神经成像数据的元分析

随着高空间分辨率神经成像技术如fMRI和PET的普及, 神经成像研究报告的数量增长迅猛。文献的积累为研究者提供了大量的数据, 研究者可以通过对文献的分析来验证研究结论以及提出新的假设。由于神经成像研究的主要目的之一在于寻求认知过程与脑区的空间位置对应关系, 基于坐标的元分析方法满足了这种需求, 成为神经成像数据元分析中主导的方法。其中, 激活可能性估计法(Activation Likelihood Estimation, ALE)由于方法上的合理性和使用上的便利, 成为当前使用最广泛的基于坐标的元分析方法。本文首先介绍了ALE方法的基本原理, 并在此基础上讨论了神经成像数据元分析的两种主要思路：寻找多个研究的一致性以及寻找脑区激活的调节变量。此外, 文章还介绍了新近流行的脑连通性元分析模型(MACM), 即使用元分析方法进行功能连通性分析。最后, 文章讨论了当前神经成像数据元分析的发展趋势。

Meta-analysis of Neuroimaging Studies

With increasing popularity of high resolution neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and position emission computerized tomography (PET), a large number of neuroimaging studies have been accumulated in the last two decades.These new data brought both opportunities and challenges for cognitive neuroscientists,enabling them to generate and examine new hypotheses. Given the main goal of neuroimaging is to explore the relationship between cognitive processes and corresponding locations in brain, coordinate-based meta-analysis become the dominant method for neuroimaging data. One such method, activation likelihood estimation (ALE), is the most widely used, because of its methodological superiority and usability. The current review first introduced basic principles of ALE method. Next, the two most common approaches of conducting meta-analysis of neuroimaging data were discussed: finding consistency across studies and finding modulators of brain activations. Furthermore, the newly emerged meta-analytic connectivity modeling (MACM), which used the meta-analysis to explore the functional connectivity of the brain, was illustrated using recent studies. Finally, the current review discussed several directions in the field of meta-analysis of neuroimaging data.