Comparison of beamformer implementations for MEG source localization

• Published in: NeuroImage (Orlando, Fla.) Vol. 216; p. 116797
• Main Authors: Jaiswal, Amit, Nenonen, Jukka, Stenroos, Matti, Gramfort, Alexandre, Dalal, Sarang S., Westner, Britta U., Litvak, Vladimir, Mosher, John C., Schoffelen, Jan-Mathijs, Witton, Caroline, Oostenveld, Robert, Parkkonen, Lauri
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-08-2020; Elsevier Limited; Elsevier; Academic Press
• Subjects: Adult; Applications; Beamformers; Brain Mapping - methods; Brain Mapping - standards; Cerebral Cortex - physiology; Computer Science; Computer Simulation; Digitization; EEG; Electroencephalography; Electroencephalography - methods; Electroencephalography - standards; Estimates; Humans; LCMV; Life Sciences; Localization; Magnetoencephalography - methods; Magnetoencephalography - standards; Medical Imaging; Medicin och hälsovetenskap; MEG; Noise; Open-source analysis toolboxes; Phantoms, Imaging; Physical Stimulation; Regularization methods; Reproducibility of Results; Sensors; Sensory integration; Signal Processing, Computer-Assisted; Simulation; Software; Source modeling; Spatial discrimination; Statistics; Visual stimuli; Finland; Source modeling; LCMV; EEG; MEG; Beamformers; Open-source analysis toolboxes
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116797

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression.
We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes.
When applied carefully to MEG data with a typical SNR (3–15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.
•Different beamformer implementations are reported to sometimes yield differing source estimates for the same MEG data.•We compared beamformers in four major open-source MEG analysis toolboxes.•All toolboxes provide consistent and accurate results with 3–15-dB input SNR.•However, localization errors are high at very high input SNR for the tested scalar beamformers.•We discuss the critical differences between the implementations.