Nuñez Ponasso Guillermo, McSweeney Ryan C, Wartman William A, Lai Peiyao, Haueisen Jens, Maess Burkhard, Knösche Thomas R, Weise Konstantin, Noetscher Gregory M, Raij Tommi, Makaroff Sergey N
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
Technische Universität Ilmenau, Ilmenau, Germany.
bioRxiv. 2024 May 21:2024.05.17.594750. doi: 10.1101/2024.05.17.594750.
To compare cortical dipole fitting spatial accuracy between the widely used yet highly simplified 3-layer and modern more realistic 5-layer BEM-FMM models with and without (AMR) methods.
We generate simulated noiseless 256-channel EEG data from 5-layer (7-compartment) meshes of 15 subjects from the Connectome Young Adult dataset. For each subject, we test four dipole positions, three sets of conductivity values, and two types of head segmentation. We use the (BEM) with (FMM) acceleration, with or without (AMR), for forward modeling. Dipole fitting is carried out with the FieldTrip MATLAB toolbox.
The average position error (across all tested dipoles, subjects, and models) is ~4 mm, with a standard deviation of ~2 mm. The orientation error is ~20° on average, with a standard deviation of ~15°. Without AMR, the numerical inaccuracies produce a larger disagreement between the 3- and 5-layer models, with an average position error of ~8 mm (6 mm standard deviation), and an orientation error of 28° (28° standard deviation).
The low-resolution 3-layer models provide excellent accuracy in dipole localization. On the other hand, dipole orientation is retrieved less accurately. Therefore, certain applications may require more realistic models for practical source reconstruction. AMR is a critical component for improving the accuracy of forward EEG computations using a high-resolution 5-layer volume conduction model.
Improving EEG source reconstruction accuracy is important for several clinical applications, including epilepsy and other seizure-inducing conditions.
比较广泛使用但高度简化的三层和现代更逼真的五层边界元法-快速多极子法(BEM-FMM)模型在有无自适应网格细化(AMR)方法时的皮质偶极子拟合空间精度。
我们从连接组青年成人数据集中的15名受试者的五层(7个隔室)网格生成模拟无噪声256通道脑电图数据。对于每个受试者,我们测试四个偶极子位置、三组电导率值和两种头部分割类型。我们使用带有快速多极子法(FMM)加速的边界元法(BEM),有或没有自适应网格细化(AMR),用于正向建模。使用FieldTrip MATLAB工具箱进行偶极子拟合。
平均位置误差(在所有测试的偶极子、受试者和模型中)约为4毫米,标准差约为2毫米。方向误差平均约为20°,标准差约为15°。没有自适应网格细化(AMR)时,数值不准确性会导致三层和五层模型之间的差异更大,平均位置误差约为8毫米(标准差6毫米),方向误差为28°(标准差28°)。
低分辨率的三层模型在偶极子定位方面提供了出色的精度。另一方面,偶极子方向的检索不太准确。因此,某些应用可能需要更逼真的模型来进行实际的源重建。自适应网格细化(AMR)是使用高分辨率五层体积传导模型提高脑电图正向计算精度的关键组件。
提高脑电图源重建精度对于包括癫痫和其他诱发癫痫的病症在内的多种临床应用非常重要。