Research Project
10464673
PROJECT SUMMARY It has now been two decades since the clinical high risk for psychosis (CHR) criteria were first formulated in service of the goal of preventing psychotic disorders, one of the most urgent unmet clinical needs in behavioral health if not in all of medicine. Despite the critical public health need, drug development for CHR is viewed in many quarters as risky. The most daunting obstacle may be the heterogeneity of CHR course. We will deeply phenotype 1040 CHR patients across the ProNET network of 26 international sites with multi-modal biomarkers that span brain structure-function (MRI and EEG), psychopathology and cognition, genetics, body fluid analytes, natural speech/language, and passive/ecological momentary digital phenotyping, and map these biomarkers onto a core set of clinical outcome measures and trajectories over a treatment-relevant time window at fourteen timepoints over 24 months. Biomarkers will be collected at two timepoints to map brain-behavior trajectories. Healthy volunteers (N=390) will complete baseline assessment and follow-up assessments (including 130 with follow-up biomarkers) to quantify typical variation. We will also pilot an evaluation of excitatory/inhibitory imbalance with MR spectroscopy for glutamate, glutamine, and GABA at 7 Tesla. We will harmonize data collection protocols with the PRESCIENT network and partner with the Data Processing, Analysis, and Coordinating Center (DPACC) for rapid data integration and NIMH Data Archive (NDA) uploads under the oversight of NIMH, FNIH, and the Accelerating Medicines Partnership - Schizophrenia. We will implement ProNET-wide standardized and near real-time data integration with the DPACC architecture to facilitate on-site monitoring, unification of standard operating procedures, and rapid data aggregation across ProNET for seamless DPACC to NDA transfer. In partnership with the other grants we will test the hypothesis that data-driven variation assessed by multivariate neural, genetic, and behavioral measures within the CHR syndrome predicts individualized clinical trajectories, expanding CHR stratification for broad clinical endpoints encompassing affect, anxiety, cognition, and positive and negative symptoms with the goal of identifying behavioral and biomarker- driven patterns that can refine the CHR syndrome and promote personalized treatment decisions. These analyses will yield expanded outcome stratification calculators for the CHR syndrome that can predict actionable mental health trajectories in individual patients. The stratification calculators will allow future clinical trial designers to select optimal samples for determining whether a novel compound improves the particular CHR outcome of interest and pave the way for phase-specific and safe interventions for patients and their families.
