By applying machine-learning models to single-cell brain recordings taken from humans in conversation, a research team identified both individual and collective neuronal activity that reflected key features of language. The work, published in Nature, offers unprecedented insight into how neurons encode linguistic information, suggesting that brain activity may one day be used to infer speech-related thoughts, which could be transformative for some patients.

“This level of granularity is necessary for us to more completely understand how the brain generates speech and, ultimately, how we can develop technologies to restore it for individuals with communication disorders,” said Debara Tucci, M.D., director of NIH’s National Institute on Deafness and Other Communication Disorders (NIDCD).