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New Statistical Methods for Multicenter Multimodal Longitudinal Neuroimaging Analysis

多中心多模态纵向神经影像分析的新统计方法

基本信息

批准号：
10320007
负责人：
Lexin Li
金额：
$ 36.76万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-02-15 至 2023-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10320007
关键词：
Address Affect Age Aging Alzheimer&apos s Disease Alzheimer&apos s disease pathology Anatomy Brain Brain imaging Classification Cohort Studies Communities Complex Computer software Data Data Set Databases Deposition Detection Development Diagnosis Dimensions Discriminant Analysis Disease Evaluation Functional Magnetic Resonance Imaging Glucose Goals Heterogeneity Image Individual International Literature Magnetic Resonance Imaging Measures Methodology Methods Modality Modeling Neurodegenerative Disorders Neurosciences Pathologic Pattern Population Procedures Proteins Reaction Time Research Scanning Site Statistical Methods Statistical Models Structure Time Validation base cerebral atrophy cognitive ability cohort disease diagnosis follow-up guar gum heterogenous data high dimensionality imaging facilities imaging modality improved in vivo multimodal data multimodal neuroimaging multimodality nervous system disorder neuroimaging normal aging novel relating to nervous system response serial imaging statistics tau Proteins theories tool

项目摘要

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.

项目摘要随着世界人口的迅速老龄化，了解，诊断和治疗阿尔茨海默病（AD）正在成为国际上的当务之急。近年来，一些大型神经影像数据库从多个成像中心收集多种成像模式，并在数年的随访中反复进行。这种多中心多模式纵向神经成像- 数据对于理解神经退行性疾病如AD特别有用。但他们提出了许多挑战，包括多维性，复杂的空间和时间相关性，高比例的缺失值、数据异质性以及缺乏正式推断或理论保证- T恤。这些挑战严重阻碍了这些大型神经影像数据库的应用，增进我们对AD和正常衰老的理解。在这份提案中，我们的目标是开发新的统计数据，解决这些挑战的方法，并回答AD领域的一些基本问题，老化研究具体而言，（1）我们开发了一个新的同时协方差推断程序，提供了一个明确的量化统计意义，大大提高了检测能力，严格的理论支持，并在多成像模式的关联分析中严格控制错误发现，（2）我们开发了一个多模态神经影像线性判别分析的综合版本基于分类和疾病诊断，旨在证明该方法保证渐进改善分类错误率时，使用多模态数据比使用单峰数据;（3）我们开发动态张量响应回归模型，可以同时处理纵向相关的图像和丢失扫描的高比例，通过稀疏和低秩结构的混合，融合正则化和张量完备化;（4）提出了一种异质性校正策略，将其嵌入张量响应回归，该回归模型可以模拟大脑图像或大脑连接的变化，随着疾病状态或年龄的变化，多个成像部位。我们的建议是由两个在体内研究的AD和正常老化：伯克利老龄化队列研究和阿尔茨海默病神经影像学倡议，而它也适用于其他神经系统疾病的研究。它解决了面临的一些首要挑战，纵向和多模式神经影像分析，并及时响应不断增长的需求用于分析大型神经成像数据库。它还有助于新的统计方法，提出了高维统计推断理论。我们的建议是，工具，特别是一个新的计算机软件，这将是免费提供给双方的最终用户，加州大学伯克利分校和整个神经科学界。