Study Rationale: Parkinson’s disease (PD) is often diagnosed when noticeable movement problems appear, but this happens later in the disease. There is a pressing need for new ways to detect early signs of the disease at a molecular level, which could lead to better and more personalized treatments. Current methods for imaging the molecules involved in PD have limitations, such as needing radioactive materials or taking a long time to complete. Recent improvements in a type of MRI called saturation-transfer-based imaging have shown promise, but the results are not always clear or specific.
Hypothesis: We believe that using advanced AI technology with a new type of MRI can help us create better ways to see and measure the molecules involved in Parkinson’s disease.
Study Design: We will develop a new MRI technique that leverages AI to quickly and accurately identify different molecules in the brain related to Parkinson’s disease. First, we will create new MRI scanning methods and test them on brain-like samples. Then, we will measure specific molecules in a mouse model that mimics early signs of PD. Finally, we will check how well our new imaging method works by comparing it with other scientific techniques.
Impact on Diagnosis/Treatment of Parkinson’s disease: If successful, this project could lead to a new way to diagnose Parkinson’s disease earlier and more accurately, as well as provide insights for more effective treatments tailored to individual patients.
Next Steps for Development: After this study, the next steps will involve refining the MRI technology for use in humans and conducting further tests to ensure it is effective for diagnosing Parkinson’s disease.