Deciphering aging-driven cognitive decline in C. elegans spatial memory and learning through an interdisciplinary approach

项目摘要 认知衰退是衰老神经生物学中的一个中心问题，这不仅是因为它具有深厚的科学意义，而且 也因为有轻度认知障碍的人患阿尔茨海默氏症或其他疾病的风险增加 痴呆症。尽管人类认知可能不同于动物认知，但像记忆这样的认知行为， 即使在无脊椎动物身上，学习、决策和环境信息处理也被广泛观察到 动物。线虫是衰老生物学和神经生物学的典范生物。 认知行为(记忆、学习和决策)正在用线虫稳定地阐明 然而，蠕虫，主要是在化学刺激的背景下；因此，人们对它们的认知衰老知之甚少 与空间导航的关系。然而，需要阐明这些过程的细胞和分子细节 仍然很紧迫。线虫作为行为和认知研究模式系统的新潜力 可以极大地帮助科学界了解衰老导致的认知衰退及其机制 在包括人类在内的高等生物中。拟议的研究重点是描述衰老驱动的特征 认知能力下降。我们假设正常衰老对多种认知功能有不同的影响，而且 特定的神经元和神经元回路更容易受到衰老效应的影响，起着“衰老中枢”的作用 确定整个网络的性能。为了测试这一点，我们计划了两个研究推进器，一个 实验和一次计算。我们将使用为蠕虫定制的认知老化学习平台， 即蠕虫迷宫。为了刻画衰老导致的认知衰退，我们将调查衰老对 空间记忆和冲突环境线索下的决策。接下来，我们将从基因上 通过沉默靶基因来灭活选定的神经元，以识别控制这些基因和神经元的基因和神经元 我们将检测每一种行为是如何受到衰老的影响的。将使用实时钙成像来跟踪 激活神经元。对于计算部分，我们将建立数学模型来描述学习、空间 记忆和决策，并预测正在老化的神经网络的性能。因此，通过 结合实验和计算的努力，我们将在与认知相关的区域中识别“老化中枢” 神经网络。拟议工作的成功结果将大大有助于理解 参与认知的神经元回路的老化。建议的研究计划是基于申请者的强有力的 神经生物学背景和她的工程经验。与职业生涯规划一起，他们的目标是 加强和扩大申请者在遗传学、行为研究和计算方面的知识 为了让她成长为当代独一无二的独立调查员 衰老的神经生物学。通过有针对性的研究活动、来自 一个由著名专家组成的多学科团队和精选的课程将在华盛顿大学举行 密歇根医学院、机械工程系和数学系。

Project Summary Cognitive decline is a central issue in neurobiology of aging, not only due to its profound scientific interest, but also because people with mild cognitive impairment are at increased risk of developing Alzheimer's or another dementia. Even though human cognition may differ from animal cognition, cognitive behaviors like memory, learning, decision making and environmental information processing are widely observed even in invertebrate animals. C. elegans nematode is an exemplary model organism in both biology of aging and neurobiology. Cognitive behaviors (memory, learning and decision making) are being steadily clarified using C. elegans worms, mainly however in the context of chemical stimuli; hence little is known about their cognitive aging in relation to spatial navigation. Still, the need for elucidating the cellular and molecular details of these processes remains pressing. The emerging potential of C. elegans as model system for behavioral and cognitive studies could significantly help the scientific community understand aging-driven cognitive decline and its mechanisms in higher organisms, including humans. The proposed research focuses on characterizing aging-driven cognitive decline. We hypothesize that normal aging differentially affects multiple cognitive functions, and that specific neurons and neuronal circuits are more susceptible to aging effects, functioning as "aging hubs" that determine the performance of entire networks. To test this, we have planned two research thrusts, one experimental and one computational. We will use a custom-made cognitive aging studying platform for worms, i.e. the Worm-Maze. To characterize aging-driven cognitive decline, we will investigate the impact of aging on spatial memory and on decision making under conflicting environmental cues. Next, we will genetically inactivate selected neurons by silencing target genes, to identify genes and neurons that govern these behaviors and we will detect how each of them is affected by aging. Live calcium imaging will be used to track firing neurons. For the computational part, we will build mathematical models to describe learning, spatial memory and decision making, and predict the performance of neuronal networks undergoing aging. Thus, by combining experimental and computational efforts, we will identify "aging hubs" in the cognition-related neuronal networks. Successful outcome of the proposed work will significantly contribute to understanding aging of neuronal circuits involved in cognition. The proposed research plan is based on the applicant's strong background in neurobiology and her engineering experience. Together with the career plan, they aim to strengthen and expand the applicant's knowledge in genetics, behavioral studies and computational neuroscience in order for her to grow into a uniquely positioned independent investigator in contemporary neurobiology of aging. Well rounded career development through targeted research activities, mentorship from a multi-disciplinary team of acclaimed experts and selected coursework will take place in the University of Michigan Medical School, Mechanical Engineering Department and Department of Mathematics.