Integrative Brain Network-Based Analysis for Heterogeneous and Multimodal

基于综合脑网络的异构和多模态分析

• 批准号: 10442961
• 负责人: Suprateek kundu
• 金额: $ 39.51万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-26 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10442961
• 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 该提案提出了最先进的方法，以解决与环境保护有关的挑战性问题。 神经生物学机制影响临床结果的利益，在存在异质性的代表 根据潜在疾病亚类和个体间症状和其他相关变量的变异性。 我们专注于开发基于脑连接体分析的综合方法，该方法结合了多个 脑功能和结构的模态成像（例如fMRI和弥散MRI），临床和行为测量， 同时考虑到样品间的异质性。我们的目标涉及神经科学中的重要问题， 到目前为止，人们的关注有限或根本没有受到关注，例如在合并的同时估计动态大脑连接性 大脑解剖结构，并随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亚类的异质性 基于对大脑底层网络的测量， 影响临床结果的白色纤维束。为了实现这些具有挑战性的目标，我们开发了 最先进的统计方法，其中包括重大创新，并依赖于多模式 神经成像数据，并使用生物信息先验，产生有意义的解决方案。激励数据集 是格雷迪创伤项目，其中包含神经成像，行为和临床数据的受试者谁是 暴露在创伤之下并发展出一定程度的创伤后应激障碍我们将在外部创伤后应激障碍上测试我们的方法 来自ENIGMA-PTSD-PGC联盟的验证数据集。我们的方法开发将包括 提出了新的方法，用于（a）使用网络值的多个图形模型的联合建模， 回归;（B）使用脑解剖学知识来告知动态连接性的估计，以及 随后使用动态功能连接来预测感兴趣的临床结果;（c）开发 本文提出了一种新的多元回归模型联合估计方法， 结合表征协变量的网络信息，以及（d）开发用于3- 使用解剖学上已知的先验对大脑中的纤维束进行尺寸形状估计，并且随后 使用所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 一个强大的战略验证所提出的方法，我们还提供了一个大纲，开发软件 并与研究人员和感兴趣的各方公开分享。本申请涉及几种临床 神经影像学研究中的重要问题，由于缺乏国家的 最先进的统计方法学，并有望使重要的方法，科学，临床和 翻译贡献。 .