Uncovering communities of pipelines in the task-fMRI analytical space

Functional magnetic resonance imaging analytical workflows are highly flexible with no definite consensus on how to choose a pipeline. While methods have been developed to explore this analytical space, there is still a lack of understanding of the relationships between the different pipelines. We use community detection algorithms to explore the pipeline space and assess its stability 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.), with relative stability across groups of participants. By visualizing the differences between these subsets, we describe the effect of pipeline parameters and derive general relationships in the analytical space.