Ponasso Guillermo Nuñez, Drumm Derek A, Wang Abbie, Noetscher Gregory M, Hämäläinen Matti, Knösche Thomas R, Maess Burkhard, Haueisen Jens, Makaroff Sergey N, Raij Tommi
Dept. of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
Graduate School of Information Sciences, Division of Mathematics, Tohoku University, Sendai, Miyagi, Japan.
bioRxiv. 2025 Mar 24:2025.03.21.644601. doi: 10.1101/2025.03.21.644601.
(MEG) source estimation relies on the computation of the gain (lead-field) matrix, which embodies the linear relationship between the amplitudes of the sources and the recorded signals. However, with a realistic forward model, the calculation of the gain matrix in a "direct" fashion is a computationally expensive task because the number of dipolar sources in standard MEG pipelines is often limited to ~10,000. We propose a fast approach based on the reciprocal relationship between MEG and transcranial magnetic stimulation (TMS). This approach couples naturally with the charge-based boundary element fast multipole method (BEM-FMM), which allows us to efficiently generate gain matrices for high-resolution multi-layer non-nested meshes involving source spaces of up to a ~1 million dipoles. We evaluate our approach by performing MEG source reconstruction against simulated data (at varying noise levels) obtained from the direct computation of MEG readings from 2000 different dipole positions over the cortical surface of 5 healthy subjects. Additionally, we test our methods with real MEG data from evoked somatosensory fields by right-hand median nerve stimulation in these same 5 subjects. We compare our experimental source reconstruction results against the standard MNE-Python source reconstruction pipeline.
（脑磁图）源估计依赖于增益（导场）矩阵的计算，该矩阵体现了源的振幅与记录信号之间的线性关系。然而，对于一个实际的正向模型，以“直接”方式计算增益矩阵是一项计算成本高昂的任务，因为标准脑磁图流程中的偶极源数量通常限制在约10000个。我们提出了一种基于脑磁图与经颅磁刺激（TMS）之间互易关系的快速方法。这种方法自然地与基于电荷的边界元快速多极子方法（BEM-FMM）相结合，这使我们能够有效地为涉及多达约100万个偶极子源空间的高分辨率多层非嵌套网格生成增益矩阵。我们通过对从5名健康受试者皮质表面2000个不同偶极子位置直接计算脑磁图读数获得的模拟数据（在不同噪声水平下）进行脑磁图源重建来评估我们的方法。此外，我们用这5名受试者右手正中神经刺激诱发体感场的真实脑磁图数据测试我们的方法。我们将实验源重建结果与标准的MNE-Python源重建流程进行比较。
