UCSF

Parkinson’s disease (PD) is one of the most prevalent progressive, neurodegenerative disorders that affects motor and cognitive function. It is characterized by tremors, rigidity and bradykinesia and eventually progresses to cognitive decline in late stages. Currently, there is no cure for PD. The standard therapy for treatment merely slows progression, to an extent, and provides temporary symptomatic relief. This is largely due to the lack of clinical biomarkers to successfully identify PD in early stages, resulting in a huge gap of knowledge surrounding the progression and disease stage. Recent advancements in MR clinical imaging have provided substantial anatomical datasets for subsets of populations affected by PD. In addition, there has been increasing focus on the implementation of artificial intelligence within the study of the brain network. In 2012, Raj et. al proposed the network diffusion model (NDM) based on the diffusion equation that provides analytical projections on baseline atrophy rate. In this study, we applied a deep learning model, autoencoder, to predict future motor and cognitive states using features acquired at baseline. We found that by implementing the autocoder with the NDM, prediction accuracy was improved when compared to using stepwise linear regression alone. This novel and innovative approach to neurodegenerative diseases, such as PD, has great potential for enhancing statistical power within the clinic, by providing clinicians to make more informed therapy decisions for PD patients. Not only does it reduce subjectivity, but it allows the clinician to assess motor and cognitive states at any given time point in the future.