Functional Regress Models with Application in Brain Imaging Studies

描述（申请人提供）：脑成像和其他成像技术为精神病学和其他医学领域的研究人员提供了强大的工具。利用正电子发射断层扫描（PET）测量各种蛋白质在整个大脑中的密度和分布，以及利用功能性磁共振成像确定脑区域功能的能力，对重度抑郁症（MDD）、阿尔茨海默病（AD）和其他神经精神疾病的生理基础产生了重要的见解。这项技术的使用使人们对这类疾病的病理生理学有了新的认识，例如，正常对照者和重度抑郁症患者之间的差异模式。成像数据的组级分析通常使用统计参数映射（SPM）等方法进行，其中统计模型分别适合于共同配准图像的每个体素。将成像数据作为“反应”变量，将患者特异性信息（治疗组、性别等）作为预测因子，计算每个体素的检验统计量。我们建议开发模型来逆转这些变量的作用，即使用图像作为预测因子，使用诸如对治疗的反应等变量作为结果。本建议的主要目标是：1。开发以二维和三维图像作为预测因子的拟合模型的方法，并根据两种一般方法对估计的模型参数进行推断，一种方法基于图像的样条表示，另一种方法基于小波分解，两者都涉及计算密集的降维算法；2. 通过模拟数据集和两种实际数据情况（一种是MDD研究中血清素能系统的PET图像，另一种是AD研究中淀粉样斑块的PET图像）来验证方法；3. 创建并提供适合这些模型的软件。除了统计方法的进步之外，这将使研究人员能够更好地了解大脑的哪个区域最能预测各种结果。

DESCRIPTION (provided by applicant): Brain imaging and other imaging technologies have provided powerful tools for researchers in psychiatry and other medical fields. The ability to measure the density and distribution of various proteins throughout the brain using positron emission tomography (PET) and to determine regional brain function using functional magnetic resonance imaging has yielded important insights as to the physiological basis of major depressive disorder (MDD), Alzheimer's disease (AD), and other neuropsychiatric illnesses. The use of this technology has led to new under- standings of the pathophysiology of such illnesses including, for instance, patterns of differences between normal controls and subjects suffering from MDD. Group-level analysis of imaging data is typically performed using methodology such as Statistical Parametric Mapping (SPM) in which a statistical model is fit separately to each voxel of the co-registered images. A test statistic is computed for each voxel, regarding the imaging data as the "response" variable and the patient-specific information (treatment group, sex, etc.) as predictors. We propose to develop models that reverse the roles of these variables - i.e., to use images as predictors and variables such as response to treatment as outcomes. The primary objectives of this proposal are: 1. to develop methodology for fitting models with two-dimensional and three-dimensional images as predictors and for inference on the estimated model parameters following two general approaches, one based on a spline representation of images and one based on a wavelet decomposition, both involving computationally intensive algorithms for dimension reduction; 2. to validate the methodology by application to simulated data sets and in two real-data situations (one with PET images of the serotonergic system in an MDD study and one with PET images of amyloid plaques in an AD study); 3. to create and make available software for fitting such models. In addition to advances in statistical methodology, this will enable researchers to better understand which areas of the brain are most predictive of various outcomes. PUBLIC HEALTH RELEVANCE: We propose to develop statistical models in which images (in combination with appropriate clinical or biological covariates) serve as predictors of scalar outcome variables. Potential applications include using brain images as predictors of outcomes such as response to a particular treatment for depression, development of Alzheimer's disease, or making a suicide attempt. Once developed and validated, this methodology could be applied to data from any imaging modality, including structural and functional magnetic resonance imaging and diffusion tensor imaging.