TY - JOUR
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.
N1 - Funding Information:
C.V. was supported by the Spanish Ministry of Economy with Grant RyC 2014-15671. G.N. was partially funded by the German Research Foundation (DFG, SFB936 Z3 and TRR169, B4). K.-R.M. work was supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115 and 01GQ0850; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689 and by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451, No. 2017-0-01779). T.W.B. was supported by a Future Fellowship from the Australian Research Council (FT180100622). V.V.N. was partially supported by the Center for Bioelectric Interfaces NRU HSE, RF Government grant, ag. No. 14.641.31.0003. The authors thank Katherina von Carlowitz-Ghori for her support with rCMC code and results.
Funding Information:
C.V. was supported by the Spanish Ministry of Economy with Grant RyC 2014-15671 . G.N. was partially funded by the German Research Foundation (DFG, SFB936 Z3 and TRR169, B4 ). K.-R.M. work was supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E , 01GQ1115 and 01GQ0850 ; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689 and by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451 , No. 2017-0-01779 ). T.W.B. was supported by a Future Fellowship from the Australian Research Council ( FT180100622 ). V.V.N. was partially supported by the Center for Bioelectric Interfaces NRU HSE, RF Government grant , ag. No. 14.641.31.0003 . The authors thank Katherina von Carlowitz-Ghori for her support with rCMC code and results.
Publisher Copyright:
© 2019
PY - 2019/11/1
Y1 - 2019/11/1
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
UR - http://www.scopus.com/inward/record.url?scp=85069638405&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2019.116009
DO - 10.1016/j.neuroimage.2019.116009
M3 - Article
C2 - 31302256
AN - SCOPUS:85069638405
SN - 1053-8119
VL - 201
JO - NeuroImage
JF - NeuroImage
M1 - 116009
ER -