Statistical methods for structural and functional integration in multi-modal neuroimaging data

多模态神经影像数据结构和功能整合的统计方法

• 批准号: 10445053
• 负责人: BRIAN Scott CAFFO
• 金额: $ 48.44万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-05 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10445053
• 关键词: Adoption; Affect; Bayesian Method; Bayesian Modeling; Biological Markers; Brain; Clinical; Cognitive; Communities; Complex; Computer software; Data; Data Set; Detection; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Disease Outcome; Disease model; Dropout; Elements; Family member; Functional Magnetic Resonance Imaging; Goals; Impaired cognition; Individual; Infrastructure; Intuition; Joints; Knowledge; Learning; Magnetic Resonance Imaging; Measures; Medical; Meta-Analysis; Methodology; Methods; Modality; Modeling; Neurons; Neurosciences; Pathway Analysis; Patients; Pattern; Performance; Persons; Procedures; Process; Research; Rest; Route; Schizophrenia; Selection Criteria; Services; Severities; Signal Transduction; Specific qualifier value; Statistical Data Interpretation; Statistical Methods; Structure; Students; System; Techniques; Training; Weight; Work; application programming interface; autism spectrum disorder; base; behavioral outcome; behavioral phenotyping; biomarker discovery; cloud platform; cost; data framework; deep learning; deep neural network; density; developmental disease; direct application; feature extraction; flexibility; imaging modality; interest; model building; motor impairment; multimodal neuroimaging; multimodality; network models; neuroimaging; neuroimaging marker; neuropsychiatric disorder; non-invasive imaging; novel; patient variability; programs; relating to nervous system; socioeconomics; software development; theories; user-friendly

Abstract Neuropsychiatric disorders, such as autism and schizophrenia, affect millions of people worldwide and place a considerable burden on both patients and family members. Existing treatments for these disorders have limited efficacy, in part due the varied clinical manifestations, and to our narrow understanding of the impacted neural processes, particularly at the system (i.e., network) level. Two key elements of networks are the underlying infrastructure or physical connections between elements and the functional signaling between entities that rides on top of this infrastructure. Recent advancements in noninvasive imaging have given us the ability to quantify structural and functional relationships in the brain via diffusion MRI, resting-state functional MRI, respectively. The size and scope of datasets measuring network structure and function are increasing in neuroimaging, and other domains, which heightens the need for new statistical frameworks that make full use of the data. Our goal is to develop frameworks for the analysis of structure-function integration in large-scale and complex networks, applied to neuroimaging studies, but also broadly applicable. This proposal will introduce three analytic paradigms: Bayesian network modeling that uses a priori structure-function knowledge for simultaneous network anomaly detection and clinical severity prediction; density regression using optimal transport theory; and end-to-end prediction using deep neural networks. In our application, infrastructure will be measured via dMRI, while function will be measured rs-fMRI. Each of our frameworks will provide a unique means to integrate these distinct imaging modalities, while also respecting the unique information provided by each data type. We also propose a unique software development effort that creates an application program interface to core software and implementations as software as as a service hosted on cloud platforms.

抽象的 自闭症和精神分裂症等神经精神疾病影响着数百万人 在世界范围内，给患者及其家属带来了相当大的负担。现存的 这些疾病的治疗效果有限，部分原因是临床情况不同 表现，以及我们对受影响的神经过程的狭隘理解，特别是 在系统（即网络）级别。网络的两个关键要素是底层 元素之间的基础设施或物理连接以及功能信号之间的连接 运行在该基础设施之上的实体。无创成像的最新进展 使我们能够通过以下方式量化大脑中的结构和功能关系 分别为扩散 MRI、静息态功能 MRI。数据集的大小和范围 在神经影像学和其他领域测量网络结构和功能正在增加， 这就更加需要充分利用数据的新统计框架。 我们的目标是开发大规模结构功能集成分析框架 和复杂的网络，应用于神经影像研究，而且适用范围广泛。这 提案将引入三种分析范式： 使用先验的贝叶斯网络建模 用于同步网络异常检测和临床严重程度的结构功能知识 预言;使用最优传输理论的密度回归；和端到端预测使用 深度神经网络。在我们的应用中，基础设施将通过 dMRI 进行测量，同时 功能将通过 rs-fMRI 进行测量。我们的每个框架都将提供独特的方法 整合这些不同的成像方式，同时也尊重独特的信息 由每种数据类型提供。我们还提出了独特的软件开发工作，以创建 核心软件的应用程序接口和软件的实现 托管在云平台上的服务。