Model-based cerebrovascular markers extracted from hemodynamic data for diagnosing MCI or AD and predicting disease progression.

“从血液动力学数据中提取的基于模型的脑血管标记物， MCI或轻度AD的非侵入性、便携式和廉价的诊断以及 疾病进展” 项目摘要 建议的多PI项目的目标是建立一个新的实用程序的概念证明 这类脑血管标志物可能有助于改善诊断和预测 轻度认知障碍（MCI）和轻度阿尔茨海默病（AD）的疾病进展。 用于获得这些标记的手段是非侵入性的、廉价的和便携的，使得它们 可用于初级保健环境中的筛查。这种新的分类的科学原理 脑血管标志物是由我们小组最近的有希望的结果提供的， 越来越多的证据表明MCI/AD和脑血管失调之间存在强相关性。 最近发表的一项对来自ADNI的1，171名受试者的大型队列的回顾性研究 数据库利用多因素数据驱动分析来评估MCI/AD之间的关系 疾病进展和常用生物标志物（从MRI/PET和血浆/CSF中获得） 并得出结论，脑血管失调是最早和最强烈的病理性 与AD进展相关的因素，证实了脑血管疾病的假设 失调 在这项大队列研究中，通过以下方法实现了脑血管失调的量化： 分析ASL-MRI脑灌注数据。我们建议写一部小说 综合动态建模方法，分析脑血流动力学的 没有认知障碍的人和MCI/AD患者使用产生输入的方法- 输出预测模型的动态关系之间的变化，心跳，心跳脑 血流速度（经颅多普勒）或脑组织氧合（近红外 光谱），以响应动脉血压和呼气末CO2数据的变化。的 所获得的基于数据的模型随后用于计算 脑血管调节所倡导的方法已取得初步成果 46名MCI患者和20名年龄匹配的对照者之间的统计学显著性描绘， 基于模型的动态血管反应性（DVR）标志物的基础。评价 DVR标志物与已建立的基于MRI和基于PET的生物标志物，以及 神经心理学测试数据，从更大的队列的拟议项目提供了承诺 便携式，非侵入性，廉价和灵敏的手段来检测脑血管 在MCI或轻度AD的早期阶段的失调，并监测疾病进展。 本研究的重要协变量包括年龄、性别、教育、ApoE基因型、研究中心和 淀粉样蛋白负荷。

"Model-based cerebrovascular markers extracted from hemodynamic data for non-invasive, portable and inexpensive diagnosis of MCI or mild AD and prediction of disease progression" PROJECT SUMMARY The goal of the proposed multi-PI project is to establish proof of concept for the utility of a new class of cerebrovascular markers that may aid in the improved diagnosis and prediction of disease progression in Mild Cognitive Impairment (MCI) and mild Alzheimer's disease (AD). The means for obtaining these markers are non-invasive, inexpensive and portable, so that they can be used for screening in a primary-care setting. The scientific rationale for this new class of cerebrovascular markers is provided by the recent promising results of our group and the mounting evidence of a strong correlation between MCI/AD and cerebrovascular dysregulation. A recently published retrospective study on a large cohort of 1,171 subjects from the ADNI database utilized multi-factorial data-driven analysis to assess the relation between MCI/AD disease progression and commonly used biomarkers (obtained from MRI/PET and plasma/CSF) and concluded that cerebrovascular dysregulation is the earliest and strongest pathologic factor associated with AD progression, corroborating the hypothesis of cerebrovascular dysregulation. Quantification of cerebrovascular dysregulation in that large-cohort study was achieved through analysis of ASL-MRI data of cerebral perfusion. We propose instead to explore a novel integrative dynamic modeling approach that analyzes the cerebral hemodynamics of persons with no cognitive impairment and MCI/AD patients with a methodology that yields input- output predictive models of the dynamic relationships between changes in beat-to-beat cerebral blood flow velocity (via Transcranial Doppler) or cerebral tissue oxygenation (via Near Infrared Spectroscopy) in response to changes in arterial blood pressure and end-tidal CO2 data. The obtained data-based models are subsequently used to compute markers of the dynamics of cerebrovascular regulation. Initial results of the advocated approach have achieved statistically significant delineation between 46 MCI patients and 20 age-matched controls on the basis of a model-based marker of dynamic vasomotor reactivity (DVR). Evaluation of the DVR marker against established MRI-based and PET-based biomarkers, as well as neuropsychological test data, from the larger cohort of the proposed project offers the promise of portable, non-invasive, inexpensive and sensitive means for detecting cerebrovascular dysregulation at the early stages of MCI or mild AD, and monitoring disease progression. Important co-variates of this study include age, gender, education, ApoE genotype, site and amyloid burden.