Impaired spatial decoding and neural population code rescaling in AD mice

阿尔茨海默病小鼠空间译码和神经种群编码重排受损 项目总结：有相当多的证据支持淀粉样β蛋白(Aβ)和tau病理 阿尔茨海默病(AD)中神经回路的完整性和功能受损。不幸的是，很少有研究证实 AD病理对受影响神经元群体内空间计算的直接影响，导致 神经网络层面的信息鸿沟。此外，检查大规模神经元的活体实验 在Aβ和tau病理的小鼠模型中，缺乏网络活动。在这个提案中，我测试了最重要的 β和tau相关神经网络功能障碍损害任务相关的空间功能障碍的假说 EC-HIPP回路内大量神经元中的信息编码，并与 这种异常活动可以恢复阿尔茨海默病小鼠的秩序，改善空间信息处理。在目标1中， 我将测试这样的假设，即Aβ和tau的寡聚体形式干扰了 内嗅皮层-海马区(EC-HIPP)回路内有大量神经元。我也会测试一下这些 寡聚肽改变了招募到负责记忆的群体编码中的神经元的数量 在空间学习和记忆任务中进行编码。在目标2和目标3中，我将利用机器的预测能力 学习破译EC-HIPP种群活动中空间信息处理的神经代码。 具体地说，我在目标2中的目标将是检查Aβ和tau病理的个体和组合影响 EC-Tau/Happ鼠系空间信息编码特征的研究。在目标3中，我将聘用 使用一种新的DREADDS配体对抗AD小鼠模型中异常神经元活动的化学遗传学， 改善神经网络中的空间信息处理的最终目标是背负着病理学的负担。 将专门针对兴奋性神经元，以更好地了解它们对受损的贡献 AD小鼠模型中的空间信息处理。 拟议的研究目的是弥合AD相关认知之间的信息鸿沟 损害和潜在的电路病理。这项导师研究科学家发展奖(K01) 将为我提供实现这一主要目标的机会，同时丰富我的技术技能并扩大 我对阿尔茨海默病的病理生理学知识。此外，我将接受的综合培训和指导将 帮助我开发空间解码分析的机器学习方面的其他专业知识。总而言之，建议的 学习和职业发展计划将确保我实现我的长期职业目标，启动一个 在一所主要研究型大学从事竞争激烈、独立研究的职业生涯。

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.