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 1 A) To develop a psychosis risk calculator through the application of machine learning (ML) 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 historical predicators;
Aim 1 B) 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 1 C): 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 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. This project will provide a next-generation CHR battery, tied to illness mechanisms and powered by cutting-edge computational methods that can be used to facilitate the earliest possible detection of psychosis risk.

Public Health Relevance

Early detection of young people at clinical high risk for psychosis offers a critical opportunity for early intervention to improve the course of illness, and perhaps even prevent onset entirely. Current interview-based methods for psychosis risk detection lack specificity, and are only available in a handful of research centers in the United States. The proposed study aims to improve accessibility and broaden impact of high risk screening by testing brief computerized measures, ultimately able to be administered on the internet, and to improve prediction by focusing on tasks specific to underlying mechanisms driving emerging psychotic symptoms.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Morris, Sarah E
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Maryland Baltimore
Schools of Medicine
United States
Zip Code