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: 10028103
• 负责人: Virendra R Mishra
• 金额: $ 76.25万
• 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/10028103
• 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.

项目概要 拟议研究的目标是确定帕金森病痴呆的最佳预测生物标志物 (PDD) 通过利用神经影像衍生测量的多模式和多变量统计模型 （弥散加权 MRI (dMRI)、静息态功能 MRI (rsfMRI) 和 T1 加权 MRI 测量）和非 影像学测量，例如人口统计（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 (LRRK2) 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) 中的痴呆症，因为大约 50-80% 的 PD 患者会出现 PDD 诊断后十二年内确定可识别帕金森病患者的基于病理生理学的生物标志物。 PDD 高风险患者的可靠诊断对于更好的预后、正确识别 PDD 的前驱期至关重要 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 PDD 的流程有两个重要组成部分。 该项目旨在通过复杂的体素分析来了解 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， 并了解网络级白质 (WM) 衍生的结构连接和 rsfMRI 衍生的功能 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神经影像来建立多元统计模型 我们将使用非影像学测量（例如临床、人口统计学、遗传学和脑脊液测量）进行测量。 招募人口统计上匹配的健康对照（HC）以及人口统计、疾病持续时间和 疾病严重程度与轻度认知障碍 (PD-MCI)、PD-非 MCI (PD-nMCI) 和 PD 患者相匹配 本项目的 PDD。我们将获取三个 b 值的多壳 dMRI 数据，即 500s/mm2、1000s/mm2 和 2500s/mm2，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 衍生测量以及非高斯 dMRI 衍生测量（例如扩散峰度） 我们将进一步比较这些测量。 组之间的测量，以确定区分组的显着 dMRI 衍生测量，以及 了解这些测量值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 这些与病理相关的神经影像学测量的网络水平差异。 将通过新颖的机器学习算法进一步与各种非成像测量相结合 我们的提案中开发的工具也具有很好的识别 PDD 的全面和最佳预测能力。 显着促进对其他神经退行性疾病（例如阿尔茨海默病）的理解的潜力 疾病（AD），从而有助于了解预测痴呆症的 AD 和 PD 特异性神经解剖学变化。