Impaired spatial decoding and neural population code rescaling in AD mice

AD小鼠的空间解码受损和神经群体代码重标度 项目摘要：大量证据支持淀粉样蛋白β（Aβ）和tau蛋白病理学 阿尔茨海默病（AD）中受损神经元回路完整性和功能。不幸的是，很少有研究 AD病理学对受影响神经元群体内空间计算的直接影响，导致 神经元网络水平的信息间隙。此外，在体内实验，检查大规模，神经元 缺乏Aβ和tau病理学小鼠模型中的网络活性。在这个提议中，我测试了 假设Aβ和tau蛋白相关的神经元网络功能障碍损害了任务相关的空间 在EC-HIPP回路内的大量神经元中的信息编码， 这种异常活动可以恢复AD小鼠的秩序并改善空间信息处理。在目标1中， 我将检验这样一个假设，即Aβ和tau蛋白的寡聚体形式干扰了编码在细胞内的空间信息内容。 内嗅皮层-海马（EC-HIPP）回路中的大量神经元。我也会测试这些 寡聚肽改变了负责记忆的群体密码中神经元的数量 空间学习和记忆任务中的编码。在目标2和3中，我将利用机器的预测能力， 学习破译EC-HIPP群体活动中空间信息处理的神经代码。 具体来说，我在目标2中的目标是研究Aβ和tau蛋白病理学的单独和联合影响 EC-Tau/hAPP小鼠系中空间信息编码的特征。在目标3中，我将使用 使用新型DREADDs配体对抗AD小鼠模型中异常神经元活性的化学遗传学， 最终目标是改善病理神经网络中的空间信息处理。 兴奋性神经元将被专门针对，以更好地了解它们对受损神经元的作用。 AD小鼠模型的空间信息处理。 建议的研究目的是为了弥合广告相关认知之间的信息鸿沟， 损伤和潜在的电路病理。指导研究科学家发展（K 01）奖 我将有机会实现这一主要目标，同时丰富我的技术技能， 我对AD病理生理学的了解此外，我将接受的综合培训和指导将 帮助我在机器学习方面发展更多的专业知识，以进行空间解码分析。在一起，拟议的 学习和职业发展计划将确保我实现我的长期职业目标， 在一所主要的研究型大学从事有竞争力的独立研究工作。

Impaired spatial decoding and neural population code rescaling in AD mice Project Summary: Considerable evidence exists to support the notion that amyloid beta (Aβ) and tau pathology impair neuronal circuit integrity and function in Alzheimer’s disease (AD). Unfortunately, few studies have tested the direct influence of AD pathology on spatial computation within affected neuronal populations, resulting in an information gap at the neuronal network level. Moreover, in vivo experiments that examine large scale, neuronal network activity in mouse models of Aβ and tau pathology are lacking. In this proposal, I test the overarching hypothesis that Aβ and tau associated neuronal network dysfunction impairs task-relevant, spatial information encoding in large populations of neurons within the EC-HIPP circuit, and that combating this aberrant activity can restore order and improve spatial information processing in AD mice. In Aim 1, I will test the hypothesis that oligomeric forms of Aβ and tau disturb spatial information content encoded within large populations of neurons in the entorhinal cortex – hippocampal (EC-HIPP) circuit. I will also test if these oligomeric peptides alter the number of neurons recruited into the population code responsible for memory encoding in a spatial learning and memory task. In Aims 2 & 3, I will leverage the predictive power of machine learning to decipher the neural code for spatial information processing in EC-HIPP population activity. Specifically, my goals in Aim 2 will be to examine the individual and combined impact of Aβ and tau pathologies on features of spatial information encoding in the EC-Tau/hAPP mouse line. In Aim 3, I will employ chemogenetics using a novel DREADDs ligand to combat aberrant neuronal activity in AD mouse models, with the ultimate goal of improving spatial information processing in neuronal networks burdened with pathology. Excitatory neurons will be specifically targeted in an effort to better understand their contribution to impaired spatial information processing in AD mouse models. The proposed research aims are designed to bridge an information gap between AD-related cognitive impairment and the underlying circuit pathology. This Mentored Research Scientist Development (K01) Award will afford me the opportunity to accomplish this major goal while enriching my technical skillset and expanding my knowledge of AD pathophysiology. In addition, the integrated training and mentorship that I will receive will help me develop additional expertise in machine learning for spatial decoding analyses. Together, the proposed studies and career development plan will ensure that I achieve my long-term career goal of launching a competitive, independent research career at a major research university.