A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses
<|reference_start|>A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses: 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.<|reference_end|>
@article{ozenne2024a,
    title={A sensitivity analysis to quantify the impact of neuroimaging
  preprocessing strategies on subsequent statistical analyses},
    author={Brice Ozenne and Martin Norgaard and Cyril Pernet and Melanie Ganz},
    journal={arXiv preprint arXiv:2404.14882},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14882},
    primaryClass={cs.CV stat.CO}
}