Max Planck Institute for Human Cognitive and Brain Sciences, P.O. Box 500355, 04303, Leipzig, Germany.
Brain Topogr. 2013 Apr;26(2):229-46. doi: 10.1007/s10548-012-0263-9. Epub 2012 Oct 31.
The high temporal resolution of EEG/MEG data offers a way to improve source reconstruction estimates which provide insight into the spatio-temporal involvement of neuronal sources in the human brain. In this work, we investigated the performance of spatio-temporal regularization (STR) in a current density approach using a systematic comparison to simple ad hoc or post hoc filtering of the data or of the reconstructed current density, respectively. For the used STR approach we implemented a frequency-specific constraint to penalize solutions outside a narrow frequency band of interest. The widely used sLORETA algorithm was adapted for STR and generally used for source reconstruction. STR and filtering approaches were evaluated with respect to spatial localization error and spatial dispersion, as well as to correlation of original and reconstructed source time courses in single source and two source scenarios with fixed source locations and oscillating source waveforms. We used extensive computer simulations and tested all algorithms with different parameter settings (noise levels and regularization parameters) for EEG data. To verify our results, we also used data from MEG phantom measurements. For the investigated scenarios, we did not find any evidence that STR-based methods outperform purely spatial algorithms applied to temporally filtered data. Furthermore, the results show very clearly that the performance of STR depends very much on the choice of regularization parameters.
脑电图/脑磁图（EEG/MEG）数据的时间分辨率很高，可用于改进源重建估计，从而深入了解人类大脑中神经元源的时空参与情况。在这项工作中，我们通过系统地比较数据或重建电流密度的简单特定或事后滤波，分别研究了电流密度方法中的时空正则化（STR）的性能。对于所使用的 STR 方法，我们实现了一个频率特定的约束，以惩罚感兴趣的窄频带之外的解。广泛使用的 sLORETA 算法被改编为 STR，并通常用于源重建。我们评估了 STR 和滤波方法在单个源和两个源场景中，对于固定源位置和振荡源波形的空间定位误差和空间分散度，以及原始和重建源时程的相关性。我们使用了广泛的计算机模拟，并针对 EEG 数据测试了所有算法的不同参数设置（噪声水平和正则化参数）。为了验证我们的结果，我们还使用了 MEG 幻影测量的数据。对于所研究的场景，我们没有发现任何证据表明基于 STR 的方法优于应用于时间滤波数据的纯空间算法。此外，结果非常清楚地表明，STR 的性能在很大程度上取决于正则化参数的选择。
