Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada.
Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada.
Gigascience. 2021 Jan 22;10(1). doi: 10.1093/gigascience/giaa155.
The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance.
Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction.
Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks.
This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
预处理流水线的选择会给神经影像学分析带来可变性，从而影响科学发现的可重复性。从结构和功能 MRI 数据中提取的特征对预处理任务（如图像归一化、配准和分割等）的算法或参数差异很敏感。因此，理解和潜在地减轻流水线的累积偏差对于区分生物学效应和方法学差异至关重要。
在这里，我们使用一个开放的结构 MRI 数据集（ABIDE），并辅以人类连接组计划，以突出说明流水线选择对皮质厚度测量的影响。具体来说，我们研究了以下因素的影响：（i）软件工具（例如，ANTS、CIVET、FreeSurfer）；（ii）皮质分割（Desikan-Killiany-Tourville、Destrieux、Glasser）；和（iii）质量控制程序（手动、自动）。我们通过（i）方法类型，即无任务（无监督）与任务驱动（监督）；和（ii）推理目标，即神经生物学组间差异与个体预测，来划分我们的统计分析。
结果表明，软件、分割和质量控制显著影响任务驱动的神经生物学推理。此外，软件选择强烈影响神经生物学（即组）和个体无任务分析，而质量控制会改变个体中心预测任务的性能。
这种比较性能评估部分解释了神经影像学发现不一致的原因。此外，它强调了需要更严格的科学工作流程和可访问的信息学资源，以复制和比较预处理流水线，以解决在大规模、数据驱动的计算神经科学时代重现性的复合问题。
