A State-Space Modeling Approach for Localization of Focal Current Sources From MEG
A State-Space Modeling Approach for Localization of Focal Current Sources From MEG
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DOI：
10.1109/tbme.2012.2189713
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发表时间：
2012-06-01
影响因子：
4.6
通讯作者：
Sato, Masa-aki
中科院分区：
文献类型：
--
作者：
Fukushima, Makoto;Yamashita, Okito;Sato, Masa-aki
状态空间建模是脑磁图（MEG）电流源重建的一种很有前途的方法，因为它以灵活的方式约束逆解的时空行为。然而，基于状态空间模型的源定位研究仍然不发达，提取空间聚焦电流源和处理的高维分布式源模型仍然存在问题。在这项研究中，我们提出了一种新的基于状态空间模型的方法，解决了这些问题，扩展我们以前的源定位方法，包括状态空间建模的时间约束。为了使焦电流重建，我们占空间不均匀的时间动态引入不同的每个皮层位置的动力学模型参数。模型参数和电流源的强度联合估计根据贝叶斯框架。我们规避高维的问题，假设先验分布的模型参数，以减少未建模组件的敏感性，并通过采用变分贝叶斯推理，以减少计算成本。通过模拟实验和应用于真实的脑磁数据，我们已经证实，我们提出的方法成功地重建了震源电流活动，演变与他们的时间动力学。
State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling of the high dimensionality of the distributed source model remain problematic. In this study, we propose a novel state-space model-based method that resolves these problems, extending our previous source localization method to include a temporal constraint by state-space modeling. To enable focal current reconstruction, we account for spatially inhomogeneous temporal dynamics by introducing dynamics model parameters that differ for each cortical position. The model parameters and the intensity of the current sources are jointly estimated according to a Bayesian framework. We circumvent the high dimensionality of the problem by assuming prior distributions of the model parameters to reduce the sensitivity to unmodeled components, and by adopting variational Bayesian inference to reduce the computational cost. Through simulation experiments and application to real MEG data, we have confirmed that our proposed method successfully reconstructs focal current activities, which evolve with their temporal dynamics.