Biostatistics for Spatial and High-Dimensional Data: New Statistical Methods for Neuroimaging and Imaging Genomics

空间和高维数据的生物统计学：神经影像和影像基因组学的新统计方法

• 批准号: RGPIN-2014-06542
• 负责人: Nathoo, Farouk
• 金额: $ 1.02万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660692
• 关键词: Biostatistics; Spatial; Dimensional; Data; New

With the rapid advancement of modern biotechnology, the world is witnessing an explosion of data. This wealth of information represents an abundant resource for tackling many of the most fundamental scientific questions, for example, questions about how the brain works, or questions about how genetic variation influences the brain. Despite this, a major challenge facing the scientific community is the sheer size and complexity of modern datasets which has created a pressing need for rigorous statistical approaches in order to process and understand it. In particular, neuroimaging studies can involve large and exquisitely detailed datasets describing the anatomy, function, and connectivity of the brain; while studies in the nascent field of imaging genomics, where interest lies in discovering the genetic variants associated with the structure and function of the brain, involves a combined analysis of both brain images as well as additional data arising from high-throughput genotyping. In the latter setting, it is not unusual to conduct well over one billion statistical tests of association in a brain-wide genome-wide study. Motivated by the challenging statistical problems arising in these areas, the research proposed here will develop new methodology for spatial and spatiotemporal statistical analysis in high-dimensional settings where the number of unknown parameters is larger than the sample size.**Increasingly, state-of-the-art neuroimaging studies collect data using multiple modalities. Such studies are well-motivated by the fact that different imaging techniques such as magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) provide complimentary sources of information, and their union provides a more complete picture describing the function of the brain. Developing rigorous approaches to analyzing such data within a unified framework is made difficult by the size of the data, the number of unknown parameters of interest, and the differing spatial and temporal scales associated with each modality. Building upon recent work focussing on spatial statistics for neuroimaging data analysis, the primary objective of the research proposed here is the development of novel, useful, and computationally tractable methods and software for the analysis of complex data arising in neuroimaging and imaging genomics. Specific research goals are as follows:**(1) To develop approaches for electromagnetic brain mapping based on the combined analysis of magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) data.**(2) To harness recent developments in nonlinear dimension reduction for extracting information and solving classification problems involving MEG/EEG data.**(3) To develop new statistical models for analysis in studies of imaging genomics that effectively exploit the spatial structure of the brain imaging data when examining an extremely large number of genetic associations.**The anticipated outcomes of the research program are the development of new statistical tools that will allow researchers to better combine and analyze information from different sources, from multiple imaging modalities; from brain imaging and high-throughput genotyping; and new classification methods that can be applied to the decoding of mental states as well as to the development of imaging biomarkers of neurological disorders.

随着现代生物技术的快速发展，世界正在见证数据的爆炸性增长。这些丰富的信息为解决许多最基本的科学问题提供了丰富的资源，例如，关于大脑如何工作的问题，或者关于基因变异如何影响大脑的问题。尽管如此，科学界面临的一个主要挑战是现代数据集的巨大规模和复杂性，这就迫切需要严格的统计方法来处理和理解它。特别是，神经成像研究可能涉及描述大脑的解剖、功能和连通性的大型且精细的数据集；而成像基因组学这一新兴领域的研究涉及对大脑图像以及高通量基因分型产生的额外数据的综合分析，其中感兴趣的是发现与大脑结构和功能相关的遗传变异。在后一种情况下，在全脑全基因组研究中进行超过10亿次的相关性统计测试并不少见。在这些领域出现的具有挑战性的统计学问题的推动下，本文提出的研究将开发新的方法，用于在未知参数数量大于样本量的高维环境中进行空间和时空统计分析。**越来越多地，最先进的神经成像研究使用多种模式收集数据。这类研究的动机很好，因为不同的成像技术，如脑磁图(MEG)、脑电(EEG)和功能磁共振成像(FMRI)提供了互补的信息来源，它们的结合提供了更完整的描述大脑功能的图像。由于数据的大小、感兴趣的未知参数的数量以及与每种模式相关的不同的空间和时间尺度，很难在统一的框架内制定严格的方法来分析这些数据。在最近专注于神经成像数据分析的空间统计学工作的基础上，这里提出的研究的主要目标是开发新的、有用的和易于计算的方法和软件，用于分析神经成像和成像基因组学中出现的复杂数据。具体研究目标如下：**(1)开发基于脑磁图(MEG)、脑电(EEG)、和功能磁共振成像(FMRI)数据。**(2)利用非线性降维的最新进展，提取信息并解决涉及脑磁图/脑电数据的分类问题。**(3)开发新的统计模型，用于成像基因组研究中的分析，在检查极大量的遗传关联时，有效地利用大脑成像数据的空间结构。**研究计划的预期结果是开发新的统计工具，使研究人员能够更好地组合和分析来自不同来源、来自多种成像模式的信息；来自大脑成像和高通量基因分型的新分类方法；以及可用于解码精神状态以及开发神经疾病成像生物标记物的新分类方法。