Abstract
Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but why is unknown. In over 700 individuals performing 7 tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that FC and overall degree of task-induced signal change, but not task-evoked activation alone, drive phenotypic prediction, and their combination further improves prediction. Inter-subject PPI demonstrates that predictive utility is highest in distributed FC patterns that are dissimilar across individuals, except in regions of group-level task activation, suggesting that task FC better predicts phenotype than rest FC for two, regionally specific reasons: (1) tasks synchronize activated regions and amplify signal components that meaningfully vary across individuals; and (2) elsewhere, prediction is driven by nodal interactions that set individuals apart. These findings offer a framework to leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.