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：
9282512
负责人：
Ying Guo
金额：
$ 43.73万
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 2020-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9282512
关键词：
Address Behavior Behavioral Sciences Biological Brain Clinical Research Cognition Communities Complex Data Data Reporting 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 high dimensionality imaging genetics improved independent component analysis infancy innovation insight interest method development multimodality 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）：精神疾病的研究已经进入了一个令人兴奋的新时代，来自神经影像和遗传学等多个平台的生物测量值正在被收集，以帮助加深对疾病的理解并改进诊断和治疗。多维数据正变得越来越普遍，并为推进心理健康研究带来了巨大希望。然而，从多维数据中提取有用和补充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尺度（连续/离散）、数据表示（标量/数组/矩阵）和维度。当前的分析方法通常在每个维度内进行单独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来说非常有限。该项目旨在开发新颖的统计独立成分分析（ICA）方法，为大规模多维数据降维、去噪和提取特征提供有效的工具。具体来说，所提出的方法将1）提供一个统一的框架来分解和整合多模态神经影像数据，如fMRI和DTI，2）提供一个离散的ICA模型，用于从大规模离散结果中提取潜在信号，如单核苷酸多态性（SNP） ) 基因型数据，3) 提供联合 ICA 模型，用于同时分解神经影像和 SNP 基因型数据，以提取综合影像遗传学特征。拟议的统计方法将应用于重度抑郁症（MDD）研究，并且将开发用户友好的软件并向一般研究团体提供。我们提出的方法开发将通过提供创新的统计工具直接有利于心理健康研究结合来自多维数据集的信息，可以促进诊断、加深对机制的理解并改善精神障碍的治疗。我们的方法也足够普遍，对统计实践普遍有用。