Lu Yingli, Grova Christophe, Kobayashi Eliane, Dubeau François, Gotman Jean
Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, Canada H3A 2B4.
Neuroimage. 2007 Jan 1;34(1):195-203. doi: 10.1016/j.neuroimage.2006.08.023. Epub 2006 Oct 11.
Research groups who study epileptic spikes with simultaneous EEG-fMRI have used mostly the general linear model (GLM). A shortcoming of the GLM is that the specification of a simple hemodynamic response function (HRF) may lead to biased results. Other methods, which predict the hemodynamic response from the measured data, have been termed "recognition models". The merit of recognition models lies in the power of estimating the region-specific or voxel-specific HRF. We propose an approach that merges these two models in a general framework: estimate the HRF on the training data sets, and applying the estimated HRF on the other part of the data sets. The merit of this framework is that it can utilize the advantages of both models. A comparison of performance is made between the GLM with three fixed HRFs and the new model with voxel-specific HRFs. The main results are as follows: (1) in 18 of the 21 patients, the new model has a higher adjusted coefficient of multiple determination than the GLM with fixed HRF; (2) in some subjects, with the new model, we found areas of activation that had not been detected with the three fixed HRFs at our threshold of significance. The results suggest that the new model can do better than the fixed HRF GLM for the analysis of epileptic activity with EEG-fMRI.
使用同步脑电图-功能磁共振成像（EEG-fMRI）研究癫痫棘波的研究小组大多采用一般线性模型（GLM）。GLM的一个缺点是，简单的血流动力学响应函数（HRF）的设定可能会导致有偏差的结果。其他从测量数据预测血流动力学响应的方法被称为“识别模型”。识别模型的优点在于能够估计特定区域或特定体素的HRF。我们提出了一种在通用框架中融合这两种模型的方法：在训练数据集上估计HRF，并将估计出的HRF应用于数据集的其他部分。该框架的优点是它可以利用两种模型的优势。对具有三个固定HRF的GLM和具有特定体素HRF的新模型进行了性能比较。主要结果如下：（1）在21名患者中的18名患者中，新模型的调整多重决定系数高于具有固定HRF的GLM；（2）在一些受试者中，使用新模型时，我们发现在我们的显著性阈值下，使用三个固定HRF未检测到的激活区域。结果表明，在通过EEG-fMRI分析癫痫活动方面，新模型比固定HRF的GLM表现更好。
