Predicting an individual’s dorsal attention network activity from functional connectivity fingerprints

The dorsal attention network (DAN) is a set of regions in frontoparietal cortex that reliably activate during attentional tasks. We designed computational models that predict the degree of an individual's DAN activation using their resting-state connectivity pattern alone. This uncovered the connectivity fingerprints of the DAN, which define it so well that we can predict how a voxel will respond to an attentional task given only its pattern of connectivity, with outstanding accuracy.