Departamento de Estadística e Investigación Operativa, Universidad Rey Juan Carlos, C/Tulipan s/n, Mostoles, Madrid, Spain.
Neuroimage. 2010 Jan 1;49(1):442-56. doi: 10.1016/j.neuroimage.2009.07.047. Epub 2009 Jul 29.
This research describes a new Bayesian spatiotemporal model to analyse block-design BOLD fMRI studies. In the temporal dimension, we parameterise the hemodynamic response function's (HRF) shape with a potential increase of signal and a subsequent exponential decay. In the spatial dimension, we use Gaussian Markov random fields (GMRF) priors on activation characteristics parameters (location and magnitude) that embody our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. The result is a spatiotemporal model with a small number of parameters, all of them interpretable. Simulations from the model are performed in order to ascertain the performance of the sampling scheme and the ability of the posterior to estimate model parameters, as well as to check the model sensitivity to signal to noise ratio. Results are shown on synthetic data and on real data from a block-design fMRI experiment.
本研究描述了一种新的贝叶斯时空模型，用于分析块设计 BOLD fMRI 研究。在时间维度上，我们用信号的潜在增加和随后的指数衰减来参数化血流动力学响应函数（HRF）的形状。在空间维度上，我们在激活特征参数（位置和幅度）上使用高斯马尔可夫随机场（GMRF）先验，这些参数体现了我们的先验知识，即诱发反应在空间上是连续的，局部上是均匀的。结果是一个具有少量参数的时空模型，所有参数都可以解释。对模型进行了模拟，以确定抽样方案的性能和后验估计模型参数的能力，以及检查模型对信噪比的敏感性。结果显示在合成数据和来自块设计 fMRI 实验的真实数据上。
