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

基本信息

  • 批准号:
    10754743
  • 负责人:
  • 金额:
    $ 52万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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.
项目总结 这项拟议的研究的目标是确定帕金森病痴呆的最佳预测生物标记物 (PDD)通过使用两种神经成像衍生测量的多模式和多变量统计模型 (扩散加权磁共振成像(DMRI)、静息状态功能磁共振成像(RsfMRI)和T1加权磁共振成像测量)和非 成像测量,如人口统计(年龄、性别、教育年限)、临床(病程和严重程度)、 遗传学(LRRK2)和脑脊液指标(总牛磺酸、β-淀粉样蛋白、α-突触核蛋白)。识别生物标志物是至关重要的 这可以预测帕金森病(PD)中的痴呆症,因为大约50%-80%的PD患者会发展成PDD 在确诊后的12年内。确定可以识别帕金森病患者的基于病理生理学的生物标志物 可靠的PDD高危人群是更好地预测、正确识别PDD的前驱症状的关键 阶段招募新的疾病修改临床试验,并更好地了解病理生理学 在PDD下划线的过程。拟议的项目有两个重要组成部分。的第一个组件 项目是通过复杂的体素来了解PDD背后的病理生理机制 使用多壳高角度和空间分辨率的dMRI数据采集估计的dMRI衍生测量, 以及了解网络级白质(WM)衍生的结构连通性和rsfMRI衍生的功能 PDD中的连接性。该项目的第二个组成部分是识别预测PDD的生物标志物 结合这些复杂的病理相关神经成像的多变量统计模型 使用非影像测量方法(如临床、人口学、遗传学和脑脊液测量)。我们会 招募人口学上匹配的健康对照(HC)以及人口学上的、病程和 疾病严重程度与轻度认知障碍的PD患者(PD-MCI)、PD-非MCI(PD-NMCI)匹配,以及 此项目的PDD。我们将以三个b值,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派生测量和非高斯dMRI派生测量,例如扩散峰度 轴突定位弥散和密度成像测量方法。我们将进一步比较这些 组之间的措施,以确定区分组的显著dMRI派生的措施,以及 了解这些测量与不同神经心理学评分之间的神经解剖学相关性。 此外,我们将估计dmri衍生的结构连通性和rsfmri衍生的功能连通性,以 了解预测PDD的网络级别差异。这些与病理相关的神经成像措施 将通过一种新颖的机器学习算法进一步结合各种非成像措施来 确定PDD的综合和最佳预测因素。在我们的提案中开发的工具也具有很好的 显著提高对阿尔茨海默氏症等其他神经退行性疾病的认识的潜力 疾病(AD),从而有助于了解AD和PD特定的神经解剖学变化预测痴呆。

项目成果

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Virendra R Mishra其他文献

Virendra R Mishra的其他文献

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{{ truncateString('Virendra R Mishra', 18)}}的其他基金

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
  • 财政年份:
    2020
  • 资助金额:
    $ 52万
  • 项目类别:
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
  • 财政年份:
    2020
  • 资助金额:
    $ 52万
  • 项目类别:

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