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Predicting Parkinson's Disease Progression Rate Using Causal Measures of Functional MRI with Deep Learning Predictive Models

使用功能 MRI 的因果测量和深度学习预测模型来预测帕金森病的进展率

基本信息

批准号：
10227044
负责人：
Cooper James Mellema
金额：
$ 3.55万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-16 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10227044
关键词：
Address Adopted Adoption Alzheimer&apos s Disease Biological Markers Brain Characteristics Clinical Clinical Trials Communities Computer software Data Data Set Development Diffusion Magnetic Resonance Imaging Disease Disease Progression Etiology Fiber Florida Functional Magnetic Resonance Imaging Image Individual Learning Literature Machine Learning Magnetic Resonance Imaging Measures Medical center Methods Modeling Movement Disorder Society Unified Parkinson&apos s Disease Rating Scale National Institute of Neurological Disorders and Stroke Nerve Degeneration Neurobiology Neurodegenerative Disorders Neurologic Noise Outcome Parkinson Disease Patients Performance Pharmaceutical Preparations Process Prognosis Research Rest Signal Transduction Site Stroke Structure Texas Time Training Universities Work base blood oxygen level dependent cognitive testing deep learning improved interest learning community learning strategy nervous system disorder neural network neuroimaging marker novel patient stratification predictive modeling prognostic value programs progression marker prospective tractography

项目摘要

Project abstract Parkinson's disease (PD) is the second most common neurodegenerative disease. A critical gap in the treatment of PD patients is that there is no clinically adopted method to predict an individual's progression rate. A predictor would enable the enrichment of disease modifying drug trials with fast progressors likely to show changes in the short duration of a clinical trial and enable a more informed discussion with patients about their prognosis. This proposal develops a composite biomarker of progression rate using the connectivity information provided by resting-state functional Magnetic Resonance Imaging (rs-fMRI) and deep learning. Deep learning (DL) is well suited to form predictive models because it learns both an optimal hierarchy of features and how to combine them for accurate prediction. In rs-fMRI the blood-oxygen level dependent signal can be analyzed to infer connectivity throughout the brain. Traditionally, connectivity has been computed as the correlation between average regional activation time courses. However correlation based connectivity is prone to inferring spurious connections due to its inability to distinguish indirect from direct connectivity and inability to distinguish bidirectional from unidirectional connectivity. A causal connectivity approach can discern these differences and thereby provide a more faithful characterization of the true neurobiological connectivity. The existing literature suggests connectivity, particularly causal connectivity, from rs-fMRI can inform the estimation of PD progression, but the attempt to predict progression rate with causal connectivity in a DL model is unique to this project. This research develops several distinct approaches for building a progression rate predictor and apply them to three datasets including: the Parkinson's Progression Markers Initiative dataset, the NINDS Parkinson's Disease Biomarkers Program (PDBP) dataset, and the University of Texas Southwestern Medical Center's prospective imaging extension to the NINDS PBDP. In these studies, individual progression rates have been tracked over multiple years using multiple clinical measures. First, causal and correlative measures will be generated regionally and used with a DL model to create a baseline predictor of progression rate. Second, voxel- level causal measures will be generated as the increased granularity is expected to improve prediction accuracy. Third, since purely data-driven DL methods can be sensitive to dataset limitations, such as insufficient subjects and noise, these limitations will be addressed by developing a new structural connectivity regularization approach that constrains causal connectivity by the subject's own diffusion MRI. This regularization method will be general and likely applicable for building predictors for other neurological disorders such as stroke and Alzheimer's disease. This proposal will yield both DL models for predicting progression rate and a novel method to calculate constrained causal connectivity. All predictive models, composite neuroimaging biomarkers of progression rate and software will be publicly disseminated for ready incorporation by the scientific and clinical communities.

项目摘要帕金森病（Parkinson's disease，PD）是第二常见的神经退行性疾病。治疗上的一个关键差距目前还没有临床上采用的方法来预测个体的进展率。预测器将使疾病修饰药物试验的丰富与快速进展可能显示出变化，临床试验持续时间短，并能够与患者就其预后进行更知情的讨论。这建议使用以下提供的连接信息开发进展率的复合生物标志物：静息态功能性磁共振成像（rs-fMRI）和深度学习。深度学习（DL）适合于形成预测模型，因为它既学习最佳的特征层次结构，又学习如何将联合收割机准确预测。在rs-fMRI中，血氧水平依赖信号可以被分析以推断连接整个大脑。传统上，连接性被计算为以下各项之间的相关性：平均区域激活时间课程。然而，基于相关性的连接性易于推断虚假的由于无法区分直接连接和间接连接，从单向连接到双向连接。因果联系方法可以识别这些差异，从而提供真实神经生物学连接的更忠实的表征。现有文献提示rs-fMRI的连接性，特别是因果连接性，可以为PD进展的估计提供信息，但在DL模型中用因果连接预测进展率的尝试是该项目所独有的。这项研究开发了几种不同的方法来建立一个进展率预测和应用他们到三个数据集，包括：帕金森病进展标志物倡议数据集，NINDS帕金森病疾病生物标志物计划（PDBP）数据集和德克萨斯大学西南医学中心的 NINDS PBDP的前瞻性成像扩展。在这些研究中，个体进展率已被使用多种临床指标跟踪多年。首先，因果和相关措施将是区域性生成并与DL模型一起使用，以创建进展率的基线预测因子。第二，体素- 将产生等级因果测量，因为预期增加的粒度将提高预测准确性。第三，由于纯数据驱动的深度学习方法可能对数据集限制敏感，例如受试者不足和噪声，这些限制将通过开发新的结构连接正则化来解决通过受试者自身的扩散MRI约束因果连接的方法。这种正则化方法将是普遍的，并可能适用于建立其他神经系统疾病，如中风和老年痴呆症该建议将产生用于预测进展率的DL模型和一种新方法来计算约束因果连通性。所有预测模型，复合神经影像学生物标志物进展率和软件将公开传播，以便科学和临床社区.