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Comparison of beamformer implementations for MEG source localization

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  • Amit Jaiswal, Megin Oy, Aalto University
  • ,
  • Jukka Nenonen, Megin Oy
  • ,
  • Matti Stenroos, Aalto University
  • ,
  • Alexandre Gramfort, Université Paris-Saclay
  • ,
  • Sarang S. Dalal
  • Britta U. Westner
  • Vladimir Litvak, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London
  • ,
  • John C. Mosher, McGovern Medical School at University of Texas Health Sciences Center at Houston
  • ,
  • Jan Mathijs Schoffelen, Radboud University of Nijmegen
  • ,
  • Caroline Witton, Aston University
  • ,
  • Robert Oostenveld, Radboud University of Nijmegen, Karolinska Institutet
  • ,
  • Lauri Parkkonen, Megin Oy, Aalto University, Helsinki University Central Hospital

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.

Original languageEnglish
Article number116797
JournalNeuroImage
Volume216
ISSN1053-8119
DOIs
Publication statusPublished - Aug 2020

    Research areas

  • Beamformers, EEG, LCMV, MEG, Open-source analysis toolboxes, Source modeling

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