Comparison of beamformer implementations for MEG source localization

Abstract

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.

Publication DOI: https://doi.org/10.1016/j.neuroimage.2020.116797
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences > Clinical and Systems Neuroscience
College of Health & Life Sciences
College of Health & Life Sciences > School of Optometry > Vision, Hearing and Language
Additional Information: Creative Commons Attribution 4.0 International (CC BY 4.0)
Publication ISSN: 1095-9572
Last Modified: 25 Mar 2024 18:06
Date Deposited: 16 Apr 2020 08:35
Full Text Link:
Related URLs: https://www.sci ... 2846?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2020-08-01
Published Online Date: 2020-04-08
Accepted Date: 2020-03-31
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 (ORCID Profile 0000-0002-5610-4234)
Oostenveld, Robert
Parkkonen, Lauri

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