Robust Bayesian Analysis of Early-Stage Parkinson's Disease Progression Using DaTscan Images

This paper proposes a mixture of linear dynamical systems model for quantifying the heterogeneous progress of Parkinson's disease from DaTscan Images. The model is fitted to longitudinal DaTscans from the Parkinson's Progression Marker Initiative. Fitting is accomplished using robust Bayesian inference with collapsed Gibbs sampling. Bayesian inference reveals three image-based progression subtypes which differ in progression speeds as well as progression trajectories. The model reveals characteristic spatial progression patterns in the brain, each pattern associated with a time constant. These patterns can serve as disease progression markers. The subtypes also have different progression rates of clinical symptoms measured by MDS-UPDRS Part III scores.

Automated summary

This study proposes a model for quantifying the heterogeneous progression of Parkinson's disease in DaTscan images, revealing different progression subtypes with varying speeds and trajectories. The model also identifies characteristic spatial progression patterns in the brain, which can serve as markers of disease progression, and the subtypes exhibit different rates of progression in clinical symptoms.