Logistic regression with PET brain images as predictors

以 PET 脑图像作为预测变量的逻辑回归

• 批准号: 6995158
• 负责人: PHILIP T REISS
• 金额: $ 2.81万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-16 至 2008-06-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6995158
• 关键词: bioimaging /biomedical imaging; brain imaging /visualization /scanning; clinical research; computational biology; computer program /software; computer system design /evaluation; human data; major depression; mathematical model; positron emission tomography; predoctoral investigator; serotonin receptor; statistics /biometry

DESCRIPTION (provided by applicant): Recent developments in statistics have extended the logistic regression model to incorporate functional data, such as curves representing time series for each individual, as predictors. The goal of this project is to extend this work further to allow two- or three-dimensional brain images, consisting of voxel-wise measures of serotonin receptor density, to serve as inputs in a logistic regression, with probability of response to treatment as output. Whereas a standard logistic regression produces coefficients indicating the extent to which each predictor influences the outcome, the proposed analysis will produce a coefficient function, itself representable as an image, which will indicate which brain regions' serotonin receptor density is most predictive of response. The same model could be applied to any depression-related binary outcome of interest. The proposed methodology will complement existing approaches, such as statistical parametric mapping, which distinguish between groups via a model treating the images as the outcome variable. It will also improve on two approaches which treat the image as a predictor-partial least squares and spline methods--by combining the advantages of both.

描述（由申请人提供）：统计学的最新发展已经扩展了逻辑回归模型，以纳入功能数据，例如代表每个人的时间序列的曲线，作为预测因子。该项目的目标是进一步扩展这项工作，以允许二维或三维大脑图像，由血清素受体密度的体素测量组成，作为逻辑回归的输入，以治疗反应的概率作为输出。标准逻辑回归产生的系数表明每个预测因素对结果的影响程度，而提议的分析将产生一个系数函数，它本身可以表示为图像，它将表明哪个大脑区域的血清素受体密度最能预测反应。同样的模型可以应用于任何与抑郁相关的二元结果。拟议的方法将补充现有的方法，如统计参数映射，通过将图像作为结果变量的模型来区分各组。它还将改进两种将图像视为预测器的方法-偏最小二乘法和样条法-通过结合两者的优点。