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Neuroscience

The data science future of neuroscience theory

Nature Methods volume 19, pages 1349–1350 (2022)Cite this article

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An approach for integrating the wealth of heterogeneous brain data — from gene expression and neurotransmitter receptor density to structure and function — allows neuroscientists to easily place their data within the broader neuroscientific context.

Imagine we conduct a human neuroimaging experiment that shows that neural activity in Brodmann area 46 scales with the demands of the task the participants performed, and that the activity is statistically higher in older than in younger adults. What do we do with this information? An expert might guess that this was a working memory or attention task, on the basis of their familiarity with the relevant neuroimaging literature. But that expert, like myself, may know relatively little about neurogenetics, neurochemistry or cortical evolution and, because of their (and my) own knowledge gaps, will be unable to see how those results fit into the broader neuroscientific context. If we are to develop anything resembling a set of theories for how the brain gives rise to behavior, cognition and disease, we need a way for placing human neuroimaging data within that greater context. The open-source Python package neuromaps, introduced by Markello, Hansen and colleagues in this issue of Nature Methods1, provides an invaluable tool for doing just that.

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Fig. 1: The potential of the neuromaps package.

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Acknowledgements

B.V. is supported by National Institute of General Medical Sciences grant R01GM134363.

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  1. Department of Cognitive Science, Halıcıoğlu Data Science Institute, Neurosciences Graduate Program, and Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA

    Bradley Voytek

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  1. Bradley Voytek
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Correspondence to Bradley Voytek.

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Voytek, B. The data science future of neuroscience theory. Nat Methods 19, 1349–1350 (2022). https://doi.org/10.1038/s41592-022-01630-z

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