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

项目总结 精神分裂症和其他精神障碍(PS)，最常见的出现在 并被认为是由于在此期间发生的对正常大脑成熟的干扰而引起的 时间到了。关键的是，偏离正常神经发育被认为是在临床上出现 几年后出现显著的PS症状，突显了他们的发现将对 精神病学研究；如果我们能成功地识别出先前的大脑异常，那么我们就可能 及早干预，降低个体患精神分裂症的风险。揭开这些先行者的大脑 异常需要建立在网络神经科学和机器的最新进展基础上的预测模型 学习；此外，这样的模型必须结合大样本的纵向神经成像和临床 以发现真正具有预测意义的生物标志物的数据。最后，在PS症状和PS中都发现了性别差异 神经发育。因此，对性行为如何与PS症状相互作用提供准确了解的研究 以及异常的神经发育是必要的。当前研究的目的是使用以下高级工具 网络神经科学和机器学习与多模式神经成像相结合，以发现 可以预测PS症状在整个发育过程中的出现。为了实现这一目标，我们将利用 多个大型横截面和纵向神经发育数据集，包括费城 神经发育队列、健康脑网络与青少年脑认知发展 研究，研究大脑结构和连通性。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，允许我们探测某个区域控制大脑变化的能力 通过白质通路的状态。与图论相比，NCT是一种当代的方法，它假设 大脑结构如何通知和约束其功能的显式模型，使人们能够机械地洞察 与PS相关的连接障碍。我们将量化NCT指标中的发育异常 一种名为标准建模的新兴机器学习技术。一个规范的模型建立了一个增长图表 将年龄和大脑之间的关系的预期变化纳入到 它的预测。然后，与这些增长图表的偏差可以用什么是什么和什么不是来理解 在一个正常的人群中是预期的。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， 目标3将调查偏离正常神经发育在多大程度上调节这种关系 性行为和PS症状之间的关系。这个独立之路奖的目标是在我强大的基础上 精神病学、多模式神经成像、网络神经科学和机器学习背景，通过扩展 我的专业知识包括发展精神病理学、NCT和纵向神经影像数据。

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.