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.