New Statistical Methods for Multicenter Multimodal Longitudinal Neuroimaging Analysis

项目摘要 随着世界人口迅速老龄化，了解、诊断和治疗阿尔茨海默病(AD) 正在成为国际社会的当务之急。近年来，一批大规模的神经影像数据库 正在出现，它们从多个成像中心收集多种成像方式，在两个基线 并在多年的跟踪调查中反复出现。这种多中心多模式的纵向神经成像- ING数据对于了解阿尔茨海默病等神经退行性疾病特别有用。然而，他们 带来许多挑战，包括超高维、复杂的空间和时间相关性， 缺失值比例高，数据异构性强，缺乏形式推理或理论保障。 发球区。这些挑战严重阻碍了这些大型神经成像数据库在 增进我们对AD和正常衰老的理解。在这项建议中，我们的目标是开发新的统计 应对这些挑战的方法，并回答AD和fi领域中的一些基本问题 老龄化研究。具体地说，(1)我们发展了一种新的同时协方差推断过程，该过程 提供了统计意义的显式量化fifi，大大提高了检测能力，严格的 理论支持，以及多成像模式关联分析中的刚性误发现控制。 (2)提出了一种用于多模式神经成像的线性判别分析的综合版本 基于经典fi判别和疾病诊断，目的是证明该方法是渐近的 与使用单模数据相比，使用多模数据可以提高Classifi检测的错误率；(3)我们开发了 一种可同时处理纵向相关的动态张量响应回归模型 通过稀疏和低等级结构的混合，图像和高比例的丢失扫描， 融合正则化和张量补全；(4)提出了一种异质性校正策略。 将其嵌入张量响应回归，该回归对脑图像或脑连接的变化进行建模 随着疾病状态或年龄的变化而变化的健康模式，同时校正来自 多个成像地点。我们的建议是基于两项关于阿尔茨海默病和正常衰老的活体研究： 伯克利老龄化队列研究和阿尔茨海默病神经成像倡议，同时它也适用于- 其他神经疾病研究的电报。它解决了面临的一些重大挑战 纵向和多模式神经成像分析，可及时响应不断增长的需求 用于大型神经影像数据库的分析。它还有助于新的统计方法，以及 提出了高维统计推断理论。我们的建议是产生一些有用的 工具，特别是新的计算机软件，将免费提供给两个最终用户，网址为 加州大学伯克利分校和整个神经科学界。

Project Summary With a rapidly aging world population, understanding, diagnosing, and treating Alzheimer's disease (AD) is becoming an international imperative. In recent years, a number of large-scale neuroimaging databases are emerging, which collect multiple imaging modalities from multiple imaging centers, at both the baseline and repeatedly over a number of years of follow-up. Such multicenter multimodal longitudinal neuroimag- ing data are particularly useful to understand neurodegenerative disorders such as AD. However, they pose numerous challenges, including ultrahigh dimensionality, complex spatial and temporal correlations, high proportion of missing values, data heterogeneity, and lack of formal inference or theoretical guaran- tee. These challenges have seriously hindered the application of those large neuroimaging databases to advance our understanding of AD and normal aging. In this proposal, we aim to develop new statistical methods to address those challenges, and to answer some fundamental questions in the field of AD and aging research. Specifically, (1) we develop a new simultaneous covariance inference procedure that provides an explicit quantification of statistical significance, a much improved detection power, a rigorous theoretical support, and a rigid false discovery control in association analysis of multiple imaging modal- ities; (2) we develop an integrative version of linear discriminant analysis for multimodal neuroimaging based classification and disease diagnosis, and aim to show the method is guaranteed to asymptotically improve the classification error rate when using multimodal data than using unimodal data; (3) we develop a dynamic tensor response regression model that can simultaneously handle the longitudinally correlated images and the high proportion of missing scans, through a mixture of sparsity and low-rank structures, fusion regularization and tensor completion; and (4) we propose a heterogeneity correction strategy and embed it with tensor response regression, which models the change of brain images or brain connectiv- ity patterns as the disease status or age changes, meanwhile correcting for potential heterogeneity from multiple imaging sites. Our proposal is motivated by two in vivo studies of AD and normal aging: the Berkeley Aging Cohort Study and the Alzheimer's Disease Neuroimaging Initiative, while it is also appli- cable to studies of other neurological disorders. It addresses a number of overarching challenges facing longitudinal and multimodal neuroimaging analysis, and offers a timely response to the growing demand for analysis of large neuroimaging databases. It also contributes to novel statistical methodology, and advances high-dimensional statistical inference theory. Our proposal is to result in a number of useful tools, in particular, a new computer software, which will be made freely available to both the end users at UC Berkeley and the neuroscience community at large.