Rosenberg et al. find that strengths of a specific set of brain connections - even when estimated from resting state data collected when subjects are not carrying out any explicit task - can be used to predict a subject's attention ability with high accuracy. A large set of connections is involved during successful attention, and a different large set correlates with lack of attention.
Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention-symptoms of attention deficit hyperactivity disorder-from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.
Functional connections predicting gradCPT performance and ADHD-RS scores. (gradCPT is a test of sustained attention and inhibition that produces a range of behavior across healthy participants, ADHD-RS is a clinical measure of attention deficit hyperactivity disorder.)
