Cognitive heterogeneity in those with high Alzheimer's 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。在此应用程序中，我们寻求利用Framingham心脏 研究(FHS)认知老化和痴呆症数据库，通过近70年的前瞻性研究获得 考试。自2005年以来，FHS独有的是收集新的NP指数(错误响应，数字 项目级延迟、零碎响应等指标)。基线数据是在以下时间收集的 这些参与者中绝大多数似乎没有症状，包括那些AD风险较高的人，这是 后来发展为AD事件以及类似的AD风险高的亚组，他们没有。我们会 利用这一史无前例的资源1)描述这些AD高危人群的认知异质性 它们会和不会发展成疾病，2)决定传统的神经成像生物标志物是否能区分 进步和非进步之间的区别以及3)开发新的机器学习方法来识别 神经影像指标甚至比传统的MRI指标更早。我们预测，与传统的 神经心理学(NP)得分，我们的新NP测量方法将产生独特的认知特征， 区分哪些高AD风险人群进展为AD，哪些进展不进展为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。我们还将开发一个对抗性学习框架来 增强基线MRI图像作为AD高危进展者和非进展者的更好预测指标 分组比原始图像更多。结果将导致更广泛的临床前疾病的鉴定 在AD风险较高的人群中的表现比以前认识到的更好，因此更好地表征了 NP表现的异质性，特别是在病程的早期，潜在地识别一个关键的 干预策略可以降低疾病风险的时期。

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 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. We will use this unprecedented resource 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 compared to traditional neuropsychological (NP) scores, our novel NP measures will produce 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 also develop 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.