MRI Signatures of Neuromelanin and Iron Pathology in Parkinsonism

帕金森病神经黑色素和铁病理学的 MRI 特征

基本信息

批准号：
10474357
负责人：
Daniel Huddleston
金额：
$ 19.26万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-30 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10474357
关键词：
Affect Autopsy Award Biological Markers Brain Clinic Clinical Clinical Research Clinical Trials Clinical Trials Design Clinical assessments Custom Data Data Science Deep Brain Stimulation Dentate nucleus Deposition Development Diagnosis Differential Diagnosis Disease Environment Freezing Future Gait Globus Pallidus Goals Image Image Analysis Individual Iron Lateral Lead Machine Learning Magnetic Resonance Imaging Measurement Measures Medial Mentors Meta-Analysis Methods Modality Monitor Movement Disorder Society Unified Parkinson&apos s Disease Rating Scale Movement Disorders Multiple System Atrophy Nerve Degeneration Neurodegenerative Disorders Neurology Operative Surgical Procedures Outcome Output Parkinson Disease Parkinsonian Disorders Pathology Patients Pattern Predisposition Process Productivity Progressive Supranuclear Palsy Public Health Questionnaires Red nucleus structure Reproducibility Research Research Personnel Structure Structure of subthalamic nucleus Subjects Selections Substantia nigra structure System Therapeutic Training Treatment outcome Universities Visit Work analysis pipeline automated analysis base candidate marker career classification algorithm clinical diagnosis clinical diagnostics cost design diagnostic accuracy diagnostic tool disorder control effective therapy follow-up image processing imaging modality imaging system improved improved outcome in vivo innovation interest locus ceruleus structure machine learning classification machine learning classifier magnetic resonance imaging biomarker neuroimaging marker neuromelanin neuron loss nigrostriatal system novel pars compacta patient screening putamen recruit relating to nervous system skills success tool

项目摘要

Parkinson’s disease (PD), multiple system atrophy parkinsonian type (MSA-P), and progressive supranuclear palsy (PSP) are costly and devastating neurodegenerative diseases. They have overlapping clinical manifestations and diagnosis remains challenging in many cases. Thus far no effective treatments have been developed to meaningfully slow or stop their progression. This is due in part to a lack of tools to objectively measure degeneration in the neural systems affected by each of these diseases. Availability of such tools would assist clinical diagnosis, facilitate selection of appropriate patient groups for trial recruitment, and enable objective measurement of treatment outcomes. Recent MRI studies suggest that disease-specific brain changes can, indeed, be identified in these parkinsonian diseases. The objective of this research is to identify univariable markers and multivariable MRI signatures that capture distinct patterns of neurodegenerative change across neural systems to accurately distinguish these diseases. To accomplish this the investigators use 3 Tesla MRI contrasts sensitive to key features of neurodegeneration to 1) identify structures damaged by PD, MSA-P and PSP and 2) to quantify the extent of damage in each neural system in parkinsonian diseases. Specifically, the investigators use neuromelanin- sensitive MRI to measure neuromelanin loss and quantitative susceptibility mapping (QSM) and R2* imaging to measure iron accumulation in patients with PD, MSA-P, and PSP. Using these contrasts and an innovative region of interest (ROI) selection approach, they reproducibly measure patterns of neurodegenerative change across neural systems. In Aim 1 the investigators use neuromelanin-sensitive MRI to study neuromelanin loss in PD, MSA-P and PSP in to identify univariable disease features that are differentially affected by parkinsonian diseases and may assist distinguishing these conditions. In Aim 2 they use QSM and R2* MRI to study ROIs differentially impacted by PD, MSA-P and PSP to identify iron accumulation biomarkers to help distinguish these diseases. In Aim 3 the investigators apply machine learning classification algorithms to identify multivariable MRI signatures of neurodegenerative change across neural systems to differentiate PD, MSA-P and PSP. Study outputs will be candidate MRI biomarkers and disease signatures. The long term goal of this research is to further develop these outputs for use as clinical diagnostic tools and as biomarkers for subject selection and outcome measurement in clinical trials. Through this career award Dr. Huddleston will gain new skills in MRI methods, data science, and neural systems imaging in parkinsonian diseases. These new skills will enable Dr. Huddleston to design and lead interdisciplinary neuroimaging biomarker studies for Parkinson’s disease and related disorders. His mentor team is comprised of leaders in their fields. This team and the dynamic research environment at Emory University provide the necessary support for Dr. Huddleston to successfully transition to scientific independence.

帕金森氏病（PD），多系统萎缩帕金森氏症类型（MSA-P）和进行性超级麻痹（PSP）是昂贵且破坏性的神经退行性疾病。他们有重叠的临床在许多情况下，表现和诊断仍然是挑战。那没有有效的治疗开发有意义地放慢或停止其进展。这部分是由于缺乏客观性的工具测量受每种疾病影响的神经系统的变性。此类工具的可用性将协助临床诊断，促进选择合适的患者群体进行试用招募，并启用治疗结果的客观测量。最近的MRI研究表明疾病特异性的大脑变化确实可以在这些帕金森氏病中确定。这项研究的目的是确定单变量标记和多变量MRI签名，可捕获整个神经退行性变化的不同模式神经系统准确区分这些疾病。为了实现这一目标，调查人员使用3个特斯拉MRI对比敏感神经变性为1）确定受PD，MSA-P和PSP损坏的结构以及2）量化的程度帕金森病疾病中每个神经元系统的损害。具体而言，研究人员使用神经丙氨酸 - 敏感的MRI测量神经元素损失和定量敏感性映射（QSM）和R2*成像测量PD，MSA-P和PSP患者的铁积累。使用这些对比和创新区域感兴趣的选择方法，它们可重复地测量整个神经退行性变化的模式神经系统。在AIM 1中，研究人员使用神经素敏感的MRI研究PD中的神经苯胺损失， MSA-P和PSP识别受帕金森氏症不同影响的单变量疾病特征疾病，可能有助于区分这些情况。在AIM 2中，他们使用QSM和R2* MRI研究ROI 受PD，MSA-P和PSP的差异影响，以识别铁积累生物标志物，以帮助区分这些生物标志物疾病。在AIM 3中，调查人员应用机器学习分类算法来识别多变量MRI 神经元系统之间神经退行性变化的特征，以区分PD，MSA-P和PSP。学习产出将是候选MRI生物标志物和疾病特征。这项研究的长期目标是进一步开发这些输出作为临床诊断工具，并用作主题选择和结果的生物标志物临床试验中的测量。通过这个职业奖，哈德勒斯顿博士将获得MRI方法，数据科学和中立的新技能帕金森病中的系统成像。这些新技能将使Huddleston博士能够设计和领导跨学科的神经影像学研究帕金森氏病和相关疾病。他的心理团队由他们领域的领导者组成。这个团队和埃默里大学的动态研究环境为Huddleston博士提供必要的支持，以成功地过渡到科学独立性。