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| This is a little text that serves as an introduction of the reason of being for this tutorial. Hopefully it will have 2 or 3 relevant links to information outside or to other tutorials. | [[https://en.wikipedia.org/wiki/Corticomuscular_coherence|Corticomuscular coherence]] relates to the synchrony between electrophisiological signals (MEG, EEG or ECoG) recorded from the contralateral motor cortex, and EMG signal from a muscle during voluntary movement. This synchrony has its origin mainly in the descending communication in corticospinal pathways between primary motor cortex (M1) and muscles. This tutorial replicates the processing pipeline and analysis presented in the [[https://www.fieldtriptoolbox.org/tutorial/coherence/|Analysis of corticomuscular coherence]] FieldTrip tutorial. |
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| == Background == [[Tutorials/Connectivity#Coherence|Coherence]] is a classic method to measure the linear relationship between two signals in the frequency domain. Previous studies ([[https://dx.doi.org/10.1113/jphysiol.1995.sp021104|Conway et al., 1995]], [[https://doi.org/10.1523/JNEUROSCI.20-23-08838.2000|Kilner et al., 2000]]) have used coherence to study the relationship between MEG signals from M1 and muscles, and they have shown synchronized activity in the 15–30 Hz range during maintained voluntary contractions. IMAGE OF EXPERIMENT, SIGNALS and COHERENCE == Dataset description == The dataset is comprised of MEG (151-channel CTF MEG system) and bipolar EMG (from left and right extensor carpi radialis longus muscles) recordings from one subject during an experiment in which the subject had to lift her hand and exert a constant force against a lever. The force was monitored by strain gauges on the lever. The subject performed two blocks of 20 trials in which either the left or the right wrist was extended for about 10 seconds. Only data for the left wrist will be analyzed in this tutorial. |
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| Example of performing citations in text, and <<latex(\LaTeX)>>. | * '''Requirements''': You should have already followed all the introduction tutorials and you have a working copy of Brainstorm installed on your computer. * '''Download the dataset''': * Download the `SubjectCMC.zip` file from FieldTrip FTP server: ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip * Unzip it in a folder that is not in any of the Brainstorm folders (program folder or database folder). * '''Brainstorm''': * Start Brainstorm (Matlab scripts or stand-alone version). * Select the menu '''''File > Create new protocol'''''. Name it '''TutorialCMC''' and select the options: '''No, use individual anatomy''', <<BR>> '''No, use one channel file per acquisition run'''. |
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| The '''imaginary coherence''' ([[https://doi.org/10.1016/j.clinph.2004.04.029|Nolte et al., 2004]]), commonly found as: <<latex($IC_{xy}(f)$)>>. | The next sections will describe how to link import the subject's anatomy, reviewing raw data, managing event markers, pre-processing, epoching, source estimation and computation of coherence in the sensor and sources domain. |
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| === A title in size 3 === ==== A title in size 4 ==== |
== Importing anatomy data == * Right-click on the '''''TutorialCMC''''' node then '''''New subject > Subject01'''''.<<BR>>Keep the default options you defined for the protocol. * Switch to the '''Anatomy''' view of the protocol. * Right-click on the '''''Subject01''''' node then '''''Import MRI''''': * Set the file format: '''All MRI file (subject space)''' * Select the file: `SubjectCMC/SubjectCMC.mri` * Compute MNI normalization, in the '''MRI viewer''' click on '''Click here to compute MNI normalization''', use the '''maff8''' method. When the normalization is complete, verify the correct location of the fiducials and click on '''Save'''. IMAGE after_mni_norm * Once the MRI has been imported and normalized, we will segment the head and brain tissues to obtain the surfaces that are needed for a realistic [[Tutorials/TutBem|BEM forward model]]. * Right-click on the '''''SubjectCMC''''' MRI node, then '''''MRI segmentation > FieldTrip: Tissues, BEM surfaces'''''. * Select all the tissues ('''scalp''', '''skull''', '''csf''', '''gray''' and '''white'''). * Click '''OK'''. * For the option '''Generate surface meshes''' select '''No'''. * After the segmentation is complete, a '''''tissues''''' node will be shown in the tree. * Rick-click on the '''''tissues''''' node and select '''Generate triangular meshes''' * Select the 5 layers to mesh * Use the default parameters: '''number of vertices''': 10,000; '''erode factor''': 0; and '''fill holes factor''' 2. As output, we get a set of (head and brain) surface files that will be used for BEM computation. By displaying the surfaces, we can note that the '''cortex''' (gray matter) overlaps heavily with the '''innerskull''' surface, so it cannot be used it for [[Tutorials/TutBem|BEM computation using OpenMEEG]]. However, as we are dealing with MEG signals, we can still compute the BEM with the [[Tutorials/HeadModel#Forward_model|overleaping-spheres method]], and obtain similar results. We can also notice that the '''cortex''' and '''white''' surfaces obtained with the method above do not register accurately the cortical surface, they can be used for [[Tutorials/TutVolSource|volume-based source estimation]], which is based on a volume grid of source points; but they do not be used for surface-based source estimation. Better surface surfaces can be obtained by doing MRI segmentation with [[Tutorials/SegCAT12|CAT12]] or [[Tutorials/LabelFreeSurfer|FreeSurfer]]. == Access the recordings == 1. How to link the MEG recordings == Handle events == Fusion all the left events == Pre-process recordings == Removing artifacts == Importing the recordings == === Epoching === == Source analysis == == Coherence == === Sensor level === === Source level === == Script == This should be label as advanced. |
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| * '''Minimum norm''': Baillet S, Mosher JC, Leahy RM<<BR>>[[http://neuroimage.usc.edu/paperspdf/BailletMosherLeahy_IEEESPMAG_Nov2001.pdf|Electromagnetic brain mapping]], IEEE SP MAG 2001. | * Conway BA, Halliday DM, Farmer SF, Shahani U, Maas P, Weir AI, et al. <<BR>> [[https://dx.doi.org/10.1113/jphysiol.1995.sp021104|Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man]]. <<BR>> The Journal of Physiology. 1995 Dec 15;489(3):917–24. |
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| * Kilner JM, Baker SN, Salenius S, Hari R, Lemon RN. <<BR>> [[https://doi.org/10.1523/JNEUROSCI.20-23-08838.2000|Human Cortical Muscle Coherence Is Directly Related to Specific Motor Parameters]]. <<BR>> J Neurosci. 2000 Dec 1;20(23):8838–45. * Liu J, Sheng Y, Liu H. <<BR>> [[https://doi.org/10.3389/fnhum.2019.00100Corticomuscular%20Coherence%20and%20Its%20Applications:%20A%20Review|https://doi.org/10.3389/fnhum.2019.00100Corticomuscular%20Coherence%20and%20Its%20Applications:%20A%20Review]]. Front Hum Neurosci. 2019 Mar 20;13:100. {{{#!wiki comment * Schoffelen J-M, Poort J, Oostenveld R, Fries P. <<BR>> [[https://doi.org/10.1523/JNEUROSCI.4882-10.2011|Selective Movement Preparation Is Subserved by Selective Increases in Corticomuscular Gamma-Band Coherence]]. Journal of Neuroscience. 2011 May 4;31(18):6750–8. }}} |
MEG corticomuscular coherence
Authors: Raymundo Cassani
Corticomuscular coherence relates to the synchrony between electrophisiological signals (MEG, EEG or ECoG) recorded from the contralateral motor cortex, and EMG signal from a muscle during voluntary movement. This synchrony has its origin mainly in the descending communication in corticospinal pathways between primary motor cortex (M1) and muscles. This tutorial replicates the processing pipeline and analysis presented in the Analysis of corticomuscular coherence FieldTrip tutorial.
Contents
Background
Coherence is a classic method to measure the linear relationship between two signals in the frequency domain. Previous studies (Conway et al., 1995, Kilner et al., 2000) have used coherence to study the relationship between MEG signals from M1 and muscles, and they have shown synchronized activity in the 15–30 Hz range during maintained voluntary contractions.
IMAGE OF EXPERIMENT, SIGNALS and COHERENCE
Dataset description
The dataset is comprised of MEG (151-channel CTF MEG system) and bipolar EMG (from left and right extensor carpi radialis longus muscles) recordings from one subject during an experiment in which the subject had to lift her hand and exert a constant force against a lever. The force was monitored by strain gauges on the lever. The subject performed two blocks of 20 trials in which either the left or the right wrist was extended for about 10 seconds. Only data for the left wrist will be analyzed in this tutorial.
Download and installation
Requirements: You should have already followed all the introduction tutorials and you have a working copy of Brainstorm installed on your computer.
Download the dataset:
Download the SubjectCMC.zip file from FieldTrip FTP server: ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip
- Unzip it in a folder that is not in any of the Brainstorm folders (program folder or database folder).
Brainstorm:
- Start Brainstorm (Matlab scripts or stand-alone version).
Select the menu File > Create new protocol. Name it TutorialCMC and select the options: No, use individual anatomy,
No, use one channel file per acquisition run.
The next sections will describe how to link import the subject's anatomy, reviewing raw data, managing event markers, pre-processing, epoching, source estimation and computation of coherence in the sensor and sources domain.
Importing anatomy data
Right-click on the TutorialCMC node then New subject > Subject01.
Keep the default options you defined for the protocol.Switch to the Anatomy view of the protocol.
Right-click on the Subject01 node then Import MRI:
Set the file format: All MRI file (subject space)
Select the file: SubjectCMC/SubjectCMC.mri
Compute MNI normalization, in the MRI viewer click on Click here to compute MNI normalization, use the maff8 method. When the normalization is complete, verify the correct location of the fiducials and click on Save.
IMAGE after_mni_norm
Once the MRI has been imported and normalized, we will segment the head and brain tissues to obtain the surfaces that are needed for a realistic BEM forward model.
Right-click on the SubjectCMC MRI node, then MRI segmentation > FieldTrip: Tissues, BEM surfaces.
Select all the tissues (scalp, skull, csf, gray and white).
Click OK.
For the option Generate surface meshes select No.
After the segmentation is complete, a tissues node will be shown in the tree.
Rick-click on the tissues node and select Generate triangular meshes
- Select the 5 layers to mesh
Use the default parameters: number of vertices: 10,000; erode factor: 0; and fill holes factor 2.
As output, we get a set of (head and brain) surface files that will be used for BEM computation. By displaying the surfaces, we can note that the cortex (gray matter) overlaps heavily with the innerskull surface, so it cannot be used it for BEM computation using OpenMEEG. However, as we are dealing with MEG signals, we can still compute the BEM with the overleaping-spheres method, and obtain similar results. We can also notice that the cortex and white surfaces obtained with the method above do not register accurately the cortical surface, they can be used for volume-based source estimation, which is based on a volume grid of source points; but they do not be used for surface-based source estimation. Better surface surfaces can be obtained by doing MRI segmentation with CAT12 or FreeSurfer.
Access the recordings
1. How to link the MEG recordings
Handle events
Fusion all the left events
Pre-process recordings
Removing artifacts
Importing the recordings
Epoching
Source analysis
Coherence
Sensor level
Source level
Script
This should be label as advanced.
Additional documentation
Articles
Conway BA, Halliday DM, Farmer SF, Shahani U, Maas P, Weir AI, et al.
Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man.
The Journal of Physiology. 1995 Dec 15;489(3):917–24.Kilner JM, Baker SN, Salenius S, Hari R, Lemon RN.
Human Cortical Muscle Coherence Is Directly Related to Specific Motor Parameters.
J Neurosci. 2000 Dec 1;20(23):8838–45.Liu J, Sheng Y, Liu H.
https://doi.org/10.3389/fnhum.2019.00100Corticomuscular%20Coherence%20and%20Its%20Applications:%20A%20Review. Front Hum Neurosci. 2019 Mar 20;13:100.
Tutorials
Tutorial: Volume source estimation
Forum discussions
Forum: Minimum norm units (pA.m): http://neuroimage.usc.edu/forums/showthread.php?1246