Statistical Methods for Biomarkers Identification Using High-resolution Diffusion MRI

使用高分辨率扩散 MRI 识别生物标志物的统计方法

基本信息

批准号：
10667994
负责人：
Ping-Shou Zhong
金额：
$ 8万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10667994
关键词：
Age Biological Calculi Characteristics Clinical Clinical Trials Collaborations Complex Data Data Set Diagnosis Diffusion Diffusion Magnetic Resonance Imaging Dimensions Disease Early Diagnosis Evaluation Exhibits Functional Imaging Goals Health Heterogeneity Human Intervention Joints Knowledge Left Magnetic Resonance Imaging Methods Mission Modeling Molecular Monitor Movement Nature Nerve Degeneration Neurodegenerative Disorders Neurologic Normal Statistical Distribution Organism Parkinson Disease Patients Physiologic pulse Procedures Relaxation Research Research Personnel Resolution Series Side Software Tools Statistical Methods Statistical Models Structure Substantia nigra structure Techniques Testing Time Tissues United States National Institutes of Health Wait Time Walking Water biomarker identification brain magnetic resonance imaging brain tissue clinical diagnosis computerized tools design diagnostic accuracy disability disease diagnosis expectation flexibility high dimensionality illness length imaging biomarker improved innovation insight model building neuropathology novel radio frequency sex simulation structural imaging tool development two-dimensional user friendly software water diffusion

项目摘要

PROJECT SUMMARY/ABSTRACT Parkinson's disease (PD) is a neurodegenerative disease that leads to the abnormalities of patients' movement and other functions. The current clinical diagnosis of PD cannot accomplish the desired accuracy. Imaging biomarkers of PD has shown great promising in improving the diagnosis accuracy. Our co-investigators developed a high spatial resolution diffusion MRI, which can improve the spatial resolution substantially and diminish geometric distortion. Pioneer studies conducted by our co-investigators identified changes in left side of substantia nigra of right-handed patients with PD. However, the potential of the data has not been fully realized due to the lack of appropriate statistical methods for this new type of data. This proposal aims to develop new statistical and computational tools to identify imaging biomarkers via parameters in the continuous time random walk (CTRW) model using the high resolution MRI data. Existing statistical methods for the CTRW model did not take the advantage of the high resolution data. The proposed statistical methods will perform high dimensional inference to integrate the information from a large number of pixels in the MRI data to achieve the power that cannot be attained by conventional low dimensional methods. This proposal has two specific objectives (1 ): develop high dimensional statistical inference methods for the CTRW model using high spatial resolution diffusion MRI; (2): integrate patients' clinical characteristics, such as disease duration, and neurological test scores and relevant biological variables such as age and sex, with imaging biomarkers in improving the diagnosis accuracy for PD. The developed statistics methods will be applied to diffusion MRI data sets of PD patients collected by co-investigators and their collaborators. User friendly software and computational tools will be made available for public use.

项目概要/摘要帕金森病（PD）是一种导致患者运动异常的神经退行性疾病和其他功能。目前PD的临床诊断还不能达到预期的准确性。影像学 PD的生物标志物在提高诊断准确性方面显示出巨大的前景。我们的共同研究人员开发了高空间分辨率扩散磁共振成像，可以大幅提高空间分辨率，减少几何变形。我们的联合研究人员进行的开创性研究发现了左侧的变化右利手 PD 患者的黑质。但数据的潜力尚未得到充分发挥由于缺乏针对这种新型数据的适当统计方法而实现。该提案旨在开发新的统计和计算工具，通过连续参数识别成像生物标志物使用高分辨率 MRI 数据的时间随机游走 (CTRW) 模型。 CTRW 的现有统计方法模型没有利用高分辨率数据。所提出的统计方法将表现出色维度推断，整合 MRI 数据中大量像素的信息，以实现传统低维方法无法获得的功率。该提案有两个具体内容目标 (1)：使用高空间数据为 CTRW 模型开发高维统计推断方法分辨率扩散磁共振成像； (2)：整合患者的临床特征，例如病程，以及神经学测试分数和相关生物变量，例如年龄和性别，以及成像生物标志物提高PD诊断的准确性。开发的统计方法将应用于扩散MRI数据由共同研究者及其合作者收集的 PD 患者组。用户友好的软件和计算工具将可供公众使用。