Scientists who use imaging to understand the brain’s complexity often focus on the strongest signals and ignore the rest. But this strategy, researchers warn, may reveal only the tip of the iceberg. A study published in Nature Human Behavior reveals that connections routinely overlooked as “noise” during neuroimaging data analysis can predict behavior with remarkable accuracy—and implicate entirely different brain networks. The finding could open many new targets for treating psychiatric illness, the researchers say.

“Many studies that rely on techniques like feature selection—which simplifies the brain down to a narrow slice—might only uncover a small part of the true neurobiology that underlies a given behavior,” says lead author Brendan Adkinson, Ph.D., an MD-Ph. D. student at Yale School of Medicine.

“Our study suggests that there may be multiple, non-overlapping networks capable of predicting a given behavior just as well.”