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: | Jialin LYU |
| Institution: | University of Leicester |
| Department: | School of Informatics |
| Country: | |
| Proposed Analysis: | Alzheimer's Disease (AD) is a degenerative brain disease and the most common cause of dementia. The symptoms of dementia include memory loss and difficulties with thinking, problem-solving or language, which seriously affect patients' daily life. Alzheimer’s Disease destroys brain cells, causing people to lose their memory, mental functions and ability to continue daily activities. It is a severe neurological brain disorder which is not curable, but earlier detection of Alzheimer’s Disease can help for proper treatment and to prevent brain tissue damage. Alzheimer's Disease can even cause death in elder people. Until now, the cause of AD is still unknown, and no effective drugs or treatments have been reported to stop or reverse AD progression. In the early stage of AD, patients will have symptoms of Mild Cognitive Impairment (MCI). As AD comes to the foreground as public awareness increases and as we approach a potential disease-modifying treatment, clinicians are encountering patients earlier in their disease journey. Early diagnosis of AD and MCI is essential for making reasonable treatment plans. Early diagnosis helps people to plan ahead while they are still able to make important decisions on their care and support needs and on financial and legal matters. It also helps them and their families to receive practical information, advice and guidance as they face new challenges. Various computer-aided approaches have been used for helping the diagnosis of AD. Several popular non-invasive neuroimaging tools are used to study AD, such as positron emission tomography (PET) and magnetic resonance imaging (MRI). MRI is one of the most popular methods, it can provide a good resolution of soft tissues within the brain, the sequence of progression of atrophy on MRI most closely fits histopathological studies. Alzheimer's Disease has a certain progressive pattern of brain tissue damage. The hippocampal formation (HF) is known from pathological and MRI studies to be severely atrophied in established Alzheimer's Disease as shown in Figure. Hippocampal formation can really help both clinicians and computer-aided approaches on decision-making. According to the existing methods, we proposed a Deep-learning model for automatic diagnosis of AD. We use our modified Deep-learning framework to extract the features in MRI data, finally, a neural network was used to finish the classification work using the extracted features. |
| Additional Investigators |
