Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data

多维数据分析与整合的统计ICA方法

基本信息

批准号：
8802230
负责人：
Ying Guo
金额：
$ 38.32万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-25 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8802230
关键词：
Address Behavior Behavioral Sciences Biological Brain Clinical Research Cognition Communities Complex Data Data Set Diagnosis Diffusion Magnetic Resonance Imaging Dimensions Disease Early Diagnosis Functional Magnetic Resonance Imaging Genes Genetic Genetic Determinism Genomics Genotype Goals Image Imaging Techniques Investigation Joints Knowledge Libraries Link Magnetic Resonance Imaging Major Depressive Disorder Measures Mental Depression Mental Health Mental disorders Methodology Methods Modeling Motivation Multimodal Imaging Nature Neurobiology Noise Outcome Pattern Research Scientific Advances and Accomplishments Signal Transduction Single Nucleotide Polymorphism Source Statistical Methods Structure System Testing Work blind discrete data genetic association genetic profiling graphical user interface improved independent component analysis infancy innovation insight interest method development neuroimaging novel public health relevance tool treatment response user friendly software

项目摘要

DESCRIPTION (provided by applicant): Study of mental disorders has entered into an exciting new era where biological measures from multiple platforms such as neuroimaging and genetics are being collected to help deepen the understanding of the disorders and improve diagnosis and treatment. Multi-dimensional data are becoming more common and hold great promise for advancing mental health research. However, effective statistical methods for extracting useful and complementary information from multi-dimensional data are still in their infancy. One of the major challenges is that multi-dimensional data often have different scales (continuous/discrete), data representations (scalar/array/matrix) and dimensions. Current analytical approaches typically conduct separate analysis within each dimension or apply simple correlative analyses. These methods are of very limited nature for uncovering latent patterns and associations in these data. This project seeks to develop novel statistical independent component analysis (ICA) methods to provide effective tools for reducing dimension, denoising and extracting features from large- scale multi-dimensional data. Specifically, the proposed methods would 1) provide a unified framework for decomposing and integrating multimodal neuroimaging data such as fMRI and DTI, 2) provide a discrete ICA model for extracting latent signals from large-scale discrete outcomes such as single-nucleotide polymorphism (SNP) genotype data, and 3) provide a joint ICA model for simultaneously decomposing neuroimaging and SNP genotype data to extract integrated imaging genetics features. The proposed statistical methods will be applied to a major depressive disorder (MDD) study, and user-friendly software will be developed and made available to general research communities. Our proposed method developments will directly benefit mental health research by providing innovative statistical tools to combine information from multi-dimensional datasets that can facilitate diagnosis, deepen mechanistic understanding and improve treatment of mental disorders. Our methods are also ubiquitous enough to be generally useful to statistical practice.

描述（由申请人提供）：精神障碍的研究已进入一个令人兴奋的新时代，其中正在收集来自多个平台的生物学措施，例如神经影像学和遗传学，以帮助加深对疾病的理解并改善诊断和治疗。多维数据变得越来越普遍，并在进行心理健康研究方面拥有巨大的希望。但是，从多维数据中提取有用和互补信息的有效统计方法仍处于起步阶段。主要挑战之一是多维数据通常具有不同的量表（连续/离散），数据表示（标量/数组/矩阵）和尺寸。当前的分析方法通常在每个维度内进行单独的分析或应用简单的相关分析。这些方法对于揭示这些数据中的潜在模式和关联的性质非常有限。该项目旨在开发新颖的独立组件分析（ICA）方法，以提供有效的工具，以降低尺寸，降低和从大型多维数据中提取特征。具体而言，提出的方法将1）提供一个统一的框架，用于分解和整合多模式神经影像学数据，例如fMRI和DTI，2）提供了一个离散的ICA模型，用于从大规模分离结果中提取潜在信号，例如从单核核苷酸（SNP）基于ICA的单个模型，并提供ICA模型，并提供ICA模型，并提供ICA模型）基因型数据提取集成成像遗传学特征。拟议的统计方法将应用于重度抑郁症（MDD）研究，并将开发用户友好的软件并将其提供给一般研究社区。我们提出的方法发展将通过提供创新的统计工具直接受益于心理健康研究结合来自多维数据集的信息，这些信息可以促进诊断，加深机理理解并改善对精神障碍的治疗。我们的方法也无处不在，通常对统计实践有用。