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: | Aya Kabbara |
| Institution: | Lebanese university |
| Department: | Fac of engineering |
| Country: | |
| Proposed Analysis: | With the aging of contemporary society, cognitive impairment and dementia in the elderly, mainly Alzheimer's disease (AD), have become increasingly serious. As a multifactor, multistage, and clinical syndrome with concomitant diseases, senile cognitive impairment will take progress to irreversible dementia several stages, so early diagnosis is essential. Early intervention of Brain's disease can effectively slow down the disease progression while reduce the burden on patients' families and our society [1] . Magnetic resonance imaging (MRI) is now considered a powerful tool for non-invasive visualization and differentiation of soft tissues. Due to its several advantages over other imaging techniques and its high contrast and spatial resolution, MRI has been widely used for the diagnosis of several brain related diseases that mostly occur due to cerebral aging such as Epilepsy, Alzheimer’s disease, Parkinson, etc. Moreover, following the progression of cerebral aging requires the quantification of the structural and topological changes that occur in time. Several studies have been conducted for the assessment of volumetric changes that appear in different brain regions during aging using structural T1-weighted images. We are interested in the textural changes that occur in these regions with age. Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled. Its algorithms use multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces... Due to its effectiveness in brain diseases such as Alzheimer’s and Parkinson, Deep Learning is becoming more and more widely used in brain age prediction and brain diseases diagnostic by training models on a given data set to complete specific tasks on new data. In this context, we investigate, in the detection of cerebral aging and the diagnosis of several brain related diseases, the most suitable Deep learning algorithm that is able to highlight change in MR images with age. This cross-sectional study will be conducted on T1- weighted MR images scanned at different ages found in the following database (https://ida.loni.usc.edu). |
| Additional Investigators |
