Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.

Real estate agents will tell you that a home’s most important feature is “location, location, location.” It’s similar in neuroscience: “Location is everything in the brain,” said Bosiljka Tasic, a self-described “biological cartographer.” Brain injury in one spot could knock out memory; damage in another could interfere with personality. Neuroscientists and doctors are lost without a good map.

Researchers have been mapping the brain for more than a century. By tracing cellular patterns that are visible under a microscope, they’ve created colorful charts and models that delineate regions and have been able to associate them with functions. In recent years, they’ve added vastly greater detail: They can now go cell by cell and define each one by its internal genetic activity. But no matter how carefully they slice and how deeply they analyze, their maps of the brain seem incomplete, muddled, inconsistent. For example, some large brain regions have been linked to many different tasks; scientists suspect that they should be subdivided into smaller regions, each with its own job. So far, mapping these cellular neighborhoods from enormous genetic datasets has been both a challenge and a chore.

Recently, Tasic, a neuroscientist and genomicist at the Allen Institute for Brain Science, and her collaborators recruited artificial intelligence for the sorting and mapmaking effort. They fed genetic data from five mouse brains — 10.4 million individual cells with hundreds of genes per cell — into a custom machine learning algorithm. The program delivered maps that are a neuro-realtor’s dream, with known and novel subdivisions within larger brain regions. Humans couldn’t delineate such borders in several lifetimes, but the algorithm did it in hours. The authors published their methods in Nature Communications in October.

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