Scalable Bayesian methods for big imaging data analysis

用于大成像数据分析的可扩展贝叶斯方法

• 批准号: 10669008
• 负责人: Timothy D Johnson
• 金额: $ 31.78万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10669008
• 关键词: Address; Adolescence; Adolescent; Affect; Age; Algorithms; Bayesian Analysis; Bayesian Method; Bayesian Modeling; Behavioral; Big Data; Biological; Brain; Brain imaging; Cations; Child; Child Health; Childhood; Clinical; Clinical Research; Complex; Computational algorithm; Computer software; Data; Data Analyses; Dependence; Development; Discipline; Electronic Health Record; Environmental Risk Factor; Etiology; Frequencies; Functional Magnetic Resonance Imaging; Funding; Goals; High Performance Computing; Human; Human Genome; Image; Individual; Intervention; Joints; Knowledge; Longitudinal Studies; Magnetic Resonance Imaging; Maps; Measurement; Mediating; Mediation; Medicine; Methods; Modeling; Modernization; Modification; Multimodal Imaging; Neural Network Simulation; Neurosciences; Outcome; Pathology; Pattern; Performance; Process; Psychology; Psychopathology; Research; Rest; Risk; Route; Sample Size; Seminal; Smoking; Sports; Statistical Data Interpretation; Statistical Methods; Structure; Substance Use Disorder; Technology; Translational Research; United States; United States National Institutes of Health; Video Games; Work; Youth; addiction; cluster computing; cognitive development; computerized tools; experience; feature selection; high dimensionality; high resolution imaging; high risk; imaging biomarker; imaging modality; innovation; neural network; neuroimaging; novel; parallel computer; precision medicine; predict clinical outcome; predictive modeling; risk prediction; screening; sleep pattern; social; social factors; social media; substance use; tool; translational applications; user friendly software; vector; web based software; web page; young adult

ABSTRACT! This proposal will address the most timely and important issues in statistical analysis of big imaging data. Our project is motivated by "The Adolescent Brain Cognitive Development (ABCD) Study”, which is the largest long-term study of brain development and child health in the United States and is funded by the National Institutes of Health (NIH). Innovative aspects of this proposal are: 1) We develop a new Bayesian image-on- vector regression model with novel sparse and smooth Gaussian process priors. It enables to perform association analysis between high-resolution images of brain activity and high-dimensional vectors of social environmental factors and clinical variables. To the best of our knowledge no existing methods can efficiently and jointly analyze high-resolution images and high-dimensional vectors of covariates simultaneously under a systematic modeling framework; 2) We develop a new Bayesian scalar-on-image neural network model with sparse, smooth, and spatially-varying coefficients. This new model has great potential to make better predictions about the risk of an adolescent initiating substance use compared to all existing methods; and more importantly, it will identify important imaging biomarkers that are associated with substance use patterns. This will provide a better understanding of the pathology of substance use initiation; 3) We propose a Bayesian model for high-dimensional mediation analysis of multimodality imaging data by combining image-on-vector regression and scalar-on-image regression with modifications. Under the potential outcome framework, ! we will define the direct effects of environmental factors/electronic health records on psychopathology, as well as their indirect effects that are mediated through the changes in brain functions and/or structures. 4) We develop scalable posterior computation algorithms for all of the proposed models. These efficient computation tools will enable the possibility to apply the statistical methods in the clinical and translational research and applications. Our methods can address two key questions about adolescent brain cognitive development: 1) they will identify important childhood experiences and social environmental factors, such as sports, video games, social media, unhealthy sleep patterns, and smoking, that affect brain development; 2) understand the inferences of brain development on the risk of substance use initiation and patterns, including detailed quantity, frequency, route of administration, and co-use patterns. ! !

抽象的！ 该提案将解决我们的大成像数据统计分析中最及时、最重要的问题。 该项目的动机是“青少年大脑认知发展（ABCD）研究”，这是迄今为止规模最大的研究 在美国对大脑发育和儿童健康进行长期研究，并由国家资助 该提案的创新之处在于： 1）我们开发了一种新的贝叶斯图像- 具有新颖的稀疏且平滑的高斯过程先验的向量回归模型能够执行。 大脑活动的高分辨率图像与社交的高维向量之间的关联分析 据我们所知，没有现有的方法可以有效地确定环境因素和临床变量。 并在a下同时联合分析高分辨率图像和协变量的高维向量 系统建模框架；2）我们开发了一种新的贝叶斯图像标量神经网络模型 这种新模型具有改善稀疏、平滑和空间变化系数的巨大潜力。 与所有现有方法相比，对青少年开始使用药物的风险进行预测； 重要的是，它将识别与物质使用模式相关的重要成像生物标志物。 将提供对物质使用起始病理学的更好理解；3）我们提出贝叶斯模型； 结合图像矢量的多模态成像数据高维中介分析模型 在潜在结果框架下，回归和图像标量回归。 我们还将定义环境因素/电子健康记录对精神病理学的直接影响 作为通过大脑功能和/或结构的变化介导的间接影响 4) 我们。 为所有提出的模型开发可扩展的后验计算算法。 工具将使统计方法在临床和转化研究中应用成为可能 我们的方法可以解决有关青少年大脑认知发展的两个关键问题：1） 他们将识别重要的童年经历和社会环境因素，例如运动、视频 影响大脑发育的游戏、社交媒体、不健康的睡眠模式和吸烟 2) 了解 大脑发育对物质使用开始风险和模式的推论，包括详细数量， 频率、给药途径和共同使用模式。！ ！

项目成果

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

对具有匹配不确定性和小样本量的非目标代谢组学数据进行贝叶斯函数分析。

• 发表时间: 2024-03-27
• 影响因子: 9.5
• 作者: Ma, Guoxuan;Kang, Jian;Yu, Tianwei
• 通讯作者: Yu, Tianwei