Towards Generating a Multimodal and Multivariate Classification Model from Imaging and Non-Imaging Measures for Accurate Diagnosis and Monitoring of Dementia in Parkinsons disease.

从影像学和非影像学测量中生成多模式和多变量分类模型，以准确诊断和监测帕金森病痴呆。

• 批准号: 10241526
• 负责人: Virendra R Mishra
• 金额: $ 75.58万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10241526
• 关键词: Age; Alzheimer' s Disease; Amyloid beta-Protein; Anatomy; Biological Markers; Brain; Classification; Clinical; Clinical Trials; Cognition; Complex; Data; Dementia; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Education; Fiber; Functional Magnetic Resonance Imaging; Functional disorder; Gaussian model; Genetic; Goals; Image; Impaired cognition; LRRK2 gene; Light; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methods; Modality; Modeling; Monitor; Neurites; Neurodegenerative Disorders; Neuropsychology; Parkinson Disease; Parkinson' s Dementia; Pathologic; Patients; Pattern; Physiological Processes; Process; Public Health; Research; Resolution; Rest; Risk; Severity of illness; Statistical Models; Structure; Validation; Variant; Water; accurate diagnosis; alpha synuclein; base; cognitive impairment in Parkinson' s; cohort; data acquisition; demographics; density; disorder subtype; gray matter; high risk; illness length; machine learning algorithm; mild cognitive impairment; multimodality; neuroimaging; non-Gaussian model; novel; pre-clinical; predictive marker; prion-like; prognostic; progression marker; rapid eye movement; recruit; sex; tau Proteins; tool; transmission process; white matter; white matter change

PROJECT SUMMARY The goal of the proposed research is to identify the best predictive biomarkers of dementia in Parkinson’s disease (PDD) through a multimodal and multivariate statistical model utilizing both neuroimaging derived measures (diffusion-weighted MRI (dMRI), resting-state functional MRI (rsfMRI), and T1-weighted MRI measures) and non- imaging measures such as demographics (age, sex, years of education), clinical (disease duration and severity), genetics (LRRK2), and CSF-measures (Total Tau, β-Amyloid, α-synuclein). It is critical to identify biomarkers that can predict dementia in Parkinson’s disease (PD) as approximately 50-80% of PD patients develop PDD within twelve years of diagnosis. Identifying pathophysiology-based biomarkers that could identify PD patients at high risk for PDD reliably is critical for better prognostication, correct identification of PDD in its prodromal stage to recruit in new disease-modifying clinical trials, and better understanding the pathophysiological processes underlining PDD. The proposed project has two important components. The first component of the project is to understand the pathophysiological mechanism underlying PDD through sophisticated voxelwise dMRI-derived measures estimated using a multi-shell high angular and spatial resolution dMRI data acquisition, and understanding network-level white matter (WM)-derived structural connectivity and rsfMRI-derived functional connectivity in PDD. The second component of the project is to identify the biomarkers that predict PDD through multivariate statistical modelling by combining these sophisticated pathologically relevant neuroimaging measures with non-imaging measures (such as clinical, demographics, genetics, and CSF-measures). We will recruit demographically matched healthy controls (HC) along with demographically, disease duration, and disease severity matched PD patients with mild cognitive impairment (PD-MCI), PD-non-MCI (PD-nMCI), and PDD for this project. We will acquire multi-shell dMRI data at three b-values, namely 500s/mm2, 1000s/mm2, and 2500s/mm2 with a high angular and spatial resolution and estimate various unbiased free-water (fiso) corrected Gaussian dMRI-derived measures along with non-Gaussian dMRI-derived measures such as diffusion kurtosis measures, and neurite orientation dispersion and density imaging measures. We will further compare these measures between the groups to identify significant dMRI-derived measures separating the groups, and understanding the neuroanatomical correlates of these measures with various neuropsychological scores. Furthermore, we will estimate dMRI-derived structural connectivity and rsfMRI-derived functional connectivity to understand network-level discrepancies predicting PDD. These pathologically relevant neuroimaging measures will be further combined with various non-imaging measures through a novel machine learning algorithm to identify the comprehensive and best predictors of PDD. The tools developed in our proposal also has great potential for significantly advancing the understanding of other neurodegenerative disorders such as Alzheimer’s disease (AD) thereby helping to understand AD- and PD-specific neuroanatomical changes predicting dementia.

项目摘要 这项研究的目的是确定帕金森病痴呆的最佳预测生物标志物 (PDD)通过多模态和多变量统计模型， （弥散加权MRI（dMRI）、静息态功能MRI（rsfMRI）和T1加权MRI测量）和非 成像测量，如人口统计学（年龄、性别、受教育年限）、临床（疾病持续时间和严重程度）， 遗传学（LRRK 2）和CSF测量（总Tau、β-淀粉样蛋白、α-突触核蛋白）。识别生物标志物至关重要 可以预测帕金森病（PD）中的痴呆，因为大约50-80%的PD患者会发展为PDD 确诊后12年内确定可识别PD患者的基于病理生理学的生物标志物 在PDD的高风险可靠是至关重要的，以更好地预测，正确识别PDD在其前驱 阶段招募新的疾病修饰临床试验，并更好地了解病理生理学 强调项目设计书的进程。拟议的项目有两个重要组成部分。的第一分量 项目是通过复杂的体素来了解PDD的病理生理机制 使用多壳高角度和空间分辨率dMRI数据采集估计的dMRI衍生测量值， 以及理解网络水平的白色物质（WM）衍生的结构连接性和rsfMRI衍生的功能性 PDD中的连接。该项目的第二个组成部分是确定预测PDD的生物标志物， 通过结合这些复杂的病理相关的神经成像， 测量与非成像测量（如临床、人口统计学、遗传学和CSF测量）。我们将 招募人口统计学匹配的健康对照（HC），沿着人口统计学、疾病持续时间和 疾病严重程度匹配的轻度认知障碍（PD-MCI）、PD-非MCI（PD-nMCI）和 这个项目的PDD。我们将在三个b值下采集多壳dMRI数据，即500 s/mm 2、1000 s/mm 2和 2500 s/mm 2，具有高角度和空间分辨率，并估计各种无偏自由水（FISO）校正 高斯dMRI导出的测量值沿着与非高斯dMRI导出的测量值（如扩散峰度） 测量，以及神经突方向分散和密度成像测量。我们将进一步比较这些 组之间的测量值，以识别区分组的重要dMRI衍生测量值，以及 了解这些测量与各种神经心理学评分的神经解剖学相关性。 此外，我们将估计dMRI衍生的结构连接和rsfMRI衍生的功能连接， 了解预测PDD的网络级差异。这些病理相关的神经影像学指标 将通过一种新的机器学习算法与各种非成像措施进一步结合， 确定PDD的全面且最佳预测因素。在我们的建议中开发的工具也有很大的 有可能显著促进对其他神经退行性疾病（如阿尔茨海默氏症）的理解 因此有助于了解AD和PD特异性神经解剖学变化预测痴呆。