Seghouane Abd-Krim
National ICT Australia, Canberra Research Laboratory and The College of Engineering and Computer Science, The Australian National University, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2910-3. doi: 10.1109/IEMBS.2010.5626278.
Hemodynamic Response Function (HRF) estimation in functional Magnetic Resonance Imaging (fMRI) experiments is an important issue in functional neuroimages analysis. Indeed, when modeling each brain region as a stationary linear system characterized by its impulse response, the HRF describes the temporal dynamic of the brain region response during activations. Using the mixed-effects model, a new algorithm for maximum likelihood HRF estimation is derived. In this model, the random effect is used to better account for the variability of the drift. Contrary to the usual approaches, the proposed algorithm has the benefit of considering an unknown drift matrix. Estimations of the HRF and the hyperparameters are derived by alternating minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the mixed-effects model and a desired family of probability distributions constrained to be concentrated on the observed data. The relevance of proposed approach is demonstrated both on simulated and real data.
在功能磁共振成像（fMRI）实验中，血流动力学响应函数（HRF）估计是功能神经影像分析中的一个重要问题。实际上，当将每个脑区建模为一个由其脉冲响应表征的平稳线性系统时，HRF描述了激活期间脑区响应的时间动态。利用混合效应模型，推导了一种用于最大似然HRF估计的新算法。在该模型中，随机效应用于更好地考虑漂移的变异性。与通常的方法不同，所提出的算法具有考虑未知漂移矩阵的优点。通过交替最小化使用混合效应模型定义的概率分布模型族与被约束集中在观测数据上的期望概率分布族之间的库尔贝克-莱布勒散度，得出HRF和超参数的估计值。所提出方法的相关性在模拟数据和真实数据上均得到了证明。
