Overlooked Brain Connections Hold Clues to Cognition and Mental Health

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 new 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, PhD, an MD-PhD 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.”

One goal of human neuroimaging is to illuminate the brain mechanisms that drive cognition and mental health. But the complexity of brain connectivity makes data interpretation challenging. To address this, researchers often use feature selection, which focuses on the strongest 10% of brain connections to make the data easier to interpret.

For the study, researchers investigated whether signals discarded by feature selection could reveal meaningful insights about brain and behavior. The team examined brain imaging and behavioral data from more than 12,000 participants across four major U.S. datasets. For every participant, the team calculated the strength of association between brain connections and the outcome they wanted to predict.

All the connections were then ranked from the strongest to weakest associated and divided into 10 non-overlapping groups. Group one contained the top 10% of connections, those that scientists usually select, while groups two through 10 held the remaining 90% of connections—the connections often dismissed as noise. The team then built 10 prediction models, one for each group.

The team found that lower-ranked connections—groups two through nine—consistently achieved prediction accuracy similar to the top 10% of connections. In some cases, models built on lower groups of connections performed better than those trained on the top group. The authors suggest this might be because predictive information is widely distributed throughout brain connections and not just concentrated within the strongest ones.

“To our surprise, even when we completely excluded the networks people usually rely on to predict behavior, we still achieved nearly the same level of accuracy using everything that’s typically left behind,” says Adkinson, who works in the lab of senior author Dustin Scheinost, PhD, associate professor of radiology and biomedical imaging at YSM and associate director of biomedical imaging technology at the Yale Biomedical Imaging Institute.

The results indicate that by narrowing their focus, scientists risk oversimplifying the brain’s complexity, especially when dealing with brain disorders. For psychiatric disorders such as depression, individuals may rely on different neural pathways for the same behavior. And if several brain circuits can achieve similar prediction accuracy, it also suggests that therapeutic targets shouldn’t be limited to only the top networks.

“While the networks traditionally targeted by interventions may work for most patients, these overlooked networks might hold more utility for certain subsets of individuals,” says Adkinson. “This could help explain why some people don’t currently respond to treatments that work for others."

With these results, the team hopes to increase the clinical efficacy of brain-based biomarkers by better reflecting the brain’s complexity and individual variability.