Department of Statistics, University of Virginia, Charlottesville, VA 22904, USA.
Department of Statistical Science, Duke University, Durham, NC 27708, USA.
Neuroimage. 2013 Jul 15;75:136-145. doi: 10.1016/j.neuroimage.2013.02.048. Epub 2013 Mar 5.
A semi-parametric model for estimating hemodynamic response function (HRF) from multi-subject fMRI data is introduced within the context of the General Linear Model. The new model assumes that the HRFs for a fixed brain voxel under a given stimulus share the same unknown functional form across subjects, but differ in height, time to peak, and width. A nonparametric spline-smoothing method is developed to evaluate this common functional form, based on which subject-specific characteristics of the HRFs can be estimated. This semi-parametric model explicitly characterizes the common properties shared across subjects and is flexible in describing various brain hemodynamic activities across different regions and stimuli. In addition, the temporal differentiability of the employed spline basis enables an easy-to-compute way of evaluating latency and width differences in hemodynamic activity. The proposed method is applied to data collected as part of an ongoing study of socially mediated emotion regulation. Comparison with several existing methods is conducted through simulations and real data analysis.
在广义线性模型的背景下，引入了一种用于从多主体功能磁共振成像数据中估计血流动力学响应函数（HRF）的半参数模型。该新模型假设，在给定刺激下，固定脑体素的 HRF 在主体间具有相同的未知功能形式，但在高度、峰值时间和宽度上有所不同。开发了一种非参数样条平滑方法来评估这种常见的功能形式，在此基础上可以估计 HRF 的特定于主体的特征。这种半参数模型明确地描述了主体间共享的共同特性，并且在描述不同区域和刺激的各种大脑血液动力学活动方面具有灵活性。此外，所采用的样条基的时间可微性使得能够以易于计算的方式评估血液动力学活动中的潜伏期和宽度差异。该方法应用于正在进行的社会介导情绪调节研究中收集的数据。通过模拟和实际数据分析，与几种现有方法进行了比较。
