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Active Investigations

There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.

Principal Investigator  
Principal Investigator's Name: Weihao Shao
Institution: Zhengzhou University
Department: Collage of Public Health
Country:
Proposed Analysis: Mild cognitive impairment (MCI) is an intermediate state between normal aging and dementia, and amnestic MCI(aMCI) is also a high-risk factor for the development of Alzheimer's disease (AD). In clinical practice, the annual conversion rate of aMCI developing into AD is 11.0%–16.5%, which is 10 times that of the normal elderly. In contrast, the probability of MCI reversing to normal cognition after reasonable intervention is up to 53%. Therefore, MCI is a high-risk and unstable stage and the best "intervention window" for the prevention and treatment of AD. Therefore, it is particularly important to identify MCI early and predict the risk of disease onset. In cognitive related research, cognitive function evaluation scales such as mini mental state examination (MMSE) and Rey auditory word learning test (RAVLT) are often used to judge whether cognitive impairment occurs. Longitudinal data repeatedly measured by cognitive function assessment indicators or survival data recording the occurrence time of cognitive impairment are usually collected in relevant follow-up investigations, but most existing prediction models only model the longitudinal data or survival data independently and ignore the potential correlation between the longitudinal data and survival data. In this study, based on the longitudinal data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) for up to 15 years, and considering the dynamic changes of individual longitudinal cognitive function evaluation indicators and the correlation with the occurrence of time endpoint event MCI, we constructed and compared the risk prediction models for the elderly with MCI based on six different cognitive function measurement scales using machine learning model, so as to provide the basis for realizing the risk prediction for MCI in the elderly.
Additional Investigators