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A data-driven approach to understanding how the brain works

Image of colorful brain

A recent Stanford study shows that different parts of the brain work together in surprising ways that differ from current neuroscientific wisdom. | iStock/dakuk

 

Feb 24 2022

By Katharine Miller

People are forever categorizing things based on superficial traits only to discover that, upon closer examination, those groupings don’t hold. Take an example from the produce aisle: Yams and sweet potatoes look and taste similar, but biologists know they come from completely unrelated plants. Meanwhile, kale, cauliflower, broccoli, and brussels sprouts seem very different from one another, but in fact they are the same species. 

These same sorts of mistaken impressions have arisen in neuroscience: We’ve lumped and split different types of mental phenomena based on long-held beliefs derived from psychology, but when we look at the data from brain scans, the categories we’ve imagined might exist aren’t always grounded in biological reality, says Ellie Beam, an MD/PhD candidate at Stanford. Issues of categorization have been particularly problematic for mental disorders: Our definitions are based on symptoms and don’t map well onto data from brain scans. 

And the ways we categorize how brain functions map onto brain structures matter: Research is proposed, funded, and organized around our existing theories about what’s commonly known in neuroscientific circles as “functional domains,” that is: which brain structures and collections of structures are responsible for which brain functions.

Beam wondered whether our current system for categorizing functional domains could be improved upon if the data could speak for itself. Would a data-driven categorization of brain functional domains better align with the neuroimaging data?