For decades, both the research and medical communities have relied on neuroimaging tools like functional magnetic resonance imaging (fMRI) to give them a window into the living human brain. Such scans have provided unprecedented insights into the brain’s structure and function – and the field, as a whole, has used this technique to better understand how the brain gives rise to thoughts, emotions, and actions. But as neuroimaging technology has advanced, so have the different analysis tools and the number of ways one can evaluate the resulting data. Now, the results of unique research project, the Neuroimaging Analysis, Replication, and Prediction Study (NARPS), suggest that different analyses can lead to strikingly different results from the same data set.

Seventy laboratories that analysed the same neuroimaging data each produced different results. This finding highlights the potential consequences of a lack of standardized pipelines for processing complex data.

For most types of big data, from genome sequences to medical images, there is no single ‘best’ way to process the data. This issue is exemplified by the substantial differences in how individual laboratories preprocess and analyse data from functional magnetic resonance imaging (fMRI) experiments, which generate information about brain activity. Indeed, a survey of fMRI studies found that nearly every study used a different analysis pipeline1. Writing in Nature, Botvinik-Nezer et al.2 provide further evidence of this variability, highlighting how analytical choices made by individual researchers can greatly influence the findings gleaned from an fMRI data set. The work is bound to spark lively discussion.

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