Hossein-Zadeh Gholam-Ali, Ardekani Babak A, Soltanian-Zadeh Hamid
Electrical and Computer Engineering Department, University of Tehran, 14399, Tehran, Iran.
Magn Reson Imaging. 2003 Oct;21(8):835-43. doi: 10.1016/s0730-725x(03)00180-2.
Many fMRI analysis methods use a model for the hemodynamic response function (HRF). Common models of the HRF, such as the Gaussian or Gamma functions, have parameters that are usually selected a priori by the data analyst. A new method is presented that characterizes the HRF over a wide range of parameters via three basis signals derived using principal component analysis (PCA). Covering the HRF variability, these three basis signals together with the stimulation pattern define signal subspaces which are applicable to both linear and nonlinear modeling and identification of the HRF and for various activation detection strategies. Analysis of simulated fMRI data using the proposed signal subspace showed increased detection sensitivity compared to the case of using a previously proposed trigonometric subspace. The methodology was also applied to activation detection in both event-related and block design experimental fMRI data using both linear and nonlinear modeling of the HRF. The activated regions were consistent with previous studies, indicating the ability of the proposed approach in detecting brain activation without a priori assumptions about the shape parameters of the HRF. The utility of the proposed basis functions in identifying the HRF is demonstrated by estimating the HRF in different activated regions.
许多功能磁共振成像（fMRI）分析方法都使用血流动力学响应函数（HRF）模型。常见的HRF模型，如高斯函数或伽马函数，其参数通常由数据分析人员事先选定。本文提出了一种新方法，该方法通过使用主成分分析（PCA）得出的三个基信号，在广泛的参数范围内对HRF进行特征描述。这三个基信号涵盖了HRF的变异性，它们与刺激模式一起定义了信号子空间，这些信号子空间适用于HRF的线性和非线性建模与识别，以及各种激活检测策略。与使用先前提出的三角子空间的情况相比，使用所提出的信号子空间对模拟fMRI数据进行分析显示检测灵敏度有所提高。该方法还应用于事件相关和组块设计实验fMRI数据的激活检测，使用了HRF的线性和非线性建模。激活区域与先前的研究一致，表明所提出的方法能够在无需对HRF的形状参数进行先验假设的情况下检测大脑激活。通过估计不同激活区域的HRF，证明了所提出的基函数在识别HRF方面的效用。
