Evolution of Psychosis in Youth: Multimodal Risk and Resilience Markers

项目摘要 在早期识别有精神病风险的个体方面的努力受到以下认识的推动，即精神病是 神经发育，伴有精神分裂症（SZ）诊断前的大脑和行为异常， 年由于未经治疗的精神病持续时间较长，预示着预后不良，因此早期识别非常重要， 使发展轨迹向有利的方向弯曲。由于目前大多数关于精神病风险的研究 根据求助样本，对精神病如何在不同社区中发展的认识存在差距 样品虽然人们普遍认为基因组和环境因素（GxE）有助于风险， 精神病，有一个互补的综合研究，可以图表因果关系的途径缺乏。基因组 SZ的“病例对照”GWAS研究确定了多个共同等位基因，允许计算多基因风险 评分（PRS）。最近，越来越多的关注已经给予童年逆境有关的SZ。的目标 拟议的R 01是建立在我们的基因型约10，000费城神经发育队列（PNC）的8， 2009-2011年研究的21岁青年，我们正在跟踪那些符合标准或有风险的人， 精神病（PS）和典型发展（TD）参与者，其当前年龄范围为15-30岁。可用 多层次的“深度表型”包括子样本的临床、神经认知和多模态神经成像 约1600。我们已经制定了一个初步的环境风险评分（ERS），并将使用它来剖析GxE。的 建议的随访设计将招募在以下方面得分最高和最低（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）。将对该样本进行临床、神经认知和多模式神经成像检查。我们将 测试假设，即基因组脆弱性，基于PRS和家族史，以及环境逆境， 基于ERS，纵向更新，通过改变大脑发育影响PS的发病和病程， 颞叶边缘区域影响着作为社会功能基础的额叶边缘连接。我们会增加 当前的数据与风险和弹性的信息和多模式的大脑行为参数，以建立 在精神病出现时大脑成熟的关键时期的发展轨迹。我们的目标 是：1.检查ERS对PS临床特征和与PRS相关的进展的影响。2.研究大脑- 从遗传和环境因素到临床表现的行为参数。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，使适应性“风险和复原力计算器”。拟定的研究将 产生的数据缺乏一个不同的美国社区样本，但需要移动精神病学到精度 医学时代该项目将提供基因组和环境风险和复原力指标的信息，提供 在通往个性化预测的阶梯上，这是实施科学的一部分。如同 PNC、数据和相关算法将成为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.