CAPER: Computerized Assessment of Psychosis Risk Supplement

项目摘要 概述： 家长资助的总体目标是创建一种新的精神病症状领域敏感(PSDS)电池，可以 用于促进尽早发现精神病风险，以便快速指导临床精神病高危人群 (CHR)青年获得适当的治疗。我们建议招募500名社区责任参与者，500名寻求帮助的个人， 和五个地点的500名健康对照，以实现以下目标： 目标1)通过应用机器学习(ML)分类方法开发精神病风险计算器 从PSDS电池的测量结果来看。在探索性的ML分析中，我们将确定组合的附加值 具有自我报告测量和临床病史预测因子的抑郁自评量表。 目的1B)我们将评估在PSD风险计算器得分上的组差异，并假设CHR的得分 群体会不同于求助和健康对照。我们进一步假设PSDS风险计算器分数为 CHR转换器将与CHR非转换器、求助和健康控制显著不同。包括 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 将那些最有可能发展成精神病的人与那些患有其他形式的精神病的人区分开来。 目标1C)通过检查以下各项来评估基线PSD表现与2年后症状结局的关系：1) 将症状性变化作为一个连续变量来处理，以及2)转化为精神病。我们假设PSDs 计算器：1)将预测症状进程，以及2)转换器和非转换器之间观察到的差异 在PSDS计算器上会比在NAPLS计算器上大。 目的2)如上所述，使用ML方法来开发计算器，该计算器可以预测A)社会和2B)角色功能变化，两者都 观察了两年多。因为已知阴性症状与功能结果的联系比 对于阳性症状，我们预测阴性症状机制任务将是功能性的最强预测因子 这两个领域都出现了下降。 Hirab将致力于这个项目，但也将在我的实验室中循环，以便接触到各种科学知识 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.