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

• 批准号: 9110314
• 负责人: Ying Guo
• 金额: $ 38.72万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-25 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9110314
• 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; Health; 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; imaging genetics; improved; independent component analysis; infancy; innovation; insight; interest; method development; neuroimaging; novel; tool; treatment response; user friendly software

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(申请人提供)：精神障碍的研究已经进入了一个令人兴奋的新时代，正在收集来自多个平台的生物测量，如神经成像和遗传学，以帮助加深对精神障碍的理解，改善诊断和治疗。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的希望。然而，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，多维数据往往具有不同的尺度(连续/离散)、数据表示(标量/数组/矩阵)和维度。目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。这些方法在揭示这些数据中的潜在模式和关联方面的性质非常有限。该项目旨在开发新的统计独立分量分析(ICA)方法，为从大规模多维数据中降维、去噪和提取特征提供有效的工具。具体地说，提出的方法将1)提供一个统一的框架来分解和集成多模式神经成像数据，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，例如单核苷酸多态(SNP)基因数据；以及3)提供一个联合ICA模型，用于同时分解神经成像和SNP基因数据，以提取集成的成像遗传学特征。拟议的统计方法将应用于一项严重抑郁障碍(MDD)的研究，并将开发用户友好的软件，供一般研究社区使用。我们建议的方法开发将通过提供创新的统计工具直接惠及心理健康研究 结合来自多维数据集的信息，以便于诊断，加深对机制的理解，并改善精神障碍的治疗。我们的方法也是无处不在的，对统计实践普遍有用。