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. .

项目摘要 该提案开发了最新方法来解决与该问题相关的挑战性问题 在存在异质性的存在下影响感兴趣的临床结果的神经生物学机制代表 通过基本的疾病子类别和症状和其他相关变量的变异性。 我们专注于开发基于大脑连接组的分析的综合方法，该方法结合了多种多样 大脑功能和结构，临床和行为度量的模态成像（例如fMRI和扩散MRI）， 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，已经受到有限或尚无关注的注意力，例如估计动态大脑连接性 大脑解剖结构，然后检查哪种动态功能连接驱动临床 结果，在预测临床结果时会考虑疾病子类别的异质性 基于位于基础大脑网络上的大脑测量值，并研究形状的差异 驱动临床结果的白质纤维束。为了解决此类具有挑战性的目标，我们发展 最先进的统计方法，结合了重大创新并依靠多模式 神经影像学数据并使用具有生物学知情的先验，从而产生有意义的解决方案。激励数据集 是Grady创伤项目，其中包含有关受试者的神经影像，行为和临床数据 暴露于创伤并发展了一定程度的PTSD。我们将在外部PTSD上测试我们的方法 来自Enigma-PTSD-PGC财团的验证数据集。我们的方法发展将包括 提出（a）使用网络值的多个图形模型的联合建模的新方法 回归； （b）使用大脑解剖学知识来告知动态连通性和 随后使用动态功能连接来预测感兴趣的临床结果； （c）发展 与各种子组相对应的多元回归模型的联合估计的新方法 合并表征协变量的网络信息，以及（d）开发3--的贝叶斯方法 使用解剖学知情的先验，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 验证拟议方法的强大策略，我们还为开发软件提供了概述 并与研究人员和感兴趣的各方公开分享它们。该应用程序解决了几个临床 由于缺乏状态，在神经影像研究中尚未探讨的重要问题 艺术统计方法论，预计将使重要的方法论，科学，临床和 翻译贡献。 。