Why experimental variation in neuroimaging should be embraced

Nature Communications volume  15, Article number: 9411 (2024 ) Cite this article

In a perfect world, scientists would develop analyses that are guaranteed to reveal the ground truth of a research question. In reality, there are countless viable workflows that produce distinct, often conflicting, results. Although reproducibility places a necessary bound on the validity of results, it is not sufficient for claiming underlying validity, eventual utility, or generalizability. In this work we focus on how embracing variability in data analysis can improve the generalizability of results. We contextualize how design decisions in brain imaging can be made to capture variation, highlight examples, and discuss how variability capture may improve the quality of results.

Central to the promise of neuroscience is the ongoing development and discovery of models linking brain structure and function to cognition, development, or clinical status. Common approaches involve generating maps of brain activation or organization through the complex analysis of imaging modalities, such as magnetic resonance imaging (MRI), electroencephalography (EEG), or magnetoencephalography (MEG). Efforts to identify associative and predictive relationships from brain imaging data have become increasingly prevalent, yielding success stories such as the identification of sex1 and age2 associations, alongside measures of health3,4 and psychiatric diagnoses5,6.

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