Abstract：
For accurate EEG/MEG source analysis it is necessary to model the head volume conductor as realistic as possible. This includes the distinction of the different conductive compartments in the human head. In this study, we investigated the influence of modeling/not modeling the conductive compartments skull spongiosa, skull compacta, cerebrospinal fluid (CSF), gray matter, and white matter and of the inclusion of white matter anisotropy on the EEG/MEG forward solution. Therefore, we created a highly realistic 6-compartment head model with white matter anisotropy and used a state-of-the-art finite element approach. Starting from a 3-compartment scenario (skin, skull, and brain), we subsequently refined our head model by distinguishing one further of the above-mentioned compartments. For each of the generated five head models, we measured the effect on the signal topography and signal magnitude both in relation to a highly resolved reference model and to the model generated in the previous refinement step. We evaluated the results of these simulations using a variety of visualization methods, allowing us to gain a general overview of effect strength, of the most important source parameters triggering these effects, and of the most affected brain regions. Thereby, starting from the 3-compartment approach, we identified the most important additional refinement steps in head volume conductor modeling. We were able to show that the inclusion of the highly conductive CSF compartment, whose conductivity value is well known, has the strongest influence on both signal topography and magnitude in both modalities. We found the effect of gray/white matter distinction to be nearly as big as that of the CSF inclusion, and for both of these steps we identified a clear pattern in the spatial distribution of effects. In comparison to these two steps, the introduction of white matter anisotropy led to a clearly weaker, but still strong, effect. Finally, the distinction between skull spongiosa and compacta caused the weakest effects in both modalities when using an optimized conductivity value for the homogenized compartment. We conclude that it is highly recommendable to include the CSF and distinguish between gray and white matter in head volume conductor modeling. Especially for the MEG, the modeling of skull spongiosa and compacta might be neglected due to the weak effects; the simplification of not modeling white matter anisotropy is admissible considering the complexity and current limitations of the underlying modeling approach.
摘要：
对于精确的EEG/MEG源分析,有必要尽可能真实地对头部体积导体进行建模。这包括人头部中不同导电隔室的区别。在这项研究中,我们调查了建模/不建模的影响，头骨致密,脑脊液（CSF），灰质,和白质以及在EEG/MEG正解上包含白质各向异性。因此,我们创建了具有白质各向异性的高度逼真的6隔室头部模型，并使用了最先进的有限元方法。从3个隔间的场景开始(皮肤，头骨,和大脑)，随后，我们通过区分上述隔室中的另一个来改进我们的头部模型。对于生成的五个头部模型中的每一个，我们测量了与高分辨率参考模型和上一个细化步骤中生成的模型相关的信号形貌和信号幅度的影响。我们使用各种可视化方法评估了这些模拟的结果，让我们获得效果强度的总体概述，触发这些影响的最重要的源参数，以及受影响最大的大脑区域。因此，从三格方法开始，我们确定了头部体积导体建模中最重要的其他细化步骤。我们能够证明包含高导电的CSF隔室，其电导率值众所周知，在两种模态中对信号地形和幅度的影响最强。我们发现灰/白质区别的影响几乎与CSF包涵体一样大，对于这两个步骤，我们都确定了效果空间分布的清晰模式。与这两个步骤相比，白质各向异性的引入导致明显较弱，但仍然坚强，效果。最后,当对均质室使用优化的电导率值时，颅骨海绵状体和致密体之间的区别在两种方式中的作用最弱。我们得出的结论是，在头部体积导体建模中包括CSF并区分灰质和白质是非常值得推荐的。特别是对于MEG，由于影响较弱，颅骨海绵状体和致密体的建模可能会被忽略；考虑到基础建模方法的复杂性和当前局限性，不建模白质各向异性的简化是可以接受的。
