Abstract

Age-related decline in skilled motor behavior is an important problem for aging adults. This decline impairs the ability of the older adults to perform activities of daily living and maintain their independence, leading to increased fall risk, inactivity and physical frailty. This in turn may amplify the cognitive dysfunction and lead to disease progression in dementia. Investigation of neural mechanisms of the age-related decline in skilled motor function has the potential to shed light on the mechanisms of cognitive decline as well as open opportunities for rehabilitative treatment strategies for the elderly. Healthy aging has substantial detrimental structural, functional and biochemical effects on the brain. In this proposal, we will investigate the neural network mechanisms of age-related decline in skilled motor control. Skilled motor control is considered a high-level cognitive function, requiring coordinated interactions of multiple cortical and subcortical regions in the brain. We will record from thousands of neurons in relevant brain regions while the mouse is performing a skilled reach-and-grasp task. We will use state-of-the-art neural recording techniques, augmented by our recently developed machine learning-based behavior analysis methods. A greater understanding of age-related changes in skilled motor control and neural networks that control this behavior will provide not only important opportunities for designing appropriate rehabilitative strategies for the elderly but also will help us understand the neural mechanisms of cognitive decline that occurs with aging.