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  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., et al. (2019). Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets. NeuroImage, 201: 116009. doi:10.1016/j.neuroimage.2019.116009.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-4B58-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-6423-4
Genre: Journal Article

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 Creators:
Vidaurre, C.1, 2, Author
Nolte, G.3, Author
de Vries, I. E. J.4, Author
Gómez, M.1, Author
Boonstra, T. W.5, 6, Author
Müller, K.-R.2, 7, 8, Author
Villringer, Arno9, Author              
Nikulin, Vadim V.9, 10, Author              
Affiliations:
1Statistics, Informatics and Mathematics Department, Public University of Navarre, Navarre, Spain, ou_persistent22              
2Machine Learning Group, Faculty of Electrical Engineering and Computer Science, TU Berlin, Germany, ou_persistent22              
3Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
4Department of Experimental and Applied Psychology, VU University Medical Center, Amsterdam, the Netherlands, ou_persistent22              
5Department of Neuropsychology and Psychopharmacology, Maastricht University, the Netherlands, ou_persistent22              
6Neuroscience Research Australia, Sydney, Australia, ou_persistent22              
7Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea, ou_persistent22              
8Max Planck Institute for Informatics, Saarbrücken, Germany, ou_persistent22              
9Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
10Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              

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

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Language(s): eng - English
 Dates: 2019-05-242019-02-152019-07-102019-07-112019-11-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2019.116009
Other: Epub ahead of print
PMID: 31302256
 Degree: -

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Title: NeuroImage
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 201 Sequence Number: 116009 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166