CRCNS: Scale-invariant navigation and its degradation in Alzheimer's disease

CRCNS：尺度不变导航及其在阿尔茨海默病中的退化

• 批准号: 10395800
• 负责人: Ehren L. Newman
• 金额: $ 33.61万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395800
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease model; Animal Model; Animals; Appearance; Behavior; Behavioral; Behavioral Mechanisms; Brain; Code; Competence; Cues; Dependence; Development; Diagnostic; Disease; Electrophysiology (science); Functional Imaging; Head; Hippocampal Formation; Human; Impairment; Individual; Instruction; Knowledge; Location; Mathematics; Memory; Modeling; Motion; Movement; Nature; Nerve Degeneration; Neurons; Outcome Measure; Performance; Principal Investigator; Recording of previous events; Resources; Rodent; Shelter facility; Signal Transduction; Source; Testing; Variant; Visual; Work; base; computer framework; density; entorhinal cortex; healthy volunteer; insight; neural circuit; neuromechanism; neurophysiology; older patient; pre-clinical; relating to nervous system; simulation environment; skills; tool; transgenic model of alzheimer disease; ultra high resolution; way finding

This project seeks to advance what is known about the neural mechanisms of navigation. Our approach is through the development and empirical testing of a computational framework for how neural circuits encode and decode information to form scale invariant memories. This framework is referred to as SIPI, short for Scale Invariant Path Integration framework. Applying the SIPI framework to navigation provides a mechanistic model for how neurons obtain spatial tuning by encoding an animals’ movements and for how neural degeneration affects path integration ability. This project will generate new tools to facilitate broader application and testing of SIPI and test strong predictions of the SIPI framework through empirical studies of rodent and human behavior and brain activity. The new tools include a simulation environment for analyzing SIPI function across parameterizations and variants of SIPI that 1) use visual input to guide navigation, 2) address how positional coding interacts with noisy self-motion cues, and 3) perform memory guided navigation. High-density single unit electrophysiology in rodents will test strong predictions of SIPI regarding whether head direction and boundary tuned neurons encode a multiscale history of movements and whether the history encoded by those neurons accounts for navigation ability in healthy and transgenic models of Alzheimer’s disease (AD). Behavioral and ultra-high resolution functional imaging in humans will test predictions from SIPI that 1) path-integration performance has diagnostic value for identifying preclinical AD, 2) that reduced velocity coding and path integration ability in elderly patients are addressed by approved AD treatments, 3) that AD is associated with increased dependence on environmental boundaries for orienting, and 4) that, in healthy volunteers, the proximity of environmental boundaries are encoded in a multiscale fashion in the entorhinal cortex. That is, this project will perform strong tests of the SIPI framework, will advance our understanding of spatial coding in the brain, and test new avenues for insight into the behavioral deficits that accompany Alzheimer’s disease. RELEVANCE (See instructions): Navigation is a core competency that depends upon intact functioning of circuits impacted first in Alzheimer’s disease. This project aims to determine the neurophysiological mechanisms that contribute to path integration and boundary coding impairments in human preclinical Alzheimer’s disease individuals and develop translatable outcome measures for assessing animal models of Alzheimer’s disease.

该项目旨在推进有关导航神经机制的了解。我们的方法是 通过开发和经验测试神经回路的计算框架 并解码信息以形成规模不变的记忆。该框架称为sipi，缩写 比例不变路径集成框架。将SIPI框架应用于导航提供了 神经元如何通过编码动物的运动以及如何获得空间调整的机械模型 神经变性会影响路径整合能力。该项目将生成新的工具，以促进更广泛的 通过实证研究应用和测试SIPI并测试SIPI框架的强有力预测 啮齿动物，人类的行为和大脑活动。新工具包括一个模拟环境 分析跨参数和SIPI变体的SIPI函数1）使用视觉输入指导 导航，2）解决位置编码如何与噪音自我运动提示相互作用，3）执行内存 导航。啮齿动物中的高密度单位电生理学将测试SIPI的强烈预测 关于头部方向和边界调谐神经​​元是否编码动作的多尺度历史 以及那些神经元对健康和转基因的导航能力的编码 阿尔茨海默氏病模型（AD）。人类的行为和超高分辨率功能成像将 来自SIPI的测试预测，即1）路径融合性能具有诊断的诊断值 临床前AD，2）降低了老年患者的速度编码和路径积分能力 通过批准的广告处理，3）AD与对环境的依赖增加有关 定向的边界，以及4）在健康的志愿者中，环境边界的距离是 以多尺度的方式编码在内嗅皮层中。也就是说，该项目将对 SIPI框架，将提高我们对大脑空间编码的理解，并测试新途径 洞悉适应阿尔茨海默氏病的行为定义。 相关性（请参阅说明）： 导航是一种核心能力，取决于首先影响电路的完整功能 阿尔茨海默氏病。该项目旨在确定有助于 人类临床前阿尔茨海默氏病中的路径整合和边界编码障碍和 制定可翻译的结果指标，以评估阿尔茨海默氏病动物模型。