Striatal shape alteration as a staging biomarker for Parkinson’s Disease

• Main Authors: Maxime Peralta, John S.H. Baxter, Ali R. Khan, Claire Haegelen, Pierre Jannin
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: NeuroImage: Clinical
• Subjects: Parkinson’s disease; Morphometric biomarkers; Machine learning; Staging biomarker; Medical imaging
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213158220301091

Parkinson’s Disease provokes alterations of subcortical deep gray matter, leading to subtle changes in the shape of several subcortical structures even before the manifestation of motor and non-motor clinical symptoms. We used an automated registration and segmentation pipeline to measure this structural alteration in one early and one advanced Parkinson’s Disease (PD) cohorts, one prodromal stage cohort and one healthy control cohort. These structural alterations are then passed to a machine learning pipeline to classify these populations. Our workflow is able to distinguish different stages of PD based solely on shape analysis of the bilateral caudate nucleus and putamen, with balanced accuracies in the range of 59% to 85%. Furthermore, we compared the significance of each of these subcortical structure, compared the performances of different classifiers on this task, thus quantifying the informativeness of striatal shape alteration as a staging bio-marker for PD.