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| [[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. | [[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. |
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| * 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` |
* 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` |
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| * 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. |
* 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'''. |
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| 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) |
IMAGE after_mni_norm |
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| 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). |
* 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. |
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| <<BR>> <<BR>> <<HR>> |
As output, we get a set of (head and brain) surface files that will be used for BEM computation. |
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| * 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. |
IMAGE result_tree By displaying the surfaces, we can note that the '''cortex''', which is related to the gray matter (shown in red) overlaps heavily with the '''innerskull''' surface (shown in gray), 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]]. IMAGE overlap cortex and innerskul |
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| 1. How to link the MEG recordings | * Switch to the '''Functional data''' view (X button). * Right-click on the '''''Subject01''''' node then '''''Review raw file''''': * Select the file format: '''MEG/EEG: CTF(*.ds; *.meg4; *.res4)''' * Select the file: `SubjectCMC.ds` * A a new folder and its content is now visible in the database explorer: * The '''''SubjectCMC''''' folder represents the MEG dataset linked to the database. Note the tag "raw" in the icon of the folder, this means that the files are considered as new continuous files. * The '''''CTF channels (191)''''' node is the '''channel file''' and defines the types and names of channels that were recorded, the position of the sensors, the head shape and other various details. This information has been read from the MEG datasets and saved as a new file in the database. The total number of data channels recorded in the file is indicated between parenthesis (191). * The '''''Link to raw file''''' node is a '''link to the original file''' that you imported. All the relevant meta-data was read from the MEG dataset and copied inside the link itself (sampling rate, number of samples, event markers and other details about the acquisition session). As it is a link, no MEG recordings were copied to the database. When we open this file, the values are read directly from the original files in the .ds folder. [[Tutorials/ChannelFile#Review_vs_Import|More information]]. IMAGE functional result_tree * Right-click on the '''''CTF channels (191)''''' node, then '''''Display sensors > CTF helmet''''' and '''''Display sensors > MEG ''''' to show a surface that represents the inner surface the helmet, and the MEG sensors respectively. Try [[Tutorials/ChannelFile#Display_the_sensors|additional display menus]]. IMAGE helmet and sensors |
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.
IMAGE result_tree
By displaying the surfaces, we can note that the cortex, which is related to the gray matter (shown in red) overlaps heavily with the innerskull surface (shown in gray), 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.
IMAGE overlap cortex and innerskul
Access the recordings
Switch to the Functional data view (X button).
Right-click on the Subject01 node then Review raw file:
Select the file format: MEG/EEG: CTF(*.ds; *.meg4; *.res4)
Select the file: SubjectCMC.ds
- A a new folder and its content is now visible in the database explorer:
The SubjectCMC folder represents the MEG dataset linked to the database. Note the tag "raw" in the icon of the folder, this means that the files are considered as new continuous files.
The CTF channels (191) node is the channel file and defines the types and names of channels that were recorded, the position of the sensors, the head shape and other various details. This information has been read from the MEG datasets and saved as a new file in the database. The total number of data channels recorded in the file is indicated between parenthesis (191).
The Link to raw file node is a link to the original file that you imported. All the relevant meta-data was read from the MEG dataset and copied inside the link itself (sampling rate, number of samples, event markers and other details about the acquisition session). As it is a link, no MEG recordings were copied to the database. When we open this file, the values are read directly from the original files in the .ds folder. More information.
IMAGE functional result_tree
Right-click on the CTF channels (191) node, then Display sensors > CTF helmet and Display sensors > MEG to show a surface that represents the inner surface the helmet, and the MEG sensors respectively. Try additional display menus.
IMAGE helmet and sensors
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