Salimi-Khorshidi Gholamreza, Smith Stephen M, Keltner John R, Wager Tor D, Nichols Thomas E
Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK.
Neuroimage. 2009 Apr 15;45(3):810-23. doi: 10.1016/j.neuroimage.2008.12.039. Epub 2008 Dec 31.
With the rapid growth of neuroimaging research and accumulation of neuroinformatic databases the synthesis of consensus findings using meta-analysis is becoming increasingly important. Meta-analyses pool data across many studies to identify reliable experimental effects and characterize the degree of agreement across studies. Coordinate-based meta-analysis (CBMA) methods are the standard approach, where each study entered into the meta-analysis has been summarized using only the (x, y, z) locations of peak activations (with or without activation magnitude) reported in published reports. Image-based meta-analysis (IBMA) methods use the full statistic images, and allow the use of hierarchical mixed effects models that account for differing intra-study variance and modeling of random inter-study variation. The purpose of this work is to compare image-based and coordinate-based meta-analysis methods applied to the same dataset, a group of 15 fMRI studies of pain, and to quantify the information lost by working only with the coordinates of peak activations instead of the full statistic images. We apply a 3-level IBMA mixed model for a "mega-analysis", and highlight important considerations in the specification of each model and contrast. We compare the IBMA result to three CBMA methods: ALE (activation likelihood estimation), KDA (kernel density analysis) and MKDA (multi-level kernel density analysis), for various CBMA smoothing parameters. For the datasets considered, we find that ALE at sigma=15 mm, KDA at rho=25-30 mm and MKDA at rho=15 mm give the greatest similarity to the IBMA result, and that ALE was the most similar for this particular dataset, though only with a Dice similarity coefficient of 0.45 (Dice measure ranges from 0 to 1). Based on this poor similarity, and the greater modeling flexibility afforded by hierarchical mixed models, we suggest that IBMA is preferred over CBMA. To make IBMA analyses practical, however, the neuroimaging field needs to develop an effective mechanism for sharing image data, including whole-brain images of both effect estimates and their standard errors.
随着神经影像学研究的迅速发展以及神经信息数据库的积累，使用荟萃分析来综合一致的研究结果变得越来越重要。荟萃分析将多个研究的数据汇总起来，以确定可靠的实验效应，并描述不同研究之间的一致性程度。基于坐标的荟萃分析（CBMA）方法是标准方法，其中纳入荟萃分析的每项研究仅使用已发表报告中报道的峰值激活的（x, y, z）位置（有或没有激活强度）进行总结。基于图像的荟萃分析（IBMA）方法使用完整的统计图像，并允许使用分层混合效应模型，该模型考虑了研究内部不同的方差以及研究间随机变异的建模。这项工作的目的是比较应用于同一数据集（一组15项关于疼痛的功能磁共振成像研究）的基于图像和基于坐标的荟萃分析方法，并量化仅使用峰值激活坐标而非完整统计图像所损失的信息。我们应用一个三级IBMA混合模型进行“大型分析”，并强调每个模型和对比规范中的重要注意事项。我们将IBMA结果与三种CBMA方法进行比较：激活似然估计（ALE）、核密度分析（KDA）和多级核密度分析（MKDA），针对各种CBMA平滑参数。对于所考虑的数据集，我们发现，在σ = 15毫米时的ALE、在ρ = 25 - 30毫米时的KDA以及在ρ = 15毫米时的MKDA与IBMA结果最为相似，并且对于这个特定数据集，ALE是最相似的，不过其骰子相似系数仅为0.45（骰子度量范围为0到1）。基于这种较差的相似性以及分层混合模型提供的更大建模灵活性，我们建议IBMA优于CBMA。然而，为了使IBMA分析切实可行，神经影像学领域需要开发一种有效的机制来共享图像数据，包括效应估计及其标准误差的全脑图像。
