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Electrophysiological Evaluation of Brain Regions Vulnerable to Alzheimers Disease

Electrophysiological Evaluation of Brain Regions Vulnerable to Alzheimers Disease
易患阿尔茨海默病的大脑区域的电生理学评估
批准号:
9973904
负责人:
Syed Abid Hussaini
金额:
$64.59万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-04-15 至 2025-03-31

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中文摘要
翻译
内嗅皮层(EC)长期以来被认为是阿尔茨海默病(AD)中最先受到影响的区域, 迷失方向、神志不清和无法导航等症状出现在生命早期。但最近, 在年轻健康成年人的脑干蓝斑(LC)中发现有tau的积聚。 使它成为大脑中第一个出现阿尔茨海默病病理的区域。众所周知,LC对于唤醒和 控制休眠/唤醒开关。LC的神经元投射到EC和海马等多个区域 它们被认为对空间记忆很重要。毫不奇怪,阿尔茨海默病的最早症状之一是 空间困难和LC的早期病理可能影响睡眠,导致空间记忆障碍。 由于LC和EC对记忆都很重要,我们的目标是确定它们中的哪一个更容易受到tau和Aβ的影响 病理学。为了探索这种可能性,我们将首先向野生型小鼠的LC和EC区域注射病理 从人类AD大脑中提取tau,使其功能失调,并评估其神经功能。我们会 还要评估睡眠,并测试相关行为任务中的记忆表现。了解β如何影响Tau 病理上,我们将把人tau注射到APP敲入小鼠中,这种小鼠具有生理数量的APP 在他们身上表现出来。我们将确定Aβ和tau是否会恶化LC和EC的神经元功能 神经元及其睡眠和记忆会进一步受损。我们将使用多区域硅探针同时 记录LC或内侧EC和海马神经元的活动。脑微血管内皮细胞和海马神经元 具有可以使用空间导航任务轻松测量的特性的。我们将利用 虚拟现实头固定装置让动物可以在其中导航,并允许我们快速测试动物的记忆 任何背景和环境。这些动物将接受物体位置记忆和上下文相关的测试 虚拟环境中的内存。我们将使用机器学习算法来解码动物在LC中的位置, MEC和HPC的神经数据,并确定它是否受到Tau或Aβ的影响,或两者兼而有之。我们还将评估睡眠 参数,并与内存相关。我们假设LC和EC中的tau会使其神经元 功能障碍,并直接影响睡眠和记忆,这与Aβ一起将加剧神经元 功能障碍会导致睡眠问题增加和空间记忆障碍,就像在AD早期看到的那样。有了这个 我们的目标是在行为障碍发作之前确定神经元功能障碍的电生理生物标记物。 麻烦。我们将测试是否增加低活动神经元的神经元放电和减少 过度活跃的神经元将恢复下游神经元功能障碍,逆转睡眠问题和认知 减损。 该提案汇集了不同的领域(神经科学、病理学和计算神经科学) 同时跨多个大脑区域应用大规模记录技术以开发分析 以及预测性测试,以询问阿尔茨海默病患者易受伤害的大脑区域的功能。
英文摘要
The entorhinal cortex (EC) is long known to be the region affected first in Alzheimer’s disease (AD) with symptoms such as disorientation, confusion and inability to navigate appearing early in life. But more recently, a region in the brainstem- locus coeruleus (LC) was found to have tau accumulation in young healthy adults, making it the first region in the brain with AD pathology. The LC is known to be important for arousal and controls the sleep/wake switch. The neurons of LC project to several regions including EC and hippocampus which are known to be important for spatial memory. Unsurprisingly, one of the earliest symptoms of AD is spatial difficulties and it is possible that early pathology in LC affects sleep leading to spatial memory deficits. With both LC and EC important for memory, we aim to identify which of them is more vulnerable to tau and Aβ pathology. To explore this possibility, we will first inject LC and EC regions of wildtype mouse with pathological tau derived from human AD brains to make them dysfunctional, and evaluate their neuronal function. We will also assess sleep and test memory performance in relevant behavior tasks. To understand how aβ affects tau pathology, we will inject human tau in the APP Knock-In mice which has physiological amounts of APP expressed in them. We will determine if Aβ together with tau worsens the neuronal function of LC and EC neurons and it sleep and memory is impaired further. We will use multi-region silicon probes to simultaneously record activity from LC or medial EC and hippocampal neurons. The MEC and hippocampal neurons are well characterized with properties that can be easily measured using spatial navigation tasks. We will make use of virtual reality head-fixed setup for the animals to navigate in, and allowing us to quickly test animal’s memory in any context and environment. The animals will be tested for object-location memory and context-dependent memory in virtual environment. We will use machine learning algorithms to decode animal’s position in the LC, MEC and HPC neural data and determine if it is affected by tau or Aβ or both. We will also assess sleep parameters and correlate with memory. We hypothesize that tau in LC and EC will make its neurons dysfunctional and directly affect sleep and memory, and this in concert with Aβ will exacerbate neuronal dysfunction leading to increased sleep problems and spatial memory impairment as seen in early AD. With this we aim to identify electrophysiological biomarker of neuronal dysfunction before the onset of behavioral troubles. We will test if increasing the neuronal firing in hypoactive neurons and reducing the firing in hyperactive neurons will restore downstream neuronal dysfunction and reverse sleep problems and cognitive impairment. The proposal brings together diverse fields (neuroscience, pathology and computational neuroscience) applying large-scale recording techniques simultaneously across multiple brain regions to develop analytical and predictive tests to interrogate function in vulnerable brain regions that are dysfunctional in AD.
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Electrophysiological Evaluation of Brain Regions Vulnerable to Alzheimers Disease
Electrophysiological Evaluation of Brain Regions Vulnerable to Alzheimers Disease
Electrophysiological Evaluation of Brain Regions Vulnerable to Alzheimers Disease
Decoding Early Signs of Alzheimer's Disease in The Lateral Entorhinal Cortex Using Machine Learning
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