Gaining a better understanding of how psychosis emerges during childhood and adolescence will help us identify causes of the illness and treatment targets to facilitate early detection and intervention. The main goals of this K01 are to 1) identify age-associated variation in resting-state functional connectivity in youth, and how that variation relates to psychosis spectrum symptoms, 2) determine to what extent these psychosis- related features are present in help-seeking youth at clinical high risk (CHR) for developing psychosis and 3) identify genetic factors that contribute to typical and atypical neurodevelopment of resting-state intrinsic functional connectivity.
Aim 1 will combine archival resting-state functional magnetic resonance imaging (rsfMRI) data from the Philadelphia Neurodevelopmental Cohort (PNC, N=907) and a highly compatible longitudinal study of normative development (Luna cohort, N=223). Graph theory measures will be calculated from the rsfMRI data and be used to determine the extent to which psychosis spectrum youth deviate from typical development.
For Aim 2, I will collect rsfMRI data on CHR youth (N=40) and typically developing controls (N=50), longitudinally, with a goal to determine to what extent age-associated alterations in between- and within- network connectivity are present in CHR youth. Positive symptoms of psychosis, psychosocial functioning, and neurocognitive measures will be collected at the baseline assessment and follow-up assessments. I will explore how intrinsic functional connectivity predicts increases in psychotic symptoms, functioning and/or cognition in this cohort.
In Aim 3, genetic information from the PNC and Luna cohort will used to determine how expected gene expression profiles of schizophrenia risk and neurodevelopmental genes are associated with development of between- and within- functional connectivity. My training plan will focus on 1) integrating genomic and neuroimaging data to understand the development of psychiatric disorders, 2) conducting analyses of high dimensional data sets to identify mechanisms of and potential risk factors for psychosis, and 3) acquiring expertise in developmental neuroscience and apply this knowledge to neurodevelopmental models of psychosis. Results from this study will help us identify mechanistic processes of brain development and function and identify to what extent age-associated changes in network-connectivity are intact or already present prior to the onset of psychosis. Through the training plan, I will become an expert in modeling high dimensional data and identifying changes in the brain during adolescence, which I will use to improve the prediction of psychosis and identify critical time periods for intervention.
Identification of how brain changes go awry in psychosis during adolescent development will help us tailor times to intervene, substantially improving clinical outcomes. Determining to what extent these brain alterations predict increases in psychotic symptoms will identify risk factors to facilitate earlier identification of those at higher risk. Linking genetic factors to typical and atypical brain changes during adolescence will help us understand how genes affect brain pathways during development.
