# Psychosis Risk Evaluation, Data Integration and Computational Technologies (PREDICT): Data Processing, Analysis, and Coordination Center

> **NIH NIH U24** · BRIGHAM AND WOMEN'S HOSPITAL · 2021 · $777,372

## Abstract

The “clinical high risk” (CHR) for psychosis syndrome is an antecedent period characterized by attenuated
psychotic symptoms marked by subtle deviations from normal development in thinking, motivation, affect,
behavior, and a decline in functioning. Early intervention in this population is critical to prevent psychosis onset
as well as other adverse outcomes. However, the presentation of symptoms and subsequent course is highly
variable, and there is a paucity of biomarkers to guide treatment development. To improve clinically relevant
predictive models, several issues need to be addressed: 1) to focus on outcomes beyond psychosis; 2) to take
into account heterogeneity in samples and outcomes; and 3) to integrate data sets with a broad array of variables
using innovative algorithms. To address these challenges, the Accelerated Medicines Partnership Schizophrenia
(AMP SCZ) study will collect diverse multi-modal data via two research networks (PRESCIENT and ProNET –
42 acquisition sites) in conjunction with the Psychosis Risk Evaluation Data Integration and Computational
Technologies: Data Processing, Analysis, and Coordination Center (PREDICT-DPACC). The ultimate goal is to
identify new CHR biomarkers, and CHR subtypes that will enhance future clinical trials and lead to effective new
treatments. The PREDICT-DPACC is tasked with, 1) providing collaborative management, direction, data
processing and coordination for the two research networks; and 2) developing and applying advanced algorithms
to identify biomarkers that predict outcomes, in addition to stratifying CHR into subtypes based on outcome
trajectories. The PREDICT-DPACC team will include multiple data types and will address the needs of the CHR
research networks and the overall AMP SCZ goals. Data will be rapidly obtained, processed, and uploaded to
the NIMH Data Archive (NDA). Planned analysis methods will be powerful and robust, leveraging the expertise
and experience of computer scientist developers, and experienced clinical researchers. This supplement will
allow the PREDICT-DPACC team to address unexpected personnel effort needs to meet the goals set forth in
the original grant submission, including, but not limited to, 1) two networks with separate and independent data
capture systems that need separate development of software tools to aggregate data, which involves twice the
effort to install, test, and deploy tools on their infrastructure for each network; 2) coordination with both networks
to ensure that the forms and data dictionaries match across networks and with the NIMH National Data Archive;
3) the study dashboard needs to be customized further to meet the visualization needs of both networks; 4) the
inclusion of additional healthy controls, and co-enrollment requirements also deviate from what was expected
and complicates the proposed analytic approaches; 5) there is also participation in additional unexpected
organizational activities such as team workgroups, which will c...

## Key facts

- **NIH application ID:** 10457174
- **Project number:** 3U24MH124629-02S1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Rene S. Kahn
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $777,372
- **Award type:** 3
- **Project period:** 2020-09-09 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10457174

## Citation

> US National Institutes of Health, RePORTER application 10457174, Psychosis Risk Evaluation, Data Integration and Computational Technologies (PREDICT): Data Processing, Analysis, and Coordination Center (3U24MH124629-02S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10457174. Licensed CC0.

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