Shen Shan, Szameitat Andre J, Sterr Annette
Department of Psychology, University of Surrey, GU2 7XH Guildford, UK.
Magn Reson Imaging. 2007 Dec;25(10):1385-96. doi: 10.1016/j.mri.2007.03.025. Epub 2007 Apr 30.
Structural neuroimaging studies are of great interest for neuroscientists, which are reflected in the rising number of papers using voxel-based morphometry (VBM). One major step in VBM is the transformation of images to a standard template, a spatial normalization necessary to ensure that homologous regions are compared while interindividual characteristics are maintained. Templates can be created in different ways, and this may affect the likelihood that differences in gray/white matter density between groups are detected. However, studies investigating the interaction of normalization template and VBM accuracy are sparse. Existing work is based on patient-control group comparisons, and the emerging results are inconclusive. The present paper therefore used simulated atrophy in a simplified one-lesion model to systematically study template effects of VBM analyses implemented in SPM. This allowed us to characterize template-specific biases in reference to a set of prespecified parameters of anatomical difference. The data suggest that the likelihood of correctly detecting the prespecified lesion is modulated by the normalization template. Thereby, the relationship between template-related VBM accuracy and specific group/study characteristics is complex, and there does not appear to be one 'best template.' Our data show that template effects are critical and clearly suggest that the choice of template needs careful consideration in relation to the specific research question and study constraints.
结构神经影像学研究引起了神经科学家的极大兴趣,这体现在使用基于体素的形态学测量(VBM)的论文数量不断增加。VBM的一个主要步骤是将图像转换为标准模板,这是一种空间归一化,对于确保在保持个体间特征的同时比较同源区域是必要的。模板可以通过不同方式创建,这可能会影响检测到组间灰质/白质密度差异的可能性。然而,研究归一化模板与VBM准确性之间相互作用的研究很少。现有工作基于患者-对照组比较,而新出现的结果尚无定论。因此,本文在一个简化的单损伤模型中使用模拟萎缩来系统地研究在SPM中实施的VBM分析的模板效应。这使我们能够根据一组预先指定的解剖差异参数来表征特定于模板的偏差。数据表明,正确检测预先指定病变的可能性受归一化模板的调节。因此,与模板相关的VBM准确性和特定组/研究特征之间的关系很复杂,似乎不存在一个“最佳模板”。我们的数据表明模板效应至关重要,并且清楚地表明模板的选择需要根据具体研究问题和研究限制进行仔细考虑。