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.

侧内肾上腺皮质（LEC）是大脑中最早受到阿尔茨海默氏症影响的区域之一疾病，对于物体识别，气味歧视和情节记忆很重要。因此，很早广告符号，例如放置对象，忘记事件和气味丢失可能是由于LEC造成的功能障碍。为了了解Aβ和TAU加速度如何影响LEC神经元，我们将使用 AD的两个小鼠模型：App敲除（App-Ki）小鼠 - 表达APP和EC-的生理水平 App/tau鼠标表达EC中APP和TAU级别的升高。两种鼠标模型都显示在LEC中有选择地脆弱性，使其成为探测LEC功能的理想候选者。我们的初步数据显示24个月的EC-APP/TAU小鼠中的行为障碍，App-Ki显示的数据显示 18个月的损害。在提案中，我们将评估年轻小鼠的LEC功能，以便在行为定义之前检测神经元变化。我们将记录LEC活动存在硅问题和测试对象，气味和时间流逝的响应。使用计算方法，例如机器学习，我们将确定LEC神经元的集合特性是否受TAU和Aβ的影响。我们假设App-KI小鼠LEC中的应用程序将使神经元功能障碍，这将是对物体，气味和临时时期的解码精度差的证据。在EC-APP/TAU小鼠中， Aβ和TAU的结合效应将使功能障碍更糟，并进一步影响解码精度可以更好地预测阿尔茨海默氏病的早期症状。该提案汇集了潜水场（电生理学，病理学和计算）神经科学）应用大规模记录技术记录神经元和开发分析性和预测性计算测试以在脆弱的大脑区域询问功能这在阿尔茨海默氏病中功能失调。