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.

该项目旨在推进对导航神经机制的了解。我们的方法是。 通过开发和实证测试神经回路如何编码的计算框架 并对信息进行解码以形成尺度不变存储器，该框架称为 SIPI，是 SIPI 的缩写。 将SIPI框架应用于导航提供了尺度不变路径集成框架。 神经元如何通过编码动物的运动来进行空间调整的机制模型以及如何 神经退化影响路径整合能力，该项目将产生新的工具以促进更广泛的发展。 SIPI的应用和测试，通过实证研究检验SIPI框架的强有力的预测 新工具包括啮齿动物和人类行为以及大脑活动的模拟环境。 跨参数化和 SIPI 变体分析 SIPI 功能，1) 使用视觉输入来指导 导航，2) 解决位置编码如何与嘈杂的自运动线索相互作用，以及 3) 执行记忆 啮齿类动物的高密度单单元电生理学将检验 SIPI 的有力预测。 关于头部方向和边界调谐神经​​元是否编码多尺度运动历史 以及这些神经元编码的历史是否解释了健康和转基因的导航能力 人类阿尔茨海默病（AD）模型的行为和超高分辨率功能成像。 SIPI 的测试预测表明：1) 路径集成性能对于识别具有诊断价值 临床前 AD，2) 解决了老年患者速度编码和路径整合能力降低的问题 通过批准的 AD 治疗，3) AD 与对环境的依赖增加有关 定向边界，4）在健康志愿者中，环境边界的接近度是 在内嗅皮层中以多尺度方式编码。也就是说，该项目将对内嗅皮层进行强有力的测试。 SIPI 框架将增进我们对大脑空间编码的理解，并测试新的途径 深入了解伴随阿尔茨海默病的行为缺陷。 相关性（参见说明）： 导航是一项核心能力，取决于首先受到影响的电路的完整功能。 该项目旨在确定导致阿尔茨海默病的神经生理机制。 人类临床前阿尔茨海默病个体的路径整合和边界编码损伤 开发可转化的结果指标来评估阿尔茨海默病的动物模型。