Comparison of beamformer implementations for MEG source localization

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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

61 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli116797
Sivut1-15
Sivumäärä15
JulkaisuNeuroImage
Vuosikerta216
DOI - pysyväislinkit
TilaJulkaistu - 1 elokuuta 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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  • Projektit

    HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography

    Iivanainen, J., Parkkonen, L., Zetter, R., Zubarev, I., Zhigalov, A., Puthanmadam Subramaniyam, N., Pfeiffer, P., Anelli, M., Henttonen, M. & Helle, L.

    22/08/201631/12/2021

    Projekti: EU: ERC grants

    Siteeraa tätä

    Jaiswal, A., Nenonen, J., Stenroos, M., Gramfort, A., Dalal, S. S., Westner, B. U., Litvak, V., Mosher, J. C., Schoffelen, J. M., Witton, C., Oostenveld, R., & Parkkonen, L. (2020). Comparison of beamformer implementations for MEG source localization. NeuroImage, 216, 1-15. [116797]. https://doi.org/10.1016/j.neuroimage.2020.116797