Advanced Modeling Techniques for Brain Imaging Data with PET

总结 精神和神经精神疾病（包括抑郁症，阿尔茨海默病和许多其他疾病）将影响 大概有20%的人在他们的一生中。通过一些测量，这些疾病代表了 是全球疾病负担的主要类别。大脑的正电子发射断层扫描（PET）已经成为 这是研究此类疾病的宝贵研究工具，因为它可以量化各种疾病的密度。 分子遍布大脑。在PET成像数据的分析的当前技术状态中，存在两个 主要缺点。首先，分析总是作为一个“两阶段”的过程来完成：第一阶段包括建模 PET数据随时间变化，以获得受体密度的单个（标量）估计，无论是对于每个体素还是对于一个 或更多感兴趣的区域。随后，在阶段2中，这些估计被有效地视为观测数据， 并且统计分析涉及在个体之间、在诊断组之间等比较这些估计。 这是对数据的无效使用，在调查一些微妙的系统性问题时， 方面的影响.第二个主要缺点是，该领域几乎完全依赖于参数模型。基本 体素或ROI中的PET数据模型是一种动力学模型，其依赖于关于PET数据的一些相当强的假设。 生物过程，虽然它们在某些情况下往往是合理的近似真理，但它们往往是 被认为是被侵犯的。通过依赖功能数据分析（FDA）的原则，我们可以开发一个强大的新的 分析结构，用于调查个体之间、群体之间的差异，并用于使个体- 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，将第1阶段和第2阶段合并为单个分析过程。 这将允许更精细的分析，可以寻找个体动力学中组间的差异 率参数，而不是只依赖于总体结果的措施。2.开发基于FDA的工具， 比较不同受试者、不同组别等的PET成像数据。这将需要新的分析方法，因为 相关的函数数据不能直接观察到，只能使用某种形式的非参数估计。 随时间推移观察到的PET数据的反卷积算法。3.结合我们的最新进展， 小组和其他人，在参数和非参数方法的背景下， 该血液数据和/或“参考区域”不可用。Aim 1适用于PET放射性示踪剂，其中 参数模型存在，并为数据提供合理的拟合。目标2是为未描述的示踪剂 以及通常的参数模型，也作为补充非参数分析。目标3将扩大范围 拓宽了PET成像的应用前景。这些新进展有可能 大大提高了我们对许多神经精神疾病的生物学基础的理解， 对治疗的反应

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.