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

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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.