Bayesian model averaging in EEG/MEG imaging
Bayesian model averaging in EEG/MEG imaging
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DOI：
10.1016/j.neuroimage.2003.11.008
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发表时间：
2004-04-01
期刊：
影响因子：
5.7
通讯作者：
Valdés-Sosa, PA
中科院分区：
文献类型：
--
作者：
Trujillo-Barreto, NJ;Aubert-Vázquez, E;Valdés-Sosa, PA
本文将贝叶斯理论应用于脑电/脑磁图的逆问题。这种提法提供了一个比较框架，广泛的逆方法，使我们能够解决模型的不确定性问题时，处理不同的解决方案，为一个单一的数据。在这种情况下，每个模型由所使用的逆方法的假设集以及数据与大脑内部的初级电流密度（PCD）之间的函数依赖性来定义。关键在于贝叶斯理论不仅提供了给定模型的感兴趣参数（PCD）的后验估计，而且还提供了在假设的模型上无条件地找到后验期望效用的可能性。在目前的工作中，这是通过考虑第三层次的推理，已系统地省略了以前的贝叶斯公式的IP。这个水平被称为贝叶斯模型平均（BMA）。新的方法示出的情况下，考虑不同的解剖约束求解IP的EEG在频域中。这种方法使我们能够解决两个主要的问题，影响线性逆解（LIS）;（a）鬼源的存在和（B）的倾向，低估了深层活动。模拟和真实的实验数据被用来证明BM，4，方法的能力，和一些结果进行了比较，使用流行的低分辨率电磁层析成像（LORETA）和它的解剖约束版本（cLORETA）获得的解决方案。(C)2004年爱思唯尔公司All rights reserved.
In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse method used, as well as by the functional dependence between the data and the Primary Current Density (PCD) inside the brain. The key point is that the Bayesian Theory not only provides for posterior estimates of the parameters of interest (the PCD) for a given model, but also gives the possibility of finding posterior expected utilities unconditional on the models assumed. In the present work, this is achieved by considering a third level of inference that has been systematically omitted by previous Bayesian formulations of the IP. This level is known as Bayesian model averaging (BMA). The new approach is illustrated in the case of considering different anatomical constraints for solving the IP of the EEG in the frequency domain. This methodology allows us to address two of the main problems that affect linear inverse solutions (LIS); (a) the existence of ghost sources and (b) the tendency to underestimate deep activity. Both simulated and real experimental data are used to demonstrate the capabilities of the BM,4, approach, and some of the results are compared with the solutions obtained using the popular low-resolution electromagnetic tomography (LORETA) and its anatomically constraint version (cLORETA). (C) 2004 Elsevier Inc. All rights reserved.