# CAPER: Computerized Assessment of Psychosis Risk Supplement

> **NIH NIH R01** · TEMPLE UNIV OF THE COMMONWEALTH · 2022 · $23,628

## Abstract

Project Summary
Overview:
The overall aim of the parent grant is to create a new psychosis symptom domain-sensitive (PSDS) battery that can be
used to facilitate the earliest possible detection of psychosis risk in order to rapidly direct clinical high risk for psychosis
(CHR) youth towards appropriate treatment. We propose to recruit 500 CHR participants, 500 help-seeking individuals,
and 500 healthy controls across five sites to address the following aims:
Aim 1A) To develop a psychosis risk calculator through the application of machine learning (ML) classification methods
to the measures from the PSDS battery. In an exploratory ML analysis, we will determine the added value of combining
the PSDS with self-report measures and clinical history predictors.
Aim 1B) We will evaluate group differences on the PSDS risk calculator score and hypothesize that the score of the CHR
group will differ from help-seeking and healthy controls. We further hypothesize that the PSDS risk calculator score of
the CHR converters will differ significantly from CHR nonconverters, help-seeking and healthy controls. The inclusion of
a clinical help-seeking group is critical for translating the risk calculator into clinical practice, where the goal is to
differentiate those at greatest risk for developing psychosis from those with other forms of psychopathology.
Aim 1C) Evaluate how baseline PSDS performance relates to symptomatic outcome 2 years later by examining: 1)
symptomatic change treated as a continuous variable, and 2) conversion to psychosis. We hypothesize that the PSDS
calculator: 1) will predict symptom course, and 2) that the differences observed between converters and nonconverters
will be larger on the PSDS calculator than on the NAPLS calculator.
Aim 2) Use ML methods, as above, to develop calculators that predict 2A) social, and 2B) role function change, both
observed over two years. Because negative symptoms are known to be more strongly linked to functional outcome than
positive symptoms, we predict that negative symptom mechanism tasks will be the strongest predictor of functional
decline in both domains.
Hirab will work on this project, but also will cycle through my lab in order to be exposed to a variety of scientific
techniques, as well as to aid him in determining his interests for an independent project.

## Key facts

- **NIH application ID:** 10540475
- **Project number:** 3R01MH120091-03S1
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** LAUREN M ELLMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $23,628
- **Award type:** 3
- **Project period:** 2020-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10540475

## Citation

> US National Institutes of Health, RePORTER application 10540475, CAPER: Computerized Assessment of Psychosis Risk Supplement (3R01MH120091-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10540475. Licensed CC0.

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