In the Search for Truth: Refining and Exploring Variability in Neuroimaging Pipelines

Neuroimaging pipelines can be designed as highly configurable software workflows whose variants may produce different analysis results and scientific conclusions. Managing this analytical variability is particularly challenging because outputs are complex, high-dimensional brain maps and no ground truth exists to assess their correctness. We model neuroimaging pipelines as a software product line and leverage the resulting feature model in a novel two-step process. First, we refine the configuration space by learning constraints from observed outputs and integrating them into the feature model. Second, we explore the refined space to identify influential features, characterize clusters, and predict unseen configurations. Evaluations on two case studies show that a small subset of features explains most observed variability and that accurate predictions can be achieved from a limited number of executed variants. They also demonstrate the complementarity of the two steps: refining the configuration space by excluding undesirable variants improves the interpretability and predictive power of subsequent exploration. Beyond neuroimaging, our results demonstrate how feature models can support both specialization and structured exploration of complex configuration spaces without ground truth.