Influence of head models on EEG simulations and inverse source localizations.
Influence of head models on EEG simulations and inverse source localizations.
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DOI：
10.1186/1475-925x-5-10
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发表时间：
2006-02-08
影响因子：
3.9
通讯作者：
Haueisen, Jens
中科院分区：
文献类型：
--
作者：
Ramon, Ceon;Schimpf, Paul H.;Haueisen, Jens
解剖表面的结构，例如，CSF以及灰质和白色物质可能严重影响头部模型中的体积电流。这反过来也会影响头皮电位和逆源定位。我们用四种不同的人头模型对此进行了详细的研究。本研究使用了由成年男性受试者的分段MR图像构建的四个有限元头部模型。这些模型是：（1）模型1：具有11个组织的完整模型，包括头皮、硬颅骨和软颅骨、CSF、灰质和白色物质以及其他突出组织的详细结构，（2）模型2是从模型1导出的，其中灰质的电导率被设置为等于白色物质，即，十种组织类型模型，（3）模型3是从模型1导出的，其中灰质和CSF的电导率被设置为等于白色物质，即，9种组织类型模型，（4）模型4由头皮、硬颅骨、CSF、灰质和白色物质组成，即，五种组织类型的模型。利用上述四种头部有限元模型研究了模型复杂度对脑电源定位的影响。对于所有四种模型，计算了由于运动皮层中的偶极源引起的导联场和头皮电位。在运动皮层区域进行了详尽的搜索模式的逆源定位。通过将不相关的高斯噪声添加到头皮电位以实现-10至30 dB的信噪比（SNR）来执行逆分析。模型1被用作参考模型。正如预期的那样，参考模型表现最好。没有CSF层的模型3表现最差。模型3的平均源定位误差（MLE）大于模型1或2。头皮电位也最受模型3中缺乏CSF几何形状的影响。Model 4的MLE也大于Model 1和2。模型4和模型3在-10 dB至0 dB的SNR范围内具有相似的MLE。然而，在5 dB至30 dB的SNR范围内，与模型3相比，模型4具有较低的MLE。这些结果表明，头部模型的复杂性强烈影响头皮电位和逆源定位。与具有较少组织表面的模型相比，更复杂的头部模型在逆源定位中表现更好。CSF层在头皮电位的修正中起着重要的作用，并且还影响反向源定位。总之，为了获得最佳结果，需要具有高度异质的头部模型，以用于头皮电位的精确模拟和逆源定位。
The structure of the anatomical surfaces, e.g., CSF and gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the scalp potentials and the inverse source localizations. This was examined in detail with four different human head models. Four finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissue-type model, (3) the Model 3 was derived from the Model 1 in which the conductivities of gray matter and CSF were set equal to the white matter, i.e., a nine tissue-type model, (4) the Model 4 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. How model complexity influences the EEG source localizations was also studied with the above four finite element models of the head. The lead fields and scalp potentials due to dipolar sources in the motor cortex were computed for all four models. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. The inverse analysis was performed by adding uncorrelated Gaussian noise to the scalp potentials to achieve a signal to noise ratio (SNR) of -10 to 30 dB. The Model 1 was used as a reference model. The reference model, as expected, performed the best. The Model 3, which did not have the CSF layer, performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The scalp potentials were also most affected by the lack of CSF geometry in the Model 3. The MLEs for the Model 4 were also larger than the Model 1 and 2. The Model 4 and the Model 3 had similar MLEs in the SNR range of -10 dB to 0 dB. However, in the SNR range of 5 dB to 30 dB, the Model 4 has lower MLEs as compared with the Model 3. These results indicate that the complexity of head models strongly influences the scalp potentials and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces. The CSF layer plays an important role in modifying the scalp potentials and also influences the inverse source localizations. In summary, for best results one needs to have highly heterogeneous models of the head for accurate simulations of scalp potentials and for inverse source localizations.