Johnston Leigh A, Gavrilescu Maria, Egan Gary F
Electrical & Electronic Engineering, University of Melbourne, & NICTA Victorian Research Laboratory, Australia.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):293-301. doi: 10.1007/978-3-642-23629-7_36.
The debate regarding how best to model variability of the hemodynamic response function in fMRI data has focussed on the linear vs. nonlinear nature of the optimal signal model, with few studies exploring the deterministic vs. stochastic nature of the dynamics. We propose a stochastic linear model (SLM) of the hemodynamic signal and noise dynamics to more robustly infer fMRI activation estimates.
The SLM models the hemodynamic signal by an exogenous input autoregressive model driven by Gaussian state noise. Activation weights are inferred by a joint state-parameter iterative coordinate descent algorithm based on the Kalman smoother.
The SLM produced more accurate parameter estimates than the GLM for event-design simulated data. In application to block-design experimental visuo-motor task fMRI data, the SLM resulted in more punctate and well-defined motor cortex activation maps than the GLM, and was able to track variations in the hemodynamics, as expected from a stochastic model.
We demonstrate in application to both simulated and experimental fMRI data that in comparison to the GLM, the SLM produces more flexible, consistent and enhanced fMRI activation estimates.
关于如何最好地模拟功能磁共振成像（fMRI）数据中血液动力学响应函数的变异性的争论主要集中在最优信号模型的线性与非线性性质上，很少有研究探讨动力学的确定性与随机性。我们提出一种血液动力学信号和噪声动力学的随机线性模型（SLM），以更稳健地推断fMRI激活估计值。
SLM通过由高斯状态噪声驱动的外生输入自回归模型对血液动力学信号进行建模。激活权重由基于卡尔曼平滑器的联合状态参数迭代坐标下降算法推断得出。
对于事件设计模拟数据，SLM产生的参数估计比广义线性模型（GLM）更准确。在应用于块设计实验视觉运动任务fMRI数据时，与GLM相比，SLM产生的运动皮层激活图更点状且定义更清晰，并且能够跟踪血液动力学的变化，这与随机模型预期一致。
我们在模拟和实验fMRI数据的应用中证明，与GLM相比，SLM产生更灵活、一致且增强的fMRI激活估计。
