An AI-assisted screening platform within a multivariate framework for biomarkers of mild cognitive impairment due to Alzheimer's disease

多变量框架内的人工智能辅助筛查平台,用于阿尔茨海默病引起的轻度认知障碍的生物标志物

基本信息

  • 批准号:
    10571773
  • 负责人:
  • 金额:
    $ 2.65万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-09-01 至 2024-02-29
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY In the past decade, the rate of deaths from Alzheimer's disease (AD) and other dementias escalated more than twice the rate of deaths from heart disease. Unfortunately, there is a lack of low-cost and non-invasive diagnostic instruments to accurately identify individuals at risk of AD and ADRD. Advanced non-invasive imaging shows that retinal neurodegeneration and visual deficits occur long before the cognitive decline in AD and ADRD. This fact raises the possibility of identifying mechanisms that drive retinal pathology in AD/ADRD that could help develop effective diagnostics tools and therapies that target early disease. The well-characterized organization of the retina, with powerful non-invasive imaging and electrophysiology techniques to monitor retinal function, make it an optimal surrogate to study early CNS pathology. The brain shares many similarities with the retina. This suggests that the retina, a more accessible organ than the cortex, may provide a viable brain biomarker for testing diagnostics tools and therapies that target early disease and prevention. Notably, we happen to live in a non-linear world surrounded by objects and processes with the property of fractality and non-linearity. For example, the deficit of fractal complexity (i.e., fractality) of environmental effects can lead to fractal complexity distortion in the brain's visual pathways and abnormalities of development or aging. Particularly, non-linear dynamics of physiological processes involved in neurodegenerative disorders have a strong base of evidence, which is seen in the impaired fractal regulation of rhythmic activity in aged and diseased brains. Our multivariate biomarker methodology relies on the fractal complexity of the retinal vasculature as a potential biomarker. However, the fractality of the time-varying electroretinogram (ERG) signal that arises from different retina layers is not yet explored. Therefore, we aim to take advantage of the current electrophysiological measurements acquired in the parent grant to investigate the distortion of fractal complexity in ERG signals correlated to AD pathology as a possible means to obtain a more comprehensive assessment for the early detection of MCI due to AD. In this project, we will further innovate our multivariate biomarker methodology by investigating the fractality of ERG signals. This investigation would make our novel method a more robust tool by incorporating the combined fractality of the retinal function (ERG signals) and structure (retinal vasculature), which can shed new light on early pathogenic mechanisms that compromise retinal and brain function much before the onset of detectable dementia. To this end, we will investigate the distortion of fractality in ERG signals and explore the discrimination power of ERG's fractality measurements between groups with the receiver operating characteristic curve, sensitivity, and specificity metrics. We will use the Youden index and the area under the curve will be calculated for the ERG device calculated features. This project may enable a more comprehensive assessment of aging on ocular and cerebral function at the early stage of cognitive impairment by identifying the most initial signs of complications in the eye and brain using relevant multimodal measures of ocular abnormalities.
项目总结 在过去的十年中,阿尔茨海默病(AD)和其他痴呆症的死亡率上升了超过 是心脏病死亡率的两倍。不幸的是,缺乏低成本和非侵入性的诊断 准确识别有AD和ADRD风险的个人的工具。先进的非侵入性成像显示 视网膜神经变性和视觉缺陷在AD和ADRD认知功能下降之前很久就发生了。这 FACT提高了识别AD/ADRD视网膜病理机制的可能性,这可能有助于 开发针对早期疾病的有效诊断工具和治疗方法。特色鲜明的组织 利用强大的非侵入性成像和电生理技术监测视网膜功能, 使其成为研究早期中枢神经系统病理的最佳替代物。大脑与视网膜有许多相似之处。 这表明,视网膜,一个比大脑皮层更容易接近的器官,可能为 测试针对早期疾病和预防的诊断工具和治疗方法。值得注意的是,我们碰巧住在一个 被具有分形性和非线性性质的对象和过程包围的非线性世界。为 例如,环境效应的分形性复杂性(即,分形性)不足会导致分形性复杂性 大脑视觉通路的扭曲和发育或衰老的异常。特别是,非线性 神经退行性疾病涉及的生理过程的动力学有很强的证据基础, 这体现在老年人和患病大脑节律性活动的分形性调节受损。我们的多元 生物标记物方法学依赖于视网膜血管系统的分形复杂性作为潜在的生物标记物。 然而,来自不同视网膜层的时变视网膜电信号(ERG)的分形性 还没有被探索过。因此,我们的目标是利用目前的电生理测量 在父母赠款中获得,以研究与AD相关的ERG信号中的分形复杂性的失真 病理学作为一种可能的手段,以获得更全面的评估,以早期发现MCI 到公元后。在这个项目中,我们将进一步创新我们的多变量生物标志物方法学,通过研究 视网膜电信号的分形性。这项研究将使我们的新方法成为一个更强大的工具,因为它将 视网膜功能(ERG信号)和结构(视网膜血管)的综合分形性,可脱落 对早在发病前损害视网膜和大脑功能的早期致病机制的新认识 可察觉的痴呆症。为此,我们将研究ERG信号中的分形失真,并探索 ERG分形性测量与接收器工作特性的组间分辨能力 曲线、灵敏度和特异度指标。我们将使用约登指数,曲线下的面积将是 计算用于ERG装置的计算功能。该项目可能使更全面的评估成为可能 通过识别最早期的认知损害来研究衰老对眼和脑功能的影响 使用眼部异常的相关多模式测量显示眼和脑并发症的迹象。

项目成果

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Delia Cabrera DeBuc其他文献

Delia Cabrera DeBuc的其他文献

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{{ truncateString('Delia Cabrera DeBuc', 18)}}的其他基金

An AI-assisted screening platform within a multivariate framework for biomarkers of mild cognitive impairment due to Alzheimer's disease
多变量框架内的人工智能辅助筛查平台,用于阿尔茨海默病引起的轻度认知障碍的生物标志物
  • 批准号:
    10552520
  • 财政年份:
    2021
  • 资助金额:
    $ 2.65万
  • 项目类别:
An AI-assisted screening platform within a multivariate framework for biomarkers of mild cognitive impairment due to Alzheimer's disease
多变量框架内的人工智能辅助筛查平台,用于阿尔茨海默病引起的轻度认知障碍的生物标志物
  • 批准号:
    10252098
  • 财政年份:
    2021
  • 资助金额:
    $ 2.65万
  • 项目类别:
Advanced imaging for diabetic retinopathy
糖尿病视网膜病变的先进成像
  • 批准号:
    8041768
  • 财政年份:
    2011
  • 资助金额:
    $ 2.65万
  • 项目类别:
Advanced imaging for diabetic retinopathy
糖尿病视网膜病变的先进成像
  • 批准号:
    8828207
  • 财政年份:
    2011
  • 资助金额:
    $ 2.65万
  • 项目类别:
Advanced imaging for diabetic retinopathy
糖尿病视网膜病变的先进成像
  • 批准号:
    8607953
  • 财政年份:
    2011
  • 资助金额:
    $ 2.65万
  • 项目类别:
Advanced imaging for diabetic retinopathy
糖尿病视网膜病变的先进成像
  • 批准号:
    8444056
  • 财政年份:
    2011
  • 资助金额:
    $ 2.65万
  • 项目类别:
Advanced imaging for diabetic retinopathy
糖尿病视网膜病变的先进成像
  • 批准号:
    8415887
  • 财政年份:
    2011
  • 资助金额:
    $ 2.65万
  • 项目类别:
Advanced imaging for diabetic retinopathy
糖尿病视网膜病变的先进成像
  • 批准号:
    8212080
  • 财政年份:
    2011
  • 资助金额:
    $ 2.65万
  • 项目类别:
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