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: | Maral Mousavi |
| Institution: | University of Southern California |
| Department: | Biomedical Engineering |
| Country: | |
| Proposed Analysis: | My laboratory develops point-of-care sensors for rapid diagnosis of diseases and frequent monitoring of disease progress.
We are working to develop a multiplexed electrochemical sensor for point of care readout of blood-based biomarkers of Alzheimer's disease (AD) from a finger stick. We are still working to identify the list of biomarkers, but so far we plan to detect amyloid beta (42/40 ratio), p-tau217, and NfL. In addition, we are hoping to develop a machine learning algorithm for classification of AD stage based on the sensor readout. We have coordinated with UCSD ADRC and USC ATRI to receive serum samples from AD patients with diagnosed AD stages, to generate the training and validation data sets for developing our machine learning algorithm. This step most likely will occur 2-3 years from now when the biosensor is developed and optimized.
While we are working on the engineering of the point-of-care sensor, we are hoping to start the data analysis and development of our machine learning algorithm using existing data. Specifically, we are interested in biomarker levels in blood, and CSF, for AD patients at different stages (Preclinical, MCI, mild, moderate, severe). We are considering the age, sex, and race of the patient as biological variables, so knowing this information about each patient is important.
What we are hoping to access from ADNI is data entailing biomarker levels in blood and CSF (or other available biofluids), tagged with AD stage, age, sex, and race of each patient. In terms of biomarkers, we are happy to receive all analyzed markers. Our literature search shows correlation of AD to amyloid levels, p-tau217, and Neurofilament light chain (NfL) to AD stage. We are interested to know levels of all biomarkers available to perform our own statistical analysis and test the sensitivity of the biomarkers for our machine learning algorithm. This data will be of great help to our work as it can inform us about the correct combination of biomarkers to adopt for developing our multiplexed point-of-care sensors.
My work is mostly on the sensor development side. I have a colleague who is collaborating with me on development of machine learning algorithm. His contact:
Prof. Meisam Razaviyayn
Assistant Professor, University of Southern California
Departments of Industrial Systems Engineering, Electrical and Computer Engineering, and Computational Biology
"Meisam Razaviyayn" |
| Additional Investigators | |
| Investigator's Name: | Meisam Razaviyayn |
| Proposed Analysis: | Same as described by Dr. Mousavi |
