Treffer: Comparison of beamformer implementations for MEG source localization

Title:
Comparison of beamformer implementations for MEG source localization
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
Contributors:
HUS Medical Imaging Center, BioMag Laboratory, Department of Diagnostics and Therapeutics, Helsinki University Hospital Area
Publisher Information:
ACADEMIC PRESS INC ELSEVIER SCIENCE
Publication Year:
2020
Collection:
Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto
Subject Terms:
MEG, EEG, Source modeling, Beamformers, LCMV, Open-source analysis toolboxes, MINIMUM-VARIANCE BEAMFORMERS, SURFACE-BASED ANALYSIS, ELECTRICAL-ACTIVITY, MAGNETOENCEPHALOGRAPHY, BRAIN, MODEL, HEAD, OSCILLATIONS, VALIDATION, Neurosciences, Neurology and psychiatry, Surgery, anesthesiology, intensive care, radiology
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
application/pdf
Language:
English
Relation:
This study has been supported by the European Union H2020 MSCA-ITN-2014-ETN program, Advancing brain research in Children's developmental neurocognitive disorders project (ChildBrain #641652). SSD and BUW have been supported by a European Research Council Starting Grant (#640448). LP was supported by European Research Council grant (#678578).; http://hdl.handle.net/10138/317773; 000541141700004
Availability:
http://hdl.handle.net/10138/317773
Rights:
cc_by ; info:eu-repo/semantics/openAccess ; openAccess
Accession Number:
edsbas.9FCC1FF2
Database:
BASE

Weitere Informationen

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. ; Peer reviewed