Naturalistic Event Representation as a Novel Biomarker of Preclinical Alzheimer's Disease

到2060年，65岁及以上的美国人口预计将增加一倍以上，从4600万增加到9800万 百万，占总人口的24%。随之而来的是阿尔茨海默病患病率的增加 (Ad)，这将给我们的社会和政府带来沉重的负担。目前，筛查工具 能够区分健康衰老和阿尔茨海默病的人在十年或更长时间后最有效 临床前阶段，潜在的治疗方法将是最有效的。因此，小说和小说的发现 评估大脑老化的特定工具是至关重要的。认知能力的典型研究 包括识别学习的对象或简单的单词联想。然而，在现实世界中， 事件的内容从多模式信息流中分割出来。分段和 事件的呈现由大脑区域的后部-内侧网络(PMN)支持。关键是， 这个完全相同的大脑网络以淀粉样蛋白病理性堆积影响的第一个区域为特征 贝塔(Aβ)，AD的一个关键特征。NIA工作组最近的一份报告定义为无症状 Aβ积聚是临床前AD的最早指标。鉴于PMN的职能作用，我们 提出这一阶段的疾病可能不是真正无症状的：细微的功能缺陷可能是 如果调查得当，这是显而易见的。为了解决这个问题，我们开发了一个自然主义范式来描述 大脑中的事件表征和随后的记忆。我们的目标是测试新的假设 在临床前AD中，大脑分割和表现复杂事件的能力受到损害， 这种干扰的程度预示着对经历过的事件记忆不足。我们会 在参与者观看描述自然主义的视频叙事时获得功能磁共振成像(FMRI)扫描 场景。我们还将测试与中描述的事件详细信息相关的内存性能 扫描仪输入和输出的视频。使用表象相似度分析(RSA)和机器 学习分析功能磁共振(FMRI)数据，我们将检查大脑方式的差异 代表患有和不患有‘无症状’淀粉样蛋白的参与者进入晚期衰老 沉积(通过现有的PET扫描数据获得状态)。这种方法的结合是非常重要的 创新，因为当前的转换方法不评估丰富的、动态的事件的内存 构成了真实世界体验的大部分。这个项目预计将显著改善我们的 了解神经和认知障碍，将健康衰老与临床前AD区分开来。 通过研究大脑如何分块和表示事件，以及这与这些事件的记忆之间的关系 在这些事件中，我们可以揭示AD相关病理如何影响日常生活的重要见解。 这可以为理解细微的、主观的记忆抱怨提供一个机械的框架。 这项工作的结果有望极大地促进我们对记忆衰退的理解 AD的最早可能阶段，为迄今必须解决的微妙问题提供了机制基础 在诊所里很难评估。

By 2060, the number of Americans 65 and older is projected to more than double from 46 million to 98 million, 24% of the total population. With this comes an increased prevalence of Alzheimer’s disease (AD), which will create significant burden on our society and government. At present, screening tools capable of differentiating healthy aging from AD are most effective a decade or more after the preclinical stage, when potential treatments would be most effective. Thus, discovery of novel and specific tools for assessing the aging brain are of utmost importance. Typical studies of cognitive ability involve recognition of learned objects or simple word associations. However, in real-world situations, the content of an event is segmented from a flow of multimodal information. Segmentation and representation of events is supported by a posterior-medial network (PMN) of brain areas. Critically, this very same brain network features the first regions affected by pathological accumulation of amyloid beta (Aβ), a key characteristic of AD. A recent report from an NIA working group defined asymptomatic Aβ accumulation as the earliest indicator of preclinical AD. Given the functional role of the PMN, we propose that this stage of disease may not be truly asymptomatic: subtle functional deficits may be evident if properly probed. To address this, we have developed a naturalistic paradigm to characterize event representation in the brain and subsequent memory. We aim to test the novel hypotheses that the brain’s ability to segment and represent complex events is compromised in preclinical AD, and that the extent of this disruption is predictive of deficient memory for the experienced events. We will acquire functional MRI (fMRI) scans while participants view a video narrative depicting naturalistic scenarios. We will additionally test memory performance related to details of the events depicted in the video both in and out of the scanner. Using representational similarity analysis (RSA) and machine learning analyses of functional MRI (fMRI) data, we will examine differences in the way the brain represents events into advanced aging in participants with and without ‘asymptomatic’ amyloid deposition (status obtained via existing PET scan data). This combination of approaches is highly innovative because current translational measures do not assess memory for rich, dynamic events that make up the majority of real-world experience. This project is expected to significantly improve our understanding of neural and cognitive disruptions that differentiate healthy aging from preclinical AD. By studying how the brain chunks and represents events, and how this relates to memory for those events, we can reveal significant insights into the way AD-related pathology affects day-to-day living. This can provide a mechanistic framework for understanding subtle, subjective memory complaints. The results of this work are anticipated to significantly advance our understanding of memory decline in the earliest possible stages of AD, providing a mechanistic basis for subtle issues that have to date been difficult to assess in the clinic.