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基于 CutFEM 的 MEG 正向建模提高了源可分离性和对类径向源的敏感性:一项体感群组研究。

CutFEM-based MEG forward modeling improves source separability and sensitivity to quasi-radial sources: A somatosensory group study.

机构信息

Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany.

Institute for Analysis and Numerics, University of Münster, Münster, Germany.

出版信息

Hum Brain Mapp. 2024 Aug 1;45(11):e26810. doi: 10.1002/hbm.26810.

Abstract

Source analysis of magnetoencephalography (MEG) data requires the computation of the magnetic fields induced by current sources in the brain. This so-called MEG forward problem includes an accurate estimation of the volume conduction effects in the human head. Here, we introduce the Cut finite element method (CutFEM) for the MEG forward problem. CutFEM's meshing process imposes fewer restrictions on tissue anatomy than tetrahedral meshes while being able to mesh curved geometries contrary to hexahedral meshing. To evaluate the new approach, we compare CutFEM with a boundary element method (BEM) that distinguishes three tissue compartments and a 6-compartment hexahedral FEM in an n = 19 group study of somatosensory evoked fields (SEF). The neural generators of the 20 ms post-stimulus SEF components (M20) are reconstructed using both an unregularized and a regularized inversion approach. Changing the forward model resulted in reconstruction differences of about 1 centimeter in location and considerable differences in orientation. The tested 6-compartment FEM approaches significantly increase the goodness of fit to the measured data compared with the 3-compartment BEM. They also demonstrate higher quasi-radial contributions for sources below the gyral crowns. Furthermore, CutFEM improves source separability compared with both other approaches. We conclude that head models with 6 compartments rather than 3 and the new CutFEM approach are valuable additions to MEG source reconstruction, in particular for sources that are predominantly radial.

摘要

脑磁图(MEG)数据的源分析需要计算大脑中电流源产生的磁场。这个所谓的 MEG 正问题包括对头内容积传导效应的精确估计。在这里,我们引入了用于 MEG 正问题的 Cut 有限元方法(CutFEM)。与四面体网格相比,CutFEM 的网格处理过程对组织解剖结构的限制较少,而与六面体网格不同,它能够对曲面几何图形进行网格划分。为了评估新方法,我们将 CutFEM 与区分三个组织隔室的边界元方法(BEM)以及在 n=19 的体感诱发电场(SEF)组研究中进行比较。使用无正则化和正则化反演方法,对 20ms 后刺激体感诱发电场(M20)的神经发生器进行重建。与 3 隔间 BEM 相比,所测试的 6 隔间 FEM 方法显著提高了与测量数据的拟合度。它们还证明了在脑回冠以下的源具有更高的准径向贡献。此外,与其他两种方法相比,CutFEM 提高了源的可分离性。我们得出结论,与 3 隔间相比,具有 6 隔间的头部模型和新的 CutFEM 方法是 MEG 源重建的有价值的补充,特别是对于主要是径向的源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7ab/11323619/38fba0a60ea0/HBM-45-e26810-g001.jpg

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