Wilke Marko, Altaye Mekibib, Holland Scott K
Department of Pediatric Neurology and Developmental Medicine, Children's Hospital and Experimental Pediatric Neuroimaging Group, Children's Hospital and Department of Neuroradiology, University of Tübingen Tübingen, Germany.
Pediatric Neuroimaging Research Consortium, Cincinnati Children's Research Foundation and Department of Pediatrics, Division of Biostatistics and Epidemiology, University of Cincinnati College of Medicine Cincinnati, OH, USA.
Front Comput Neurosci. 2017 Feb 22;11:5. doi: 10.3389/fncom.2017.00005. eCollection 2017.
Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.
脑图像空间归一化和组织分割依赖于先前的组织概率图。在研究“特殊”人群，如幼儿或老年受试者时，正确选择这些组织图尤为重要。在创建此类先验图时，必须权衡施加更多变形的缺点与获得更清晰图像的益处。我们之前曾提出，对人口统计学变量进行统计建模，而非简单地对图像求平均值，是有益的。本文探讨了这两个方面（更多与更少的变形以及建模与平均）。我们使用了1914名年龄在13个月至75岁之间受试者的成像数据，并采用多元自适应回归样条来对年龄、场强、性别和数据质量的影响进行建模。在spm/cat12框架内，我们将仅仿射变换方法与低维和高维扭曲方法进行了比较。正如预期的那样，个体层面上更多的变形会导致组间差异降低。因此，在使用更广泛的变形方案时，年龄效应在最终的组织图中尤其不明显。利用统计描述的参数，可以为整个年龄范围生成高质量的组织概率图；与基于25、50或100名受试者传统生成的先验图相比，它们始终更接近金标准。观察到了场强、性别和数据质量的明显影响。我们得出结论，为生成组织先验图进行广泛匹配可能会对数据集中固有的许多变异性进行建模，而这些变异性随后不会包含在最终的先验图中。此外，对相关参数进行统计描述（使用回归样条）能够在控制已知混杂因素的同时生成高质量的组织概率图。所得的CerebroMatic工具箱可从http://irc.cchmc.org/software/cerebromatic.php下载。
