Goutte C, Nielsen F A, Hansen L K
Department of Mathematical Modeling, Technical University of Denmark, Lyngby.
IEEE Trans Med Imaging. 2000 Dec;19(12):1188-201. doi: 10.1109/42.897811.
Modeling the haemodynamic response in functional magnetic resonance (fMRI) experiments is an important aspect of the analysis of functional neuroimages. This has been done in the past using parametric response function, from a limited family. In this contribution, we adopt a semi-parametric approach based on finite impulse response (FIR) filters. In order to cope with the increase in the number of degrees of freedom, we introduce a Gaussian process prior on the filter parameters. We show how to carry on the analysis by incorporating prior knowledge on the filters, optimizing hyper-parameters using the evidence framework, or sampling using a Markov Chain Monte Carlo (MCMC) approach. We present a comparison of our model with standard haemodynamic response kernels on simulated data, and perform a full analysis of data acquired during an experiment involving visual stimulation.
在功能磁共振成像（fMRI）实验中对血流动力学响应进行建模是功能神经影像分析的一个重要方面。过去曾使用有限族的参数响应函数来完成这一工作。在本论文中，我们采用基于有限脉冲响应（FIR）滤波器的半参数方法。为了应对自由度数量的增加，我们在滤波器参数上引入高斯过程先验。我们展示了如何通过纳入关于滤波器的先验知识、使用证据框架优化超参数或采用马尔可夫链蒙特卡罗（MCMC）方法进行采样来开展分析。我们在模拟数据上对我们的模型与标准血流动力学响应核进行了比较，并对在涉及视觉刺激的实验中采集的数据进行了全面分析。
