Decoding Early Signs of Alzheimer's Disease in The Lateral Entorhinal Cortex Using Machine Learning

使用机器学习解码外侧内嗅皮层阿尔茨海默病的早期症状

基本信息

批准号：
10017142
负责人：
Syed Abid Hussaini
金额：
$ 20.25万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
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：
2019-09-15 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10017142
关键词：
Affect Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease model Alzheimer&apos s disease pathology Alzheimer’s disease biomarker Amyloid beta-Protein Animals Artificial Intelligence Behavioral Biological Markers Brain Brain region Cells Clinical Complex Computer Analysis Computer Models Confusion Cues Data Development Discrimination Disease Drops Electroencephalography Electrophysiology (science)Environment Episodic memory Event Fire - disasters Functional disorder Goals Human Immunohistochemistry Impaired cognition Impairment Knock-in Knock-in Mouse Lateral Lead Lesion Location Longitudinal Studies Machine Learning Memory Memory impairment Monitor Mus Neurobehavioral Manifestations Neuronal Dysfunction Neurons Odors Onset of illness Pathology Pattern Physiological Population Positioning Attribute Property Psychometrics Rewards Role Silicon Smell Perception Structure Symptoms Techniques Testing Time Time Perception Training abeta accumulation base behavioral impairment computational neuroscience digital entorhinal cortex experience forgetting in vivo mild cognitive impairment mouse model neuronal patterning novel novel marker object recognition response sugar tau Proteins tau aggregation tool virtual reality environment

项目摘要

The lateral entorhinal cortex (LEC) is one of the first regions in the brain to be affected in Alzheimer’s disease, and is important for object recognition, odor discrimination and episodic memory. Hence, early AD symptoms such as misplacing objects, forgetting events and loss of smell could be due to LEC dysfunction. In order to understand how Aβ and tau accumulation impacts the LEC neurons, we will use two mouse models of AD: APP knockin (APP-KI) mice- expressing physiological levels of APP and EC- APP/Tau mice- expressing elevated levels of APP and tau in the EC. Both mouse models show selectively vulnerability in the LEC, making them ideal candidates to probe LEC function. Our preliminary data shows behavioral impairment in the EC-APP/Tau mice at 24 months and data on APP-KI show impairment at 18 months. In the proposal we will evaluate LEC function in the younger mice in order to detect neuronal changes prior to behavioral deficits. We will record LEC activity with silicon probes and test responses towards objects, odors and passage of time. Using computational approach such as machine learning, we will determine if ensemble properties of LEC neurons are affected by tau and Aβ. We hypothesize that APP in the LEC of APP-KI mice will make the neurons dysfunctional which will be evident with poor decoding accuracy for objects, odors and temporal epochs. In the EC-APP/Tau mice, combined effect of Aβ and tau will make the dysfunction worse and affect the decoding accuracy further allowing better prediction of early symptoms of Alzheimer’s disease. The proposal brings together diverse fields (electrophysiology, pathology and computational neuroscience) applying large-scale recording techniques to record ensemble populations of neurons and develop analytical and predictive computational tests to interrogate function in a vulnerable brain region that is dysfunctional in Alzheimer’s disease.

外侧内嗅皮层 (LEC) 是大脑中最先受到阿尔茨海默病影响的区域之一疾病，并且对于物体识别、气味辨别和情景记忆很重要。 AD 症状，如放错物品、忘记事件和失去嗅觉，可能是由 LEC 引起的为了了解 Aβ 和 tau 蛋白积累如何影响 LEC 神经元，我们将使用。 AD 的两种小鼠模型：APP 敲入 (APP-KI) 小鼠 - 表达 APP 和 EC 的生理水平 - APP/Tau 小鼠——两种小鼠模型的 EC 中 APP 和 tau 表达水平升高。 LEC 中的选择性漏洞，使它们成为探索 LEC 功能的理想候选者。数据显示 EC-APP/Tau 小鼠在 24 个月时出现行为障碍，APP-KI 数据显示在该提案中，我们将评估年轻小鼠的 LEC 功能，以便在行为缺陷之前检测神经变化我们将用硅探针记录 LEC 活动并使用计算方法（例如）测试对物体、气味和时间流逝的反应。通过机器学习，我们将确定 LEC 神经元的整体特性是否受到 tau 和 Aβ 的影响。我们发现 APP-KI 小鼠 LEC 中的 APP 会使神经元功能失调，从而导致在 EC-APP/Tau 小鼠中，物体、气味和时间时期的解码精度很差。 Aβ和tau的共同作用会使功能障碍更加严重，进一步影响解码精度可以更好地预测阿尔茨海默病的早期症状。该提案汇集了不同的领域（电生理学、病理学和计算学）神经科学）应用大规模记录技术来记录神经元群和开发分析和预测计算测试来询问脆弱大脑区域的功能这在阿尔茨海默病中是功能失调的。