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.

翻译：尽管新型成像技术在研究脑结构和功能方面取得了成功，但测量到的生物信号常受到多种噪声源的污染，这些噪声源于被扫描个体的头部运动、有限的空间/时间分辨率，或每种成像技术特有的其他问题。因此，数据预处理（例如去噪）至关重要。多年来，预处理流程日益复杂，但也更加灵活，而此种灵活性可能对特定研究的最终结果和结论产生显著影响。这一庞大的参数空间常被称为多元宇宙分析。本文为能够聚合多个流程结果的统计分析提供了概念性和实用性工具，并基于此提出了一种新的敏感性分析方法，用于检验跨流程的假设，例如"所有流程均无效应"或"至少有一个流程无效应"。所提出的框架具有通用性，可应用于任何多元宇宙场景，但我们基于正电子发射断层扫描数据展示了其应用。