CAPER: Computerized Assessment of Psychosis Risk Supplement

项目总结

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.