Predicting psychosis risk in youth using a novel structural neuroimaging score that measures deviation from normative development. Can we bring it to communities using portable, low-field MRI?

Project Summary/Abstract Converging lines of evidence support the hypothesis that deviations from typical brain structure development take place prior to psychosis onset, while ‘big data’ neuroimaging studies of adults with psychosis find subtle, widespread gray matter disruptions in the brain. In this proposal, we will synergize knowledge about normative structural neurodevelopment and findings of structural brain aberrations in adults with psychosis to develop cost-effective brain-based markers of psychosis risk in youth. To improve identification of those at greatest risk, we leverage results from large-scale structural neuroimaging studies of psychosis to create a ‘Psychosis Neuroimaging Score’, a cumulative summary score that reflects one’s psychosis liability. We first aim to transport the Psychosis Neuroimaging Score to youth by incorporating crucial aspects of structural brain development. In Aim 1, we will characterize the normative developmental trajectory of the Psychosis Neuroimaging Score by harmonizing many archival datasets of normative development (N>5,000, 2-30 years old). We will then evaluate how greater age-associated deviation from the aggregate Psychosis Neuroimaging Score differentiates youth with psychosis spectrum symptoms from typically developing youth in the Philadelphia Neurodevelopmental Cohort (N=1209, 10-22 years old). In Aim 2, we plan to examine how greater age-associated deviation from the aggregate Psychosis Neuroimaging Score predicts distinct developmental trajectories associated with psychotic-like experiences in youth from the Adolescent Brain and Cognitive Development Study (N=11,875). We will also assess the extent to which known psychosis risk factors (e.g., family history of psychosis, obstetric complications, trauma) contribute to characterization of these trajectories. Finally, in Aim 3, we propose to use measurement-in-error modeling to establish a functional relationship between Psychosis Neuroimaging scores generated from 3T MRI scans and those generated using low-field MRI scans in a community sample of youth. Results from this study will allow us to create more affordable, clinically accessible biological indicators of severe psychopathology, ultimately improving identification of young people at greatest risk and allowing earlier, more effective interventions.