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Functional Regress Models with Application in Brain Imaging Studies

功能回归模型在脑成像研究中的应用

基本信息

批准号：
8246500
负责人：
TODD OGDEN
金额：
$ 21.05万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2014-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8246500
关键词：
Aftercare Algorithms Alzheimer&apos s Disease Area Biological Brain Brain imaging Categories Clinical Computer software Confidence Intervals Data Data Set Development Diagnosis Diagnostic Diffusion Magnetic Resonance Imaging Dimensions Disease Functional Magnetic Resonance Imaging Functional disorder Future Genetic Goals Image Image Analysis Imaging technology Individual Investigation Linear Models Literature Major Depressive Disorder Maps Measures Medical Medicine Mental Depression Methodology Methods Modeling Outcome Patients Pattern Physiological Positron-Emission Tomography Proteins Psychiatry Research Personnel Role Senile Plaques Sensory Receptors Severities Signal Transduction Simulate Statistical Models Suicide attempt Symptoms System Technology Testing Three-Dimensional Imaging Treatment outcome Work base clinically relevant cognitive function comparison group density imaging modality improved insight neuropsychiatry public health relevance response sex statistics theories tool two-dimensional user friendly software

项目摘要

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.

描述（由申请人提供）：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。使用正电子发射断层扫描（PET）测量整个大脑中各种蛋白质的密度和分布以及使用功能性磁共振成像确定局部脑功能的能力已经产生了关于重度抑郁症（MDD），阿尔茨海默病（AD）和其他神经精神疾病的生理基础的重要见解。该技术的使用导致了对这些疾病的病理生理学的新理解，包括例如正常对照和患有MDD的受试者之间的差异模式。成像数据的组级分析通常使用诸如统计参数映射（SPM）的方法来执行，其中统计模型被单独地拟合到共同配准的图像的每个体素。针对每个体素计算检验统计量，将成像数据视为“响应”变量，并将患者特定信息（治疗组、性别等）作为预测者。我们建议开发一些模型来颠倒这些变量的作用，即，使用图像作为预测因子，并使用诸如对治疗的反应等变量作为结果。本提案的主要目标是：1.开发用于以二维和三维图像作为预测因子拟合模型的方法，以及用于按照两种一般方法对估计的模型参数进行推断的方法，一种基于图像的样条表示，一种基于小波分解，两者都涉及用于降维的计算密集型算法; 2.通过应用于模拟数据集和两种真实数据情况（一种是MDD研究中的β-肾上腺素能系统的PET图像，另一种是AD研究中的淀粉样斑块的PET图像）来验证该方法; 3.创建并提供用于拟合此类模型的软件。除了统计方法的进步，这将使研究人员能够更好地了解大脑的哪些区域最能预测各种结果。公共卫生相关性：我们建议开发统计模型，其中图像（结合适当的临床或生物学协变量）作为标量结果变量的预测因子。潜在的应用包括使用大脑图像作为结果的预测因子，例如对抑郁症特定治疗的反应，阿尔茨海默病的发展或自杀企图。一旦开发和验证，这种方法可以应用于任何成像模式的数据，包括结构和功能磁共振成像和扩散张量成像。