Advanced Modeling Techniques for Brain Imaging Data with PET

使用 PET 进行脑成像数据的先进建模技术

基本信息

批准号：
9980905
负责人：
TODD OGDEN
金额：
$ 36.02万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-17 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9980905
关键词：
Address Affect Age Algorithms Alzheimer&apos s Disease Amyloid beta-Protein Anxiety Disorders Binding Biological Biological Process Bipolar Disorder Blood Brain Brain imaging Categories Clinical Research Communities Complex Computer software DNA Data Data Analyses Depressed mood Diagnostic Disease Human Image Individual Kinetics Measurement Measures Mental Depression Methodology Methods Modeling Outcome Measure Patients Pattern Population Positron-Emission Tomography Post-Traumatic Stress Disorders Procedures Process Proteins Psyche structure Research Schizophrenia Serotonergic System Statistical Data Interpretation Structure Techniques Testing Time Tracer analytical tool base burden of illness clinical application cost data modeling density flexibility improved interest kinetic model method development neuropsychiatric disorder neuropsychiatry radiotracer receptor density sugar suicide attempter tool treatment response virtual

项目摘要

Summary Mental and neuropsychiatric illnesses (including depression, Alzheimer's Disease, and many others) will affect roughly 20% of the population sometime during their lifetimes. By some measurements these illnesses represent the leading category of disease burden worldwide. Positron Emission Tomography (PET) of the brain has become an invaluable research tool for studying such illnesses because it allows quantiﬁcation of the density of various molecules throughout the brain. In the current state of the art in the analysis of PET imaging data, there are two major drawbacks. The ﬁrst is that analysis is always done as a “two-stage” process: Stage 1 consists of modeling the PET data over time to get a single (scalar) estimate of receptor density, either for each voxel or for each of one or more regions of interest. Subsequently, in Stage 2 these estimates are effectively regarded as the observed data, and statistical analysis involves comparing these estimates across individuals, between diagnostic groups, etc. This is an inefﬁcient use of data and it does not allow good precision when investigating some subtle systematic effects. The second major drawback is that the ﬁeld relies almost exclusively on parametric models. The basic model for PET data in a voxel or ROI is a kinetic model that relies on some fairly strong assumptions about the biological processes that, while they are often reasonable approximations to the truth in some instances, are often thought to be violated. By relying on principles of functional data analysis (FDA), we can open up a powerful new analysis structure for investigating differences among individuals, among groups, and for making individual- level predictions (e.g., response to treatment). This project will undertake the following three aims. 1. To develop methodology based on parametric models that combines both Stage 1 and Stage 2 into a single analysis process. This will allow for much more reﬁned analysis that can look for differences between groups in individual kinetic rate parameters, rather than relying only on aggregate outcome measures. 2. To develop FDA-based tools for comparing PET imaging data across subjects, across groups, etc. This will require new analysis methods since the relevant functional data are not observed directly but can only be estimated using some form of nonparametric deconvolution algorithm of the observed PET data over time. 3. To incorporate recent advances made by our group and others, in the contexts of both the parametric and the nonparametric approaches, to the situation in which blood data and/or a “reference region” is not available. Aim 1 is intended for PET radiotracers in which parametric models exist and provide a reasonable ﬁt for the data. Aim 2 is intended both for tracers not described well by usual parametric models and also as supplementary nonparametric analysis. Aim 3 will extend the reach of these methods and widen the potential application of PET imaging. These new advances have the potential to greatly enhance our understanding of the biological underpinnings of many neuropsychiatric diseases as well as response to treatment.

概括精神和神经精神疾病（包括抑郁症，阿尔茨海默氏病等）将影响一生中大约有20％的人口。通过一些测量，这些疾病代表伯恩在全球范围内的主要类别。大脑的正电子发射断层扫描（PET）已成为一种用于研究此类疾病的宝贵研究工具，因为它可以定量各种密度整个大脑的分子。在目前的宠物成像数据分析中，有两个主要缺点。首先是分析始终是“两个阶段”过程：阶段1包括建模随着时间的推移，宠物数据以获得每个体素的单个（标量）估计值或更多关注区域。随后，在第2阶段中，这些估计值有效地视为观察到的数据，统计分析涉及比较个人之间，诊断组之间的这些估计值。这是数据的不足用途，在研究一些微妙的系统性时，它不允许精确效果。第二个主要缺点是该领域几乎完全依赖于参数模型。基本 Voxel或ROI中PET数据的模型是一个动力学模型，它依赖于一些相当有力的假设生物学过程在某些情况下通常是对真理的合理近似的生物过程，但通常是被认为是违反的。通过依靠功能数据分析的原则（FDA），我们可以打开一个有力的新的新分析结构，用于调查个体之间，群体之间的差异以及使个人的差异水平预测（例如，对治疗的反应）。该项目将实现以下三个目标。 1。发展基于参数模型的方法将阶段1和第2阶段结合到单个分析过程中。这将允许更多的改进分析，可以在单个动力学中寻找组之间的差异速率参数，而不是仅依靠汇总结果度量。 2。开发基于FDA的工具比较跨受试者，跨组等的PET成像数据。这将需要新的分析方法，因为没有直接观察到相关的功能数据，但只能使用某种形式的非参数估算随着时间的推移，观察到的PET数据的反向卷积算法。 3。结合我们的最新进展在参数和非参数方法的背景下，团体和其他人都应对哪些血液数据和/或“参考区域”不可用。 AIM 1适用于宠物放射性示例存在参数模型并为数据提供合理的拟合。 AIM 2是针对未描述的示踪剂的两者通过通常的参数模型，也可以作为补充非参数分析。 AIM 3将延伸到覆盖范围在这些方法中，扩大了PET成像的潜在应用。这些新进步有可能极大地增强了我们对许多神经精神疾病的生物基础的理解以及对治疗的反应。