Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline. While it has been shown that the use of different pipelines might lead to different results, there is still a lack of understanding of the factors that drive these differences and of the stability of these differences across contexts. We use community detection algorithms to explore the pipeline space and assess the stability of pipeline relationships across different contexts. We show that there are subsets of pipelines that give similar results, especially those sharing specific parameters (e.g. number of motion regressors, software packages, etc.). Those pipeline-to-pipeline patterns are stable across groups of participants but not across different tasks. By visualizing the differences between communities, we show that the pipeline space is mainly driven by the size of the activation area in the brain and the scale of statistic values in statistic maps.

翻译：功能磁共振成像的分析流程具有高度灵活性，但关于如何选择分析管道的最佳实践却十分有限。尽管已有研究表明，使用不同的分析管道可能导致不同的结果，但对于驱动这些差异的因素以及这些差异在不同情境下的稳定性，目前仍缺乏深入理解。本研究采用社区发现算法探索分析管道的空间结构，并评估不同情境下管道间关系的稳定性。我们发现存在能够产生相似结果的管道子集，尤其是那些共享特定参数（如运动回归因子数量、软件包等）的管道。这些管道间的关联模式在参与者群体间保持稳定，但在不同任务间并不稳定。通过可视化不同群落间的差异，我们证明分析管道的空间结构主要受大脑激活区域的大小以及统计图中统计值尺度的影响。