Collaborative Research: Statistical Modeling and Inference for High-dimensional Multi-Subject Neuroimaging Data

合作研究：高维多主体神经影像数据的统计建模和推理

• 批准号: 1208983
• 负责人: Fan Li
• 金额: $ 7.11万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208983&HistoricalAwards=false
• 关键词: Collaborative; Research; Statistical; Modeling; Inference

This project consists of two components, each motivated by the inference problem for functional magnetic resonance imaging (fMRI) data. In the first part, within the framework of generalized functional linear model (GFLM), a flexible semi-parametric model for neural hemodynamic response in the form of slope functions is introduced. To accommodate the variation of brain activity across different regions, stimulus types, and subjects, the new approach assumes the slope functions share the same but unknown functional shape for a given region and stimulus, while having subject-specific height, time to peak, and width. Several fast algorithms based on B-spline smoothing are proposed to estimate the model parameters for whole-brain analysis. The second part of the research focuses on building a novel Bayesian variable selection framework to study the relationship between individual traits and brain activity. The spline estimates of the brain hemodynamic responses from the first part are taken as predictors in a regression model where the response is the individual traits. Two types of priors are introduced jointly to achieve simultaneous variable selection and clustering.FMRI is one of the most effective neuroimaging technologies for understanding brain activity. In recent years, fMRI data collected from complex studies with multiple subjects have been widely used in psychological and medical research. This project will provide tools for modeling, analysis and computation for this type of fMRI data. Project findings will advance basic understanding of the inter-relations between nature and nurture in shaping individual differences in brain function and behavior, and suggest new directions for interdisciplinary research that combines statistics, neuroscience and psychology. The open source R/Matlab software developed from the research will provide valuable data analysis and educational tools for the scientific community.

该项目由两个部分组成，每个部分都是由功能磁共振成像 (fMRI) 数据的推理问题推动的。第一部分，在广义函数线性模型（GFLM）的框架内，介绍了一种灵活的斜率函数形式的神经血流动力学响应半参数模型。为了适应不同区域、刺激类型和受试者之间大脑活动的变化，新方法假设斜率函数对于给定区域和刺激具有相同但未知的功能形状，同时具有受试者特定的高度、达到峰值的时间和宽度。 提出了几种基于 B 样条平滑的快速算法来估计全脑分析的模型参数。研究的第二部分重点是构建一个新颖的贝叶斯变量选择框架来研究个体特征与大脑活动之间的关系。第一部分的大脑血流动力学响应的样条估计被视为回归模型中的预测因子，其中响应是个体特征。联合引入两种类型的先验以实现同时变量选择和聚类。FMRI 是了解大脑活动最有效的神经成像技术之一。近年来，从多个受试者的复杂研究中收集的功能磁共振成像数据已广泛应用于心理学和医学研究。该项目将为此类功能磁共振成像数据提供建模、分析和计算工具。项目研究结果将促进对先天与后天之间相互关系在塑造大脑功能和行为个体差异方面的基本理解，并为结合统计学、神经科学和心理学的跨学科研究提出新方向。该研究开发的开源 R/Matlab 软件将为科学界提供有价值的数据分析和教育工具。