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

自然事件表示作为临床前阿尔茨海默病的新型生物标志物

基本信息

批准号：
9912695
负责人：
Charan Ranganath
金额：
$ 15.7万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-15 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9912695
关键词：
Address Affect Age-Related Memory Disorders Aging Alzheimer&apos s Disease Alzheimer’s disease biomarker American Amyloid beta-Protein Amyloid deposition Area Biological Markers Brain Brain imaging Characteristics Clinic Clinical Clinical assessments Cognition Cognitive Complex Data Deposition Diagnosis Diagnostic Disease Elderly Episodic memory Event Exhibits Functional Magnetic Resonance Imaging Goals Government Hippocampus (Brain)Impairment Individual Knowledge Laboratories Lead Machine Learning Maps Measures Medial Memory Memory Loss Memory impairment Methods Neuropsychological Tests Participant Pathologic Pathology Pattern Performance Population Positron-Emission Tomography Prevalence Reporting Research Retrieval Role Scanning Screening procedure Senile Plaques Sensory Societies Stream Structure Television Testing Visual Work abeta accumulation abeta deposition aging brain behavior measurement behavior test cognitive ability cognitive process experience functional MRI scan healthy aging improved innovation insight movie multimodality neural patterning novel novel marker pre-clinical relating to nervous system tool working group

项目摘要

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.

到 2060 年，65 岁及以上的美国人数量预计将增加一倍以上，从 4600 万增至 98 人万人，占总人口的24%。随之而来的是阿尔茨海默病的患病率增加（AD），这会给我们的社会和政府带来沉重的负担。目前筛选工具能够区分健康衰老和 AD 的方法在疾病发生十年或更长时间后最有效临床前阶段，此时潜在的治疗方法最为有效。因此，新奇的发现和评估大脑老化的具体工具至关重要。认知能力的典型研究涉及对学习对象的识别或简单的单词关联。然而，在现实世界的情况下，事件的内容是从多模式信息流中分段的。细分和事件的表征由大脑区域的后内侧网络（PMN）支持。关键的是，这个完全相同的大脑网络具有受淀粉样蛋白病理性积累影响的第一个区域 β (Aβ)，AD 的一个关键特征。 NIA 工作组最近的一份报告定义了无症状感染者 Aβ 积累是临床前 AD 的最早指标。鉴于 PMN 的功能作用，我们提出这个阶段的疾病可能并不是真正的无症状：微妙的功能缺陷可能是如果探测得当，就会很明显。为了解决这个问题，我们开发了一种自然主义范式来表征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大脑分割和表示复杂事件的能力在临床前 AD 中受到损害，并且这种破坏的程度预示着对经历过的事件的记忆不足。我们将当参与者观看描绘自然主义的视频叙述时，获取功能性 MRI (fMRI) 扫描场景。我们还将额外测试与中描述的事件细节相关的内存性能扫描仪输入和输出的视频。使用代表性相似性分析（RSA）和机器学习分析功能性核磁共振（fMRI）数据，我们将检查大脑方式的差异代表有或没有“无症状”淀粉样蛋白的参与者的晚期衰老事件沉积（通过现有 PET 扫描数据获得的状态）。这种方法的组合非常有效创新是因为当前的翻译测量并没有评估丰富的、动态的事件的记忆，构成了现实世界经验的大部分。该项目预计将显着改善我们的了解区分健康衰老与临床前 AD 的神经和认知干扰。通过研究大脑如何划分和表示事件，以及这与记忆的关系通过研究这些事件，我们可以揭示 AD 相关病理如何影响日常生活的重要见解。这可以提供一个机制框架来理解微妙的、主观的记忆抱怨。这项工作的结果预计将显着增进我们对记忆衰退的理解 AD 的最早可能阶段，为迄今为止的微妙问题提供了机械基础在临床上很难评估。