Germani Elodie, Rolland Xavier, Maurel Pierre, Maumet Camille
Univ Rennes, Inria, CNRS, Inserm, Rennes, France.
Imaging Neurosci (Camb). 2025 Apr 28;3. doi: 10.1162/imag_a_00522. eCollection 2025.
In neuroimaging and functional magnetic resonance imaging (fMRI), many derived data are made openly available in public databases. These can be re-used to increase sample sizes in studies and thus, improve robustness. In fMRI studies, raw data are first preprocessed using a given analysis pipeline to obtain subject-level contrast maps, which are then combined into a group analysis. Typically, the subject-level analysis pipeline is identical for all participants. However, derived data shared on public databases often come from different workflows, which can lead to different results. Here, we investigate how this analytical variability, if not accounted for, can induce false positive detections in mega-analyses combining subject-level contrast maps processed with different pipelines. We use the Human Connectome Project (HCP) multi-pipeline dataset, containing contrast maps for N = 1,080 participants of the HCP Young-Adult dataset, whose raw data were processed and analyzed with 24 different pipelines. We performed between-groups analyses with contrast maps from different pipelines in each group and estimated the rates of pipeline-induced detections. We show that, if not accounted for, analytical variability can lead to inflated false positive rates in studies combining data from different pipelines.
在神经成像和功能磁共振成像（fMRI）中，许多衍生数据在公共数据库中公开提供。这些数据可被重新利用以增加研究中的样本量，从而提高稳健性。在fMRI研究中，原始数据首先使用给定的分析流程进行预处理，以获得个体水平的对比图，然后将这些对比图合并进行组分析。通常，所有参与者的个体水平分析流程都是相同的。然而，公共数据库中共享的衍生数据往往来自不同的工作流程，这可能导致不同的结果。在这里，我们研究了这种分析变异性（如果不加以考虑）如何在对使用不同流程处理的个体水平对比图进行合并的大型分析中诱发假阳性检测。我们使用人类连接组计划（HCP）多流程数据集，该数据集包含HCP青年数据集N = 1,080名参与者的对比图，其原始数据使用24种不同的流程进行了处理和分析。我们对每组中来自不同流程的对比图进行组间分析，并估计流程诱导检测的发生率。我们表明，如果不加以考虑，分析变异性会导致在合并来自不同流程的数据的研究中出现过高的假阳性率。
