Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth

用于表征和预测青少年精神病理学的个性化功能网络模型

• 批准号: 10304463
• 负责人: Yong Fan
• 金额: $ 65.54万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-02 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10304463
• 关键词: Address; Adolescent; Adopted; Adoption; Anxiety; Architecture; Attention deficit hyperactivity disorder; Award; Brain; Brain imaging; Clinical Data; Communication; Communities; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Development; Diagnostic; Dimensions; Engineering; Ensure; Environment; Functional Magnetic Resonance Imaging; Future; Human; Image; Individual; Learning; Magnetic Resonance Imaging; Measurement; Measures; Mental Depression; Methods; Modeling; Nature; Neurons; Neurosciences; Paper; Pennsylvania; Persons; Philadelphia; Procedures; Psychopathology; Psychoses; Reproducibility; Research; Site; Source Code; Statistical Models; Supervision; Symptoms; System; Techniques; Universities; Work; Youth; analytical tool; base; cognitive development; cohort; computational platform; connectome; data harmonization; data structure; deep learning; deep learning algorithm; deep neural network; human data; image processing; improved; interest; large scale data; learning strategy; multiple datasets; mutual learning; network architecture; network models; neural network architecture; neuroimaging; neuropsychiatry; novel; novel diagnostics; open source; outcome prediction; personalized medicine; portability; predictive modeling; programs; psychiatric symptom; recurrent neural network; relating to nervous system; success; tool; translational scientist; user-friendly

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.

抽象的 固有功能连接性磁共振成像是研究组织的强大工具 人脑中的功能网络（FNS）。富裕和积累的证据表明FNS 在青年中经历可预测的规范发展，并且异常发展与 多样化的心理病理学。基于图像分析的进步的最新工作已经确定FN在 事实特定于人。当与大规模神经影像数据集配对时，特定于人的FN提供 开发新诊断的空前的转化机会，可以指导 神经精神疾病的个性化治疗方法。但是，特定于人的翻译承诺 目前，FNS受到了几个障碍的阻碍。首先，当前方法在特定的 规模，尽管有明确的证据表明大脑是具有分层功能组织的多尺度系统。 第二，在不同主题之间执行信函，通常在下面计算 某些约束，可能会产生偏见的结果。第三，深度学习在 由于通常采用了临时网络体系结构的事实和 通过采用预先设计的而不是学习的功能，通常会剥夺功能学习能力。第四，到 来自多个大规模神经影像学研究数据集的神经成像测量的正确位点效应 当前方法通常在统计建模之前尝试协调数据，从而导致损失有价值 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一点是，已经开发了深度学习算法 分析FCMRI数据，但通常以记录不足的源代码发布，从而限制了可重复性和 翻译研究人员的收养。在此申请中，我们以先前裁决期的成功为基础 通过开发，验证和传播表征大脑功能的工具来解决这些限制 在个人主题层面上组织。我们将利用大脑的互补大规模研究 开发以验证我们的方法并描述FNS的异常发展与 青年心理病理学的主要维度，包括抑郁，焦虑，精神病和多动症 - 谱系 症状。具体而言，我们将开发新的方法至1）准确地确定没有偏见的个性化FNS 一个多尺度等级组织； 2）使用个性化预测精神病症状维度 具有优化深度神经网络架构和集成的站点效果校正的FNS，以及3）有效 模拟FNS的纵向数据，以创建精神病理学的预测模型。这些工具将在 一个免费的容器软件包，以确保跨计算平台的无摩擦可移植性 和完整的可重复性。