CAPER: Computerized Assessment of Psychosis Risk Supplement

项目摘要 概述： 父母补助金的总体目标是创建一个新的精神病症状领域敏感（PSDS）电池，可以 用于促进尽早检测精神病风险，以便快速指导临床高风险精神病 (CHR)青少年接受适当的治疗。我们建议招募500名参加者，500名寻求帮助的个人， 和500名健康对照，分布在五个地点，以实现以下目标： 目的1A）通过应用机器学习（ML）分类方法开发精神病风险计算器 PSDS电池的措施。在探索性ML分析中，我们将确定组合 PSDS与自我报告的措施和临床病史预测。 目的1B）我们将评估PSDS风险计算器评分的组间差异，并假设PSDS风险计算器评分 组将不同于求助和健康对照。我们进一步假设PSDS风险计算器得分为 转换者与非转换者、寻求帮助者和健康对照者有很大不同。列入 临床求助小组对于将风险计算器转化为临床实践至关重要，其目标是 将那些最有可能患上精神病的人与那些患有其他形式精神病理学的人区分开来。 目的1C）通过检查以下内容评价基线PSDS表现与2年后症状结局的关系：1） 症状变化作为连续变量处理，和2）转化为精神病。我们假设PSDS 计算器：1）将预测症状过程，2）转换器和非转换器之间观察到的差异 在PSDS计算器上比在NAPLS计算器上更大。 目标2）使用ML方法，如上所述，开发预测2A）社会和2B）角色功能变化的计算器， 观察了两年多。因为已知阴性症状与功能结果的联系比 积极的症状，我们预测，消极的症状机制任务将是最强的预测功能 这两个领域的下降。 Hirab将在这个项目上工作，但也将在我的实验室循环，以便接触各种科学 技术，以及帮助他确定他对一个独立项目的兴趣。

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.