MULTIVARIATE VOXELWISE ANALYSIS OF MULTIMODALITY IMAGING

这个子项目是许多利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 这是Imaging Core项目1的新增子项目 总体目标： 目前，大多数脑图像分析，特别是在神经退行性疾病的研究中，集中于单一成像模态，例如结构磁共振成像（sMRI）、扩散张量成像（DTI）、灌注MRI、正电子发射断层扫描（PET）、功能性MRI。不同的成像方式提供了互补的，但不一定是独立的，关于大脑的信息。新的见解可以通过同时执行几个模态的综合分析来获得。这种分析不仅能够发现模态之间的关系，而且还可以发现神经退行性疾病的影响，在任何一个或可能几个modales.The项目的目标是开发一个通用的统计方法，可以用来分析几个成像模态同时，以增加发现疾病的局部特征的统计能力，同时也揭示了这些模式之间以及大脑中不同位置之间的关系。为此目的，我们假设成像数据是作为一组共同注册的标量图像从一些主题，并对应于各种成像方式。这些图像可能是从应用于sMRI的TBM获得的体积扩张/收缩、从灌注MRI获得的血流测量结果以及从DTI获得的分数各向异性等标量摘要。 该分项目的具体目标如下。 目的1：开发一种多元统计方法，用于测试疾病状态对每个体素同时进行多模态成像的影响。这包括： a）、 单变量和多变量回归方法的比较 B） 通过模拟进行性能评估 目的2：开发一种多变量统计方法，用于测试疾病状态对 多模态成像同时在不同的体素。这包括： a）、 互相关分析与典型相关分析的比较 B） 通过模拟进行性能评估 目的3：在R软件中实现上述方法，并将其应用于ADNI数据库。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This is an added subproject to Imaging Core, Project 1 Overall Goal: Currently, most brain image analyses, particularly in the study of neurodegenerative diseases concentrate on a single imaging modality, e.g. structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), perfusion MRI, positron emission tomography (PET), functional MRI. Different imaging modalities provide complementary, but not necessarily independent, information about the brain. New insights may be obtained by performing an integrated analysis of several modalities simultaneously. Such analysis not only enables the discovery of relationships between the modalities, but also allows the discovery of neurodegenerative effects in either one or perhaps several modalities simultaneously.The goal of this project is to develop a general statistical methodology that can be used to analyze several imaging modalities simultaneously in order to increase the statistical power of finding localized characteristics of disease, as well as revealing relationships between the modalities and between different locations in the brain. For this purpose, we assume that the imaging data is given as a set of co-registered scalar images from a number of subjects and corresponding to various imaging modalities. These images may be, among others, volume expansion/contraction obtained from TBM applied to sMRI, blood flow measurements obtained from perfusion MRI, and scalar summaries such as fractional anisotropy obtained from DTI. The specific aims of this subproject are the following. Aim 1: Develop a multivariate statistical methodology for testing the effect of disease status on multimodality imaging simultaneously at each voxel. This includes: a) Comparison of univariate and multivariate regression approaches b) Performance evaluation via simulations Aim 2: Develop a multivariate statistical methodology for testing the effect of disease status of multimodality imaging simultaneously at different voxels. This includes: a) Comparison of cross-correlation analysis and canonical correlation analysis b) Performance evaluation via simulations Aim 3: Implementation of the above methods in the R software and apply them to the ADNI data base.