Robust Bayesian Analysis of Early-Stage Parkinson’s Disease Progression Using DaTscan Images
| Autor: | Yuan Zhou, Sule Tinaz, Hemant D. Tagare |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Parkinson's disease
Computer science Disease Bayesian inference Article 030218 nuclear medicine & medical imaging Linear dynamical system 03 medical and health sciences Bayes' theorem symbols.namesake 0302 clinical medicine Robust Bayesian analysis medicine Humans Electrical and Electronic Engineering Time series Radiological and Ultrasound Technology business.industry Disease progression Linear model Brain Bayes Theorem Parkinson Disease Pattern recognition medicine.disease Computer Science Applications Disease Progression Linear Models symbols Artificial intelligence business Software Gibbs sampling |
| Zdroj: | IEEE Trans Med Imaging |
| ISSN: | 1558-254X 0278-0062 |
| DOI: | 10.1109/tmi.2020.3031478 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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