Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses
Functional brain connectivity is increasingly being seen as critical for cognition, perception and motor control. Magnetoencephalography and electroencephalography are modalities that offer noninvasive mapping of electrophysiological interactions among brain regions, yet suffer from signal leakage a...
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2020-03-01
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doaj-e5a9844da99e409993b01352b7f6c3392020-11-25T03:28:15ZengElsevierNeuroImage1095-95722020-03-01208116386Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analysesAdonay S. Nunes0Alexander Moiseev1Nataliia Kozhemiako2Teresa Cheung3Urs Ribary4Sam M. Doesburg5Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada; Corresponding author. 2708, 250 - 13450, 102nd Avenue, Surrey, BC V3T 0A3, Canada.Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, BC, CanadaBiomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada; Corresponding author.School of Engineering Science, Simon Fraser University, Burnaby, BC, CanadaBehavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, BC, Canada; Department Pediatrics and Psychiatry, University of British Columbia, Vancouver, BC, Canada; B.C. Children’s Hospital Research Institute, Vancouver, BC, Canada; Department Psychology, Simon Fraser University, Burnaby, BC, CanadaBiomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada; Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, BC, CanadaFunctional brain connectivity is increasingly being seen as critical for cognition, perception and motor control. Magnetoencephalography and electroencephalography are modalities that offer noninvasive mapping of electrophysiological interactions among brain regions, yet suffer from signal leakage and signal cancellation when estimating brain activity. This leads to biased connectivity values which complicate interpretation. In this study, we test the hypothesis that a Multiple Constrained Minimum Variance beamformer (MCMV) outperforms the more traditional Linearly Constrained Minimum Variance beamformer (LCMV) for estimation of electrophysiological connectivity. To this end, MCMV and LCMV performance is compared in task related analyses with both simulated data and human MEG recordings of visual steady state signals, and in resting state analyses with simulated data and human MEG data of 89 subjects. In task related scenarios connectivity was estimated using coherence and phase locking values, whereas envelope correlations were used for the resting state data. We also introduce a novel Augmented Pairwise MCMV (APW-MCMV) approach for signal leakage suppression in resting state analyses and assess its performance against LCMV and more conventional MCMV approaches. We demonstrate that with MCMV effects of signal mixing and coherent source cancellation are greatly reduced in both task related and resting state conditions, while in contrast to other approaches 0- and short time lag interactions are preserved. In addition, we demonstrate that in resting state analyses, APW-MCMV strongly reduces spurious connections while better controlling for false negatives compared to more conservative measures such as symmetrical orthogonalization.http://www.sciencedirect.com/science/article/pii/S1053811919309772 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Adonay S. Nunes Alexander Moiseev Nataliia Kozhemiako Teresa Cheung Urs Ribary Sam M. Doesburg |
spellingShingle |
Adonay S. Nunes Alexander Moiseev Nataliia Kozhemiako Teresa Cheung Urs Ribary Sam M. Doesburg Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses NeuroImage |
author_facet |
Adonay S. Nunes Alexander Moiseev Nataliia Kozhemiako Teresa Cheung Urs Ribary Sam M. Doesburg |
author_sort |
Adonay S. Nunes |
title |
Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses |
title_short |
Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses |
title_full |
Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses |
title_fullStr |
Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses |
title_full_unstemmed |
Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses |
title_sort |
multiple constrained minimum variance beamformer (mcmv) performance in connectivity analyses |
publisher |
Elsevier |
series |
NeuroImage |
issn |
1095-9572 |
publishDate |
2020-03-01 |
description |
Functional brain connectivity is increasingly being seen as critical for cognition, perception and motor control. Magnetoencephalography and electroencephalography are modalities that offer noninvasive mapping of electrophysiological interactions among brain regions, yet suffer from signal leakage and signal cancellation when estimating brain activity. This leads to biased connectivity values which complicate interpretation. In this study, we test the hypothesis that a Multiple Constrained Minimum Variance beamformer (MCMV) outperforms the more traditional Linearly Constrained Minimum Variance beamformer (LCMV) for estimation of electrophysiological connectivity. To this end, MCMV and LCMV performance is compared in task related analyses with both simulated data and human MEG recordings of visual steady state signals, and in resting state analyses with simulated data and human MEG data of 89 subjects. In task related scenarios connectivity was estimated using coherence and phase locking values, whereas envelope correlations were used for the resting state data. We also introduce a novel Augmented Pairwise MCMV (APW-MCMV) approach for signal leakage suppression in resting state analyses and assess its performance against LCMV and more conventional MCMV approaches. We demonstrate that with MCMV effects of signal mixing and coherent source cancellation are greatly reduced in both task related and resting state conditions, while in contrast to other approaches 0- and short time lag interactions are preserved. In addition, we demonstrate that in resting state analyses, APW-MCMV strongly reduces spurious connections while better controlling for false negatives compared to more conservative measures such as symmetrical orthogonalization. |
url |
http://www.sciencedirect.com/science/article/pii/S1053811919309772 |
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