Investigating the fPDRP and fPDCP brain patterns in Parkinson’s disease.
Main applicant: Dr. Rick Helmich, Dr. Amgad Droby and Dr. An Vo
Affiliation(s): Radboud University Medical Center, Nijmegen (NL), Tell Aviv Sourasky Medical Center (IL), Feinstein Institute for Medical Research, Manhasset (US)
Abstract: Currently, the main way to confirm Parkinson's disease (PD) through imaging is by using a special type of scan called dopamine-transporter SPECT (DaT-SPECT). However, DaT-SPECT has some drawbacks, such as being expensive, using radioactive materials, and not being widely available. These limitations make it less effective for monitoring early stages of the disease, which can start up to 20 years before symptoms appear. MRI (Magnetic Resonance Imaging) is a non-invasive alternative that can help study the disease throughout its course. One type of MRI, called resting-state functional MRI (rs-fMRI), measures brain activity by tracking blood oxygen levels. This method can show changes in brain networks due to ongoing disease processes, even before symptoms start. We identified previously specific brain activity patterns related to PD using another imaging technique called FDG-PET. These patterns, known as PD-related patterns (PDRP) and PD cognition-related patterns (PDCP), have been linked to PD symptoms like movement issues and cognitive decline. PDRP shows increased brain activity in certain regions and decreased activity in others, while PDCP is associated with changes in brain areas linked to cognitive functions. Similar brain patterns were identified using rs-fMRI as well, which confirms the brain activity changes seen in FDG-PET scans. In a recent study, we used advanced methods to analyze brain scans from 60 newly diagnosed PD patients and 58 healthy people of the same age and sex. We identified brain patterns that help differentiate between PD patients and healthy individuals with an accuracy of up to 83%. These patterns are linked to both movement and cognitive changes in PD. This project aims to test the AI model on a new group of drug-naive PD patients (De Novo) and healthy controls to see how well it works. Additionally, we seek to validate the identified brain patterns and explore their relationship with clinical performance scores.
Related Publications: Parkinson’s disease-related network topographies characterized with resting state functional MRI.