Conditionally Gaussian Hypermodels for Cerebral Source Localization
Conditionally Gaussian Hypermodels for Cerebral Source Localization
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DOI：
10.1137/080723995
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发表时间：
2009-01-01
影响因子：
2.1
通讯作者：
Somersalo, Erkki
中科院分区：
文献类型：
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作者：
Calvetti, Daniela;Hakula, Harri;Somersalo, Erkki
脑磁图和脑电图模式的贝叶斯建模和分析提供了一个灵活的框架，用于引入与测量数据互补的先验信息。这些先验信息本质上通常是定性的，使得将可用信息转化为计算模型成为一项具有挑战性的任务。我们提出了一种广义的超先验伽马族，它允许外加电流成为焦点，并且我们提倡一种快速高效的迭代算法，即用于计算最大后验（MAP）估计的迭代交替顺序算法。此外，我们表明，对于指定超先验的标量参数的特定选择，该算法有效地近似流行的正则化策略，例如最小当前估计和最小支持估计。还指出了先验条件和自适应正则化方法之间的联系。通过适合该超模型家族的马尔可夫链蒙特卡罗策略来探索后验密度。计算实验表明，相对于深层源，浅层源的正则化方法的已知偏好仅是 MAP 估计器的属性，并且层次模型中后验均值的估计更适合定位深层源。
Bayesian modeling and analysis of the magnetoencephalography and electroencephalography modalities provide a flexible framework for introducing prior information complementary to the measured data. This prior information is often qualitative in nature, making the translation of the available information into a computational model a challenging task. We propose a generalized gamma family of hyperpriors which allows the impressed currents to be focal and we advocate a fast and efficient iterative algorithm, the iterative alternating sequential algorithm for computing maximum a posteriori (MAP) estimates. Furthermore, we show that for particular choices of the scalar parameters specifying the hyperprior, the algorithm effectively approximates popular regularization strategies such as the minimum current estimate and the minimum support estimate. The connection between priorconditioning and adaptive regularization methods is also pointed out. The posterior densities are explored by means of a Markov chain Monte Carlo strategy suitable for this family of hyper-models. The computed experiments suggest that the known preference of regularization methods for superficial sources over deep sources is a property of the MAP estimators only, and that estimation of the posterior mean in the hierarchical model is better adapted for localizing deep sources.