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| The dataset is comprised of MEG and EMG recordings recorded from one subject in a experiment in which the subject 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. We will calculate the coherence between the MEG and the EMG when the subject extended her LEFT wrist, while keeping the right forearm muscle relaxed. The first step is to read the data. In this section we will apply automatic artifact rejection. Preprocessing requires the original MEG dataset, which is available from ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip. |
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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| * Go to the [[http://neuroimage.usc.edu/bst/download.php|Download]] page of this website, and download the file: '''sample_epilepsy.zip''' * Unzip it in a folder that is __not__ in any of the Brainstorm folders (program folder or database folder) * Start Brainstorm (Matlab scripts or stand-alone version) * Select the menu File > Create new protocol. Name it "'''TutorialEpilepsy'''" and select the options: * "'''No, use individual anatomy'''", * "'''No, use one channel file per acquisition run'''". |
* Download the dataset `SubjectCMC.zip` from FieldTrip FTP server: ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip |
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=== Download and installation === ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip 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. Only data for the lifting with the left hand is analyzed. |
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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| * Liu J, Sheng Y, Liu H. <<BR>> https://doi.org/10.3389/fnhum.2019.00100Corticomuscular%20Coherence%20and%20Its%20Applications:%20A%20Review. Front Hum Neurosci. 2019 Mar 20;13:100. | * 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. |
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| {{{#!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 have already followed all the introduction tutorials and you have a working copy of Brainstorm installed on your computer.
Download the dataset SubjectCMC.zip from FieldTrip FTP server: ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip
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
1. Create protocol 1.
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