Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

C. Vidaurre, G. Nolte, I. E.J. de Vries, M. Gómez, T. W. Boonstra, K. R. Müller, A. Villringer, V. V. Nikulin

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

Original languageEnglish
Article number116009
Pages (from-to)1-11
Number of pages11
JournalNeuroImage
Volume201
Early online date11 Jul 2019
DOIs
Publication statusE-pub ahead of print - 11 Jul 2019

Fingerprint

Electroencephalography
Complex Mixtures
Head
Population
Datasets

Keywords

  • Coherence optimization
  • Cortico-muscular coherence (CMC)
  • Electroencephalography (EEG)
  • Electromyography (EMG)
  • High density electromyography (HDsEMG)
  • Local field potentials (LFP)
  • Magnetoencephalography (MEG)
  • Multimodal methods
  • Multivariate methods

Cite this

Vidaurre, C. ; Nolte, G. ; de Vries, I. E.J. ; Gómez, M. ; Boonstra, T. W. ; Müller, K. R. ; Villringer, A. ; Nikulin, V. V. / Canonical maximization of coherence : A novel tool for investigation of neuronal interactions between two datasets. In: NeuroImage. 2019 ; Vol. 201. pp. 1-11.
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Vidaurre, C, Nolte, G, de Vries, IEJ, Gómez, M, Boonstra, TW, Müller, KR, Villringer, A & Nikulin, VV 2019, 'Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets' NeuroImage, vol. 201, 116009, pp. 1-11. https://doi.org/10.1016/j.neuroimage.2019.116009

Canonical maximization of coherence : A novel tool for investigation of neuronal interactions between two datasets. / Vidaurre, C.; Nolte, G.; de Vries, I. E.J.; Gómez, M.; Boonstra, T. W.; Müller, K. R.; Villringer, A.; Nikulin, V. V.

In: NeuroImage, Vol. 201, 116009, 01.11.2019, p. 1-11.

Research output: Contribution to JournalArticleAcademicpeer-review

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T1 - Canonical maximization of coherence

T2 - A novel tool for investigation of neuronal interactions between two datasets

AU - Vidaurre, C.

AU - Nolte, G.

AU - de Vries, I. E.J.

AU - Gómez, M.

AU - Boonstra, T. W.

AU - Müller, K. R.

AU - Villringer, A.

AU - Nikulin, V. V.

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N2 - Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

AB - Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

KW - Coherence optimization

KW - Cortico-muscular coherence (CMC)

KW - Electroencephalography (EEG)

KW - Electromyography (EMG)

KW - High density electromyography (HDsEMG)

KW - Local field potentials (LFP)

KW - Magnetoencephalography (MEG)

KW - Multimodal methods

KW - Multivariate methods

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