Examining the Interplay of Clinical Symptoms, Neurocognition, Functioning, and Stressful Events in Individuals at Clinical High Risk for Developing Psychosis: A Network Analysis Approach - Project Summary Schizophrenia is a severe form of mental illness, characterized by psychotic symptoms, large cognitive impairments, and often life-long functional disability. While advances in early detection enable us to identify youth at clinical high risk for psychosis (CHR-P), a critical problem remains. To date, there are no gold- standard treatments to prevent the onset of the full disorder. A major barrier to developing such interventions is the clinical heterogeneity of the CHR-P population. This stems from the underlying complexity of the illness state itself, an active system of interacting factors. While we know many factors are associated with risk, we lack a clear understanding of the mechanisms by which these factors interact and reinforce each other to create the pathways that drive the transition to psychosis. This knowledge gap prevents the development of targeted, precision interventions. This project addresses this challenge by adopting a powerful alternative to traditional research models. We will apply cutting-edge network theory to a large sample of over 2,000 CHR-P individuals, modeling the high-risk state as a system of interacting factors. This approach allows us to move beyond simply listing risk factors to identifying those that are most central and influential in driving the illness forward. Specifically, we will construct the first multi-domain network in this population, comprehensively mapping the interplay between clinical symptoms, neurocognitive deficits, social functioning, and environmental stressors to reveal how they interact to accelerate illness progression. To achieve this, our research plan is threefold. First, we will establish the comprehensive baseline network structure of the CHR-P state, providing a foundational map of its interacting components (Aim 1). Second, we will isolate the critical network differences between youth who later develop psychosis and those who do not, pinpointing the specific interactions most predictive of illness onset (Aim 2). Third, we will incorporate longitudinal data to model how these network connections change over time, mapping the precise temporal pathways that constitute the progression to disease (Aim 3). This research is expected to provide a data-driven roadmap to the most potent and direct targets for preventative intervention. By identifying the system’s key drivers, our work will transform our understanding of psychosis risk. Ultimately, this will accelerate the development of personalized, mechanism-based treatments designed to disrupt the pathways to psychosis and prevent its onset in vulnerable young people.
