Graduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, Japan.
IEEE Trans Biomed Eng. 2012 Jun;59(6):1561-71. doi: 10.1109/TBME.2012.2189713. Epub 2012 Mar 1.
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.
状态空间建模是一种有前途的方法，可以从脑磁图（MEG）中重建电流源，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不发达；提取空间聚焦电流源和处理分布式源模型的高维性仍然是问题。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来解决这些问题，将我们之前的源定位方法扩展到包括通过状态空间建模的时间约束。为了实现聚焦电流重建，我们通过引入每个皮质位置不同的动态模型参数来考虑空间不均匀的时间动态。根据贝叶斯框架，共同估计模型参数和电流源的强度。我们通过假设模型参数的先验分布来规避问题的高维性，以减少对未建模成分的敏感性，并采用变分贝叶斯推断来降低计算成本。通过模拟实验和对真实 MEG 数据的应用，我们已经证实，我们提出的方法成功地重建了随时间动态演变的聚焦电流活动。
