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: 10586155
• 负责人: BRIAN Scott CAFFO
• 金额: $ 47.75万
• 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/10586155
• 关键词: Adoption; Affect; Bayesian Method; Bayesian Modeling; Biological Markers; Brain; Clinical; Cognitive; Communities; Complex; Computer software; Data; Data Set; Detection; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Disease Outcome; Disease model; Dropout; Economic Burden; Elements; Family member; Functional Magnetic Resonance Imaging; Goals; Impaired cognition; Individual; Infrastructure; Intuition; Joints; Knowledge; Learning; Measures; Medical; Meta-Analysis; Methodology; Methods; Modality; Modeling; Neurons; Neurosciences; Pathway Analysis; Patients; Pattern; Performance; Persons; Procedures; Process; Research; Rest; 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; behavioral outcome; behavioral phenotyping; biomarker discovery; clinical predictors; 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; neural; neuroimaging; neuroimaging marker; neuropsychiatric disorder; non-invasive imaging; novel; patient variability; programs; 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 神经精神疾病，例如自闭症和精神分裂症，会影响数百万人 在全球范围内，并给患者和家人带来了巨大的负担。现存的 这些疾病的治疗效果有限，部分原因是临床的多样 表现形式，以及我们对受影响的神经过程的狭narly理解 在系统（即网络）级别上。网络的两个关键要素是基础 元素与功能信号之间的基础架构或物理连接 骑在本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 扩散MRI，静止状态功能性MRI。数据集的大小和范围 测量网络结构和功能在神经影像中和其他领域的增加， 这增加了对充分利用数据的新统计框架的需求。 我们的目标是开发框架以分析大规模的结构功能集成 和复杂的网络，适用于神经影像学研究，但也广泛适用。这 建议将介绍三个分析范式：使用先验的贝叶斯网络建模 同时网络异常检测和临床严重程度的结构功能知识 预言;使用最佳运输理论的密度回归；并使用 深神经网络。在我们的应用中，基础架构将通过DMRI进行测量，而 功能将测量RS-FMRI。我们的每个框架都将为 整合这些独特的成像方式，同时尊重独特的信息 由每种数据类型提供。我们还提出了一个独特的软件开发工作，可以创建 应用程序程序接口到核心软件和实现的软件作为 服务托管在云平台上。

项目成果

Multivariate associations between behavioural dimensions and white matter across children and adolescents with and without attention-deficit/hyperactivity disorder.

• DOI: 10.1111/jcpp.13689
• 发表时间: 2023-02
• 期刊: JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY
• 影响因子: 7.6
• 作者: Bu, Xuan;Gao, Yingxue;Liang, Kaili;Bao, Weijie;Chen, Ying;Guo, Lanting;Gong, Qiyong;Lu, Hanzhang;Caffo, Brian;Mori, Susumu;Huang, Xiaoqi
• 通讯作者: Huang, Xiaoqi