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

基于 DaTscan 的早期帕金森病疾病进展模型

• 批准号: 9902564
• 负责人: Hemant D Tagare
• 金额: $ 35.32万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9902564
• 关键词: Algorithms; Anterior; Bilateral; Biological Models; Clinical; Clinical Data; Corpus striatum structure; Coupled; Coupling; Data; Data Analyses; Data Set; Disease; Disease Progression; Enrollment; Exhibits; Glean; Globus Pallidus; Goals; Handedness; Image; Joints; Left; Machine Learning; Mathematics; Measures; Methodology; Methods; Modeling; Motor; National Institute of Neurological Disorders and Stroke; Parkinson Disease; Pattern; Recommendation; Research; Research Design; Signal Transduction; Systems Theory; Work; base; disease heterogeneity; dopamine transporter; dynamic system; expectation; insight; interest; machine learning method; model design; neuron loss; presynaptic; progression marker; putamen; research clinical testing; single photon emission computed tomography; uptake

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.

项目总结