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

外侧内嗅皮层(LEC)是阿尔茨海默病患者大脑中最早受到影响的区域之一 疾病，对物体识别、气味辨别和情景记忆很重要。因此，早些时候 AD症状，如放错物体、忘记事件和气味丧失，可能是由于LEC 功能障碍。为了了解Aβ和tau的积累如何影响LEC神经元，我们将使用 两种AD小鼠模型：APP-KI小鼠-表达生理水平的APP和EC- APP/Tau小鼠-在EC中表达高水平的APP和tau。两个鼠标型号都显示了 LEC中的选择性脆弱性，使它们成为探测LEC功能的理想候选者。我们的预赛 数据显示EC-APP/Tau小鼠24个月时行为障碍，APP-KI数据显示 18个月时的减损。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元的变化。我们将用硅探针记录LEC活动，并 测试对物体、气味和时间流逝的反应。使用计算方法，例如 通过机器学习，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APP将使神经元功能障碍，这将是 很明显，对物体、气味和时间纪元的解码精度较差。在EC-APP/Tau小鼠中， β和Tau的联合作用会加重功能障碍，进一步影响译码的准确性 从而能够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 神经科学])应用大规模记录技术来记录神经元和 开发分析和预测性计算测试以询问脆弱大脑区域的功能 这在阿尔茨海默病中是一种功能障碍。

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.