Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors
Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors
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DOI：
10.1109/tmi.2020.3025608
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发表时间：
2021-01-01
影响因子：
10.6
通讯作者：
Purdon, Patrick L.
中科院分区：
文献类型：
--
作者：
Liu, Feng;Wang, Li;Purdon, Patrick L.
脑源成像是利用脑电(EEG)或脑磁图(MEG)记录对脑活动进行非侵入性表征的重要方法。传统的EEG/MEG源成像(ESI)方法通常假设不同时间点的源活动是无关的，并且在源激活时没有利用时间结构，这使得ESI分析对噪声敏感。一些方法可能在整个时间进程中鼓励非常相似的激活模式，并且可能无法计算沿时间进程的变化。为了有效地处理噪声，同时保持脑激活模式之间的灵活性和连续性，我们提出了一种新的基于层次图先验的概率ESI模型。在我们的方法中，生成树约束确保了活动模式具有时空连续性。提出了一种基于交替凸搜索的高效算法来解决所提出的模型的收敛问题。在传感器和信源空间的不同信噪比(SNR)水平下，使用真实大脑模型上的合成数据进行了全面的数值研究。我们还在两个实际应用中检查了EEG/MEG数据集，在这两个应用中，我们的ESI重建在神经学上是可信的。实验结果表明，与基准方法相比，该方法在信源定位性能方面有了显著的改善，尤其是在高噪声环境下。
Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. Comprehensive numerical studies using synthetic data on a realistic brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels.