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: | Dongmei Gu |
| Institution: | Peking University |
| Department: | Peking University Institute of Mental Health |
| Country: | |
| Proposed Analysis: | Dementia has become a major public health problem for its heavy economic and disease burden. It was estimated that the number of people with dementia would increase from 57·4 million cases globally in 2019 to 152·8 million cases in 2050. It was one of major causes of disability in older people and the fifth largest cause of death. Mild cognitive impairment (MCI) is the abnormality of cognitive functions in one or more cognitive domains, but without loss of functional abilities and skills in everyday social and occupational life. MCI may result from a variety of underlying causes, including Alzheimer’s disease. It represents a transitional stage between healthy aging and dementia, and affects 10-15% of the population over the age of 65. Due to lack of effective therapies for advanced dementia, diagnosis and disease intervention at an early stage, particularly at MCI stage, has been widely accepted as a critical strategy in disease management that could reduce the prevalence and costs of dementia profoundly. Early detection and diagnosis of dementia and MCI is therefore of paramount importance. This strategy will result in unprecedented demand for cognitive performance assessments including large-scale cognitive screening in primary care. However, lacking suitable screening tools for practical use is one of the barriers for early detection of MCI in primary care practice. Currently, various brief cognitive tools have been introduced to detect cognitive decline as first-line screening methods. These tests need to be administered by trained physicians and require extensive amounts of time. The limited length of time of the average primary care physicians visit requires tests to be conducted in 10 minutes or less. This constraint introduces a major limitation, as cognition is multifaceted, and many different cognitive domains can be impacted by MCI. Testing all domains of cognition in a short test is likely not feasible, so tools must strike a proper balance between time and depth of testing to maximize their utility. In primary care practice, there is an urgent need for time- saving, easily administered, sensitive, specific and reliable cognitive tools to identify the early stages of subtle cognitive decline. Our previously study had formed Chinese Neuropsychological Consensus Battery (CNCB) via Delphi method and all the tests in it are culturally appropriate and have been validated in Chinese. The CNCB neurocognitive tests cover six subdomains, including attention, memory, executive function, language, visuospatial and social cognition. We further digitized this comprehensive cognitive battery, and it could be administered on a touchscreen computer. The computerized CNCB is potentially useful in the detection of MCI and Dementia but time-consuming as it contains to many tests. In this study, based on the study mentioned above, we aim to further use machine-learning method to develop a brief and accurate combination of cognitive tests from the CNCB for the early detection of MCI in community and primary care. |
| Additional Investigators |
