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

• 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
• 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

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.

项目摘要/摘要 帕金森病（Parkinson's disease，PD）是一种导致患者运动功能异常的神经退行性疾病 等功能目前PD的临床诊断不能达到预期的准确性。成像 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。我们的合作研究者 开发了一种高空间分辨率的弥散MRI，它可以大大提高空间分辨率， 减少几何失真。我们的合作研究者进行的先驱研究确定了左侧的变化 右利手帕金森病患者的黑质然而，数据的潜力尚未得到充分发挥。 这是因为缺乏适当的统计方法来处理这种新的数据。这项建议旨在 开发新的统计和计算工具，通过参数识别成像生物标志物， 时间随机游走（CTRW）模型使用高分辨率MRI数据。现金转拨报告的现有统计方法 模型没有利用高分辨率数据的优势。建议的统计方法将执行高 维推断，以整合来自MRI数据中的大量像素的信息，以实现 这是传统的低维方法无法达到的。该提案有两个具体的 目标（1）：利用高空间分辨率，建立CTRW模型的高维统计推断方法 （2）结合患者的临床特征，如病程， 神经学测试评分和相关生物学变量，如年龄和性别，以及成像生物标志物， 提高了PD诊断的准确性。所开发的统计方法将应用于扩散MRI数据 共同研究者及其合作者收集的PD患者集。用户友好的软件和 计算工具将提供给公众使用。