Cognitive Heterogeneity in those with high Alzheimer's Disease Risk

阿尔茨海默病高风险人群的认知异质性

• 批准号: 9975371
• 负责人: Rhoda Au
• 金额: $ 153.27万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9975371
• 关键词: Age; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-Protein; Apolipoprotein E; Blood Vessels; Brain; Brain imaging; Clinical; Cognition; Cognitive; Cognitive aging; Collection; Communities; Data; Databases; Dementia; Detection; Diagnosis; Disease; Elderly; Female; Framingham Heart Study; Future; Generations; Genotype; Goals; Health; Heterogeneity; Hippocampus (Brain); Image; Impaired cognition; Individual; Intervention; Learning; Link; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Masks; Measurement; Measures; Memory Loss; Methods; Modeling; National Institute on Aging; Nerve Degeneration; Neuropsychology; Noise; Participant; Pathology; Performance; Plasma; Positron-Emission Tomography; Reporting; Research Priority; Resources; Risk; Risk Factors; Structure; Subgroup; Symptoms; Time; Update; White Matter Hyperintensity; Work; apolipoprotein E-4; base; cardiovascular risk factor; cerebral atrophy; cognitive ability; cognitive performance; cohort; comorbidity; critical period; digital; disorder risk; effective therapy; high risk; high risk population; hippocampal atrophy; imaging biomarker; improved; in vivo; indexing; longitudinal database; machine learning method; magnetic resonance imaging biomarker; network models; neuroimaging; neuroimaging marker; novel; pre-clinical; prognostic; prospective; response; sex; tau Proteins; virtual

The lack of an effective treatment for Alzheimer's disease (AD) has led to a call to detect the disease earlier in its course but AD's insidious onset that can span many years, adds complexity to doing so. As a result, the National Institute on Aging (NIA) has identified to understand the heterogeneity of AD, particularly at the asymptomatic stages as a research priority. While there are well-documented high AD risk factors (e.g., age, apolipoprotein E4, cardiovascular risk, amyloid and tau pathology), diagnosis is not inevitable, but it remains unknown why only some of those with high AD risk progress to disease and others do not. We contend that one challenge for answering this question is that by the time traditional AD preclinical symptoms of memory decline and/or hippocampal atrophy emerge, the neurodegenerative trajectory is already on a near irreversible course. We further hypothesize that traditional measurement methods produce crude measures that mask the broader range of clinical expression in the preclinical period and preclude the earliest opportunity to detect the beginning of the neurodegenerative trajectory. In this updated application, we seek to leverage the Framingham Heart Study (FHS) cognitive aging and dementia database, acquired through nearly 7 decades of prospective examination. Unique to FHS since 2005 has been the collection of novel NP indices (error responses, digital metrics such as item-level latencies, fragmented responses). Baseline data were collected at a time when the vast majority of these participants appeared asymptomatic, including those who are at high AD risk, a subset of which have since progressed to incident AD as well as similarly high AD risk subgroups who did not. Through a one year R56, we provide new preliminary data in support of our aims to 1) characterize the cognitive heterogeneity of these high AD risk groups as they do and do not progress to disease, 2) determine whether traditional neuroimaging biomarkers differentiate between progressors and non- progressors and 3) develop novel machine learning methods to identify neuroimaging indices even earlier than traditional MRI measures. We predict that with additional analyses we will identify unique cognitive profiles that better differentiate those at high AD risk who do and do not progress to AD, that the NP profiles of high AD risk progressors will be associated with AD neuroimaging markers (e.g. decline in total brain and hippocampal volume, increase in white matter hyperintensities) while the NP profiles of high AD risk non-progressors will not show similar evidence of brain structure changes. We will further build on our preliminary work of developing an adversarial learning framework to enhance baseline MRI images to serve as better predictors of high AD risk progressor and non-progressor groups than the original images. Results will lead to identification of a broader spectrum of preclinical presentation in those with high AD risk than has been previously recognized and thus better characterize the heterogeneity of NP performance, particularly earlier in the disease course, potentially identifying a critical period in which intervention strategies can mitigate disease risk.

由于阿尔茨海默病(AD)缺乏有效的治疗方法，导致人们呼吁早些时候发现这种疾病 它的过程，但AD的隐匿性发病可能跨越多年，增加了这一点的复杂性。因此， 国家老龄研究所(NIA)已确定了解AD的异质性，特别是在 将无症状期作为研究重点。虽然有充分记录的高AD风险因素(例如，年龄， 载脂蛋白E4，心血管风险，淀粉样蛋白和tau病理)，诊断不是不可避免的，但它仍然 不知道为什么只有一些AD风险高的人会进展为疾病，而其他人则不会。我们认为 回答这个问题的一个挑战是，当传统的阿尔茨海默病临床前症状记忆 下降和/或出现海马区萎缩，神经退行性变化的轨迹已经近乎不可逆转 当然了。我们进一步假设，传统的测量方法产生的粗略测量掩盖了 临床前期的临床表现范围更广，排除了最早检测到 神经退化轨迹的开始。在此更新的应用程序中，我们寻求利用 弗雷明翰心脏研究(FHS)认知老化和痴呆症数据库，通过近70年的 前瞻性考试。自2005年以来，FHS唯一的是收集新的NP指数(错误 回复、数字指标，如项目级延迟、碎片化回复)。基准数据收集于 当时，这些参与者中的绝大多数似乎没有症状，包括那些处于兴奋状态的人 AD风险，其子集已发展为事件AD以及类似高AD风险子组 谁没有呢。通过一年的R56，我们提供新的初步数据来支持我们的目标1) 描述这些AD高危人群的认知异质性，因为他们已经和不会进展到 疾病，2)确定传统的神经成像生物标记物是否区分进展者和非进展者 和3)开发新的机器学习方法来识别神经成像指标，甚至比 传统的MRI检查方法。我们预测，通过额外的分析，我们将识别出独特的认知特征， 更好地区分高AD风险人群，谁进展为AD，谁不进展为AD，高AD风险的NP特征 进展者将与阿尔茨海默病的神经成像标志物(例如，全脑和海马体的下降)相关 体积，白质高信号增加)，而高危AD非进展者的NP谱不会 显示出类似的大脑结构变化的证据。我们将在前期工作的基础上，进一步发展 增强基线MRI图像以更好地预测高AD的对抗性学习框架 风险进展者和非进展者组比原始图像。结果将导致识别一种 阿尔茨海默病高风险人群的临床前表现比以前认识到的更广泛 从而更好地表征NP表现的异质性，特别是在疾病过程的早期， 潜在地确定干预策略可以降低疾病风险的关键时期。