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: 10612018
• 负责人: Raquel E Gur
• 金额: $ 71.46万
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612018
• 关键词: 21 year old; Address; Adolescence; Affect; African American population; 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; 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; Psychoses; Psychotic Disorders; Public Domains; Race; Recording of previous events; Resource Sharing; Resources; Risk; Sampling; Schizophrenia; Severities; Sex Differences; Shapes; Social Functioning; Structure; Symptoms; System; Testing; Update; Work; Youth; aged; behavior measurement; behavioral phenotyping; brain behavior; case control; childhood adversity; cohort; critical period; data anonymization; design; duration of untreated psychosis; 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; multimodal neuroimaging; multimodality; novel; personalized predictions; polygenic risk score; precision medicine; predictive modeling; psychiatric genomics; psychosis risk; recruit; resilience; schizophrenia risk; 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.

项目摘要 意识到精神病是在早期识别有精神病风险的人的努力。 神经发育，大脑和行为异常，精神分裂症（SZ）的诊断。 年。随着未经治疗的精神病的持续时间更长，预后差，早期识别对于 沿着有利的方向弯曲发展轨迹。由于目前关于精神病风险的大多数研究是 基于寻求帮助的样本，关于潜水员社区中精神病的发展方面存在差距 样品。虽然通常认为基因组和环境因素（GXE）有助于冒险 精神病，完全综合研究可能会绘制因果途径。基因组 SZ的“病例对照” GWAS研究确定了多个普通等位基因，允许计算多基因风险 得分（PR）。最近，对与SZ相关的儿童逆境的关注越来越高。目标 拟议的R01是基于我们的基因分型〜10,000个费城神经发育队列（PNC）8至8 21岁的年轻人在2009 - 2011年研究，我们遵循符合条件或有风险的人 精神病（PS）和通常发展的（TD）参与者，其当前年龄范围为15-30岁。可用的 多层“深度表型”包括子样本上的临床，神经认知和多模式神经影像学 〜1600。我们已经开发了初步的环境风险评分（ERS），并将使用它来剖析GXE。这 拟议的后续设计将招募PS和TD参与者，得分最高和最低分数（四分位数） ERS以及在这四个单元中的每个细胞中，我们将检查120个人，60名男性和60个女性（总数 n = 480）。该样本将在临床，神经认知和多模式神经影像上进行检查。我们将 检验以下假设，即基于PRS和家族史以及环境广告的基因组脆弱性， 基于ERS，纵向更新，通过改变大脑发育的影响和PS的过程 模板边缘区域影响社会功能构成的额叶连通性。我们将增加 当前数据具有有关风险和弹性的信息以及建立多模式的脑行为参数 精神病出现时大脑成熟的关键时期的发育轨迹。我们的目标 为：1。检查ER对PS临床特征的影响和与PR相关的进展。 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，相关大脑参数和神经认知缺陷的发展轨迹，并应用 新颖的计算模型，以实现自适应的“风险和弹性计算器”。拟议的研究将 为美国潜水员社区样本缺乏数据，但需要将精神病学转移到精确度中 医学时代。该项目将告知基因组和环境风险和弹性指标，并提供 梯子中的基本梯级朝个性化预测，这是实施科学的一部分。和 PNC，数据和相关算法将是与科学界共享的资源。

项目成果

Network controllability mediates the relationship between rigid structure and flexible dynamics.

• DOI: 10.1162/netn_a_00225
• 发表时间: 2022-02
• 期刊: NETWORK NEUROSCIENCE
• 影响因子: 4.7
• 作者: Gu, Shi;Fotiadis, Panagiotis;Parkes, Linden;Xia, Cedric H.;Gur, Ruben C.;Gur, Raquel E.;Roalf, David R.;Satterthwaite, Theodore D.;Bassett, Dani S.
• 通讯作者: Bassett, Dani S.

Modeling environment through a general exposome factor in two independent adolescent cohorts.

• DOI: 10.1093/exposome/osac010
• 发表时间: 2022
• 期刊: Exposome

Exposome and Trans-syndromal Developmental Trajectories Toward Psychosis.

• DOI: 10.1016/j.bpsgos.2022.05.001
• 发表时间: 2022-07
• 期刊: Biological psychiatry global open science

Development of brain behavior integration systems related to criminal culpability from childhood to young adulthood: Does it stop at 18 years?

• DOI: 10.1007/s40817-021-00101-1
• 发表时间: 2021-06
• 期刊: Journal of pediatric neuropsychology
• 作者: Gur RC