A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses

TL;DR

This paper introduces a sensitivity analysis framework for neuroimaging preprocessing pipelines, enabling researchers to assess the robustness of statistical results across multiple preprocessing strategies, illustrated with PET data.

Contribution

It provides a novel, generic framework for aggregating and testing hypotheses across diverse preprocessing pipelines in neuroimaging studies.

Findings

Framework effectively tests hypotheses across multiple pipelines

Demonstrated robustness of results in PET data analysis

Tools facilitate understanding of preprocessing impact on conclusions

Abstract

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.