Comparison of beamformer implementations for MEG source localization
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 r...
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doaj-5fa8c32191574851923e146e5b095e212020-11-29T04:14:12ZengElsevierNeuroImage1095-95722020-08-01216116797Comparison of beamformer implementations for MEG source localizationAmit Jaiswal0Jukka Nenonen1Matti Stenroos2Alexandre Gramfort3Sarang S. Dalal4Britta U. Westner5Vladimir Litvak6John C. Mosher7Jan-Mathijs Schoffelen8Caroline Witton9Robert Oostenveld10Lauri Parkkonen11Megin Oy, Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Corresponding author. Megin Oy, Helsinki, Finland.Megin Oy, Helsinki, FinlandDepartment of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, FinlandUniversité Paris-Saclay, Inria, CEA, Palaiseau, FranceCenter of Functionally Integrative Neuroscience, Aarhus University, DenmarkCenter of Functionally Integrative Neuroscience, Aarhus University, DenmarkThe Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UKDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USADonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the NetherlandsAston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, UKDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, SwedenMegin Oy, Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki, FinlandBeamformers 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.http://www.sciencedirect.com/science/article/pii/S1053811920302846MEGEEGSource modelingBeamformersLCMVOpen-source analysis toolboxes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amit Jaiswal Jukka Nenonen Matti Stenroos Alexandre Gramfort Sarang S. Dalal Britta U. Westner Vladimir Litvak John C. Mosher Jan-Mathijs Schoffelen Caroline Witton Robert Oostenveld Lauri Parkkonen |
spellingShingle |
Amit Jaiswal Jukka Nenonen Matti Stenroos Alexandre Gramfort Sarang S. Dalal Britta U. Westner Vladimir Litvak John C. Mosher Jan-Mathijs Schoffelen Caroline Witton Robert Oostenveld Lauri Parkkonen Comparison of beamformer implementations for MEG source localization NeuroImage MEG EEG Source modeling Beamformers LCMV Open-source analysis toolboxes |
author_facet |
Amit Jaiswal Jukka Nenonen Matti Stenroos Alexandre Gramfort Sarang S. Dalal Britta U. Westner Vladimir Litvak John C. Mosher Jan-Mathijs Schoffelen Caroline Witton Robert Oostenveld Lauri Parkkonen |
author_sort |
Amit Jaiswal |
title |
Comparison of beamformer implementations for MEG source localization |
title_short |
Comparison of beamformer implementations for MEG source localization |
title_full |
Comparison of beamformer implementations for MEG source localization |
title_fullStr |
Comparison of beamformer implementations for MEG source localization |
title_full_unstemmed |
Comparison of beamformer implementations for MEG source localization |
title_sort |
comparison of beamformer implementations for meg source localization |
publisher |
Elsevier |
series |
NeuroImage |
issn |
1095-9572 |
publishDate |
2020-08-01 |
description |
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. |
topic |
MEG EEG Source modeling Beamformers LCMV Open-source analysis toolboxes |
url |
http://www.sciencedirect.com/science/article/pii/S1053811920302846 |
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