Integrative Brain Network-Based Analysis for Heterogeneous and Multimodal Neuroimaging Data

基于综合脑网络的异构和多模态神经影像数据分析

• 批准号: 10002306
• 负责人: Suprateek kundu
• 金额: $ 40.74万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002306
• 关键词: 3-Dimensional; Accounting; Address; Affect; Algorithms; Anatomy; Attention; Bayesian Method; Behavioral; Biological; Biomedical Research; Brain; Categories; Clinical; Clinical Data; Communities; Computer software; Data; Data Set; Development; Diffusion Magnetic Resonance Imaging; Disease; Effectiveness; Environmental Exposure; Fiber; Functional Magnetic Resonance Imaging; Goals; Heterogeneity; Individual; Information Networks; Joints; Knowledge; Literature; Measurement; Mental disorders; Methodology; Methods; Modeling; Multimodal Imaging; Network-based; Neurosciences; Outcome; Population; Post-Traumatic Stress Disorders; Regression Analysis; Research; Research Personnel; Sample Size; Sampling; Shapes; Structure; Subgroup; Symptoms; Testing; Translational Research; Trauma; Validation; Visualization; base; behavior measurement; cohort; connectome; heterogenous data; high dimensionality; innovation; interest; multimodality; network models; neural circuit; neurobiological mechanism; neuroimaging; novel; novel strategies; open source; patient subsets; predict clinical outcome; response; simulation; software development; stem; tool; trauma exposure; user-friendly; white matter

PROJECT SUMMARY This proposal develops state of the art approaches for addressing challenging questions related to the neurobiological mechanisms affecting clinical outcomes of interest in the presence of heterogeneity represented by underlying disease sub-categories and variability in symptoms and other relevant variables across individuals. We focus on developing integrative approaches for brain connectome based analyses, which combines the multi- modal imaging (e.g. fMRI and diffusion MRI) of brain function and structure, clinical and behavioral measures, while accounting for heterogeneity across samples. Our goals involve important questions in neuroscience which have received limited or no attention so far, such as estimating dynamic brain connectivity while incorporating brain anatomical structure, and subsequently examining which dynamic functional connections drive the clinical outcome, accounting for heterogeneity in terms of disease sub-categories when predicting the clinical outcome based on brain measurements which lie on an underlying brain network, and investigating differences in shapes of white matter fiber bundles which drive the clinical outcome. To address such challenging goals, we develop state-of-the-art statistical approaches which incorporate significant innovations and rely on multi-modal neuroimaging data and uses biologically informed priors which yield meaningful solutions. The motivating dataset is the Grady Trauma Project, which contains neuroimaging, behavioral, and clinical data on subjects who were exposed to trauma and developed some degree of PTSD. We will test our approaches on an external PTSD validation dataset from the ENIGMA-PTSD-PGC consortium. Our methodology development will include proposing novel approaches for (a) the joint modeling of multiple graphical models using network-valued regression; (b) using brain anatomical knowledge to inform the estimation of dynamic connectivity and subsequently using the dynamic functional connections to predict the clinical outcome of interest; (c) developing novel approaches for the joint estimation of multiple regression models corresponding to varying subgroups while incorporating network information characterizing the covariates, and (d) developing Bayesian approaches for 3- dimensional shape estimation for fiber tracts in the brain using anatomically informed priors, and subsequently using the shapes of the estimated fiber bundles to predict the clinical outcomes of interest. We also develop a robust strategy for the validation of the proposed methods and we also provide an outline for developing software and sharing them openly with researchers and interested parties. This application addresses several clinical significant questions in neuroimaging research which have not been explored before due to the lack of state of the art statistical methodology, and is expected to make important methodological, scientific, clinical and translational contributions. .

项目概要 该提案开发了最先进的方法来解决与 在存在异质性的情况下影响感兴趣的临床结果的神经生物学机制 通过潜在的疾病子类别和症状的变异性以及个体之间的其他相关变量。 我们专注于开发基于大脑连接组的分析的综合方法，该方法结合了多种方法 脑功能和结构、临床和行为测量的模态成像（例如功能磁共振成像和扩散磁共振成像）， 同时考虑样本之间的异质性。我们的目标涉及神经科学中的重要问题 到目前为止受到的关注有限或没有受到关注，例如在整合的同时估计动态大脑连接 大脑解剖结构，然后检查哪些动态功能连接驱动临床 结果，在预测临床结果时考虑疾病子类别的异质性 基于底层大脑网络的大脑测量，并研究形状差异 驱动临床结果的白质纤维束。为了解决这些具有挑战性的目标，我们开发 最先进的统计方法，融合了重大创新并依赖于多模式 神经影像数据并使用生物学知识先验，从而产生有意义的解决方案。激励数据集 是格雷迪创伤项目，其中包含接受过创伤治疗的受试者的神经影像、行为和临床数据。 遭受创伤并出现一定程度的创伤后应激障碍（PTSD）。我们将在外部 PTSD 上测试我们的方法 来自 ENIGMA-PTSD-PGC 联盟的验证数据集。我们的方法开发将包括 提出新方法（a）使用网络值对多个图形模型进行联合建模 回归； （b）利用大脑解剖知识来估计动态连接性和 随后使用动态功能连接来预测感兴趣的临床结果； (c) 发展 联合估计对应于不同子组的多重回归模型的新方法，同时 纳入表征协变量的网络信息，以及 (d) 开发 3- 的贝叶斯方法 使用解剖学先验知识对大脑中的纤维束进行维度形状估计，然后 使用估计的纤维束的形状来预测感兴趣的临床结果。我们还开发了一个 用于验证所提出的方法的稳健策略，我们还提供了开发软件的大纲 并与研究人员和感兴趣的各方公开分享。该应用程序解决了几个临床 由于缺乏神经影像学研究的状态，以前从未探讨过这些重大问题 艺术统计方法论，预计将在方法论、科学、临床和 翻译贡献。 。