Network controllability in transmodal cortex predicts psychosis spectrum symptoms

TL;DR

This study uses Network Control Theory to analyze how both direct and indirect brain connections in transmodal cortex relate to psychosis symptoms, revealing that indirect connections improve prediction accuracy especially in association cortex.

Contribution

It introduces the use of average controllability to incorporate indirect structural connections in predicting psychosis spectrum symptoms, advancing understanding of transmodal cortex's role.

Findings

Average controllability predicts positive psychosis symptoms better than strength.

Prediction accuracy is higher in association cortex for average controllability.

Indexing indirect connections enhances understanding of psychosis-related dysconnectivity.

Abstract

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.