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?

项目总结/摘要 越来越多的证据支持这一假设，即偏离典型的大脑结构的发展 发生在精神病发作之前，而对患有精神病的成年人的“大数据”神经成像研究发现， 大脑中广泛的灰质破坏在本提案中，我们将协同有关规范性 成年精神病患者结构神经发育和脑结构畸变研究 具有成本效益的基于大脑的青少年精神病风险标志物。为了更好地识别那些风险最大的人， 我们利用精神病的大规模结构神经成像研究的结果来创建一个“精神病”， 神经影像学评分，反映一个人精神病倾向的累积汇总评分。我们的目标首先是 通过纳入结构性大脑的关键方面，将精神病神经影像评分转移到青年人中 发展在目标1中，我们将描述精神病的规范发展轨迹 通过协调许多规范性发育的档案数据集进行神经影像学评分（N> 5，000，2-30年 旧）。然后，我们将评估如何更大的年龄相关的偏离，从总精神病神经影像学 分数区分青年与精神病谱的症状，从典型的发展青年， 费城神经发育队列（N=1209，10-22岁）。在目标2中，我们计划研究如何 与总精神病神经影像评分的年龄相关偏差越大， 与青少年大脑中的精神病样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 因素（例如，精神病家族史，产科并发症，创伤）有助于这些特征的描述 轨迹最后，在目标3中，我们提出使用误差测量建模来建立一个泛函 3 T MRI扫描生成的精神病神经影像学评分与 使用低场MRI扫描在一个社区样本的青年。这项研究的结果将使我们能够创造更多 负担得起的，临床上可获得的严重精神病理学生物指标，最终改善 确定风险最大的年轻人，并允许更早、更有效的干预。

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.