  Even though novel imaging techniques have been successful in studying brain
structure and function, the measured biological signals are often contaminated
by multiple sources of noise, arising due to e.g. head movements of the
individual being scanned, limited spatial/temporal resolution, or other issues
specific to each imaging technology. Data preprocessing (e.g. denoising) is
therefore critical. Preprocessing pipelines have become increasingly complex
over the years, but also more flexible, and this flexibility can have a
significant impact on the final results and conclusions of a given study. This
large parameter space is often referred to as multiverse analyses. Here, we
provide conceptual and practical tools for statistical analyses that can
aggregate multiple pipeline results along with a new sensitivity analysis
testing for hypotheses across pipelines such as "no effect across all
pipelines" or "at least one pipeline with no effect". The proposed framework is
generic and can be applied to any multiverse scenario, but we illustrate its
use based on positron emission tomography data.
