DaTscan-based Disease Progression Models for Early-stage Parkinson’s Disease

项目摘要 帕金森病（PD）是异质性的：它有许多亚型，疾病在不同的时间进展。 不同亚型的发病率。早期PD中的疾病进展被观察为SPECT中的信号变化 使用123 I-FP-CIT进行成像，这被称为DaTscan成像，或简称DaTscan。研究的目标 这里提出的是使用DaTscan图像创建异质性PD进展的精确模型。等 模型不仅提供了对PD的进一步了解，而且在评估PD的过程中也至关重要。 神经保护治疗的效果。 提出了一种新的线性动力系统混合模型（MLDS）来描述早期状态 在DaTscans中显示的PD进展。MLDS模型联合收割机机器学习方法与线性 动力系统理论它们捕捉了早期PD进展的许多特征：偏侧性、非线性 进展，以及PD异质性。初步结果表明，MLDS是准确的，发现进展 亚型，与临床数据（MDS-MRS运动评分）相关良好，并提供了关于PD的真正新见解 进展 该研究的目的是发展MLDS方法在区域的利益，以及基于体素 框架。详细讨论了MLDS理论、模型拟合以及与临床数据的关系， 包括.纵向DaTscan图像以及MDS-MLS RS运动评分可用于超过440 受试者来自帕金森病进展标志物倡议（PPMI），该数据集将与沿着使用 MLDS用于创建PD进展模型。

Project Summary Parkinson's disease (PD) is heterogeneous: it has many subtypes and the disease progresses at different rates in different subtypes. Disease progression in early-stage PD is observed as signal changes in SPECT imaging with 123I-FP-CIT, which is called DaTscan imaging, or simply DaTscan. The goal of the research proposed here is to create accurate models of heterogeneous PD progression using DaTscan images. Such models will not only provide additional insight into PD, but they are also critically important in assessing the effect of neuro-protective therapy. A new set of models called mixtures of linear dynamical systems (MLDS) are proposed to model early-state PD progression as it manifests in DaTscans. MLDS models combine machine-learning methods with linear dynamical system theory. They capture many features of early-stage PD progression: laterality, non-linear progression, as well as PD heterogeneity. Preliminary results show that, MLDS is accurate, finds progression subtypes, relates well to clinical data (MDS-UPDRS motor scores), and gives genuinely new insights about PD progression. The proposed research aims to develop the MLDS methodology in region-of-interest as well as voxel-based frameworks. A detailed discussion of the MLDS theory, model fitting, and the relation to clinical data is included. Longitudinal DaTscan images as well as MDS-UPDRS motor scores are available for over 440 subjects from the Parkinson's Progression Markers Initiative (PPMI), and this data set will be used along with MLDS to create the PD progression models.