Differences between revisions 10 and 17 (spanning 7 versions)

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
Dataset description
Download and installation
Importing anatomy data
Access the recordings
Handle events
Pre-process recordings
Importing the recordings
1. Epoching
Source analysis
Coherence
1. Sensor level
2. Source level
Script
Additional documentation

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
In the viewer click in Click here to compute MNI normalization
Use the maff8 method
Then click on Save
Right-click on MRI > MRI segmentation > FieldTrip: Tissues, BEM surfaces
Select all the (5) tissues, then OK
Generate surface meshes? NO
Now there is some processing in the background, and the tissues node appears.
Rick-click the tissues node and Generate triangular meshes
Select the 5 layers to mesh
Use the default parameters: number of vertices 10,000; erode factor 0; fill holes factor 2.

In output: we get a list of files that can be used for BEM computation (head, innerskull, outerskull) + brain (white and grey matter). However, the cortex (grey matter) overlaps heavily with the innerskull, so we can't use it for BEM computation. => For this reason, I suggest selecting the WHITE surface as the default one (we won't need this anyway, since we'll only use Overlapping Spheres I guess)

Add a comment about the quality of the cortex and white surfaces: These can be used for VOLUME source estimation (to define the volume grid of source points), but should not be used for surface-based source models (point at CAT or FreeSurfer for these cases).

<<HR>>

Do you want to apply the transformation to the MRI file? YES
The MRI viewer opens automatically. Click on "Click here to compute MNI transformation". It computes an affine transformation between the subject space and the MNI ICBM152 space, and sets default positions for all the anatomical landmarks.
Click on [Save] to close the MRI viewer.

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

-  ⇤ ← Revision 10 as of 2021-08-06 15:30:33 → 
  Size: 3014
  Editor: RaymundoCassani
  Comment:
+   ← Revision 17 as of 2021-08-10 19:52:15 → ⇥
  Size: 7289
  Editor: RaymundoCassani
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 10:
-== Background == 
[[Link-tocnx-tutorial|Coherence]] is a classic metric to evaluate the synchrony between two signals. Previous studies (conway 1995, review: https://www.frontiersin.org/articles/10.3389/fnhum.2019.00100/full) have reported the use of coherence to study the connectivity between the primary motor cortex and muscles. The results of such studies, show synchronized activity in the 15–30 Hz range during maintained voluntary contractions. Kilkner 2000
+== Background ==
[[https://neuroimage.usc.edu/brainstorm/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.
 Line 16:
-=== License ===
+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.
 Line 18:
-MEG EMG equipment trials duration which ones will be analyzed
+== 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''', <<BR>> '''No, use one channel file per acquisition run'''.
-Line 20:
+Line 27:
-=== Download and installation ===
You should have Brainstorm Download data from Fieldtrip FTP

Example of performing citations in text, and <<latex(\LaTeX)>>.

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.
-Line 28:
+Line 30:
-. Create protocol 1.
+ * 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''''':<<BR>>Set the file format: '''All MRI file (subject space)'''<<BR>>Select the file: `SubjectCMC/SubjectCMC.mri`

 * In the viewer click in Click here to compute MNI normalization
 * Use the maff8 method 
 * Then click on Save
 * Right-click on MRI > MRI segmentation > FieldTrip: Tissues, BEM surfaces
 * Select all the (5) tissues, then OK 
 * Generate surface meshes? NO 
 * Now there is some processing in the background, and the tissues node appears.
 * Rick-click the tissues node and Generate triangular meshes
 * Select the 5 layers to mesh
 * Use the default parameters: number of vertices 10,000; erode factor 0; fill holes factor 2.

In output: we get a list of files that can be used for BEM computation (head, innerskull, outerskull) + brain (white and grey matter).
However, the cortex (grey matter) overlaps heavily with the innerskull, so we can't use it for BEM computation.
=> For this reason, I suggest selecting the WHITE surface as the default one (we won't need this anyway, since we'll only use Overlapping Spheres I guess)

Add a comment about the quality of the cortex and white surfaces:
These can be used for VOLUME source estimation (to define the volume grid of source points), but should not be used for surface-based source models (point at CAT or FreeSurfer for these cases).

<<BR>>
<<BR>>
<<HR>> 

 * Do you want to apply the transformation to the MRI file? '''YES'''
 * The MRI viewer opens automatically. Click on "[[http://neuroimage.usc.edu/brainstorm/Tutorials/ImportAnatomy#MNI_normalization|Click here to compute MNI transformation]]".  It computes an affine transformation between the subject space and the  MNI ICBM152 space, and sets default positions for all the anatomical landmarks.<<BR>> {{attachment:mni_transformation.gif}}
 * Click on [Save] to close the MRI viewer.
-Line 41:
+Line 72:
-Line 43:
+Line 73:
-Line 46:
+Line 75:
-Line 48:
+Line 76:
-Line 54:
+Line 81:
- * '''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.
-Line 56:
+Line 83:
+ * 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.
}}}

Software

Users

Development