  The psychosis spectrum is associated with structural dysconnectivity
concentrated in transmodal association cortex. However, understanding of this
pathophysiology has been limited by an exclusive focus on the direct
connections to a region. Using Network Control Theory, we measured variation in
both direct and indirect structural connections to a region to gain new
insights into the pathophysiology of the psychosis spectrum.
  We used psychosis symptom data and structural connectivity in 1,068 youths
aged 8 to 22 years from the Philadelphia Neurodevelopmental Cohort. Applying a
Network Control Theory metric called average controllability, we estimated each
brain region's capacity to leverage its direct and indirect structural
connections to control linear brain dynamics. Next, using non-linear
regression, we determined the accuracy with which average controllability could
predict negative and positive psychosis spectrum symptoms in out-of-sample
testing. We also compared prediction performance for average controllability
versus strength, which indexes only direct connections to a region. Finally, we
assessed how the prediction performance for psychosis spectrum symptoms varied
over the functional hierarchy spanning unimodal to transmodal cortex.
  Average controllability outperformed strength at predicting positive
psychosis spectrum symptoms, demonstrating that indexing indirect structural
connections to a region improved prediction performance. Critically, improved
prediction was concentrated in association cortex for average controllability,
whereas prediction performance for strength was uniform across the cortex,
suggesting that indexing indirect connections is crucial in association cortex.
  Examining inter-individual variation in direct and indirect structural
connections to association cortex is crucial for accurate prediction of
positive psychosis spectrum symptoms.
