Evolution of Psychosis in Youth: Multimodal Risk and Resilience Markers

青年精神病的演变：多模式风险和弹性标记

• 批准号: 9978131
• 负责人: Raquel E Gur
• 金额: $ 78.76万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9978131
• 关键词: 21 year old; Address; Affect; African American; Age; Algorithms; Alleles; Attention; Behavioral; Benign; Brain; Cells; Childhood; Clinical; Clinical assessments; Cognition; Cognitive; Communities; Computer Models; Data; Development; Diagnosis; Dimensions; Disease; Early Intervention; Early identification; Environment; Environmental Risk Factor; Epigenetic Process; Evaluation; Evolution; Family; Family history of; Female; Genetic; Genetic Predisposition to Disease; Genetic Risk; Genomics; Genotype; Goals; Heterogeneity; Image; Individual; Knowledge; Language; Lead; Linear Regressions; Longitudinal Studies; Machine Learning; Measures; Mediating; Modeling; Neurocognition; Neurocognitive; Neurocognitive Deficit; Neurosciences; Outcome; Participant; Pathway interactions; Performance; Phenotype; Philadelphia; Positioning Attribute; Process; Psychiatry; Psychotic Disorders; Public Domains; Race; Recording of previous events; Resource Sharing; Resources; Risk; Sampling; Schizophrenia; Severities; Sex Differences; Social Functioning; Structure; Symptoms; System; Testing; Update; Work; Youth; aged; base; behavior measurement; behavioral phenotyping; brain behavior; case control; childhood adversity; cohort; critical period; data anonymization; design; early adolescence; emerging adult; follow-up; genome wide association study; genomic variation; help-seeking behavior; high risk; implementation science; improved; innovation; machine learning algorithm; machine learning method; male; multidisciplinary; multimodality; neuroimaging; novel; personalized predictions; polygenic risk score; precision medicine; predictive modeling; predictive test; psychiatric genomics; recruit; resilience; sex; social

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.

项目概要 认识到精神病是一种疾病，从而推动早期识别有精神病风险的个体。 神经发育，在诊断精神分裂症 (SZ) 之前存在大脑和行为异常 由于未经治疗的精神病持续时间较长预示着不良结果，因此早期识别对于治疗很重要。 由于目前大多数关于精神病风险的研究都在朝着有利的方向弯曲发展轨迹。 根据寻求帮助的样本，对于精神病在不同社区中如何发展存在知识差距 虽然人们普遍认为基因组和环境因素 (GxE) 会导致风险。 精神病，缺乏可以绘制因果路径的补充综合研究。 SZ 的“病例对照”GWAS 研究确定了多个常见等位基因，可以计算多基因风险 最近，与 SZ 相关的童年逆境受到越来越多的关注。 拟议的 R01 是建立在我们基因分型的约 10,000 个费城神经发育队列 (PNC) 的基础上，其中 8 至 2009 年至 2011 年研究的 21 岁青少年，我们正在跟踪那些符合标准或有风险的人 精神病 (PS) 和典型发育 (TD) 参与者，目前年龄范围为 15-30 岁。 多层次“深度表型分析”包括子样本的临床、神经认知和多模式神经影像 我们已经制定了初步的环境风险评分 (ERS)，并将用它来剖析 GxE。 拟议的后续设计将招募具有最高和最低得分者（四分位数）的 PS 和 TD 参与者 ERS，在这四个单元中的每一个单元中，我们将检查 120 个人，其中 60 名男性和 60 名女性（总共 N=480）。我们将对这个样本进行临床、神经认知和多模式神经影像检查。 检验基于 PRS 和家族史以及环境逆境的基因组脆弱性的假设， 基于 ERS，纵向更新，通过改变大脑发育来影响 PS 的发生和过程 影响社会功能基础的额边缘连接的颞边缘区域。 当前数据包含有关风险和复原力的信息以及多模式大脑行为参数，以建立 当精神病出现时，大脑成熟的关键时期的发展轨迹。 是： 1. 检查 ERS ​​对 PS 临床特征和与 PRS 相关的进展的影响 2. 研究脑- 3. 建立从遗传和环境因素到临床表现的行为参数。 PS 特征、相关大脑参数和神经认知缺陷的发育轨迹，并应用 拟议的研究将采用新颖的计算模型来实现自适应“风险和弹性计算器”。 生成美国不同社区样本所缺少的数据，但需要将精神病学推向精确 该项目将提供基因组和环境风险及复原力指标的信息，提供 与实施科学一样，个性化预测是迈向个性化预测的重要一步。 PNC、数据和相关算法将成为与科学界共享的资源。