Predicting psychosis risk in youth using a novel structural neuroimaging score that measures deviation from normative development. Can we bring it to communities using portable, low-field MRI?

使用一种新颖的结构神经影像评分来预测青少年的精神病风险，该评分可测量偏离规范发展的情况。

• 批准号: 10435204
• 负责人: MARIA JALBRZIKOWSKI
• 金额: $ 75.34万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10435204
• 关键词: 20 year old; Adolescence; Adolescent; Adult; Age; Archives; Base of the Brain; Behavioral; Big Data; Biological; Biological Markers; Brain; Childhood; Classification; Clinical; Communities; Complement; Data; Data Set; Development; Diagnosis; Distress; Early Diagnosis; Early Intervention; Early identification; Family history of; Future; Genetic; Gold; Growth; Height; Heterogeneity; Image; Individual; Knowledge; Logistics; MRI Scans; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mental disorders; Methodology; Methods; Modeling; Performance; Persons; Philadelphia; Prevention approach; Primary Prevention; Proxy; Psychopathology; Psychoses; Psychotic Disorders; Research; Risk; Risk Factors; Risk Marker; Sampling; Schizophrenia; Severities; Structure; Symptoms; Testing; Time; Translating; Trauma; Weight; Youth; base; case control; cognitive development; cohort; community setting; cost effective; cost effectiveness; effective intervention; emerging adult; gray matter; improved; improved outcome; indexing; innovation; neurodevelopment; neuroimaging; novel; obstetrical complication; pediatrician; polygenic risk score; portability; prevent; psychosis risk; psychotic symptoms; psychotic-like experiences; relating to nervous system; risk prediction

Project Summary/Abstract Converging lines of evidence support the hypothesis that deviations from typical brain structure development take place prior to psychosis onset, while ‘big data’ neuroimaging studies of adults with psychosis find subtle, widespread gray matter disruptions in the brain. In this proposal, we will synergize knowledge about normative structural neurodevelopment and findings of structural brain aberrations in adults with psychosis to develop cost-effective brain-based markers of psychosis risk in youth. To improve identification of those at greatest risk, we leverage results from large-scale structural neuroimaging studies of psychosis to create a ‘Psychosis Neuroimaging Score’, a cumulative summary score that reflects one’s psychosis liability. We first aim to transport the Psychosis Neuroimaging Score to youth by incorporating crucial aspects of structural brain development. In Aim 1, we will characterize the normative developmental trajectory of the Psychosis Neuroimaging Score by harmonizing many archival datasets of normative development (N>5,000, 2-30 years old). We will then evaluate how greater age-associated deviation from the aggregate Psychosis Neuroimaging Score differentiates youth with psychosis spectrum symptoms from typically developing youth in the Philadelphia Neurodevelopmental Cohort (N=1209, 10-22 years old). In Aim 2, we plan to examine how greater age-associated deviation from the aggregate Psychosis Neuroimaging Score predicts distinct developmental trajectories associated with psychotic-like experiences in youth from the Adolescent Brain and Cognitive Development Study (N=11,875). We will also assess the extent to which known psychosis risk factors (e.g., family history of psychosis, obstetric complications, trauma) contribute to characterization of these trajectories. Finally, in Aim 3, we propose to use measurement-in-error modeling to establish a functional relationship between Psychosis Neuroimaging scores generated from 3T MRI scans and those generated using low-field MRI scans in a community sample of youth. Results from this study will allow us to create more affordable, clinically accessible biological indicators of severe psychopathology, ultimately improving identification of young people at greatest risk and allowing earlier, more effective interventions.

项目摘要/摘要 融合的证据线支持偏离典型大脑结构发展的假设 发生在精神病发作之前，而对精神病成年人的“大数据”神经影像学发现微妙， 大脑中宽度的灰质破坏。在此提案中，我们将协同有关正常的知识 成人精神病的结构神经发育和结构性脑像差的发现 成本效益的基于大脑的精神病风险标志。为了提高对风险最大的人的识别， 我们利用大规模的精神病结构神经影像学研究的结果来创建“精神病” 神经影像学评分，这是反映一个人的精神病责任的累积摘要分数。我们首先要 通过结合结构大脑的关键方面，将精神病神经影像评分传输到青年 发展。在AIM 1中，我们将表征精神病的正常发育轨迹 通过协调许多正常开发的档案数据集（n> 5,000，2-30年），神经影像学评分 老的）。然后，我们将评估与总体精神病神经影像学的年龄相关的程度 得分将青年与精神病谱系症状与通常发展的青年区分开 费城神经发育队列（n = 1209，10-22岁）。在AIM 2中，我们计划研究如何 与年龄相关的偏离总体精神病神经影像学评分预测不同 与青少年大脑青年的精神病经历相关的发展轨迹 认知发展研究（n = 11,875）。我们还将评估已知的精神病风险的程度 因素（例如，精神病的家族史，产科并发症，创伤）有助于表征这些 轨迹。最后，在AIM 3中，我们建议使用纠错模型来建立功能 由3T MRI扫描产生的精神病神经影像学评分与产生的分数之间的关系 在社区青年样本中使用低场MRI扫描。这项研究的结果将使我们能够创造更多 负担得起的，临床上可访问的严重心理病理学生物学指标，最终改善 确定年轻人的风险最大，并允许更早，更有效的干预措施。