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From Brain Blobs to Research Data Management

Posted in UC3

If you spend some time browsing the science section of a publication like the New York Times you’ll likely run across an image that looks something like the one below: A cross section of a brain covered in colored blobs. These images are often used to visualize the results of studies using a technique called functional magnetic resonance imaging (fMRI), a non-invasive method for measuring brain activity (or, more accurately, a correlate of brain activity) over time. Researchers who use fMRI are often interested in measuring the activity associated with a particular mental process or clinical condition.

A visualization of the results of an fMRI study. These images are neat to look at but not particularly useful without information the underlying data and analysis.

Because of the size and complexity of the datasets involved, research data management (RDM) is incredibly important in fMRI research. In addition to the brain images, a typical fMRI study involves the collection of questionnaire data, behavioral measures, and sensitive medical information. Analyzing all this data often requires the development of custom code or scripts. This analysis is also iterative and cumulative, meaning that a researcher’s decisions at each step along the way can have significant effects on both the subsequent steps and what is ultimately reported in a presentation, poster, or journal article. Those blobby brain images may look cool, but they aren’t particularly useful in the absence of information about the underlying data and analyses.

In terms of both the financial investment and researcher hours involved, fMRI research is quite expensive. Throughout fMRI’s relatively short history, data sharing has been proposed multiple times times as a method for maximizing the value of individual datasets and for overcoming the field’s ongoing methodological issues. Unfortunately, a very practical issue has hampered efforts to foster the open sharing of fMRI data- researchers have historically organized, documented, and saved their data (and code) in very different ways.

What we are doing and why

Recently, following concerns about sub-optimal statistical practices and long-standing software errors, fMRI researchers have begun to cohere around a set of standards regarding how data should be collected, analyzed, and reported. From a research data management perspective, it’s also very exciting to see that there is also an emerging standard regarding how data should be organized and described. But, even with these emerging standards, our understanding of the data-related practices actually employed by fMRI in the lab and how those practices relate to data sharing and other open science-related activities remains mostly anecdotal.

To help fill this knowledge gap and hopefully advance some best practices related to data management and sharing, Dr. Ana Van Gulick and I are conducting a survey of fMRI researchers. Developed in consultation with members of the open and reproducible neuroscience communities, our survey asks researchers about their own data-related practices, how they view the field as a whole, their interactions with RDM service providers, and the degree to which they’ve embraced developments like registrations and pre-prints. Our hope is that our results will be useful for both the community of researchers who use fMRI but and for data service providers looking to engage with researchers on their own terms.

If you are a researcher who uses fMRI and would like to complete our survey, please follow this link. We estimate that the survey should take between 10 and 20 minutes.

If you are a data service provider and would like to chat with us about what we’re doing and why, please feel free to either leave a comment or contact me directly.

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