A guideline for head volume conductor modeling in EEG and MEG
A guideline for head volume conductor modeling in EEG and MEG
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DOI：
10.1016/j.neuroimage.2014.06.040
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发表时间：
2014-10-15
期刊：
影响因子：
5.7
通讯作者：
Wolters, Carsten H.
中科院分区：
文献类型：
--
作者：
Vorwerk, Johannes;Cho, Jae-Hyun;Wolters, Carsten H.
为了准确的EEG/MEG源分析，有必要尽可能逼真地建模头部体积导体。这包括人头部中不同导电隔室的区别。在这项研究中，我们研究了建模/不建模的导电隔室颅骨海绵体，颅骨海绵体，脑脊液（CSF），灰质，和白色物质和包含白色物质各向异性的EEG/MEG前向解决方案的影响。因此，我们创建了具有白色物质各向异性的高度逼真的6室头部模型，并使用了最先进的有限元方法。从一个3区室场景（皮肤、头骨和大脑）开始，我们随后通过区分上述区室中的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在先前细化步骤中生成的模型相关的对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，使我们能够获得效应强度的总体概述，触发这些效应的最重要的源参数，以及受影响最严重的大脑区域。因此，从3房室方法开始，我们确定了头容积导体建模中最重要的附加细化步骤。我们能够证明，包含高导电性CSF室，其导电率值是众所周知的，在两种模式中对信号地形图和幅度的影响最大。我们发现灰/白色物质区分的影响几乎与CSF包含的影响一样大，并且对于这两个步骤，我们确定了影响的空间分布的清晰模式。与这两个步骤相比，引入白色物质各向异性导致明显较弱但仍然较强的效果。最后，当使用均质化隔室的优化电导率值时，颅骨海绵体和骨海绵体之间的区别在两种模式中引起最弱的影响。我们的结论是，它是高度可扩展的，包括CSF和区分灰色和白色物质在头部体积导体建模。特别是对于MEG，由于效果较弱，可能会忽略颅骨海绵体和骨皮质的建模;考虑到基础建模方法的复杂性和当前限制，不对白色物质各向异性建模的简化是可以接受的。(C)2014 Elsevier Inc. All rights reserved.
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. (C) 2014 Elsevier Inc. All rights reserved.