Statistical Methods for Integrative Analysis of Large Scale Neuroimaging Data

大规模神经影像数据综合分析的统计方法

基本信息

批准号：
10647855
负责人：
Quefeng Li
金额：
$ 36.92万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10647855
关键词：
Accounting Address Affect Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis American Area Bioconductor Biomedical Research Communities Computer software Confidence Intervals Data Data Collection Dementia Development Dimensions Disease Early Diagnosis Elderly Electronic Health Record Genetic Grant Heterogeneity Literature Longitudinal cohort Measurement Measures Methods Modality Modeling Modernization Neurosciences Pattern Performance Population Prevention Progressive Disease Property Public Health Research Sampling Statistical Methods Statistical Models Statistical Study Testing cohort computer studies computerized tools effective therapy flexibility high dimensionality imaging biomarker imaging modality improved innovation longitudinal analysis microbiome research morphogens multidimensional data multimodal data multimodal neuroimaging multimodality neuroimaging novel resilience factor simulation statistics theories tool user friendly software

项目摘要

Abstract Integrative analysis methods are in great needs as multimodal multi-cohort neuroimaging data rapidly emerge in neuro science. In Alzheimer's Disease (AD) studies, many research relies on multimodal neuroimaging data to identify key image biomarkers for the early diagnosis of AD. Despite great endeavors in data collection, there still lacks rigorous statistical methods and efﬁcient computational tools to properly integrate big neuroimaging data in a statistical model and carry out inference to address practical problems. Important problems such as missing data and adjustment for between-subject heterogeneity still remain unsolved. In this proposal, we propose to build two integrative models, one handles multimodal data and the other handles longitudinal multi-cohort data. They will be built under a generic M-estimation framework that covers many widely used statistical models as its special cases. We will provide various inference tools for these models and develop efﬁcient algorithms to solve the M-estimation problem in presence of block missing values. In Aim 1, we propose a factor-adjusted integrative model for multimodal data and provide a complete set of inference tools. These tools can test the signiﬁcance of one whole data modality as well as the signiﬁcance of multiple linear combinations of predictors from one or more modalities. In Aim 2, we provide a powerful computational tool to handle block missing values of multimodal data. Such a tool does not need to perform ad-hoc imputation on missing values, but rather relies on an innovative mini- batch gradient descent algorithm to yield a good estimator. In Aim 3, we will develop an interactive factor model to jointly model longitudinal data coming from multiple cohorts. We show that such a model includes the standard random effects model as a special case and is more ﬂexible modeling the longitudinal data and accounting for the between-subject heterogeneity. The proposed research will likely transform how we analyze neuroimaging data and enhance our understanding of Alzheimer's Disease and its relation to public health.

抽象的综合分析方法符合多模式的多模式神经影像学数据，迅速出现在神经科学。在阿尔茨海默氏病（AD）研究中，许多研究依赖于多模式神经图像数据确定关键图像生物标志物以早期诊断AD。尽管数据收集方面有很多努力，但仍然存在缺乏严格的统计方法和有效的计算工具，可以正确整合大型神经影像数据统计模型并进行推理以解决实际问题。重要问题，例如失踪受试者间异质性的数据和调整仍未解决。在此提案中，我们建议构建两个集成模型，一个处理多模式数据，另一个处理纵向多核管数据。它们将建立在通用M估计框架下，该框架涵盖了许多广泛使用的统计模型特殊情况。我们将为这些模型提供各种推理工具，并开发有效的算法来解决在存在块缺失值的情况下，M估计问题。在AIM 1中，我们提出了一个因子调整的集成多模式数据的模型，并提供一组完整的推理工具。这些工具可以测试一个或多个预测变量组合的整个数据模式以及一个或多个线性组合的重要性方式。在AIM 2中，我们提供了一个强大的计算工具来处理多模式数据的块缺失值。这样的工具不需要在缺失值上进行临时插补，而是依赖于创新的迷你批处理梯度下降算法可得出良好的估计器。在AIM 3中，我们将开发一个交互式因素模型共同模拟来自多个队列的纵向数据。我们证明这样的模型包括标准随机效应模型是一种特殊情况，并且更灵活地建模纵向数据并考虑受试者之间的异质性。拟议的研究可能会改变我们分析神经影像数据的方式并增强我们对阿尔茨海默氏病及其与公共卫生的关系的理解。