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.

帕金森病会引起皮质下深部灰质的改变，甚至在运动和非运动临床症状出现之前就导致几个皮质下结构的形状发生微妙的变化。我们使用自动注册和分割流程来测量一个早期和一个晚期帕金森病 (PD) 队列、一个前驱阶段队列和一个健康对照队列中的这种结构变化。然后，这些结构改变被传递到机器学习管道以对这些群体进行分类。我们的工作流程能够仅根据双侧尾状核和壳核的形状分析来区分 PD 的不同阶段，平衡准确度在 59% 至 85% 范围内。此外，我们比较了每个皮质下结构的重要性，比较了不同分类器在该任务中的表现，从而量化了纹状体形状改变作为 PD 分期生物标志物的信息量。