# CAPER: Computerized Assessment of Psychosis Risk

> **NIH NIH R01** · TEMPLE UNIV OF THE COMMONWEALTH · 2021 · $317,000

## Abstract

Project Summary/Abstract
Research suggests that early identification of individuals at clinical high risk (CHR) for psychosis may be able
to improve illness course. Studies suggest that early identification of CHR using specialized interviews with
help-seeking individuals (with attenuated psychosis symptoms) is a useful approach. This work has two major
limitations: 1) interview methods have limited specificity as only 20% of CHR individuals convert to psychosis,
and 2) the expertise needed to make CHR diagnosis is only accessible in a few academic centers. We propose
to develop a new psychosis symptom domain sensitive (PSDS) battery, prioritizing tasks that show correlations
with the symptoms that define psychosis and are tied to the neurobiological systems and computational
mechanisms implicated in these symptoms. To promote accessibility, we utilize behavioral tasks that could be
administered over the internet; this will set the stage for later research testing widespread screening that would
identify those most in need of in-depth assessment. To reach that goal we first need determine which tasks are
effective for predicting illness course and how this strategy compares to published prediction methods. We
propose to recruit 500 CHR participants, 500 help-seeking individuals, and 500 healthy controls across 5 sites
with the following Aims: Aim 1A) To develop a psychosis risk calculator through the application of machine
learning (ML) methods to the measures from the PSDS battery. In determine an exploratory ML analysis, we will
the added value of combining the PSDS with self-report measures and historical predicators; Aim
1B) We will evaluate group differences on the risk calculator score and hypothesize that the risk calculator
score of the CHR group will differ from help-seeking and healthy controls. We further hypothesize that the risk
calculator score of the CHR converters will differ significantly from groups of CHR nonconverters, help-seeking
and healthy controls. The inclusion of a help-seeking group is critical for translating the risk-calculator into
clinical practice, where the goal is to differentiate those at greatest risk for psychosis from those with other
forms of psychopathology; Aim 1C): Evaluate how baseline PSDS performance relates to symptomatic
outcome 2 years later examining: 1) symptomatic worsening 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 deterioration, both observed over two years. Because negative symptoms are
strongly linked t o functional outcome than positive symptoms, we predict that negative symptom
tasks will be the strongest predictor of functional decline in both domains....

## Key facts

- **NIH application ID:** 10136105
- **Project number:** 5R01MH120091-02
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** LAUREN M ELLMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $317,000
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10136105

## Citation

> US National Institutes of Health, RePORTER application 10136105, CAPER: Computerized Assessment of Psychosis Risk (5R01MH120091-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10136105. Licensed CC0.

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