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

项目总结 这项提案开发了最先进的方法来解决与 异质性存在时影响感兴趣临床结果的神经生物学机制 根据潜在的疾病亚类以及症状和其他相关变量在个人中的可变性。 我们专注于开发基于脑连接组的综合分析方法，它结合了多个 脑功能和结构、临床和行为测量的模式成像(例如功能磁共振成像和扩散磁共振成像)， 同时考虑了样品之间的异质性。我们的目标涉及神经科学中的重要问题 到目前为止，已经得到了有限的关注或没有得到关注，比如在合并的同时估计动态的大脑连接 大脑解剖结构，并随后检查哪些动态功能连接推动了临床 结果，在预测临床结果时，考虑到疾病子类别的异质性 基于建立在大脑底层网络上的大脑测量，并研究形状的差异 影响临床结果的白质纤维束。为了解决这些具有挑战性的目标，我们开发了 包含重大创新并依赖于多模式的最先进的统计方法 神经成像数据，并使用生物知情的先验，从而产生有意义的解决方案。激动人心的数据集 是格雷迪创伤项目，该项目包含神经成像、行为和临床数据，受试者 暴露在创伤中并发展成某种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 提出了用于(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)使用大脑解剖知识来提供动态连通性的估计和 随后使用动态功能连接来预测感兴趣的临床结果；(C)发展 不同子群对应的多元回归模型联合估计的新方法 结合表征协变量的网络信息，以及(D)开发贝叶斯方法以用于3- 使用解剖学信息的先验数据估计大脑中纤维束的空间形状，并随后 使用估计的纤维束的形状来预测感兴趣的临床结果。我们还开发了一种 为验证所提出的方法提供了可靠的策略，并提供了软件开发的大纲 并与研究人员和感兴趣的各方公开分享。这个应用程序解决了几个临床 神经影像研究中的重大问题，这些问题以前没有被探索过，因为缺乏 ART统计方法论，预计将在方法学、科学、临床和 翻译投稿。 。

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. .