Bayesian M/EEG source reconstruction with spatio-temporal priors
Bayesian M/EEG source reconstruction with spatio-temporal priors
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DOI：
10.1016/j.neuroimage.2007.07.062
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发表时间：
2008-01
期刊：
影响因子：
5.7
通讯作者：
N. Trujillo-Barreto;E. Aubert-Vázquez;W. Penny
中科院分区：
文献类型：
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作者：
N. Trujillo-Barreto;E. Aubert-Vázquez;W. Penny
本文提出了一种用于M/EEG数据源重建的贝叶斯时空模型。通常的两级概率模型隐含在大多数分布式源的解决方案是通过添加第三个层次，它描述了时间的演变神经元电流源使用时域一般线性模型（GLM）扩展。这些包括一组用于描述事件相关的M/EEG响应的时间基函数。这将M/EEG分析置于与PET和fMRI非常相似的统计框架中。实验设计可以在设计矩阵中进行编码，感兴趣的效果使用对比进行表征，并使用后验概率图进行推断。重要的是，与单受试者fMRI分析一样，试验被视为固定效应，该方法考虑了试验间方差，允许对单受试者数据进行有效的推断。建议的概率模型是有效地反演使用变分贝叶斯框架下的一个方便的平均场近似（VB-GLM）。新方法进行了测试与生物医学现实的模拟数据和结果进行比较，与传统的空间方法，如流行的低分辨率电磁断层扫描（LORETA）和最小方差波束形成器。最后，VB-GLM方法被用来分析一个人脸处理实验的EEG数据集。
This article proposes a Bayesian spatio-temporal model for source reconstruction of M/EEG data. The usual two-level probabilistic model implicit in most distributed source solutions is extended by adding a third level which describes the temporal evolution of neuronal current sources using time-domain General Linear Models (GLMs). These comprise a set of temporal basis functions which are used to describe event-related M/EEG responses. This places M/EEG analysis in a statistical framework that is very similar to that used for PET and fMRI. The experimental design can be coded in a design matrix, effects of interest characterized using contrasts and inferences made using posterior probability maps. Importantly, as is the case for single-subject fMRI analysis, trials are treated as fixed effects and the approach takes into account between-trial variance, allowing valid inferences to be made on single-subject data. The proposed probabilistic model is efficiently inverted by using the Variational Bayes framework under a convenient mean-field approximation (VB-GLM). The new method is tested with biophysically realistic simulated data and the results are compared to those obtained with traditional spatial approaches like the popular Low Resolution Electromagnetic TomogrAphy (LORETA) and minimum variance Beamformer. Finally, the VB-GLM approach is used to analyze an EEG data set from a face processing experiment.