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: | Fadi Thabtah |
| Institution: | University of Huddersfield |
| Department: | Psychology |
| Country: | |
| Proposed Analysis: | The current study aims to develop new classification models based on Ensemble learners to predict traits associated with dementia. The study will conduct an evaluation of the impactful dementia features that are extracted from pathological, clinical and non-clinical features in the dementia dataset of ADNI. This evaluation of the massive features from different cases and controls will lead us to identify specific characteristics related to early diagnosis of dementia and other mild cognitive impairment (MCI). Therefore, we can define metrics that can possibly quantify the level of impairments and discriminate influential characteristics. This indeed will help neurologists, diagnosticians, psychologists and clinicians among others to measure components defined in in the DSM-5 in order to appropriately placing cases on the right impairment levels. More importantly, the proposed solution can be implemented to enhance both the classification process of pre-dementia diagnosis as well as speed up that process. |
| Additional Investigators | |
| Investigator's Name: | David Peebles |
| Proposed Analysis: | The current study aims to develop new classification models based on Ensemble learners to predict traits associated with dementia. The study will conduct an evaluation of the impactful dementia features that are extracted from pathological, clinical and non-clinical features in the dementia dataset of ADNI. This evaluation of the massive features from different cases and controls will lead us to identify specific characteristics related to early diagnosis of dementia and other mild cognitive impairment (MCI). Therefore, we can define metrics that can possibly quantify the level of impairments and discriminate influential characteristics. This indeed will help neurologists, diagnosticians, psychologists and clinicians among others to measure components defined in in the DSM-5 in order to appropriately placing cases on the right impairment levels. More importantly, the proposed solution can be implemented to enhance both the classification process of pre-dementia diagnosis as well as speed up that process. |
