Pseudotime Models of Disease Progression in Parkinson’s Disease

Music City Center 

Parkinson's disease (PD) is characterized by long-term degeneration of neurons that leads to debilitating impairments. Assessing PD progression often focus on clinical scales, which can be limited due to variability. Integrating other measurements, such as clinical imaging, can help define alterative metrics of progression that better represent the underlying disease. Novel modeling approaches can lead to discovery of new disease states or improve the statistical efficiency of clinical trials. To approximate biological timing of PD progression, we leveraged the Parkinson's Progression Markers Initiative (PPMI) data set and applied pseudotime approaches. We derived lower-dimensional representations of the data to identify cluster centroids that serve as anchor points in the disease trajectory. We inferred pseudotime values through a curve fitting method. We found that the inferred pseudotime has a good association with the progression of calendar time, as well as existing clinical measurements. Particularly, we have identified that clinical imacharacteristics have strong correlation with pseudotime, suggesting potential of this measurement modality in defining disease progression.

Parkinson’s Disease

Pseudotime

Disease Progression Modeling

Clinical Trials

Multi-modal data analysis 

Biopharmaceutical Section