Evolution of Psychosis in Youth: Multimodal Risk and Resilience Markers

项目总结 人们认识到精神病是一种疾病，这推动了早期识别精神病风险个体的努力。 神经发育，脑和行为异常在诊断精神分裂症(SZ)之前 好几年了。由于未经治疗的精神病持续时间较长，预示着预后不佳，因此及早识别对 让发展轨迹朝着有利的方向弯曲。由于目前大多数关于精神病风险的研究都是 根据寻求帮助的样本，对于精神病是如何在不同的社区展开的，人们的认识存在差距 样本。虽然人们普遍认为基因组和环境因素(GxE)会导致 在精神病方面，缺乏能够绘制因果路径的互补性综合研究。基因组学 SZ的“病例对照”GWAS研究确定了允许计算多基因风险的多个共同等位基因 得分(RPS)。最近，与SZ相关的童年逆境受到越来越多的关注。的目标是 建议的R01是在我们8到10,000名费城神经发育队列(PNC)基因分型的基础上建立的 21岁的年轻人在2009-2011年间学习，在那里我们跟踪那些符合标准或有患病风险的人 精神病(PS)和典型发展中(TD)参与者，目前年龄范围为15-30岁。可用 多层次的“深层表型”包括临床、神经认知和多模式神经成像。 约1600年。我们已经开发了一个初步的环境风险评分(ERS)，并将用它来剖析GxE。这个 建议的后续设计将招募得分最高和最低(四分位数)的PS和TD参与者 ERS，在这四个细胞中，我们将检查120个个体，60个男性和60个女性(总计 N=480)。该样本将进行临床、神经认知和多模式神经成像检查。我们会 基于PR和家族病史以及环境逆境，测试基因组脆弱性的假设， 基于ERS的纵向更新，通过改变脑发育来影响PS的发病和病程 影响作为社会功能基础的额缘-边缘连接的颞缘区域。我们将增加 目前的数据包括关于风险和复原力的信息以及多模式大脑行为参数 在这个大脑成熟的关键时期，当精神病出现时的发展轨迹。我们的目标 1.研究ERS对PS的影响、临床特征和进展与PR的关系。2.调查大脑-- 行为参数是从遗传和环境因素到临床表现之间的桥梁。3.建立 PS特征、相关脑参数和神经认知缺陷的发展轨迹，并应用 新的计算模型，支持自适应的“风险和复原力计算器”。拟议的研究将 提供多样化的美国社区样本所不具备的数据，但需要将精神病学带入精确度 医学时代。该项目将通报基因组和环境风险和复原力指标，提供 个性化预测的基本阶梯，这是实施科学的一部分。就像 PNC、数据和相关算法将是与科学界共享的资源。

PROJECT SUMMARY Efforts at early identification of individuals at risk for psychosis are propelled by the realization that psychosis is neurodevelopmental, with brain and behavioral abnormalities anteceding diagnosis of schizophrenia (SZ) by years. As longer duration of untreated psychosis portends poor outcome, early identification is important to bend the developmental trajectory in a favorable direction. Since most current studies of psychosis risk are based on help-seeking samples, there is a gap in knowledge on how psychosis unfolds in diverse community samples. While it is generally recognized that genomic and environmental factors (GxE) contribute to risk for psychosis, there is a paucity of complementary integrative studies that can chart causal pathways. Genomic “case-control” GWAS studies of SZ identified multiple common alleles permitting calculation of a polygenic risk score (PRS). Recently, increased attention has been given to childhood adversity related to SZ. The goal of the proposed R01 is to build on our genotyped ~10,000 Philadelphia Neurodevelopmental Cohort (PNC) of 8 to 21 years old youths studied in 2009-2011, where we are following those who meet criteria or are at risk for psychosis (PS) and typically developing (TD) participants, whose current age range is 15-30 years. Available multi-level “deep phenotyping” includes clinical, neurocognition and multi-modal neuroimaging on a subsample of ~1600. We have developed a preliminary environmental risk score (ERS) and will use it to dissect GxE. The proposed followup design will recruit PS and TD participants with the highest and lowest scorers (quartile) on the ERS, and within each of these four cells we will examine 120 individuals, 60 males and 60 females (total N=480). This sample will be examined clinically, neurocognitively and with multimodal neuroimaging. We will test the hypothesis that genomic vulnerabilities, based on PRS and family history, and environmental adversity, based on ERS, updated longitudinally, affect onset and course of PS by altering brain development in temporolimbic regions affecting fronto-limbic connectivity that underlies social functioning. We will augment current data with information on risk and resilience and multimodal brain-behavior parameters to establish developmental trajectories during this critical period of brain maturation when psychosis emerges. Our aims are: 1. Examine effects of ERS on PS clinical features and progression in relation to PRS. 2. Investigate brain- behavior parameters that bridge from genetic and environmental factors to clinical manifestations. 3. Establish developmental trajectories for PS features, associated brain parameters and neurocognitive deficits, and apply novel computational models to enable an adaptive “risk and resilience calculator”. The proposed study will produce the data absent for a diverse US community sample but needed to move psychiatry into the precision medicine era. The project will inform on genomic and environmental risk and resilience indicators, offering an essential rung in the ladder toward individualized prediction, a part of implementation science. As with the PNC, data and associated algorithms will be a resource shared with the scientific community.