Cognitive Heterogeneity in those with high Alzheimer's Disease Risk

由于缺乏有效的治疗阿尔茨海默病（AD）的方法，人们呼吁在早期发现这种疾病。 但AD的潜伏发作可能持续多年，这增加了这样做的复杂性。结果导致 国家老龄化研究所（NIA）已经确定要了解AD的异质性，特别是在 无症状阶段作为研究重点。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蛋白病理学），诊断并非不可避免，但仍 不知道为什么只有一些高AD风险的人进展为疾病，而其他人则没有。我们坚持认为 回答这个问题的一个挑战是，到了记忆的传统AD临床前症状出现的时候， 当出现衰退和/或海马萎缩时，神经退行性疾病的轨迹已经处于几乎不可逆的状态。 当然了我们进一步假设，传统的测量方法产生的粗糙的措施，掩盖了 在临床前阶段的临床表达范围更广，并排除了最早检测 神经退行性疾病的开始在这个更新的应用程序中，我们寻求利用 心脏研究（FHS）认知老化和痴呆数据库，通过近70年的 前瞻性检查自2005年以来，FHS的独特之处在于收集了新的NP指数（错误 响应、数字度量，例如项目级延迟、碎片化响应）。基线数据收集于 当时绝大多数参与者都没有出现症状，包括那些处于高血糖状态的人。 AD风险，其中一个亚组已进展为AD事件以及类似的高AD风险亚组 谁没有。通过为期一年的R56，我们提供了新的初步数据，以支持我们的目标： 描述这些AD高危人群的认知异质性，因为他们确实和不进展到 疾病，2）确定传统的神经成像生物标志物是否区分进展者和非进展者， 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。我们预测，通过进一步的分析，我们将确定独特的认知特征， 更好地区分那些有AD高风险的人，他们是否进展为AD， 进展者将与AD神经影像学标志物（例如，全脑和海马神经元的下降）相关。 体积，白色高信号增加），而高AD风险非进展者的NP特征将不会 显示出类似的大脑结构变化我们将进一步加强我们的初步工作， 对抗性学习框架，以增强基线MRI图像，作为高AD的更好预测因子 风险进展者和非进展者组比原始图像。结果将导致确定一个 AD高风险患者的临床前表现谱比以前认识到的更广 从而更好地表征NP性能的异质性，特别是在病程的早期， 潜在地确定干预策略可以减轻疾病风险的关键时期。

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.