High-definition MEG source estimation using the reciprocal boundary element fast multipole method

Magnetoencephalographic (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”...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Jg. 320; S. 121452
Hauptverfasser: Núñez Ponasso, Guillermo, Drumm, Derek A., Wang, Abbie, Noetscher, Gregory M., Hämäläinen, Matti, Knösche, Thomas R., Maess, Burkhard, Haueisen, Jens, Makaroff, Sergey N., Raij, Tommi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 15.10.2025
Elsevier Limited
Schlagworte:
Adult
Brain - physiology
Brain Mapping - methods
Electroencephalography
Evoked Potentials, Somatosensory - physiology
Female
Humans
Localization
Magnetic fields
Magnetoencephalography
Magnetoencephalography - methods
Male
Median nerve
Radiology/Diagnostic Imaging
Sensors
Signal Processing, Computer-Assisted
Software
Transcranial magnetic stimulation
Transcranial Magnetic Stimulation - methods
ISSN:1053-8119, 1095-9572, 1095-9572
Online-Zugang:Volltext
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Zusammenfassung:Magnetoencephalographic (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, forcing the number of dipolar sources in standard MEG pipelines to be typically limited to 10,000. We propose a fast computational approach to calculate the gain matrix, which is based on the reciprocal relationship between MEG and transcranial magnetic stimulation (TMS), and which we couple with the charge-based boundary element fast multipole method (BEM-FMM). Our method allows us to efficiently generate gain matrices for high-resolution multi-layer non-nested meshes involving source spaces of up to 1 million dipoles. We employed the gain matrices generated with our approach to perform minimum norm estimate (MNE) source localization against simulated data (at varying noise levels) and experimental MEG data of evoked somatosensory fields elicited by right-hand median nerve stimulation on 5 healthy participants. Additionally, we compare our experimental source estimates against the standard 1- and 3-layer BEM models of the MNE-Python source estimation pipeline, and against a 3-layer isotropic FEM model. •Reciprocal boundary element fast multipole method allows million-dipole source models.•Our method enables forward models involving many high-resolution non-nested layers.•High source density inverse models reliably localize somatosensory evoked fields.•Analysis on simulated data reveal robustness of inverse methods against noise.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2025.121452