Discovering prognostic neuroimaging biomarkers of the psychosis spectrum using network control theory

项目摘要 精神分裂症和其他精神病谱（PS）疾病，最常见的出现在整个 发展，并被认为是由破坏正常的大脑成熟过程中发生的， 时间重要的是，偏离正常的神经发育被认为是在临床上出现神经发育障碍之前。 显著的PS症状几年，突出了他们的发现将对 精神病学研究;如果我们能够成功地识别出先前的大脑异常，那么我们就可以 及早干预，降低患精神分裂症的风险。揭开这些先行的大脑 异常需要建立在网络神经科学和机器的最新进展的预测模型 此外，这种模型必须与纵向神经成像和临床的大样本相结合， 数据来揭示真正的预后生物标志物。最后，在PS症状和 神经发育因此，研究提供了一个精确的了解如何性与PS症状相互作用， 和异常的神经发育。目前研究的目的是使用先进的工具， 网络神经科学和机器学习结合多模态神经成像，以发现生物标志物， 可以预测整个发育过程中PS症状的出现。为了实现这一目标，我们将借鉴 多个大规模的横截面和纵向神经发育数据集，包括费城 神经发育队列、健康脑网络和青少年脑认知发展 研究大脑的结构和连通性。我们使用网络控制理论（NCT）来研究连通性。 NCT将大脑视为一个动态系统，允许我们探测一个区域控制大脑变化的能力。 通过白色物质的途径。与图论相比，NCT是一种当代的方法，它假定 大脑结构如何通知和约束其功能的明确模型，使机械洞察力， 与PS相关的连接障碍。我们将量化NCT指标中的发育异常， 这是一种新兴的机器学习技术，被称为规范建模。一个规范的模型建立一个增长图 将年龄和大脑之间关系的预期变化纳入大脑发育的过程中， 其预测。然后，偏离这些增长图表可以理解的条款是什么和什么不是 在正常人群中。在这里，我们将构建横截面（目标1）和纵向（目标2） NCT指标的标准模型，并使用多变量偏差来预测样本外的PS症状。最后， 目的3将调查偏离正常神经发育的程度， 性行为和PS症状之间的联系这个独立之路奖的目标是建立在我强大的 在精神病学，多模式神经成像，网络神经科学和机器学习的背景，通过扩大 我在发展精神病理学、NCT和纵向神经成像数据方面的专长。

PROJECT SUMMARY Schizophrenia, and other disorders of the psychosis spectrum (PS), most commonly emerge throughout development and are thought to be caused by disruptions to normative brain maturation occurring during this time. Critically, deviations from normative neurodevelopment are thought to precede the emergence of clinically significant PS symptoms by several years, highlighting the profound impact that their discovery would have for psychiatry research; if we can successfully identify the antecedent brain abnormalities, then we may be able to intervene early and reduce the risk of individuals developing schizophrenia. Uncovering these antecedent brain abnormalities requires predictive models built upon recent advances in network neuroscience and machine learning; moreover, such models must be coupled with large samples of longitudinal neuroimaging and clinical data to uncover truly prognostic biomarkers. Finally, sex differences are found in both the PS symptoms and neurodevelopment. Thus, studies that provide a precise understanding of how sex interacts with PS symptoms and abnormal neurodevelopment are needed. The purpose of the current study is to use advanced tools from network neuroscience and machine learning coupled with multi-modal neuroimaging to uncover biomarkers that can predict the emergence of PS symptoms throughout development. To achieve this goal, we will draw on multiple largescale cross-sectional and longitudinal neurodevelopmental datasets, including the Philadelphia Neurodevelopmental Cohort, the Healthy Brain Network, and the Adolescent Brain Cognitive Development study, to study brain structure and connectivity. We use Network Control Theory (NCT) to study connectivity. NCT treats the brain as a dynamical system allowing us to probe a region's capacity to control changes in brain states via white matter pathways. Compared to graph theory, NCT is a contemporary approach that posits an explicit model of how the brain's structure informs and constrains its function, enabling mechanistic insight into the dysconnectivity associated with the PS. We will quantify developmental abnormalities in NCT metrics using a nascent machine learning technique known as normative modeling. A normative model builds a growth chart of brain development that incorporates the expected variation in the relationship between age and the brain into its predictions. Then, deviations from these growth charts can be understood in terms of what is and what is not expected in a normative population. Here, we will build cross-sectional (Aim 1) and longitudinal (Aim 2) normative models of NCT metrics and use multivariate deviations to predict PS symptoms out-of-sample. Finally, Aim 3 will investigate the extent to which deviations from normative neurodevelopment mediate the relationship between sex and PS symptoms. The goal of this Pathway to Independence award is to build on my strong background in psychiatry, multimodal neuroimaging, network neuroscience, and machine learning by expanding my expertise to developmental psychopathology, NCT, and longitudinal neuroimaging data.