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.

项目总结