Principal Investigator: Dr Kun Hu
Brigham and Womens Hospital, Boston, Massachusetts, USATags: 33883, actigraphy, Alzheimer's Disease, Circadian, cognition, Dementia, fractal
Collaborators: Dr Richa Saxena, Broad Institute, Boston, Massachusetts, USA
Dr Lei Yu, Rush University Medical Center, Chicago, Illinois, USA
Dr Michael Weedon, University of Exeter, Exeter, UK
Dr Martin Rutter, University of Manchester, Manchester, UK
Dr Aiden Doherty, University of Oxford, Oxford, UK
The goal of this 5-year project is to determine the ability of a nonlinear pattern in motor activity control, namely, multiscle activity control (MAC), to predict the risk of Alzheimer’s disease (AD) in people at their middle to old ages. We aim to answer two questions: (1) Can MAC alterations at younger ages (<65 years) predict cognitive decline and incident AD? (2) Are the patterns of MAC alterations due to AD different from those due to other neuropathological diseases? Earlier identification of people at risk for developing Alzheimer’s disease (AD) is important for better health outcomes for these individuals and their caregivers. The project will provide valuable information about the degradation of behavioral control with aging and during the development of AD in people at middle to old ages, and the specificity of MAC alterations to reflect AD neuropathology. The results will be useful for the potential application of MAC in the clinical practice for the early diagnosis of AD and AD prognosis, which is in line with the UK Biobank’s purpose. We will examine the temporal patterns in daily motor activity fluctuations that reflect a multiscale activity control (MAC) as we demonstrated in our previous studies. We will study people at middle to old ages to deteremine (1) how MAC measures change with aging; (2) how MAC measures are affected by genetic mutations that are associated with AD risk; (3) how MAC at baseline predicts cognitive decline and incident AD in those people without AD at baseline; and (4) how MAC is affected by neuroanatomical changes in the brain. This project is focused on the mechanistic understanding of how MAC degrades with aging and during the development of AD at early stages. The knowledge to be obtained from the proposed research is not only scientifically important but also may lay the groundwork for the application of fractal physiology in clinical practice. The test MAC measures may be used as an unobtrusive, low-cost tool to narrow down or identify the individuals at a high risk for AD at younger ages such that these people and their caregivers can take certain earlier actions for better outcomes. In addition, improving MAC or decelerating its degradation with aging may become a new target for AD prevention and AD treatment.