Chen Huafu, Yao Dezhong, Liu Zuxiang
School of Life Science and Technology, School of Applied Math., University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Magn Reson Imaging. 2005 Jan;23(1):83-8. doi: 10.1016/j.mri.2004.11.002.
Blood oxygenation level-dependent (BOLD) contrast-based functional magnetic resonance imaging (fMRI) has been widely utilized to detect brain neural activities and great efforts are now stressed on the hemodynamic processes of different brain regions activated by a stimulus. The focus of this paper is the comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response. The convolutions are between the perfusion function of the neural response to a stimulus and a Gaussian or Gamma function. The parameters of the two models are estimated by a nonlinear least-squares optimal algorithm for the fMRI data of eight subjects collected in a visual stimulus experiment. The results show that the Gaussian model is better than the Gamma model in fitting the data. The model parameters are different in the left and right occipital regions, which indicate that the dynamic processes seem different in various cerebral functional regions.
基于血氧水平依赖（BOLD）对比的功能磁共振成像（fMRI）已被广泛用于检测大脑神经活动，目前人们非常关注由刺激激活的不同脑区的血液动力学过程。本文的重点是比较fMRI BOLD反应的伽马和高斯动态卷积模型。卷积是在对刺激的神经反应的灌注函数与高斯或伽马函数之间进行的。通过非线性最小二乘优化算法对在视觉刺激实验中收集的八名受试者的fMRI数据估计这两个模型的参数。结果表明，高斯模型在拟合数据方面优于伽马模型。左右枕叶区域的模型参数不同，这表明不同脑功能区域的动态过程似乎有所不同。
