Background 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. Methods 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 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. Conclusions 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.

中文翻译：

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了解预处理流程对神经影像皮层表面分析的影响

背景 预处理流程的选择会引入神经影像分析的可变性，从而影响科学发现的可重复性。从结构和功能 MRI 数据导出的特征对预处理任务的算法或参数差异很敏感，例如图像归一化、配准和分割等。因此，为了区分生物效应和方法学差异，了解并潜在地减轻管道的累积偏差至关重要。方法在这里，我们使用开放结构 MRI 数据集 (ABIDE)，并辅以人类连接组项目，以强调管道选择对皮质厚度测量的影响。具体来说，我们研究了 (i) 软件工具（例如 ANTS、CIVET、FreeSurfer）、(ii) 皮质分区（Desikan-Killiany-Tourville、Destrieux、Glasser）和 (iii) 质量控制程序（手动、自动）的效果）。我们按（i）方法类型划分统计分析，即无任务（无监督）与任务驱动（监督）；(ii) 推理目标，即神经生物学群体差异与个体预测。结果结果表明，软件、分割和质量控制显着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（即群体）和个体无任务分析，并且质量控制改变以个体为中心的预测任务的性能。结论 这种比较性能评估部分解释了神经影像学结果不一致的根源。此外，它强调需要更严格的科学工作流程和可访问的信息学资源来复制和比较预处理流程，以解决大规模、数据驱动的计算神经科学时代的复杂的再现性问题。