Department of Systems and Computational Biology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA; Department of Biochemistry, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA.
Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA; Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA; Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA; Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA.
Neuroimage. 2021 Feb 15;227:117657. doi: 10.1016/j.neuroimage.2020.117657. Epub 2020 Dec 15.
Many clinical and scientific conclusions that rely on voxel-wise analyses of neuroimaging depend on the accurate comparison of corresponding anatomical regions. Such comparisons are made possible by registration of the images of subjects of interest onto a common brain template, such as the Johns Hopkins University (JHU) template. However, current image registration algorithms are prone to errors that are distributed in a template-dependent manner. Therefore, the results of voxel-wise analyses can be sensitive to template choice. Despite this problem, the issue of appropriate template choice for voxel-wise analyses is not generally addressed in contemporary neuroimaging studies, which may lead to the reporting of spurious results.
We present a novel approach to determine the suitability of a brain template for voxel-wise analysis. The approach is based on computing a "distance" between automatically-generated atlases of the subjects of interest and templates that is indicative of the extent of subject-to-template registration errors. This allows for the filtering of subjects and candidate templates based on a quantitative measure of registration quality. We benchmark our approach by evaluating alternative templates for a voxel-wise analysis that reproduces the well-known decline in fractional anisotropy (FA) with age. Our results show that filtering registrations minimizes errors and decreases the sensitivity of voxel-wise analysis to template choice. In addition to carrying important implications for future neuroimaging studies, the developed framework of template induction can be used to evaluate robustness of data analysis methods to template choice.
许多依赖于神经影像学体素分析的临床和科学结论都依赖于对相应解剖区域的准确比较。这种比较可以通过将感兴趣的受试者的图像注册到共同的大脑模板（如约翰霍普金斯大学（JHU）模板）上实现。然而，当前的图像配准算法容易出现以模板为依赖的分布错误。因此，体素分析的结果可能对模板选择敏感。尽管存在这个问题，但在当代神经影像学研究中，通常不会解决体素分析的适当模板选择问题，这可能导致虚假结果的报告。
我们提出了一种新的方法来确定大脑模板是否适合体素分析。该方法基于计算自动生成的感兴趣受试者图谱与模板之间的“距离”，该距离表示受试者与模板配准误差的程度。这允许根据配准质量的定量度量来过滤受试者和候选模板。我们通过评估复制年龄相关分数各向异性（FA）下降的体素分析的替代模板来验证我们的方法。我们的结果表明，过滤配准可以最小化误差并降低体素分析对模板选择的敏感性。除了对未来的神经影像学研究具有重要意义外，所开发的模板诱导框架还可以用于评估数据分析方法对模板选择的稳健性。
