuss some methods for making such predictions.