Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth

摘要 内在功能连接性磁共振成像是研究组织的有力工具， 功能网络（Functional Networks，简称FN）。丰富和积累的证据表明， 在青年中经历可预测的规范发展，而异常发展与 不同的精神病理学基于图像分析进展的最新工作已经确定， 事实上，人的具体。当与大规模神经成像数据集配对时，个人特定的FN提供了 为开发新的诊断方法提供了前所未有的转化机会， 神经精神疾病的个性化治疗。然而，特定于人的翻译承诺 FN目前受到几个障碍的阻碍。首先，当前的方法在特定的阈值下计算个性化的FN。 规模，尽管有明确的证据表明，大脑是一个多尺度系统与层次功能组织。 第二，为了在不同主体之间强制对应，个性化FN通常在以下条件下计算： 某些限制，这可能会产生有偏见的结果。第三，深度学习取得了喜忧参半的成功， 神经成像数据分析部分地由于通常采用自组织网络架构的事实， 特征学习能力通常由于采用预先设计的而不是学习的特征而被剥夺。四是大力 从大规模神经影像学研究的多个数据集校正神经影像学测量的部位效应 目前的方法通常试图在统计建模之前协调数据，导致有价值的数据丢失。 信息.第五，纵向神经影像学和临床数据越来越多，但有效的分析 纵向数据的工具很少。最后但并非最不重要的是，深度学习算法已经开发出来， 分析fcMRI数据，但通常作为记录不佳的源代码发布，限制了可重复性和 翻译研究者的采纳。在此应用程序中，我们建立在前一个奖励期的成功基础上， 通过开发、验证和传播表征大脑功能的工具来解决这些局限性 在个体主体层面上。我们将利用互补的大规模大脑研究， 发展，以验证我们的方法，并描绘FN的异常发展是如何与 青年精神病理学的主要方面，包括抑郁、焦虑、精神病和ADHD谱 症状具体来说，我们将开发新的方法，以1）准确地识别无偏见的个性化FN， 多尺度层次组织; 2）使用个性化的 具有优化的深度神经网络架构和集成的站点效应校正的FN，以及3）有效地 对FN的纵向数据进行建模，以创建精神病理学的预测模型。这些工具将于 一个免费提供的容器化软件包，可确保跨计算平台的无摩擦可移植性 和完全再现性。

ABSTRACT Intrinsic functional connectivity magnetic resonance imaging is a powerful tool to study the organization of functional networks (FNs) in the human brain. Rich and accumulating evidence demonstrates that FNs undergo predictable normative development in youth, and that abnormal development is associated with diverse psychopathology. Recent work based on advances in image analytics has established that FNs are in fact person-specific. When paired with large-scale neuroimaging datasets, person-specific FNs provide unprecedented translational opportunities for the development of new diagnostics that could guide personalized treatments for neuropsychiatric illnesses. However, the translational promise of person-specific FNs is at present hindered by several obstacles. First, current methods compute personalized FNs at a specific scale, despite clear evidence that the brain is a multi-scale system with a hierarchical functional organization. Second, to enforce correspondence across different subjects personalized FNs are typically computed under certain constraints, which may yield biased results. Third, deep learning has achieved mixed success in neuroimaging data analysis partially due to the fact that ad-hoc network architecture is typically adopted and feature learning capability is often deprived by adopting pre-engineered rather than learned features. Fourth, to correct site effects of neuroimaging measures from multiple datasets of large-scale neuroimaging studies current methods typically attempt to harmonize data prior to statistical modeling, resulting in loss of valuable information. Fifth, longitudinal neuroimaging and clinical data are increasingly available, but effective analytic tools for longitudinal data are scarce. Last but not least, deep learning algorithms have been developed to analyze fcMRI data but are often released as poorly documented source code, limiting both reproducibility and adoption by translational researchers. In this application, we build on the success of the prior award period to address these limitations by developing, validating, and disseminating tools that characterize brain functional organization at an individual subject level. We will leverage complementary large-scale studies of brain development to validate our methods and delineate how abnormal development of FNs is associated with major dimensions of psychopathology in youth, including depression, anxiety, psychosis, and ADHD-spectrum symptoms. Specifically, we will develop novel methods to 1) accurately identify bias-free personalized FNs with a multiscale hierarchical organization; 2) robustly predict psychiatric symptom dimensions using personalized FNs with optimized deep neural network architecture and integrated site-effect correction, and 3) effectively model longitudinal data of FNs to create predictive models of psychopathology. These tools will be released in a freely available, containerized software package to ensure frictionless portability across computing platforms and full reproducibility.