Norris Carly, Lisinski Jonathan, McNeil Elizabeth, VanMeter John W, VandeVord Pamela, LaConte Stephen M
Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States.
Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.
Neuroimage. 2021 Jul 15;235:118015. doi: 10.1016/j.neuroimage.2021.118015. Epub 2021 Mar 30.
The pig is growing in popularity as an experimental animal because its gyrencephalic brain is similar to humans. Currently, however, there is a lack of appropriate brain templates to support functional and structural neuroimaging pipelines. The primary contribution of this work is an average volume from an iterative, non-linear registration of 70 five- to seven-month-old male Yucatan minipigs. In addition, several aspects of this study are unique, including the comparison of linear and non-linear template generation, the characterization of a large and homogeneous cohort, an analysis of effective resolution after averaging, and the evaluation of potential in-template bias as well as a comparison with a template from another minipig species using a "left-out" validation set. We found that within our highly homogeneous cohort, non-linear registration produced better templates, but only marginally so. Although our T1-weighted data were resolution limited, we preserved effective resolution across the multi-subject average, produced templates that have high gray-white matter contrast and demonstrate superior registration accuracy compared to an alternative minipig template.
猪作为实验动物越来越受欢迎，因为其脑回脑与人类相似。然而，目前缺乏合适的脑模板来支持功能和结构神经成像流程。这项工作的主要贡献是通过对70只5至7个月大的雄性尤卡坦小型猪进行迭代非线性配准得到的平均体积。此外，本研究的几个方面是独特的，包括线性和非线性模板生成的比较、对一个大型且同质队列的特征描述、平均后有效分辨率的分析、模板内潜在偏差的评估以及使用“留出”验证集与另一种小型猪物种的模板进行比较。我们发现，在我们高度同质的队列中，非线性配准产生了更好的模板，但只是略微好一些。尽管我们的T1加权数据受分辨率限制，但我们在多主体平均中保留了有效分辨率，生成了具有高灰白质对比度的模板，并且与另一种小型猪模板相比，显示出更高的配准精度。
