Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX 78229-3900, USA.
Neuroimage. 2010 Jun;51(2):677-83. doi: 10.1016/j.neuroimage.2010.02.048. Epub 2010 Mar 1.
Spatial normalization of neuroimaging data is a standard step when assessing group effects. As a result of divergent analysis procedures due to different normalization algorithms or templates, not all published coordinates refer to the same neuroanatomical region. Specifically, the literature is populated with results in the form of MNI or Talairach coordinates, and their disparity can impede the comparison of results across different studies. This becomes particularly problematic in coordinate-based meta-analyses, wherein coordinate disparity should be corrected to reduce error and facilitate literature reviews. In this study, a quantitative comparison was performed on two corrections, the Brett transform (i.e., "mni2tal"), and the Lancaster transform (i.e., "icbm2tal"). Functional magnetic resonance imaging (fMRI) data acquired during a standard paired associates task indicated that the disparity between MNI and Talairach coordinates was better reduced via the Lancaster transform, as compared to the Brett transform. In addition, an activation likelihood estimation (ALE) meta-analysis of the paired associates literature revealed that a higher degree of concordance was obtained when using the Lancaster transform in the form of fewer, smaller, and more intense clusters. Based on these results, we recommend that the Lancaster transform be adopted as the community standard for reducing disparity between results reported as MNI or Talairach coordinates, and suggest that future spatial normalization strategies be designed to minimize this variability in the literature.
神经影像学数据的空间标准化是评估组效应时的标准步骤。由于不同的归一化算法或模板导致分析过程存在差异,并非所有已发表的坐标都指向相同的神经解剖区域。具体来说,文献中充斥着 MNI 或 Talairach 坐标形式的结果,它们之间的差异可能会阻碍不同研究结果的比较。在基于坐标的荟萃分析中,这种情况尤其成问题,因为需要对坐标差异进行校正以减少误差并促进文献综述。在这项研究中,对两种校正方法,即 Brett 变换(即“mni2tal”)和兰卡斯特变换(即“icbm2tal”)进行了定量比较。在标准配对联想任务期间采集的功能磁共振成像(fMRI)数据表明,与 Brett 变换相比,Lancaster 变换能更好地减小 MNI 和 Talairach 坐标之间的差异。此外,对配对联想文献的激活似然估计(ALE)荟萃分析表明,当以更少、更小和更强烈的簇的形式使用 Lancaster 变换时,获得了更高的一致性程度。基于这些结果,我们建议采用 Lancaster 变换作为减少以 MNI 或 Talairach 坐标报告的结果之间差异的社区标准,并建议未来的空间标准化策略旨在最小化文献中的这种变异性。