Statistical Methods for Integrative Analysis of Large Scale Neuroimaging Data

摘要 随着多模式多队列神经成像数据的迅速涌现，对综合分析方法的需求非常大 神经科学。在阿尔茨海默病(AD)的研究中，许多研究依赖于多模式神经成像数据来 确定AD早期诊断的关键影像生物标志物。尽管在数据收集方面付出了巨大的努力，但仍有 缺乏严格的统计方法和有效的计算工具，无法正确地将大量神经成像数据整合到 建立统计模型并进行推理，以解决实际问题。失踪等重要问题 研究对象间异质性的数据和调整仍未解决。在这项建议中，我们建议 建立两个综合模型，一个处理多模式数据，另一个处理纵向多队列数据。 它们将建立在通用的M-估计框架下，该框架涵盖了许多广泛使用的统计模型，如ITS 特例。我们将为这些模型提供各种推理工具，并开发有效的fi算法来求解 存在块缺失值的M-估计问题。在目标1中，我们提出了一种因子调整的综合算法 为多模式数据建立模型，并提供一整套推理工具。这些工具可以测试fi的重要性 一个完整的数据形态以及来自一个或多个预测器的多个线性组合的显著fi 医疗模式。在目标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。 这样的工具不需要对缺失的值执行特别的推算，而是依赖于一个创新的迷你 批量梯度下降算法产生一个很好的估计器。在目标3中，我们将开发一个交互因素模型 对来自多个队列的纵向数据进行联合建模。我们证明了这样的模型包含了标准 随机效应模型作为一种特例，具有更强的fl灵活性，可以对纵向数据进行建模，并考虑到 主体间的异质性。这项拟议的研究可能会改变我们分析神经成像数据的方式 并加深我们对阿尔茨海默氏症及其与公共健康的关系的了解。

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.