Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
Neuroimage. 2023 Oct 1;279:120292. doi: 10.1016/j.neuroimage.2023.120292. Epub 2023 Aug 11.
Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.
体素形态计量学（VBM）分析常用于局部定量灰质体积（GMV）。有几种替代方案可用于实现 VBM 管道。然而，这些替代方案如何进行比较以及它们在应用中的实用性，例如估计衰老效应，在很大程度上仍不清楚。这使得研究人员想知道他们应该在项目中使用哪个 VBM 管道。在这项研究中，我们从用户为中心的角度出发，系统比较了五个 VBM 管道，以及分别与通用模板或研究特定模板的配准，利用了三个大型数据集（每个数据集的 n>500）。考虑到衰老对 GMV 的已知影响，我们首先比较了这些管道在个体水平年龄预测能力方面的差异，结果存在明显差异。为了检查这些结果是否是由于管道之间的系统差异引起的，我们根据它们的 GMV 对其进行分类，结果接近完美的准确性。为了获得更深入的见解，我们使用管道之间的区域相似性检查了不同 VBM 步骤的影响。结果显示出明显的差异，主要由分割和配准步骤驱动。我们观察到在个体识别准确性方面存在很大的可变性，突出了 GMV 个体水平量化的管道间差异。作为一个有生物学意义的标准，我们将区域 GMV 与年龄相关联。结果与年龄预测分析一致，CAT 管道和 fMRIPrep 用于组织特征化与 FSL 用于配准的组合，更好地反映了年龄信息。
