# ProNET: Psychosis-Risk Outcomes Network

> **NIH NIH U01** · YALE UNIVERSITY · 2021 · $2,115,894

## Abstract

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.

## Key facts

- **NIH application ID:** 10464673
- **Project number:** 3U01MH124639-02S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** CARRIE E BEARDEN
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,115,894
- **Award type:** 3
- **Project period:** 2020-09-08 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10464673

## Citation

> US National Institutes of Health, RePORTER application 10464673, ProNET: Psychosis-Risk Outcomes Network (3U01MH124639-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10464673. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*