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Corticomuscular coherence (CTF MEG)

[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE]

Authors: Raymundo Cassani, Francois Tadel & Sylvain Baillet.

Corticomuscular coherence measures the degree of similarity between electrophysiological signals (MEG, EEG, ECoG sensor traces or source time series, especially over the contralateral motor cortex) and the EMG signal recorded from muscle activity during voluntary movement. This signal similarity is due mainly to the descending communication along corticospinal pathways between primary motor cortex (M1) and muscles. For consistency and reproducibility purposes across major software toolkits, the present tutorial replicates the processing pipeline "Analysis of corticomuscular coherence" by FieldTrip.

Contents

Dataset description
Download and installation
Importing anatomy
MEG and EMG recordings
Pre-processing
Epoching
Coherence: EMG x MEG
Source estimation
Coherence: EMG x Sources
Coherence: EMG x Scouts
Additional documentation
Scripting

Dataset description

The dataset is distributed as part of the FieldTrip tutorial Analysis of corticomuscular coherence:

One participant
MEG recordings: 151-channel CTF MEG system
Bipolar EMG recordings: from left and right extensor carpi radialis longus muscles
EOG recordings: used for detection and attenuation of ocular artifacts
MRI: 1.5T Siemens system
Task: The participant was tasked to lift their hand and exert a constant force against a lever for about 10 seconds. The force was monitored by strain gauges on the lever. The participant performed two blocks of 25 trials using either the left or right wrist.
We will analyze the data from the left-wrist trials in the present tutorial. Replicating the pipeline with right-wrist data is a good exercise to do next!

Corticomuscular coherence:

Coherence measures the linear relationship between two signals in the frequency domain.
Previous studies (Conway et al., 1995, Kilner et al., 2000) have reported corticomuscular coherence effects in the 15–30 Hz range during maintained voluntary contractions.
TODO: IMAGE OF EXPERIMENT, SIGNALS and COHERENCE

Download and installation

Requirements

Please make sure you have completed the get-started tutorials and that you have a working copy of Brainstorm installed on your computer.
In addition, you need to install the plugin CAT12, used for MRI segmentation.

Download the dataset

Download SubjectCMC.zip from FieldTrip FTP server:
ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip
Unzip it in a folder not located in any of Brainstorm folders (the app or its database).

Brainstorm

Launch Brainstorm (via Matlab command line or the Matlab-free 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.

Importing anatomy

Right-click on the newly created TutorialCMC node > New subject > Subject01.
Keep the default options defined for the study (aka "protocol" in Brainstorm jargon).
Switch to the Anatomy view of the protocol ().
Right-click on the Subject01 > Import MRI:
- Select the file format: All MRI files (subject space)
- Select the file: SubjectCMC/SubjectCMC.mri
This will open the MRI viewer showing the coronal, sagittal and axial views of the MRI. In addition, three anatomical fiducials: left and right pre-auricular points (LPA and RPA), and nasion (NAS) are automatically identified. These fiducials are located near the left/right ears and just above the nose respectively. Click on Save.
In a typical Brainstorm workflow, as illustrated in all the other tutorials, we would recommend running the full segmentation of the MRI at this stage, in order to have the anatomy of the subject fully prepared before importing the functional data. In the case of this tutorial, we will proceed differently, in order to follow better the original FieldTrip pipeline and to obtain sensor-level coherence results much faster. We will run the segmentation of the anatomy just before moving to the source-level analysis.
For validating the registration between the MRI and the MEG, we will now limit the anatomy processing to the reconstruction of the head surface from the MRI.
Right-click on the MRI () > MRI segmentation > Generate head surface.
At the end, you would get one new head surface. Double-click on it to display it.

MEG and EMG recordings

Link the recordings

Switch now to the Functional data view ().
Right-click on the Subject01 > Review raw file:
- Select the file format: MEG/EEG: CTF(*.ds; *.meg4; *.res4)
- Select the file: SubjectCMC.ds
A new folder SubjectCMC is created in the Brainstorm database explorer. Note the "RAW" tag over the icon of the folder (), indicating the files contain unprocessed, continuous data. It contains:
CTF channels (191): Channel file with all channel types, names, locations, etc. The number of channels available (MEG, EMG, EOG etc.) is indicated between parentheses.
Link to raw file: Provides access to the original data file. All the relevant metadata was read from the dataset and copied inside the node itself (e.g., sampling rate, number of time samples, event markers). Note that Brainstorm logic is not to import/duplicate the raw unprocessed data directly into the database. Instead, Brainstorm provides a link to that raw file for further review and data extraction (more information).

MEG-MRI coregistration

This step, sometimes simply named registration, refers to the alignment of the sensors on the anatomy of the subject (more info). For this tutorial, data registration is carried out using only three anatomical landmarks present in the MRI and MEG data. According to the description of the data in the FieldTrip tutorial:
"To measure the head position with respect to the sensors, three coils were placed at anatomical landmarks of the head (nasion, left and right ear canal). [...] During the MRI scan, ear molds containing small containers filled with vitamin E marked the same landmarks. This allows us, together with the anatomical landmarks, to align source estimates of the MEG with the MRI."
To verify the registration, right-click on the CTF channels node > MRI registration > Check. This opens a 3D figure showing the inner surface of the MEG helmet, the head surface, and the fiducials and axes that comprise the subject coordinate system (SCS).

Reviewing

Right-click on Link to raw file > Switch epoched/continuous to convert the file to continuous, a technical detail proper to CTF file formatting.
Right-click on Link to raw file > MEG > Display time series (or double-click). This will open a new visualization window to explore data time series, also enabling the Time panel and the Record tab in the main Brainstorm window (see how to best use all controls in this panel and tab to explore data time series).
Right-click on Link to raw file > EMG > Display time series.

Event markers

The colored dots above the data time series indicate event markers (or triggers) saved with this dataset. The trial onset information of the left-wrist and right-wrist trials is saved in an auxiliary channel of the raw data named Stim. To add these markers, these events need to be decoded as follows:

While the time series figure is open, go to the Record tab and File > Read events from channel. Event channels = Stim, select Value, and click Run.
This creates new event markers now shown in the Events section of the tab, along with previous event categories. In this tutorial, we will only use events U1 through U25, which correspond to the beginning of each of the 25 trials of 10 seconds with left-wrist movements. To make sure we reproduce FieldTrip tutorial, we need to reject trial #7, event U7.
Delete unused events: Select all the events except U1-U6 and U8-25 (Ctrl+click / Shift+click), then menu Events > Delete group (or press the Delete key).
Merge events: Select all the event groups, then menu Events > Merge group > "Left". This new event category references 24 trials of the left-wrist condition, i.e. 10-second blocks of left-wrist movements.

Pre-processing

In this tutorial, we will analyze only the Left trials (left-wrist extensions). In the following sections, we will process only the first 330 s of the recordings, where the left-wrist trials were performed.

Power line artifacts

In the Process1 box: Drag and drop the Link to raw file.
Run process Frequency > Power spectrum density (Welch):
- Time window: 0-330 s
- Window length: 10 s
- Overlap: 50%
- Sensor types: MEG, EMG
Double-click on the new PSD file to visualize the power spectrum density of the data.
The PSD plot shows two groups of sensors: EMG (highlighted in red above) and the MEG spectra below. Peaks at 50Hz and its harmonics (100, 150, 200Hz and above) correspond to the European power line and are clearly visible. We will use notch filters to attenuate power line contaminants at 50, 100 and 150 Hz.
In the Process1 box: Drag and drop the Raw | clean node.
Run the process Pre-processing > Notch filter with:
- Check Process the entire file at once
- Sensor types: MEG, EMG
- Frequencies to remove (Hz): 50, 100, 150
Troubleshooting in case of memory error:
These MEG recordings have been saved before applying the CTF 3rd-order gradient compensation. The compensation weights are applied on the fly when Brainstorm reads data from the file, however this requires reading all the channels at once. By default, the frequency filter are optimized to process the channels sequentially, which is incompatible with applying the CTF compensation on the fly. This setting can be overridden with the option Process the entire file at once, but this solution has the effect of loading the entire file in memory at once, which can crash on computers with limited memory (RAM < 8Gb). If this happens to you: run the process Artifacts > Apply SSP & CTF compensation on the file first, then the notch filter without the option "Process the entire file at once" (more information).
A new folder named SubjectCMC_clean_notch is created. Estimate the PSD of these signals to appreciate the effect of the notch filters applied. As above, please remember to indicate a Time window restricted from 0 to 330 s in the options of the PSD process.

EMG: Filter and rectify

Two typical pre-processing steps for EMG consist in high-pass filtering and rectifying.

In the Process1 box: drag and drop the Raw | notch(50Hz 100Hz 150Hz) node.
Add the process Pre-process > Band-pass filter
- Sensor types = EMG
- Lower cutoff frequency = 10 Hz
- Upper cutoff frequency = 0 Hz
Add the process Pre-process > Absolute values
- Sensor types = EMG
Run the pipeline
Delete intermediate files that won't be needed anymore: Select folders SubjectCMC_notch and SubjectCMC_notch_high, then press the Delete key (or right-click > File > Delete).

MEG: Blink SSP and bad segments

Stereotypical artifacts such eye blinks and heartbeats can be identified from their respective characteristic spatial distributions. Their contamination of MEG signals can then be attenuated specifically using Signal-Space Projections (SSPs). For more details, consult the dedicated tutorials about the detection and removal of artifacts with SSP. The present tutorial dataset features an EOG channel but no ECG. We will perform only the removal of eye blinks.

Blink correction with SSP

Right-click on the pre-processed file > MEG > Display time series and EOG > Display time series.
In the Record tab: Artifacts > Detect eye blinks, and use the parameters:
- Channel name= EOG
- Time window = 0 - 330 s
- Event name = blink
Three categories of blink events are created. Review the traces of EOG channels around a few of these events to ascertain they are related to eye blinks. In the present case, we note that the blink group contains genuine eye blinks, and that groups blink2 and blink3 capture saccade events.
To remove blink artifacts with SSP, go to Artifacts > SSP: Eye blinks:
- Event name=blink
- Sensors=MEG
Display the time series and topographies of the first two SSP components identified. In the present case, only the first SSP component can be clearly related to blinks: percentage between brackets much higher than the others, typical spatial topography, time series highly correlated with the EOG signal. Select only component #1 for removal.
The second SSP component could be related with other ocular artifacts. For a more precise characterization of this artifact, it could be better indicated to use the other events detected on the EOG (blink2 and blink3). We will not do this here because advanced SSP cleaning is not the main topic of this tutorial.
Close all figures: Click on the large × at the top-right of the main Brainstorm window.

Detection of "bad" data segments

Here we will use the automatic detection of artifacts to identify data segments contaminated by e.g., large eye and head movements and muscle contractions.

Display the MEG and EOG time series. In the Record tab, select Artifacts > Detect other artifacts and enter the following parameters:
- Time window = 0 - 330 s
- Sensor types=MEG
- Sensitivity=3
- Check both frequency bands 1-7 Hz and 40-240 Hz
We encourage users to review all the segments marked using this procedure. In the present case, all the segments detected clearly point at artifacts.
Select the 1-7Hz and 40-240Hz event groups and select Events > Mark group as bad. Alternatively, you can add the prefix bad_ to the event names. Brainstorm will automatically discard these data segments from further processing.
Close all visualization windows and reply "Yes" to the save the modifications query.

Epoching

We are finished with the pre-processing of the EMG and MEG recordings. We will now extract and import specific data segments of interest into the Brainstorm database for further derivations. As mentioned previously, we will focus on the Left category of events (left wrist movements). To follow the same pipeline as the FieldTrip tutorial: we will consider 8 seconds of recordings after each trigger (out of the 10s of each trial), and split them in epochs of 1 second. In addition DC offset is removed, only for MEG signals.

In the Process1 box: Drag-and-drop the pre-processed file.
Select the process Import > Import recordings > Import MEG/EEG: Events:
- Subject name = Subject01
- Folder name = empty
- Event names = Left
- Time window = 0 - 330 s
- Epoch time = 0 - 8000 ms
- Split recordings in time blocks = 1 s
- Uncheck Create a separate folder for each event type
- Check Ignore shorter epochs
- Check Use CTF compensation
- Check Use SSP/ICA projectors
Add the process Pre-process > Remove DC offset:
- Baseline = All file
- Sensor types = MEG
Run the pipeline

A new folder SubjectCMC_notch_high_abs without the 'raw' indication is created, including 192 epochs (24 trials x 8 epochs each). The epochs overlapping with a "bad" event are marked as bad and identified with an exclamation mark in a red circle (). The bad epochs will be automatically ignored by the Process1 and Process2 tabs, and therefore excluded from further processing.

Comparison with FieldTrip

The figures below represent the EMG and MRC21 channels (sensor over the left motor-cortex) from the epoch #1.1, in Brainstorm (left) and in the FieldTrip tutorial (right).

Coherence: EMG x MEG

Let's compute the magnitude square coherence (MSC) between the left EMG and the MEG channels.

In the Process1 box, drag and drop the Left (192 files) trial group.
Select the process Connectivity > Coherence 1xN [2021]:
- Time window = All file
  The imported epochs have different times (e.g. Left#1.1: 0-1s, Left#1.2: 1-2s, Left#1.8: 7-8s), but the process offers by default only the time window of the first file, which would fail reading the next files will fail. Selecting "All file" ensures that the entire time window is read for each epoch.
- Source channel = EMGlft
- Do not check Include bad channels nor Remove evoke response
- Magnitude squared coherence
- Window length for PSD estimation = 0.5 s
- Overlap for PSD estimation = 50%
- Highest frequency of interest = 80 Hz
- Average cross-spectra of input files (one output file)
- More details on the Coherence process can be found in the connectivity tutorial.
Add the process File > Add tag with the following parameters:
- Tag to add = MEG sensors
- Select Add to file name
Run the pipeline

Double-click on the resulting node mscohere(0.6Hz,555win): EMGlft | MEG sensors to display the MSC spectra. Click on the maximum peak in the 15 to 20 Hz range, and press Enter to plot the selected sensor in a new figure. This spectrum corresponds to channel MRC21, and shows a large peak at 17.58 Hz. You can also use the frequency slider (under the Time panel) to explore the MSC output more precisely across frequencies.
Right-click on the spectrum and select 2D Sensor cap for a topographical representation of the magnitude of the coherence results across the sensor array. You may also use the shortcut Ctrl-T. The sensor locations can be displayed with a right-click and by selecting Channels > Display sensors from the contextual menu (shortcut Ctrl-E).
We can now average magnitude of the MSC over the beta band (15-20 Hz).
In the Process1 box, select the new mscohere file.
Run process Frequency > Group in time or frequency bands:
- Select Group by frequency bands
- Type cmc_band / 15, 20 / mean in the text box.
The resulting file mscohere...|tfbands has only one MSC value for each sensor (the MSC average in the 15-20 Hz band). Right-click on the file to display a 2D or 3D topography.
We can observe higher MSC values between the EMG signal and MEG sensor signals over the contralateral set of central sensors in the beta band. Unfortunately, sensor-level connectivity is difficult to interpret. In the rest of this tutorial, we will compute coherence at the source level.

Source estimation

MRI segmentation

In order to estimate the brain sources for these MEG recordings, we first need to reconstruct the cortex surface from the T1 MRI imported at the beginning of this tutorial. For this puropose, we decided to use CAT12 because it is fast (30-60min) and fully integrated with Brainstorm as a plugin.

Switch back to the Anatomy view of the protocol ().
Right-click on the MRI () > MRI segmentation > CAT12:
- Number of vertices: 15000
- Anatomical parcellations: Yes
- Cortical maps: No
Keep the low-resolution central surface selected as the default cortex (central_15002V). This surface is the primary output of CAT12, and is located half-way between the pial envelope and the grey-white interface (more information). The head surface was recomputed during the process, you now have two identical head surfaces: you can either delete one or simply ignore this detail.
For quality control, double-click on the head and central_15002V surfaces to display them.

Head models

We will perform source modeling using a distributed model approach for two different source spaces: the cortex surface and the entire MRI volume. The forward model, labelled head model in Brainstorm, accounts for how neural electrical currents produce magnetic fields captured by sensors outside the head, considering head tissues electromagnetic properties and geometry, independently of actual empirical measurements (more information). As the head model depends on the source space, a distinct head model is required for the surface and volume source spaces: we will compute them both now.

Surface

Go back to the Functional data view of the database.
Right-click on the channel file of the imported epochs folder > Compute head model.
- Comment = Overlapping spheres (surface)
- Source space = Cortex surface
- Forward model = MEG Overlapping spheres.

Volume

Right-click on the channel file again > Compute head model.
- Comment = Overlapping spheres (volume)
- Source space = MRI volume
- Forward model = Overlapping spheres.
- Select Regular grid and Brain
- Grid resolution = 5 mm
The Overlapping spheres (volume) head model is now added to the database explorer. The green color indicates this is the default head model for the current folder: this can be changed by double clicking on a different head model.

Noise covariance

The recommendation for MEG is to extract basic noise statistics from empty-room recordings. When not available, resting-state data can be used as proxies for MEG noise covariance. We will use a segment of the MEG recordings, away from the task or major artifacts: 18s-29s.

Right-click on the clean continuous file > Noise covariance > Compute from recordings.
Right-click on the Noise covariance () > Copy to other folders.

Inverse models

We will now compute three inverse models, with different source spaces: cortex surface with constrained (normal to the cortex) dipole orientations, cortex surface with unconstrained orientation, and MRI volume (more information).

Surface

Right-click on Overlapping spheres (surface) > Compute sources:
- Minimum norm imaging
- Current density map
- Constrained: Normal to the cortex
- Comment = MN: MEG (surface)
Repeat the previous step, but this time select Unconstrained in the Dipole orientations field.

Volume

Right-click on the Overlapping spheres (volume) > Compute sources:
- Current density map
- Unconstrained
- Comment = MN: MEG (volume)
Three imaging kernels () are now available in the database explorer. Note that each trial is associated with three source links ().

Coherence: EMG x Sources

We can now compute the coherence between the EMG signal and the brain source time series, for each of the source models. Let's start with the surface/constrained model.

To select the source maps we want to include in the coherence estimation, click on the Search Database button (), and select New search. Set the parameters as shown below, and click on Search.
It creates a new tab in the database explorer, showing only the files that match the criteria.
Click the Process2 tab at the bottom of the main Brainstorm window.
- Files A: Drag-and-drop the Left (192 files) group, select Process recordings ().
- Files B: Drag-and-drop the Left (192 files) group, select Process sources ().
- Objective: Extract from the same files the EMG recordings (Files A) and the sources time series (Files B), then compute coherence between these two sets. Note that the blue labels over the file lists indicate that there are 185 "good" files (7 bad epochs).
Select the process Connectivity > Coherence AxB [2021]:
- Time window = All file
- Source channel (A) = EMGlft
- Uncheck Use scouts (B)
- Do not Remove evoked responses from each trial
- Magnitude squared coherence
- Window length = 0.5 s, Overlap = 50%
- Highest frequency = 80 Hz
- Average cross-spectra.
Add the process File > Add tag:
- Tag to add = (surface)(Constr)
- Select Add to file name
Run the pipeline

Repeat the steps above to compute the EMG-sources coherence for the other source models: surface/unconstrained and volume:
- Edit the search criteria of an open serach tab: right-click on the tab > Edit search.
- In Process2: Right-click on one of the file lists > Clear list.
- Select again the Left group in FilesA (recrodings) and FilesB (sources).
- Select the processes: Do not forget to update the tag to be added to the output file.
Go to the Database tab of the database explorer and refresh it (with [F5]) to show the resulting 1xN connectivity file ()

Surface

Double-click the 1xN connectivity files for the (surface) source space to show the results on the cortex. If you are not familiar with the options in the cortex figures, check Display: Cortex surface

Find the location and frequency with the highest coherence value.

To compare the results, set manually the colormap range to [0 - 0.07]
In the Surface tab
- Set the Smooth slider to 30%
- Adjust the amplitude threshold to 95%
Explore with coherence spectra with frequency slider

The highest coherence value is located on the right primary motor cortex (precentral gyrus) around 14.65 Hz for the analysis using constrained and unconstrained sources. Set the amplitude threshold to 0% to see the extension of the high coherence values.


MSC @ 14.65Hz (surface)(Constr)		MSC @ 14.65Hz (surface)(Unconstr)

We observe that results obtained with constrained and unconstrained sources agree in the location and frequency of the peak coherence. The main difference between these results is that coherence values obtained with unconstrained sources appear smoother, this caused by the maximum aggregation performed across directions, explained at detail in the next section.

Finally, right-click on any of the cortex figures and select Get coordinates. Then click on the right motor cortex with the crosshair cursor that appears. The SCS coordinates will be useful in the next section.
SCS coordinates X:39.2, Y:-22.3 and Z: 113.0

Volume

Double-click the 1xN connectivity file for the (volume) source space. Note that this time the results are shown in the MRI viewer rather than the cortical surface.

Set frequency slider to 14.65 Hz
Set the SCS coordinates to X:39.2, Y:-22.3 and Z: 113.0
Set the Transparency of the coherence values (Data) in the Surface tab to 20%

MSC @ 14.65Hz (volume)(Unconstr)

We note that all the results obtained with constrained (surface) and unconstrained (surface and volume) sources agree with each other, and in the location and frequency of the peak coherence. Moreover, they agree with our hypothesis, previous results in the literature [REFS], and the results presented in the FieldTrip tutorial.

Coherence with constrained and unconstrained sources

For constrained sources, each vertex in the source grid is associated with ONE time series, as such, when coherence is computed with the EMG signal (also one time series), the result is ONE coherence spectrum per vertex. In other words, for each frequency bin, there is coherence brain map.

In the case of unconstrained sources, each vertex in the grid is associated with THREE time series, each one corresponding to the X, Y and Z directions. Thus, when coherence is computed with the EMG signal (one time series), there are THREE coherence spectra. This complicates its representation in the brain, thus; these THREE coherence spectra need to be flattened into one, resulting in one coherence spectrum per vertex, the maximum across directions is found for each frequency bin for each vertex.

An alternative approach in the literature, to address the 3-dimensional nature of the unconstrained sources, consists in flattening the vertex X, Y and Z time series before the coherence computation; resulting in a similar case as the constrained sources (REF). Common methods for this flattening include: PCA (only first component is kept), and (Euclidean) norm. This flattening of the time series can be performed in Brainstorm with the process: Sources > Unconstrained to flat map.

Flattened sources are saved as full rather than recordings+kernel.
We have tested this flattening approach with simulations and the real data (from this tutorial) and we have found decrimental effects on the expected results.

Coherence: EMG x Scouts

So far, we have computed coherence at the source level, thus, a coherence spectrum is computed for each of the 15002 source points. This large dimension hinders later analysis of the results. Therefore, the strategy is to reduce the dimensionality of the source space by using a surface- or volume-parcellation scheme, in Brainstorm jargon this is an atlas that is made of scouts. See the scout tutorial for detail information on atlases and scouts in Brainstorm.

Under this approach, instead of providing one result (coherence spectrum) per source vertex, one result is computed for each scout. When computing coherence (or other connectivity metrics) in the scout level, it is necessary to provide two parameters that define how the data is aggregated per scout:

The scout function (mean is often used), and
When the within-scout aggregation takes place. Either before or after the coherence computation.
- Before: The scout function is applied for each direction on the vertices' source time series that make up a scout; resulting in one time series per direction per scout. Then, the scout time series are used to compute coherence with the reference signal (EMG in this tutorial), and the coherence spectra for each scout are aggregated across dimensions, as shown previously, to obtain one coherence spectrum per scout.

After: Coherence is computed between the reference signal and each direction of the vertices' source time series, as in the previous section. Then, the scout function is applied on the coherence spectra for each direction of the vertices within a scout, finally these spectra are aggregated across dimensions to obtain a coherence spectrum per scout.

As it can be seen, the After option takes longer and used more resources as it computes the coherence spectrum for each vertex in the scouts, and then, the coherence spectra are aggregated.

Let's here compute the coherence using scouts, using mean as scout function alongside with the Before option. We will use the Schaefer 100 parcellation atlas on the results from constrained sources.

Use Search Database () to select the Left trials with their respective (surface)(Constr) source maps, as shown in the previous section.
On the Process2 tab drag-and-drop the Left (192 files) trial group into the Files A and Files B boxes. Select Process recordings () for Files A, and Process sources () for Files B. There should be 185 files in each side.

Open the Pipeline editor:

Add the process Connectivity > Coherence AxB [2021] with the following parameters:
- Time window = 0 - 1000 ms or check All file
- Source channel (A) = EMGlft
- Check Use scouts (B)
- From the menu at the right, select Schaefer_100_17net
- Select all the scouts
- Scout function: Mean
- When to apply the scout function: Before
- Do not Remove evoked responses from each trial
- Magnitude squared coherence, Window length = 0.5 s
- Overlap = 50%
- Highest frequency = 80 Hz
- Average cross-spectra.
Add the process File > Add tag with the following parameters:
- Tag to add = (surface)(Constr)
- Select Add to file name
Run the pipeline

Open the file: mscohere(0.6Hz,555win): EMGlft x 102 scouts, mean before | (surface)(Constr) by double-clicking on it. This time the coherence spectra are not displayed on the cortex, but they are plotted for each scout. Moreover, 1xN connectivity file can be shown as image.

Note that for 14.65 Hz, the highest two peaks correspond to the SomMotA_4 R and SomMotA_2 R scouts, both located over the right primary motor cortex.

The choice of the optimal parcellation scheme for the source space is not easy.
The optimal choice is to choose a parcellation based on anatomy, for example the Brodmann parcellation.
In Brainstorm these atlases are imported in Brainstorm as scouts (cortical regions of interest), and saved directly in the surface files as explained in this tutorial here.

BRAINSTORM TEAM
Due to the current implementation of the bst_connectivity, the full source map for each trial (185) are loaded in memory, thus replicate the After, only for the (surface)(Constr) option ~30GB of RAM are needed! (Unconstrained take 3 times that).

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.
Corticomuscular Coherence and Its Applications: A Review.
Front Hum Neurosci. 2019 Mar 20;13:100.
Sadaghiani S, Brookes MJ, Baillet S.
Connectomics of human electrophysiology.
NeuroImage. 2022 Feb;247:118788.

Tutorials

Scripting

The following script from the Brainstorm distribution reproduces the analysis presented in this tutorial page: brainstorm3/toolbox/script/tutorial_coherence.m

function tutorial_coherence(tutorial_dir, reports_dir)
% TUTORIAL_COHERENCE: Script that runs the Brainstorm corticomuscular coherence tutorial
% https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence
%
% INPUTS: 
%    - tutorial_dir : Directory where the SubjectCMC.zip file has been unzipped
%    - reports_dir  : Directory where to save the execution report (instead of displaying it)

% @=============================================================================
% This function is part of the Brainstorm software:
% https://neuroimage.usc.edu/brainstorm
% 
% Copyright (c) University of Southern California & McGill University
% This software is distributed under the terms of the GNU General Public License
% as published by the Free Software Foundation. Further details on the GPLv3
% license can be found at http://www.gnu.org/copyleft/gpl.html.
% 
% FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE
% UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY
% WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
% MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY
% LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE.
%
% For more information type "brainstorm license" at command prompt.
% =============================================================================@
%
% Author: Raymundo Cassani & Francois Tadel, 2022

% Output folder for reports
if (nargin < 2) || isempty(reports_dir) || ~isfolder(reports_dir)
    reports_dir = [];
end
% You have to specify the folder in which the tutorial dataset is unzipped
if (nargin == 0) || isempty(tutorial_dir) || ~file_exist(tutorial_dir)
    error('The first argument must be the full path to the dataset folder.');
end

% Protocol name
ProtocolName = 'TutorialCMC';
% Subject name
SubjectName = 'Subject01';
% Channel selection
emg_channel = 'EMGlft';  % Name of EMG channel
meg_sensor  = 'MRC21';   % MEG sensor over the left motor-cortex (MRC21)
% Coherence parameters
cohmeasure = 'mscohere'; % Magnitude-squared Coherence |C|^2 = |Cxy|^2/(Cxx*Cyy)
win_length = 0.5;        % 500ms
overlap    = 50;         % 50%
maxfreq    = 80;         % 80Hz

% Build the path of the files to import
MriFilePath = fullfile(tutorial_dir, 'SubjectCMC', 'SubjectCMC.mri');
MegFilePath = fullfile(tutorial_dir, 'SubjectCMC', 'SubjectCMC.ds');
% Check if the folder contains the required files
if ~file_exist(MriFilePath) || ~file_exist(MegFilePath)
    error(['The folder ' tutorial_dir ' does not contain the folder from the file SubjectCMC.zip.']);
end


%% ===== CREATE PROTOCOL =====
% Start brainstorm without the GUI
if ~brainstorm('status')
    brainstorm nogui
end
% Delete existing protocol
gui_brainstorm('DeleteProtocol', ProtocolName);
% Create new protocol
gui_brainstorm('CreateProtocol', ProtocolName, 0, 0);
% Start a new report
bst_report('Start');
% Reset colormaps
bst_colormaps('RestoreDefaults', 'meg');
% Hide scouts
panel_scout('SetScoutShowSelection', 'none');


%% ===== IMPORT ANATOMY =====
% Process: Import MRI
bst_process('CallProcess', 'process_import_mri', [], [], ...
    'subjectname', SubjectName, ...
    'mrifile',     {MriFilePath, 'ALL'}, ...
    'nas',         [0, 0, 0], ...
    'lpa',         [0, 0, 0], ...
    'rpa',         [0, 0, 0], ...
    'ac',          [0, 0, 0], ...
    'pc',          [0, 0, 0], ...
    'ih',          [0, 0, 0]);
% Process: Generate head surface
bst_process('CallProcess', 'process_generate_head', [], [], ...
    'subjectname', SubjectName, ...
    'nvertices',   10000, ...
    'erodefactor', 0, ...
    'fillfactor',  2);


%% ===== LINK TO RAW FILE AND DISPLAY REGISTRATION =====
% Process: Create link to raw files
sFileRaw = bst_process('CallProcess', 'process_import_data_raw', [], [], ...
    'subjectname',  SubjectName, ...
    'datafile',     {MegFilePath, 'CTF'}, ...
    'channelalign', 1);
% Process: Snapshot: Sensors/MRI registration
bst_process('CallProcess', 'process_snapshot', sFileRaw, [], ...
    'target',   1, ...  % Sensors/MRI registration
    'modality', 1, ...  % MEG (All)
    'orient',   1, ...  % left
    'Comment',  'MEG/MRI Registration');
% Process: Convert to continuous (CTF): Continuous
bst_process('CallProcess', 'process_ctf_convert', sFileRaw, [], ...
    'rectype', 2);  % Continuous


%% ===== EVENT MARKERS =====
% Process: Read from channel
sFileRaw = bst_process('CallProcess', 'process_evt_read', sFileRaw, [], ...
    'stimchan',     'Stim', ...
    'trackmode',    1, ...  % Value: detect the changes of channel value
    'zero',         0, ...
    'min_duration', 12);
% Load all Event group labels
DataMat = in_bst_data(sFileRaw.FileName, 'F');
eventList = {DataMat.F.events.label};
% Labels for Event groups to keep
eventKeep = cellfun(@(c)sprintf('U%d',c), num2cell([1:6, 8:25]), 'UniformOutput', 0);
% Find useless Events
eventDelete = setdiff(eventList, eventKeep);
% Process: Delete events
sFileRaw = bst_process('CallProcess', 'process_evt_delete', sFileRaw, [], ...
    'eventname', strjoin(eventDelete, ', '));
% Process: Merge events
sFileRaw = bst_process('CallProcess', 'process_evt_merge', sFileRaw, [], ...
    'evtnames', strjoin(eventKeep, ', '), ...
    'newname',  'Left');


%% ===== REMOVAL OF POWER LINE ARTIFACTS =====
% Process: Notch filter: 50Hz 100Hz 150Hz
sFileRawNotch = bst_process('CallProcess', 'process_notch', sFileRaw, [], ...
    'freqlist',    [50, 100, 150], ...
    'sensortypes', 'MEG, EMG', ...
    'read_all',    1);
% Process: Power spectrum density (Welch)
sFilesPsd = bst_process('CallProcess', 'process_psd', [sFileRaw, sFileRawNotch], [], ...
    'timewindow',  [0 330], ...
    'win_length',  10, ...
    'win_overlap', 50, ...
    'clusters',    {}, ...
    'sensortypes', 'MEG, EMG', ...
    'edit', struct(...
         'Comment',    'Power', ...
         'TimeBands',  [], ...
         'Freqs',      [], ...
         'ClusterFuncTime', 'none', ...
         'Measure',    'power', ...
         'Output',     'all', ...
         'SaveKernel', 0));
% Process: Snapshot: Frequency spectrum
bst_process('CallProcess', 'process_snapshot', sFilesPsd, [], ...
    'target',   10, ...  % Frequency spectrum
    'Comment',  'Power spectrum density');


%% ===== EMG PRE-PROCESSING =====
% Process: High-pass:10Hz
sFileRawNotchHigh = bst_process('CallProcess', 'process_bandpass', sFileRawNotch, [], ...
    'sensortypes', 'EMG', ...
    'highpass',    10, ...
    'lowpass',     0, ...
    'tranband',    0, ...
    'attenuation', 'strict', ...  % 60dB
    'ver',         '2019', ...  % 2019
    'mirror',      0, ...
    'read_all',    0);
% Process: Absolute values
sFileRawNotchHighAbs = bst_process('CallProcess', 'process_absolute', sFileRawNotchHigh, [], ...
    'sensortypes', 'EMG');
% Process: Delete folders
bst_process('CallProcess', 'process_delete', [sFileRawNotch, sFileRawNotchHigh], [], ...
    'target', 2);  % Delete folders


%% ===== MEG PRE-PROCESSING =====
% Process: Detect eye blinks
bst_process('CallProcess', 'process_evt_detect_eog',  sFileRawNotchHighAbs, [], ...
    'channelname', 'EOG', ...
    'timewindow',  [0 330], ...
    'eventname',   'blink');
% Process: SSP EOG: blink
bst_process('CallProcess', 'process_ssp_eog', sFileRawNotchHighAbs, [], ...
    'eventname',   'blink', ...
    'sensortypes', 'MEG', ...
    'usessp',      1, ...
    'select',      1);
% Process: Snapshot: SSP projectors
bst_process('CallProcess', 'process_snapshot', sFileRawNotchHighAbs, [], ...
    'target',  2, ...  % SSP projectors
    'Comment', 'SSP projectors');

% Process: Detect other artifacts
bst_process('CallProcess', 'process_evt_detect_badsegment', sFileRawNotchHighAbs, [], ...
    'timewindow',  [0 330], ...
    'sensortypes', 'MEG', ...
    'threshold',   3, ...  % 3
    'isLowFreq',   1, ...
    'isHighFreq',  1);
% Process: Rename event (1-7Hz > bad_1-7Hz)
bst_process('CallProcess', 'process_evt_rename', sFileRawNotchHighAbs, [], ...
    'src',  '1-7Hz', ...
    'dest', 'bad_1-7Hz');
% Process: Rename event (40-240Hz > bad_40-240Hz)
bst_process('CallProcess', 'process_evt_rename', sFileRawNotchHighAbs, [], ...
    'src',  '40-240Hz', ...
    'dest', 'bad_40-240Hz');


%% ===== IMPORTING DATA EPOCHS =====
% Process: Import MEG/EEG: Events
sFilesEpochs = bst_process('CallProcess', 'process_import_data_event', sFileRawNotchHighAbs, [], ...
    'subjectname',   SubjectName, ...
    'condition',     '', ...
    'eventname',     'Left', ...
    'timewindow',    [0, 330], ...
    'epochtime',     [0, 7.9992], ...
    'split',         1, ...
    'createcond',    0, ...
    'ignoreshort',   1, ...
    'usectfcomp',    1, ...
    'usessp',        1, ...
    'freq',          [], ...
    'baseline',      'all', ...
    'blsensortypes', 'MEG');
% Process: Uniform epoch time
sFilesEpochs = bst_process('CallProcess', 'process_stdtime', sFilesEpochs, [], ...
    'method',    'spline', ...  % spline
    'overwrite', 1);
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sFilesEpochs(1), [], ...
    'type',           'data', ...  % Recordings time series
    'modality',       1, ... % MEG (All)
    'rowname',        meg_sensor, ...
    'Comment',        ['Epoch #1: ' meg_sensor]);
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sFilesEpochs(1), [], ...
    'type',           'data', ...  % Recordings time series
    'modality',       10, ... % EMG
    'rowname',        '', ...
    'Comment',        'Epoch #1: EMG');


%% ===== COHERENCE: EMG x MEG =====
% Process: Coherence NxN [2023]
sFileCoh1N = bst_process('CallProcess', 'process_cohere1', {sFilesEpochs.FileName}, [], ...
    'timewindow',    [], ...
    'src_channel',   emg_channel, ...
    'dest_sensors',  'MEG', ...
    'includebad',    0, ...
    'removeevoked',  0, ...
    'cohmeasure',    cohmeasure, ...  % Magnitude-squared coherence
    'tfmeasure',     'stft', ...  % Fourier transform
    'tfedit',        struct(...
         'Comment',         'Complex', ...
         'TimeBands',       [], ...
         'Freqs',           [], ...
         'StftWinLen',      win_length, ...
         'StftWinOvr',      overlap, ...
         'StftFrqMax',      maxfreq, ...
         'ClusterFuncTime', 'none', ...
         'Measure',         'none', ...
         'Output',          'all', ...
         'SaveKernel',      0), ...
    'timeres',       'none', ...  % None
    'avgwinlength',  1, ...
    'avgwinoverlap', 0, ...
    'outputmode',    'avg');  % across combined files/epochs
% Process: Add tag
sFileCoh1N = bst_process('CallProcess', 'process_add_tag', sFileCoh1N, [], ...
    'tag',      'MEG sensors', ...
    'output',   1);  % Add to file name
% Set selected sensors
bst_figures('SetSelectedRows', {meg_sensor});
% Process: Snapshot: Frequency spectrum
bst_process('CallProcess', 'process_snapshot', sFileCoh1N, [], ...
    'target',   10, ...  % Frequency spectrum
    'freq',     17.58, ...
    'Comment',  ['MSC ' emg_channel '  x MEG']);
% Process: Snapshot: Frequency spectrum
bst_process('CallProcess', 'process_snapshot', sFileCoh1N, [], ...
    'target',   10, ...  % Frequency spectrum
    'freq',     17.58, ...
    'rowname',  meg_sensor, ...
    'Comment',  ['MSC ' emg_channel ' x ', meg_sensor]);
% Process: Snapshot: Recordings topography (one time)
bst_process('CallProcess', 'process_snapshot', sFileCoh1N, [], ...
    'type',     'topo', ...  % Recordings topography (one time)
    'modality', 1, ...  % MEG (All)
    'freq',     17.58, ...
    'Comment',  ['2D sensor cap MSC ' emg_channel ' x MEG']);


%% ===== COHERENCE: EMG x MEG (FREQUENCY BAND) =====
% Process: Group in time or frequency bands
sFileCoh1NBand = bst_process('CallProcess', 'process_tf_bands', sFileCoh1N, [], ...
    'isfreqbands', 1, ...
    'freqbands',   {'cmc_band', '15, 20', 'mean'}, ...
    'istimebands', 0, ...
    'timebands',   '', ...
    'overwrite',   0);
% Process: Snapshot: Recordings topography (one time)
bst_process('CallProcess', 'process_snapshot', sFileCoh1NBand, [], ...
    'type',     'topo', ...  % Recordings topography (one time)
    'modality', 1, ...  % MEG (All)
    'Comment',  ['2D topography MSC 15-20Hz ' emg_channel ' x MEG']);


%% ===== SOURCE ESTIMATION =====
% Process: Segment MRI with CAT12
bst_process('CallProcess', 'process_segment_cat12', [], [], ...
    'subjectname', SubjectName, ...
    'nvertices',   15000, ...
    'tpmnii',      {'', 'Nifti1'}, ...
    'sphreg',      1, ...
    'vol',         1, ...
    'extramaps',   0, ...
    'cerebellum',  0);
% Process: Compute covariance (noise or data)
bst_process('CallProcess', 'process_noisecov', sFileRawNotchHighAbs, [], ...
    'baseline',       [18, 29], ...
    'datatimewindow', [], ...
    'sensortypes',    'MEG', ...
    'target',         1, ...  % Noise covariance     (covariance over baseline time window)
    'dcoffset',       1, ...  % Block by block, to avoid effects of slow shifts in data
    'identity',       0, ...
    'copycond',       1, ...
    'copysubj',       0, ...
    'copymatch',      0, ...
    'replacefile',    1);  % Replace

% Process: Compute head model (surface)
bst_process('CallProcess', 'process_headmodel', sFilesEpochs(1).FileName, [], ...
    'Comment',      'Overlapping spheres (surface)', ...
    'sourcespace',  1, ... % Cortex
    'meg',          3);    % Overlapping spheres
% Process: Compute sources [2018]
bst_process('CallProcess', 'process_inverse_2018', sFilesEpochs(1).FileName, [], ...
    'output',  1, ...  % Kernel only: shared
    'inverse', struct(...
         'Comment',        'MN: MEG (surface)', ...
         'InverseMethod',  'minnorm', ...
         'InverseMeasure', 'amplitude', ...
         'SourceOrient',   {{'fixed'}}, ...
         'Loose',          0.2, ...
         'UseDepth',       1, ...
         'WeightExp',      0.5, ...
         'WeightLimit',    10, ...
         'NoiseMethod',    'reg', ...
         'NoiseReg',       0.1, ...
         'SnrMethod',      'fixed', ...
         'SnrRms',         1e-06, ...
         'SnrFixed',       3, ...
         'ComputeKernel',  1, ...
         'DataTypes',      {{'MEG'}}));
% Process: Compute sources [2018]
bst_process('CallProcess', 'process_inverse_2018', sFilesEpochs(1).FileName, [], ...
    'output',  1, ...  % Kernel only: shared
    'inverse', struct(...
         'Comment',        'MN: MEG (surface)', ...
         'InverseMethod',  'minnorm', ...
         'InverseMeasure', 'amplitude', ...
         'SourceOrient',   {{'free'}}, ...
         'Loose',          0.2, ...
         'UseDepth',       1, ...
         'WeightExp',      0.5, ...
         'WeightLimit',    10, ...
         'NoiseMethod',    'reg', ...
         'NoiseReg',       0.1, ...
         'SnrMethod',      'fixed', ...
         'SnrRms',         1e-06, ...
         'SnrFixed',       3, ...
         'ComputeKernel',  1, ...
         'DataTypes',      {{'MEG'}}));

% Process: Compute head model (volume)
bst_process('CallProcess', 'process_headmodel', sFilesEpochs(1).FileName, [], ...
    'Comment',     'Overlapping spheres (volume)', ...
    'sourcespace', 2, ...  % MRI volume
    'volumegrid',  struct(...
         'Method',        'isotropic', ...
         'nLayers',       17, ...
         'Reduction',     3, ...
         'nVerticesInit', 4000, ...
         'Resolution',    0.005, ...
         'FileName',      []), ...
    'meg',         3 );  % Overlapping spheres
% Process: Compute sources [2018]
bst_process('CallProcess', 'process_inverse_2018', sFilesEpochs(1).FileName, [], ...
    'output',  1, ...  % Kernel only: shared
    'inverse', struct(...
         'Comment',        'MN: MEG (volume)', ...
         'InverseMethod',  'minnorm', ...
         'InverseMeasure', 'amplitude', ...
         'SourceOrient',   {{'free'}}, ...
         'Loose',          0.2, ...
         'UseDepth',       1, ...
         'WeightExp',      0.5, ...
         'WeightLimit',    10, ...
         'NoiseMethod',    'reg', ...
         'NoiseReg',       0.1, ...
         'SnrMethod',      'fixed', ...
         'SnrRms',         1e-06, ...
         'SnrFixed',       3, ...
         'ComputeKernel',  1, ...
         'DataTypes',      {{'MEG'}}));


%% ===== COHERENCE: EMG x SOURCES =====
% Process: Select data files
sFilesRecEmg = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname',   SubjectName, ...
    'condition',     '', ...
    'tag',           'Left', ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);
% Coherence between EMG signal and sources (for different source types)
sourceTypes = {'(surface)(Constr)', '(surface)(Unconstr)', '(volume)(Unconstr)'};
for ix = 1 :  length(sourceTypes)
    sourceType = sourceTypes{ix};
    % Process: Select results files
    sFilesResMeg = bst_process('CallProcess', 'process_select_files_results', [], [], ...
        'subjectname',   SubjectName, ...
        'condition',     '', ...
        'tag',           sourceType, ...
        'includebad',    0, ...
        'includeintra',  0, ...
        'includecommon', 0);
    % Process: Coherence NxN [2023]
    sFileCoh1N = bst_process('CallProcess', 'process_cohere2', sFilesRecEmg, sFilesResMeg, ...
        'timewindow',    [], ...
        'src_channel',   emg_channel, ...
        'dest_scouts',   {}, ...
        'flatten',       1, ...
        'scouttime',     'after', ...  % after    connectivity metric
        'scoutfunc',     'mean', ...  % Mean
        'scoutfuncaft',  'mean', ...  % Mean
        'pcaedit',       struct(...
             'Method',         'pcaa', ...
             'Baseline',       [], ...
             'DataTimeWindow', [], ...
             'RemoveDcOffset', 'file'), ...
        'removeevoked',  0, ...
        'cohmeasure',    cohmeasure, ...  % Magnitude-squared coherence
        'tfmeasure',     'stft', ...  % Fourier transform
        'tfedit',        struct(...
             'Comment',         'Complex', ...
             'TimeBands',       [], ...
             'Freqs',           [], ...
             'StftWinLen',      win_length, ...
             'StftWinOvr',      overlap, ...
             'StftFrqMax',      maxfreq, ...
             'ClusterFuncTime', 'none', ...
             'Measure',         'none', ...
             'Output',          'all', ...
             'SaveKernel',      0), ...
        'timeres',       'none', ...  % None
        'avgwinlength',  1, ...
        'avgwinoverlap', 50, ...
        'outputmode',    'avg');  % across combined files/epochs
    % Process: Add tag
    sFileCoh1N = bst_process('CallProcess', 'process_add_tag', sFileCoh1N, [], ...
        'tag',           sourceType, ...
        'output',        1);  % Add to file name
    % View surface
    if ~isempty(strfind(sourceType, 'surface'))
        % Process: Snapshot: Sources (one time)
        bst_process('CallProcess', 'process_snapshot', sFileCoh1N, [], ...
            'type',        'sources', ...  % Sources (one time)
            'orient',      3, ...  % top
            'threshold',   0, ...
            'surfsmooth',  30, ...
            'freq',        14.65, ...
            'Comment',     ['MSC 14.65Hz ',  sourceType]);
    % View volume
    elseif ~isempty(strfind(sourceType, 'volume'))
        % Process: Snapshot: Sources (MRI Viewer)
        bst_process('CallProcess', 'process_snapshot', sFileCoh1N, [], ...
            'type',        'mriviewer', ...  % MRI viewer
            'threshold',   0, ...
            'freq',        14.65, ...
            'mni',         [26, -11, 73], ...
            'Comment',     ['MSC 14.65Hz ',  sourceType]);
    end
end


%% ===== COHERENCE: EMG x SCOUTS (BEFORE) =====
% Process: Select data files
sFilesRecEmg = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname',   SubjectName, ...
    'condition',     '', ...
    'tag',           'Left', ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);
% Only performed for (surface)(Constrained)
sourceType = '(surface)(Constr)';
sFilesResSrfCon = bst_process('CallProcess', 'process_select_files_results', [], [], ...
    'subjectname',   SubjectName, ...
    'condition',     '', ...
    'tag',           sourceType, ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);
% Process: Coherence NxN [2023]
sFileCoh1N = bst_process('CallProcess', 'process_cohere2', sFilesRecEmg, sFilesResSrfCon, ...
    'timewindow',    [], ...
    'src_channel',   emg_channel, ...
    'dest_scouts',  {'Schaefer_100_17net', {'Background+FreeSurfer_Defined_Medial_Wall L', 'Background+FreeSurfer_Defined_Medial_Wall R', 'ContA_IPS_1 L', 'ContA_IPS_1 R', 'ContA_PFCl_1 L', 'ContA_PFCl_1 R', 'ContA_PFCl_2 L', 'ContA_PFCl_2 R', 'ContB_IPL_1 R', 'ContB_PFCld_1 R', 'ContB_PFClv_1 L', 'ContB_PFClv_1 R', 'ContB_Temp_1 R', 'ContC_Cingp_1 L', 'ContC_Cingp_1 R', 'ContC_pCun_1 L', 'ContC_pCun_1 R', 'ContC_pCun_2 L', 'DefaultA_IPL_1 R', 'DefaultA_PFCd_1 L', 'DefaultA_PFCd_1 R', 'DefaultA_PFCm_1 L', 'DefaultA_PFCm_1 R', 'DefaultA_pCunPCC_1 L', 'DefaultA_pCunPCC_1 R', 'DefaultB_IPL_1 L', 'DefaultB_PFCd_1 L', 'DefaultB_PFCd_1 R', 'DefaultB_PFCl_1 L', 'DefaultB_PFCv_1 L', 'DefaultB_PFCv_1 R', 'DefaultB_PFCv_2 L', 'DefaultB_PFCv_2 R', 'DefaultB_Temp_1 L', 'DefaultB_Temp_2 L', 'DefaultC_PHC_1 L', 'DefaultC_PHC_1 R', 'DefaultC_Rsp_1 L', 'DefaultC_Rsp_1 R', 'DorsAttnA_ParOcc_1 L', 'DorsAttnA_ParOcc_1 R', 'DorsAttnA_SPL_1 L', 'DorsAttnA_SPL_1 R', 'DorsAttnA_TempOcc_1 L', 'DorsAttnA_TempOcc_1 R', 'DorsAttnB_FEF_1 L', 'DorsAttnB_FEF_1 R', 'DorsAttnB_PostC_1 L', 'DorsAttnB_PostC_1 R', 'DorsAttnB_PostC_2 L', 'DorsAttnB_PostC_2 R', 'DorsAttnB_PostC_3 L', 'LimbicA_TempPole_1 L', 'LimbicA_TempPole_1 R', 'LimbicA_TempPole_2 L', 'LimbicB_OFC_1 L', 'LimbicB_OFC_1 R', 'SalVentAttnA_FrMed_1 L', 'SalVentAttnA_FrMed_1 R', 'SalVentAttnA_Ins_1 L', 'SalVentAttnA_Ins_1 R', 'SalVentAttnA_Ins_2 L', 'SalVentAttnA_ParMed_1 L', 'SalVentAttnA_ParMed_1 R', 'SalVentAttnA_ParOper_1 L', 'SalVentAttnA_ParOper_1 R', 'SalVentAttnB_IPL_1 R', 'SalVentAttnB_PFCl_1 L', 'SalVentAttnB_PFCl_1 R', 'SalVentAttnB_PFCmp_1 L', 'SalVentAttnB_PFCmp_1 R', 'SomMotA_1 L', 'SomMotA_1 R', 'SomMotA_2 L', 'SomMotA_2 R', 'SomMotA_3 R', 'SomMotA_4 R', 'SomMotB_Aud_1 L', 'SomMotB_Aud_1 R', 'SomMotB_Cent_1 L', 'SomMotB_Cent_1 R', 'SomMotB_S2_1 L', 'SomMotB_S2_1 R', 'SomMotB_S2_2 L', 'SomMotB_S2_2 R', 'TempPar_1 L', 'TempPar_1 R', 'TempPar_2 R', 'TempPar_3 R', 'VisCent_ExStr_1 L', 'VisCent_ExStr_1 R', 'VisCent_ExStr_2 L', 'VisCent_ExStr_2 R', 'VisCent_ExStr_3 L', 'VisCent_ExStr_3 R', 'VisCent_Striate_1 L', 'VisPeri_ExStrInf_1 L', 'VisPeri_ExStrInf_1 R', 'VisPeri_ExStrSup_1 L', 'VisPeri_ExStrSup_1 R', 'VisPeri_StriCal_1 L', 'VisPeri_StriCal_1 R'}}, ...
    'flatten',       0, ...
    'scouttime',     'before', ...  % before    connectivity metric
    'scoutfunc',     'mean', ...  % Mean
    'scoutfuncaft',  'mean', ...  % Mean
    'pcaedit',       struct(...
         'Method',         'pcaa', ...
         'Baseline',       [], ...
         'DataTimeWindow', [], ...
         'RemoveDcOffset', 'file'), ...
    'removeevoked',  0, ...
    'cohmeasure',    cohmeasure, ...  % Magnitude-squared coherence
    'tfmeasure',     'stft', ...  % Fourier transform
    'tfedit',        struct(...
         'Comment',         'Complex', ...
         'TimeBands',       [], ...
         'Freqs',           [], ...
         'StftWinLen',      win_length, ...
         'StftWinOvr',      overlap, ...
         'StftFrqMax',      maxfreq, ...
         'ClusterFuncTime', 'none', ...
         'Measure',         'none', ...
         'Output',          'all', ...
         'SaveKernel',      0), ...
    'timeres',       'none', ...  % None
    'avgwinlength',  1, ...
    'avgwinoverlap', 50, ...
    'outputmode',    'avg');  % across combined files/epochs
% Process: Add tag
sFileCoh1N = bst_process('CallProcess', 'process_add_tag', sFileCoh1N, [], ...
    'tag',           sourceType, ...
    'output',        1);  % Add to file name
% Highlight scout of interest
bst_figures('SetSelectedRows', {'SomMotA_2 R', 'SomMotA_4 R'});
% Process: Snapshot: Frequency spectrum
bst_process('CallProcess', 'process_snapshot', sFileCoh1N, [], ...
    'target',   10, ...  % Frequency spectrum
    'freq',     14.65, ...
    'Comment',  ['MSC ,' sourceType, ' Before']);
% Process: Snapshot: Connectivity matrix
bst_process('CallProcess', 'process_snapshot', sFileCoh1N, [], ...
    'type',     'connectimage', ...  % Connectivity matrix
    'freq',     14.65, ...
    'Comment',  ['MSC 14.65Hz,' sourceType, ' Before']);


%% ===== COHERENCE: SCOUTS x SCOUTS (BEFORE) =====
% Only performed for (surface)(Constrained)
sourceType = '(surface)(Constr)';
sFilesResSrfCon = bst_process('CallProcess', 'process_select_files_results', [], [], ...
    'subjectname',   SubjectName, ...
    'condition',     '', ...
    'tag',           sourceType, ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);
% Process: Coherence NxN [2023]
sFileCohNN = bst_process('CallProcess', 'process_cohere1n', sFilesResSrfCon, [], ...
    'timewindow',    [], ...
    'scouts',        {'Schaefer_100_17net', {'SomMotA_2 R', 'SomMotA_4 R', 'VisCent_ExStr_2 L'}}, ...
    'flatten',       0, ...
    'scouttime',     'before', ...  % before
    'scoutfunc',     'mean', ...  % Mean
    'scoutfuncaft',  'mean', ...  % Mean
    'pcaedit',       struct(...
         'Method',         'pcaa', ...
         'Baseline',       [], ...
         'DataTimeWindow', [], ...
         'RemoveDcOffset', 'file'), ...
    'removeevoked',  0, ...
    'cohmeasure',    'mscohere', ...  % Magnitude-squared coherence
    'tfmeasure',     'stft', ...  % Fourier transform
    'tfedit',        struct(...
         'Comment',         'Complex', ...
         'TimeBands',       [], ...
         'Freqs',           [], ...
         'StftWinLen',      0.5, ...
         'StftWinOvr',      50, ...
         'StftFrqMax',      80, ...
         'ClusterFuncTime', 'none', ...
         'Measure',         'none', ...
         'Output',          'all', ...
         'SaveKernel',      0), ...
    'timeres',       'none', ...  % None
    'avgwinlength',  1, ...
    'avgwinoverlap', 50, ...
    'outputmode',    'avg');  % across combined files/epochs
% Process: Add tag
sFileCohNN = bst_process('CallProcess', 'process_add_tag', sFileCohNN, [], ...
    'tag',           sourceType, ...
    'output',        1);  % Add to file name
% Highlight scout of interest
bst_figures('SetSelectedRows', {'SomMotA_2 R', 'SomMotA_4 R'});
% Process: Snapshot: Frequency spectrum
bst_process('CallProcess', 'process_snapshot', sFileCohNN, [], ...
    'target',   10, ...  % Frequency spectrum
    'freq',     14.65, ...
    'Comment',  ['MSC ,' sourceType, ' Before']);
% Process: Snapshot: Connectivity matrix
bst_process('CallProcess', 'process_snapshot', sFileCohNN, [], ...
    'type',     'connectimage', ...  % Connectivity matrix
    'freq',     14.65, ...
    'Comment',  ['MSC 14.65Hz,' sourceType, ' Before']);
% Process: Snapshot: Connectivity graph
bst_process('CallProcess', 'process_snapshot', sFileCohNN, [], ...
    'type',     'connectgraph', ...  % Connectivity graph
    'freq',     14.65, ...
    'threshold',      0, ...
    'Comment',  ['MSC 14.65Hz,' sourceType, ' Before']);


%% ===== SAVE REPORT =====
% Save and display report
ReportFile = bst_report('Save', []);
if ~isempty(reports_dir) && ~isempty(ReportFile)
    bst_report('Export', ReportFile, reports_dir);
else
    bst_report('Open', ReportFile);
end

disp([10 'DEMO> Corticomuscular coherence tutorial completed' 10]);


-  ⇤ ← Revision 267 as of 2022-02-21 10:06:37 → 
  Size: 50779
  Editor: FrancoisTadel
  Comment:
+   ← Revision 331 as of 2022-03-12 10:37:40 → ⇥
  Size: 43722
  Editor: FrancoisTadel
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
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-<<HTML(<style>.backtick {font-size: 16px;}</style>)>><<HTML(<style>abbr {font-weight: bold;}</style>)>> <<HTML(<style>em strong {font-weight: normal; font-style: normal; padding: 2px; border-radius: 5px; background-color: #EEE; color: #111;}</style>)>>
+<<HTML(<style>tt {font-size: 16px;}</style>)>><<HTML(<style>abbr {font-weight: bold;}</style>)>> <<HTML(<style>em strong {font-weight: normal; font-style: normal; padding: 2px; border-radius: 5px; background-color: #EEE; color: #111;}</style>)>>
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-<<TableOfContents(2,2)>>

== Background ==
[[Tutorials/Connectivity#Coherence|Coherence]] measures 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 reported corticomuscular coherence effects in the 15–30 Hz range during maintained voluntary contractions.

IMAGE OF EXPERIMENT, SIGNALS and COHERENCE
+<<TableOfContents(3,2)>>
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+Line 13:
-The dataset comprises MEG recordings (151-channel CTF MEG system) and bipolar EMG recordings (from left and right extensor carpi radialis longus muscles) from one participant who was tasked to lift their hand and exert a constant force against a lever for about 10 seconds. The force was monitored by strain gauges on the lever. The participant performed two blocks of 25 trials using either the left or right wrist. EOG signals were also recorded, which will be useful for detection and attenuation of ocular artifacts. We will analyze the data from the left-wrist trials in the present tutorial. Replicating the pipeline with right-wrist data is a good exercise to do next!
+The dataset is distributed as part of the FieldTrip tutorial [[https://www.fieldtriptoolbox.org/tutorial/coherence/|Analysis of corticomuscular coherence]]:

 * One participant
 * MEG recordings: 151-channel CTF MEG system
 * Bipolar EMG recordings: from left and right extensor carpi radialis longus muscles
 * EOG recordings: used for detection and attenuation of ocular artifacts
 * MRI: 1.5T Siemens system
 * Task: The participant was tasked to lift their hand and exert a constant force against a lever for about 10 seconds. The force was monitored by strain gauges on the lever. The participant performed two blocks of 25 trials using either the left or right wrist.
 * We will analyze the data from the left-wrist trials in the present tutorial. Replicating the pipeline with right-wrist data is a good exercise to do next!

Corticomuscular coherence:

 * [[Tutorials/Connectivity#Coherence|Coherence]] measures 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 reported corticomuscular coherence effects in the 15–30 Hz range during maintained voluntary contractions.
 * TODO: IMAGE OF EXPERIMENT, SIGNALS and COHERENCE
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+Line 30:
- * '''Requirements''':
  * Please make sure you have completed the [[Tutorials|get-started tutorials]] and that you have a working copy of Brainstorm installed on your computer.
  * In addition, you need to [[Tutorials/SegCAT12#Install_CAT12|install CAT12 plugin]], used here for MRI segmentation.
 * '''Download the dataset''':
  * Download `SubjectCMC.zip` from FieldTrip FTP server:<<BR>> ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip
  * Unzip it in a folder not located in any of Brainstorm folders (the app or its database).
 * '''Brainstorm''':
  * Launch Brainstorm (via Matlab command line or the Matlab-free stand-alone version).
  * Select the menu '''''File > Create new protocol'''''. Name it `TutorialCMC` and select the options:<<BR>> '''No, use individual anatomy''', <<BR>> '''No, use one channel file per acquisition run'''.

== Importing and processing anatomy data ==
 * Right-click on the newly created '''TutorialCMC''' node in your Brainstorm data tree then: <<BR>> '''''New subject > Subject01'''''.<<BR>>Keep the default options defined for the study (aka "protocol" in Brainstorm jargon).

 * Switch to the '''Anatomy''' view of the protocol (<<Icon(iconSubjectDB.gif)>>).
 * Right-click on the '''Subject01''' node then '''''Import MRI''''':
  * Select the adequate file format from the pull-down menu: '''All MRI file (subject space)'''
+'''Requirements'''

 * Please make sure you have completed the [[Tutorials|get-started tutorials]] and that you have a working copy of Brainstorm installed on your computer.
 * In addition, you need to [[Tutorials/SegCAT12#Install_CAT12|install the plugin CAT12]], used for MRI segmentation.

'''Download the dataset'''

 * Download `SubjectCMC.zip` from FieldTrip FTP server:<<BR>> ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip
 * Unzip it in a folder not located in any of Brainstorm folders (the app or its database).

'''Brainstorm'''

 * Launch Brainstorm (via Matlab command line or the Matlab-free stand-alone version).
 * Select the menu '''File > Create new protocol'''. Name it `TutorialCMC` and select the options:<<BR>> No, use individual anatomy, <<BR>> No, use one channel file per acquisition run.

== Importing anatomy ==
 * Right-click on the newly created TutorialCMC node > '''New subject > Subject01'''.<<BR>>Keep the default options defined for the study (aka "protocol" in Brainstorm jargon).

 * Switch to the Anatomy view of the protocol (<<Icon(iconSubjectDB.gif)>>).
 * Right-click on the Subject01 > '''Import MRI''':
  * Select the file format: '''All MRI files (subject space)'''
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- * This will open the '''MRI viewer''' showing the coronal, sagittal and axial views of the MRI. In addition, [[CoordinateSystems|three anatomical fiducials]]: left and right pre-auricular points (LPA and RPA), and nasion (NAS) are automatically identified. These fiducials are located near the left/right ears and just above the nose respectively. Click on '''Save'''.

 . [[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=mri_viewer.png|{{attachment:mri_viewer.png|https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=mri_viewer.png}}]]

We then need to segment the head tissues to obtain the surfaces required to derive a realistic MEG [[Tutorials/HeadModel|head models (aka "forward models")]]. Here, we will perform [[Tutorials/SegCAT12|MRI segmentation with CAT12]], this process takes between 30 to 60 minutes.

 * Right-click on the '''SubjectCMC''' MRI node (<<Icon(iconMri.gif)>>), then select '''''MRI segmentation > CAT12: Cortex, atlases, tissues'''''. This will prompt a series of windows to set the parameters for the MRI segmentation, use the following parameters:
  * '''Number of vertices on the cortex surface''' use `15000`
  * '''Compute anatomical parcellations?''' select `Yes`
  * '''Compute cortical maps''' select `Yes`

 . {{attachment:cat12.png||width="100%"}}

CAT12 computes the '''scalp''' surface (<<Icon(iconScalp.gif)>>), and the '''cortex''' surfaces (<<Icon(iconCortexCut.gif)>>) for white matter, pial envelope and the midpoint between them. The default surface of each type is indicated in green. Moreover, as part of the MRI segmentation with CAT12, the anatomy data is normalized in the MNI space, and several anatomical parcellations (<<Icon(iconMriAtlas.gif)>>) are computed. For further information on the anatomy files see the [[Tutorials/ExploreAnatomy|Display the anatomy tutorial]].

 . {{attachment:import_result.png||width="40%"}}

 * Right-click on the '''pial_15002''' surface as set it as default
 * Double-click on the '''head mask''' and then on the '''pial_15002''' surface to display them.
 * In addition, the registration between the MRI and the surfaces can be checked with context menu '''MRI registration > Check MRI/surface registration...'''

 . {{attachment:fig_anat_srf.png||width="60%"}}

 . {{attachment:fig_anat_registration.png}}

== Review the MEG and EMG recordings ==
=== Link the recordings to Brainstorm database ===
 * Switch now to the '''Functional data''' view (<<Icon(iconStudyDBSubj.gif)>>).
 * Right-click on the '''Subject01''' node then '''''Review raw file''''':
  * Select the file format of current data from the pulldown menu options:<<BR>> '''MEG/EEG: CTF(*.ds; *.meg4; *.res4)'''
+ * This will open the MRI viewer showing the coronal, sagittal and axial views of the MRI. In addition, [[CoordinateSystems|three anatomical fiducials]]: left and right pre-auricular points (LPA and RPA), and nasion (NAS) are automatically identified. These fiducials are located near the left/right ears and just above the nose respectively. Click on '''Save'''. <<BR>><<BR>> {{attachment:mri_viewer.gif||width="344",height="295"}}

 * In a typical Brainstorm workflow, as illustrated in all the other tutorials, we would recommend running the full segmentation of the MRI at this stage, in order to have the anatomy of the subject fully prepared before importing the functional data. In the case of this tutorial, we will proceed differently, in order to follow better the original FieldTrip pipeline and to obtain sensor-level coherence results much faster. We will run the segmentation of the anatomy just before moving to the source-level analysis.
 * For validating the registration between the MRI and the MEG, we will now limit the anatomy processing to the reconstruction of the head surface from the MRI.
 * Right-click on the MRI (<<Icon(iconMri.gif)>>) > MRI segmentation > '''Generate head surface'''. <<BR>><<BR>> {{attachment:head_process.gif}}
 * At the end, you would get one new head surface. Double-click on it to display it.<<BR>><<BR>> {{attachment:head_display.gif}}

== MEG and EMG recordings ==
=== Link the recordings ===
 * Switch now to the '''Functional data '''view (<<Icon(iconStudyDBSubj.gif)>>).
 * Right-click on the Subject01 > '''Review raw file''':
  * Select the file format: '''MEG/EEG: CTF(*.ds; *.meg4; *.res4)'''
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-A new folder is now created in Brainstorm database explorer and contains:

 * '''SubjectCMC''': a folder that provides access to the MEG dataset. Note the "RAW" tag over the icon of the folder (<<Icon(iconRawFolderClose.gif)>>), indicating the files contain unprocessed, continuous data.
 * '''CTF channels (191)''': a node containing '''channel information''' with all channel types, names, locations, etc. The number of channels available (MEG, EMG, EOG etc.) is indicated between parentheses '''(here, 191)'''.
 * '''Link to raw file''' provides access to '''the original data file'''. All the relevant metadata was read from the dataset and copied inside the node itself (e.g., sampling rate, number of time samples, event markers). Note that Brainstorm logic is not to import/duplicate the raw unprocessed data directly into the database. Instead, Brainstorm provides a link to that raw file for further review and data extraction ([[Tutorials/ChannelFile#Review_vs_Import|more information]]).
+ * A new folder '''SubjectCMC '''is created in the Brainstorm database explorer. Note the "RAW" tag over the icon of the folder (<<Icon(iconRawFolderClose.gif)>>), indicating the files contain unprocessed, continuous data. It contains:
 * '''CTF channels (191)''': Channel file with all channel types, names, locations, etc. The number of channels available (MEG, EMG, EOG etc.) is indicated between parentheses.
 * '''Link to raw file''': Provides access to the original data file. All the relevant metadata was read from the dataset and copied inside the node itself (e.g., sampling rate, number of time samples, event markers). Note that Brainstorm logic is not to import/duplicate the raw unprocessed data directly into the database. Instead, Brainstorm provides a link to that raw file for further review and data extraction ([[Tutorials/ChannelFile#Review_vs_Import|more information]]).
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-=== Display MEG helmet and sensors ===
 * Right-click on the '''CTF channels (191)''' node, then select '''''Display sensors > CTF helmet''''' from the contextual menu and '''''Display sensors > MEG. '''''This will open a new display window showing the inner surface of the MEG helmet, and the lo MEG sensors respectively. Try [[Tutorials/ChannelFile#Display_the_sensors|additional display menus]].

|| {{attachment:fig_helmet.png}} || || {{attachment:fig_sensors.png}} ||


=== Reviewing continuous recordings ===
 * Right-click on the '''Link to raw file''' node, then '''''Switch epoched/continuous''''' to convert the file to '''continuous''', a technical detail proper to CTF file formatting.
 * Right-click again on the '''Link to raw file''' node, then '''''MEG > Display time series''''' (or double-click on the node). This will open a new visualization window to explore data time series, also enabling the '''''Time''''' panel and the '''''Record''''' tab in the main Brainstorm window (see how to best use all controls in this panel and tab to [[Tutorials/ReviewRaw|explore data time series]]).
 * We will also display EMG traces by right-clicking on the '''Link to raw file''' node, then '''''EMG > Display time series'''''.
+=== MEG-MRI coregistration ===
 * This step, sometimes simply named ''registration'', refers to the alignment of the sensors on the anatomy of the subject ([[https://neuroimage.usc.edu/brainstorm/Tutorials/ChannelFile#Automatic_registration|more info]]). For this tutorial, data registration is carried out using only three anatomical landmarks present in the MRI and MEG data. According to the description of the data in the [[https://www.fieldtriptoolbox.org/tutorial/coherence/|FieldTrip tutorial]]:<<BR>>''"To measure the head position with respect to the sensors, three coils were placed at anatomical landmarks of the head (nasion, left and right ear canal). [...] During the MRI scan, ear molds containing small containers filled with vitamin E marked the same landmarks. This allows us, together with the anatomical landmarks, to align source estimates of the MEG with the MRI." ''
 * To verify the registration, right-click on the '''CTF channels''' node > '''MRI registration > Check'''. This opens a 3D figure showing the inner surface of the MEG helmet, the head surface, and the fiducials and axes that comprise the [[CoordinateSystems#Subject_Coordinate_System_.28SCS_.2F_CTF.29|subject coordinate system (SCS)]].<<BR>><<BR>> {{attachment:fig_registration.gif||width="209",height="204"}}

=== Reviewing ===
 * Right-click on Link to raw file > '''Switch epoched/continuous''' to convert the file to continuous, a technical detail proper to CTF file formatting.
 * Right-click on Link to raw file > '''MEG > Display time series''' (or double-click). This will open a new visualization window to explore data time series, also enabling the Time panel and the Record tab in the main Brainstorm window (see how to best use all controls in this panel and tab to [[Tutorials/ReviewRaw|explore data time series]]).
 * Right-click on Link to raw file > '''EMG > Display time series'''.
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+Line 85:
-The colored dots above the data time series indicate [[Tutorials/EventMarkers|event markers]] (or triggers) saved with this dataset. The trial onset information of the left-wrist and right-wrist trials is saved in an auxiliary channel of the raw data named '''Stim'''. To add these markers, these events need to be decoded as follows:

 * While the time series figure is open, go to the '''''Record''''' tab and '''''File > Read events from channel'''''. From the options of the '''Read from channel''' process window, set '''Event channels''' = `Stim`, select '''Value''', and click '''Run'''.
+The colored dots above the data time series indicate [[Tutorials/EventMarkers|event markers]] (or triggers) saved with this dataset. The trial onset information of the left-wrist and right-wrist trials is saved in an auxiliary channel of the raw data named ''Stim''. To add these markers, these events need to be decoded as follows:

 * While the time series figure is open, go to the Record tab and '''File > Read events from channel'''. Event channels = `Stim`, select Value, and click '''Run'''.
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-This procedure creates new event markers now shown in the '''''Events''''' section of the tab. along with previous event categories. In this tutorial, we will only use events '''U1''' through '''U25''', which correspond to how each of the 25 left-wrist trials had been encoded in the study. We will now delete other events of no interest, and merge the left trial events under a single event category, for convenience.

 * Delete other events: select the events to delete in the event box/list with '''Ctrl+click''', then in the menu '''''Events > Delete group''''' and confirm. Alternatively, you can select all events with '''Ctrl+A''' and deselect the '''U1''' to '''U25''' events by clicking on them.
 * To make sure we reproduce FieldTrip tutorial, we need to reject trial #7: Select events '''U1''' to '''U6''' and '''U8''' to '''U25''', then from the '''Events '''menu, select''' Merge group''' and type in new label ('''Left_01''') to describe this as the left-wrist condition.

 . {{attachment:left_24.png}}

These events correspond to the beginning of 10-s trials of left-wrist movements. We will compute coherence over 1-s epochs over the first 8 s of each trial. To that purpose, we will now create extra events to define these epochs.

 * Duplicate 7 times the '''Left''' events by selecting '''''Duplicate group''''' in the '''''Events''''' menu. The groups '''Left_02''' to '''Left_08''' will be created.
 * For each copy of the '''Left''' events, we will add a time offset of 1 s for '''Left02''', 2 s for '''Left03''', and so on. Select the '''Left '''event group to add a 1,000 ms time offset, by going to the menu '''''Events > Add time offset'', '''enter 1,000 in the text box. Repeat for each other group, entering 2,000, then 3,000 etc.

 . {{attachment:dup_offset.png}}

 * Once done for '''Left_08''', merge all these '''Left*''' events into a single '''Left '''category, and select '''''Save modifications''''' in the '''''File''''' menu in the '''''Record''''' tab.

 . {{attachment:left_192.png}}

== Pre-process ==
+ * This creates new event markers now shown in the Events section of the tab, along with previous event categories. In this tutorial, we will only use events '''U1''' through '''U25''', which correspond to the beginning of each of the 25 trials of 10 seconds with left-wrist movements. To make sure we reproduce FieldTrip tutorial, we need to reject trial #7, event '''U7'''.

 * Delete unused events: Select all the events '''except''' '''U1-U6''' and '''U8-25''' (Ctrl+click / Shift+click), then menu '''Events > Delete group''' (or press the Delete key).

 * Merge events: Select all the event groups, then menu '''Events > ''' Merge group > '''"Left"'''. This new event category references 24 trials of the left-wrist condition, i.e. 10-second blocks of left-wrist movements.<<BR>><<BR>> {{attachment:left_24.gif}}

== Pre-processing ==
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+Line 101:
-Another idiosyncrasy of the present dataset is that the CTF MEG data were saved without the desired 3-rd order gradient compensation for optimal denoising. We will now apply this compensation as follows:

 * In the '''''Process1''''' box: Drag and drop the '''Link to raw file''' node.
 * Run process '''''Artifacts > Apply SSP & CTF compensation''''':<<BR>>

 . {{attachment:pro_ctf_compensation.png||width="60%"}}

This process creates the '''SubjectCMC_clean''' folder that contains a copy of the '''channel file''' and a link to the raw file '''Raw | clean''', which points to the original data and to the fact that the 3-rd order gradient compensation will be applied. Brainstorm does not create a physical copy of the actual, large dataset at this stage.

 . {{attachment:tre_raw_clean.png||width="40%"}}

=== Removal of power line artifacts ===
We will start with identifying the spectral components of power line contamination of MEG and EMG recordings.

 * In the '''''Process1''''' box: Drag and drop the '''Raw | clean''' node.
+=== Power line artifacts ===
 * In the Process1 box: Drag and drop the '''Link to raw file'''.
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+Line 104:
-  * '''Time window''': `0 - 330 s`
  * '''Window length='''`10 s`
  * '''Overlap'''=`50%`
  * '''Sensor types'''=`MEG, EMG`
+  * '''Time window''': `0-330 s`
  * '''Window length''':''' '''`10 s`
  * '''Overlap''': `50%`
  * '''Sensor types''': `MEG, EMG`
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+Line 111:
- * Double-click on the new '''PSD''' file to visualize the power spectrum density of the data.<<BR>>
+ * Double-click on the new PSD file to visualize the power spectrum density of the data.<<BR>>
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+Line 115:
- * In the '''''Process1''''' box: Drag and drop the '''Raw | clean''' node.

 * Run the process '''''Pre-processing > Notch filter''''' with: <<BR>>
  * '''Sensor types''' = `MEG, EMG`
  * '''Frequencies to remove (Hz)''' = `50, 100, 150`
+ * In the Process1 box: Drag and drop the '''Raw | clean''' node.

 * Run the process '''Pre-processing > Notch filter''' with: <<BR>>
  * Check '''Process the entire file at once'''
  * '''Sensor types''': `MEG, EMG`
  * '''Frequencies to remove (Hz)''': `50, 100, 150`
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+Line 124:
-A new '''raw''' folder named '''SubjectCMC_clean_notch''' is created. Estimate the PSD of these signals to appreciate the effect of the notch filters applied. As above, please remember to indicate a '''Time window''' restricted from 0 to 330 s in the options of the PSD process.
+ * Troubleshooting in case of memory error:<<BR>>These MEG recordings have been saved before applying the CTF 3rd-order gradient compensation. The compensation weights are applied on the fly when Brainstorm reads data from the file, however this requires reading all the channels at once. By default, the frequency filter are optimized to process the channels sequentially, which is incompatible with applying the CTF compensation on the fly. This setting can be overridden with the option '''Process the entire file at once''', but this solution has the effect of loading the entire file in memory at once, which can crash on computers with limited memory (RAM < 8Gb). If this happens to you: run the process  '''Artifacts > Apply SSP & CTF compensation''' on the file first, then the notch filter without the option "Process the entire file at once" ([[https://neuroimage.usc.edu/brainstorm/Tutorials/TutMindNeuromag#Existing_SSP_and_pre-processing|more information]]).
 * A new folder named '''SubjectCMC_clean_notch''' is created. Estimate  the PSD of these signals to appreciate the effect of the notch filters  applied. As above, please remember to indicate a '''Time window''' restricted from 0 to 330 s in the options of the PSD process.<<BR>><<BR>>
-Line 160:
+Line 128:
-=== EMG pre-processing ===
+=== EMG: Filter and rectify ===
-Line 163:
+Line 131:
- * In the '''''Process1''''' box: drag and drop the '''Raw | notch(50Hz 100Hz 150Hz)''' node.
 * Add the process '''''Pre-process > Band-pass filter'''''
+ * In the Process1 box: drag and drop the '''Raw | notch(50Hz 100Hz 150Hz)''' node.
 * Add the process '''Pre-process > Band-pass filter'''
-Line 169:
+Line 137:
- * Add the process '''''Pre-process > Absolute values'''''
+ * Add the process '''Pre-process > Absolute values'''
-Line 176:
+Line 144:
-Two new folders '''SubjectCMC_clean_notch_high''' and '''SubjectCMC_clean_notch_high_abs''' are added to Brainstorm database explorer. We can now safely delete folders that are not needed anymore:

 * Delete '''SubjectCMC_clean_notch''' and '''SubjectCMC_clean_notch_high '''by selecting both before pressing Delete (or right-click '''''File > Delete''''').

=== MEG pre-processing ===
We need to remove more artifacts from the MEG traces via the:

 1. '''Detection and removal of stereotypical artifacts with SSP'''

 1. '''Detection of noisy (bad) data segments.'''

==== Detection and removal of artifacts with SSP (Signal Space Projection) ====
Stereotypical artifacts such eye blinks and heartbeats can be identified from their respective characteristic spatial distributions. Their contamination of MEG signals can then be attenuated specifically using Signal-Space Projections (SSPs). For more details, consult the dedicated tutorials about the [[Tutorials/ArtifactsDetect|detection]] and [[Tutorials/ArtifactsSsp|removal of artifacts with SSP]]. The present tutorial dataset features an EOG channel but no ECG. We will perform only the removal of eye blinks.

 * Display the MEG and EOG time series: Right-click on the pre-processed (for EMG) continuous file '''Raw | clean | notch(...''' (in the '''SubjectCMC_clean_notch_high_abs''' folder) then '''''MEG > Display time series''''' and '''''EOG > Display time series'''''.

 * In the '''Events''' section of the '''''Record''''' tab, select '''''Artifacts > Detect eye blinks''''', and use the parameters:
+ * Delete intermediate files that won't be needed anymore: Select folders '''SubjectCMC_notch''' and '''SubjectCMC_notch_high''',''' '''then press the Delete key (or right-click > File > Delete).<<BR>><<BR>> {{attachment:db_filters.gif}}

=== MEG: Blink SSP and bad segments ===
Stereotypical artifacts such eye blinks and heartbeats can be identified  from their respective characteristic spatial distributions. Their  contamination of MEG signals can then be attenuated specifically using  Signal-Space Projections (SSPs). For more details, consult the dedicated  tutorials about the [[Tutorials/ArtifactsDetect|detection]] and [[Tutorials/ArtifactsSsp|removal of artifacts with SSP]]. The present tutorial dataset features an EOG channel but no ECG. We will perform only the removal of eye blinks.

==== Blink correction with SSP ====
 * Right-click on the pre-processed file > '''MEG > Display time series''' and '''EOG > Display time series'''.

 * In the Record tab: '''Artifacts > Detect eye blinks''', and use the parameters:
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+Line 163:
- * To [[Tutorials/ArtifactsSsp|remove blink artifacts with SSP]] go to '''''Artifacts > SSP: Eye blinks''''', and use the parameters:
+ * To [[Tutorials/ArtifactsSsp|remove blink artifacts with SSP]], go to '''Artifacts > SSP: Eye blinks''':
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+Line 166:
-  * Check '''Compute using existing SSP/ICA projectors'''
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+Line 169:
- * Display the time series and topographies of the first two (dominant) SSP components identified. In the present case, only the first SSP component can be clearly related to blinks. Select only component #1 for removal.
+ * Display the time series and topographies of the first two SSP components identified. In the present case, only the first SSP component can be clearly related to blinks: percentage between brackets much higher than the others, typical spatial topography, time series highly correlated with the EOG signal. Select only '''component #1''' for removal.
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- * Follow the same procedure for the other blink events ('''blink2''' and '''blink3'''). As mentioned above, none of the first two SSP components seem to be related to ocular artifacts. The figure below shows the visualization of the first two components for the '''blink2''' group.

 . {{attachment:ssp_blink2.png||width="100%"}}

 . We therefore recommend unselecting the '''blink2''' and '''blink3''' groups from the '''Select Active Projectors''' panel (see below) rather than removing spatial components which nature remains ambiguous.

 . {{attachment:ssp_active_projections.png||width="60%"}}

 * Click on the large crosshair at the top right of the main Brainstorm window to close all visualization windows.

==== Detection of "bad" data segments: ====
+ * The second SSP component could be related with other ocular artifacts. For a more precise characterization of this artifact, it could be better indicated to use the other events detected on the EOG (blink2 and blink3). We will not do this here because advanced SSP cleaning is not the main topic of this tutorial.

 * Close all figures: Click on the large '''×''' at the top-right of the main Brainstorm window.

==== Detection of "bad" data segments ====
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- * Display the MEG and EOG time series. In the '''''Record''''' tab, select '''''Artifacts > Detect other artifacts''''' and enter the following parameters:
+ * Display the MEG and EOG time series. In the '''Record''' tab, select '''Artifacts > Detect other artifacts''' and enter the following parameters:
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+Line 188:
-We encourage users to review and validate the segments marked using this procedure. In the present case, the segments detected as bad clearly point at contaminated MEG data segments, which we will now label these as "bad".

 * Select the '''1-7Hz''' and '''40-240Hz''' event groups and select '''Events > Mark group as bad''' from the contextual menu. Alternatively, you can also rename the events created above and append the '''bad_''' prefix to their name: Brainstorm will automatically discard these data segments from further processing.
+ * We encourage users to review all the segments marked using this procedure. In the present case, all the segments detected clearly point at artifacts.

 * Select the '''1-7Hz''' and '''40-240Hz''' event groups and select '''Events > Mark group as bad'''. Alternatively, you can add the prefix  '''bad_''' to the event names. Brainstorm will automatically discard these data segments from further processing.
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-== Importing data epochs ==
At this point we are finished with the pre-processing of the EMG and MEG recordings. We will now extract and import specific data segments of interest into the Brainstorm database for further derivations. We refer to these segments as '''epochs''' or '''trials'''. As mentioned previously, we will focus on the '''Left''' (wrist) category of events.

 * Right-click on the filtered continuous file '''Raw | clean | notch(...''' (in the '''SubjectCMC_clean_notch_high_abs''' condition), then '''''Import in database'''''.

 . {{attachment:import_menu.png||width="40%"}}

 * Enter the following parameter values:
+== Epoching ==
We are finished with the pre-processing of the EMG and MEG recordings. We will now extract and import specific data segments of interest into the Brainstorm database for further derivations. As mentioned previously, we will focus on the '''Left''' category of events (left wrist movements). To follow the same pipeline as the [[https://www.fieldtriptoolbox.org/tutorial/coherence/|FieldTrip tutorial]]: we will consider 8 seconds of recordings after each trigger (out of the 10s of each trial), and split them in epochs of 1 second. In addition DC offset is removed, only for MEG signals.

 * In the Process1 box: Drag-and-drop the pre-processed file.

 * Select the process '''Import > Import recordings > Import MEG/EEG: Events''':
  * '''Subject name''' = `Subject01`
  * '''Folder name''' = empty
  * '''Event names''' = `Left`
-Line 252:
+Line 206:
-  * Check '''Use events''' and highlight the '''Left(x192)''' event group
  * '''Epoch time''' = `0 - 1000 ms`
  * Check '''Apply SSP/ICA projectors'''
  * Check '''Remove DC offset''' and select '''All recordings'''

 . {{attachment:import_options.png||width="80%"}}

A new folder '''SubjectCMC_clean_notch_high_abs''' without the 'raw' indication is created for '''Subject01'''. Let's rename it as '''SubjectCMC_preprocessed'''.

 * Select the folder and press '''[F2]''' to set the new name. This action can also be done with the contextual menu '''File > Rename'''.

The new folder contains a copy of the '''channel file''' from the original raw file, and individual trials tagged as '''Left '''in a new trial group. Expand the trial group and note there are trials marked with an exclamation mark in a red circle (<<Icon(iconModifBad.gif)>>). This indicates trials that contain segments of signal occurred in the '''bad''' segments identified in the previous section. All the bad trials are automatically ignored for further processing, whenever dropped into the '''''Process1''''' and '''''Process2''''' tabs.
+  * '''Epoch time''' = `0 - 8000 ms`
  * '''Split recordings in time blocks''' = `1 s`
  * Uncheck '''Create a separate folder for each event type'''
  * Check '''Ignore shorter epochs'''
  * Check '''Use CTF compensation'''
  * Check '''Use SSP/ICA projectors'''

 * Add the process '''Pre-process > Remove DC offset''':
  * '''Baseline''' = `All file`
  * '''Sensor types''' = `MEG`

 * Run the pipeline

|| {{attachment:pro_import.png}} || || {{attachment:pro_remove_dc.png}} ||


 * A new folder '''SubjectCMC_notch_high_abs''' without the 'raw' indication is created, including  '''192 epochs''' (24 trials x 8 epochs each). The epochs overlapping with a "bad" event are marked as bad and identified with an exclamation mark in a red circle (<<Icon(iconModifBad.gif)>>). The bad epochs will be automatically ignored by the '''Process1''' and '''Process2''' tabs, and therefore excluded from further processing.
-Line 267:
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-To have a glance of the signals after the pre-processing, plot the MEG signal for one sensor overlying the left motor-cortex (MRC21) and the EMG signals, both for trial 1. Note that these traces are similar to the ones obtained in the [[https://www.fieldtriptoolbox.org/tutorial/coherence/|FieldTrip tutorial]].

 . {{attachment:meg_trial_1.png||width="70%"}}

 . {{attachment:emg_trial_1.png||width="70%"}}

== Coherence 1xN (sensor level) ==
We will now compute the '''magnitude square coherence (MSC)''' between the '''left EMG''' signal and each of the MEG sensor data.

 * In the '''''Process1''''' box, drag and drop the '''Left (192 files)''' trial  group. Note that the number between square brackets is '''[185]''', as the 7 '''bad''' trials will be ignored by the MSC process.
+==== Comparison with FieldTrip ====
The figures below represent the EMG and MRC21 channels (sensor over the left motor-cortex) from the epoch #1.1, in Brainstorm (left) and in the [[https://www.fieldtriptoolbox.org/tutorial/coherence/|FieldTrip tutorial]] (right).

{{attachment:bst_ft_trial1.png||width="100%"}}

== Coherence: EMG x MEG ==
Let's compute the '''magnitude square coherence (MSC)''' between the '''left EMG''' and the '''MEG''' channels.

 * In the Process1 box, drag and drop the '''Left (192 files)''' trial  group.
-Line 279:
+Line 237:
- * Run the process '''''Connectivity > Coherence 1xN [2021]''''' with the following parameters:
  * '''Time window''' = `0 - 1000 ms` or check '''All file'''
+ * Select the process '''Connectivity > Coherence 1xN [2021]''':
  * '''Time window''' = '''All file'''<<BR>>The imported epochs have different times (e.g.  Left#1.1: 0-1s, Left#1.2: 1-2s, Left#1.8: 7-8s), but the process offers  by default only the time window of the first file, which would fail  reading the next files will fail. Selecting "All file" ensures that the  entire time window is read for each epoch.
-Line 290:
+Line 248:
- . {{attachment:coh_meg_emgleft.png||width="60%"}}

 * Double-click on the resulting node '''mscohere(0.6Hz,555win): EMGlft''' to display the MSC spectra. Click on the maximum peak in the 15 to 20 Hz range, and press `Enter` to plot it in a new figure. This spectrum corresponds to channel '''MRC21''', and shows a large peak at 17.58 Hz. You can also use the frequency slider (under the '''''Time''''' panel) to explore the MSC output more precisely across frequencies.

 * Right-click on the spectrum and select '''2D Sensor cap''' for a topographical representation of the magnitude of the coherence results across the sensor array. You may also use the shortcut `Ctrl-T`. The sensor locations can be displayed with a right-click and by selecting '''''Channels > Display sensors'' '''from the contextual menu (shortcut `Ctrl-E)`.
+ * Add the process '''File > Add tag''' with the following parameters:
  * '''Tag to add''' = `MEG sensors`
  * Select '''Add to file name'''

 * Run the pipeline

|| {{attachment:coh_meg_emgleft.png}} || || {{attachment:coh_meg_emgleft2.png}} ||


 * Double-click on the resulting node '''mscohere(0.6Hz,555win): EMGlft | MEG sensors''' to display the MSC spectra. Click on the maximum peak in the 15 to 20 Hz range, and press `Enter` to plot the selected sensor in a new figure. This spectrum corresponds to channel '''MRC21''', and shows a large peak at 17.58 Hz. You can also use the frequency slider (under the Time panel) to explore the MSC output more precisely across frequencies.

 * Right-click on the spectrum and select '''2D Sensor cap''' for a topographical representation of the magnitude of the coherence results across the sensor array. You may also use the shortcut `Ctrl-T`. The sensor locations can be displayed with a right-click and by selecting '''Channels > Display sensors''' from the contextual menu (shortcut `Ctrl-E)`.
-Line 298:
+Line 263:
-We can now average magnitude of the MSC across a frequency band of interest (15-20 Hz):

 * In the '''''Process1''''' box, drag-and-drop the '''mscohere(0.6Hz,555win): EMGlft''' node, and add the process '''''Frequency > Group in time or frequency bands''''' with the parameters:
  * Select '''Group by frequency'''
+ * We can now average magnitude of the MSC over the beta band (15-20 Hz). <<BR>>In the Process1 box, select the new '''mscohere''' file.
 * Run process '''Frequency > Group in time or frequency bands''':
  * Select '''Group by frequency bands'''
-Line 306:
+Line 270:
-The resulting file '''mscohere(0.6Hz,555win): EMGlft | tfbands''' has only one MSC value for each sensor (the MSC average in the 15-20 Hz band). You may visualize the topography of this MSC statistics via 3 possible representations: '''2D Sensor cap''', '''2D Sensor cap''' and '''2D Disk''', which are all accessible via a right-click over the MSC node. We clicked on sensor '''MRC21''' below; it is shown in red.

 . {{attachment:res_coh_meg_emgleft1520.png||width="100%"}}

We can observe higher MSC values between the EMG signal and MEG sensor signals over the contralateral set of central sensors in the beta band. Unfortunately, [[Tutorials/Connectivity#Sensor-level|sensor-level connectivity presents the disadvantages]] of not being interpretable, and being subject to spurious results due to volume conduction.

In the next section we will compute coherence in the source level. To do this, we first need to estimate the sources time series from the sensor data.

== MEG source modelling ==
We will perform source modelling using a [[Tutorials/HeadModel#Dipole_fitting_vs_distributed_models|distributed model]] approach for two possible source spaces:  the '''cortex surface''' and the entire '''MRI volume'''. In the '''surface space''', a source grid is made with the source located on the cortical surface obtained from the participant's MRI. For the '''volume space''', the source grid consists of elementary sources uniformly distributed across the entire brain volume. Before estimating the brain sources, we need to derive the sensor '''noise covariance matrix''', and the '''head model'''.

=== Noise covariance ===
The [[Tutorials/NoiseCovariance#The_case_of_MEG|recommendation for MEG]], is to extract basic noise statistics from empty-room recordings. However, when recommended empty-room recordings are not available, as with this tutorial data, resting-state data can be used as proxies for MEG noise covariance. See the [[Tutorials/NoiseCovariance|noise covariance tutorial]] for more details.

 * In the raw '''SubjectCMC_clean_notch_high_abs '''node, right-click over '''Raw | clean | notch(...'''and select '''''Noise covariance > Compute from recordings'''''. Please enter the following parameters:
  * '''Baseline:''' from `18 to 30 s`
  * Select the '''Block by block''' option.

 . {{attachment:pro_noise_cov.png||width="60%"}}

 * Copy the '''Noise covariance''' (<<Icon(iconNoiseCov.gif)>>) node to the '''SubjectCMC_preprocessed''' folder. This can be done using the shortcuts `Ctrl-C` and `Ctrl-V`.

 . {{attachment:tre_covmat.png||width="50%"}}

=== Head model ===
The [[Tutorials/HeadModel|head model]], aka forward model, accounts for how neural electrical currents (in a source space) produce magnetic fields captured by sensors outside the head, considering head tissues electromagnetic properties and geometry, independently of actual empirical measurements. As the head model depends on the source space, a distinct head model is required for the surface and volume source spaces. Please refer to the [[Tutorials/HeadModel|head model tutorial]] for more in-depth explanations.
+ * The resulting file '''mscohere...|tfbands''' has only one MSC value for each sensor (the MSC average in the 15-20 Hz band). Right-click on the file to display a 2D or 3D topography. <<BR>><<BR>> {{attachment:res_coh_tfgroup.gif}}
 * We can observe higher MSC values between the EMG signal and MEG sensor signals over the contralateral set of central sensors in the beta band. Unfortunately, [[Tutorials/Connectivity#Sensor-level|sensor-level connectivity]] is difficult to interpret. In the rest of this tutorial, we will compute coherence at the source level.

== Source estimation ==
=== MRI segmentation ===
In order to estimate the brain sources for these MEG recordings, we  first need to reconstruct the cortex surface from the T1 MRI imported at  the beginning of this tutorial. For this puropose, we decided to use [[https://neuroimage.usc.edu/brainstorm/Tutorials/SegCAT12|CAT12]] because it is fast (30-60min) and fully integrated with Brainstorm as a plugin.

 * Switch back to the Anatomy view of the protocol  (<<Icon(iconSubjectDB.gif)>>).
 * Right-click on the MRI (<<Icon(iconMri.gif)>>) > '''MRI segmentation > CAT12''':
  * '''Number of vertices'''{{{: }}}`15000`
  * '''Anatomical parcellations''': `Yes`
  * '''Cortical maps''': {{{No}}}

 . {{attachment:cat12.png||width="100%"}}

 * Keep the low-resolution central surface selected as the default cortex ('''central_15002V'''). This surface is the primary output of CAT12, and is located half-way between the pial envelope and the grey-white interface ([[https://neuroimage.usc.edu/brainstorm/Tutorials/SegCAT12|more information]]). The head surface was recomputed during the process, you now have two identical head surfaces: you can either delete one or simply ignore this detail.
 * For quality control, double-click on the head and central_15002V surfaces to display them.<<BR>><<BR>> {{attachment:cat12_files.gif}}

=== Head models ===
We will perform source modeling using a [[Tutorials/HeadModel#Dipole_fitting_vs_distributed_models|distributed model]] approach for two different source spaces:  the '''cortex surface''' and the entire '''MRI volume'''. The forward model, labelled ''head model'' in Brainstorm, accounts for how neural electrical currents produce magnetic fields captured by sensors outside the  head, considering head tissues electromagnetic properties and geometry,  independently of actual empirical measurements ([[http://neuroimage.usc.edu/brainstorm/Tutorials/HeadModel|more information]]). As the head model  depends on the source space, a distinct head model is required for the  surface and volume source spaces: we will compute them both now.
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- * Go to the '''Anatomy''' view of the database and verify that the '''pial_15002V''' surface is the default (green characters) cortex surface. Otherwise, right-click on it and select '''Set as default cortex''' in the contextual menu.
 * Go back '''Functional data''' view of the database, and inside the '''SubjectCMC_preprocessed''' folder, right-click over '''CTF channels (191)''' and select '''Compute head model '''from the contextual menu. Run the process with the options as indicated below:
+ * Go back to the '''Functional data''' view of the database.
 * Right-click on the channel file of the imported epochs folder > '''Compute head model'''.
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-The cortical head model will be derived from each of the 15,000 sources (surface vertices) as defined in the default cortex.

 . {{attachment:pro_head_model_srf.png||width="40%"}}

The '''Overlapping spheres (surface)''' head model (<<Icon(iconHeadModel.gif)>>) now appears in the database explorer.
+ . {{attachment:pro_head_model_srf.gif}}
-Line 347:
+Line 301:
- * In the '''SubjectCMC_preprocessed''' folder, right-click over the '''CTF channels (191)''' node and select '''''Compute head model'''''. Set the option values to:
+ * Right-click on the channel file again > '''Compute head model'''.
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+Line 305:
- . {{attachment:pro_head_model_vol.png||width="40%"}}

 * In the '''Volume source grid''' window, specify the following parameters that will produce around '''11,500''' source grid points across the brain volume.
-Line 358:
+Line 308:
- . {{attachment:pro_grid_vol.png||width="60%"}}

The '''Overlapping spheres (volume)''' head model is now added to the database explorer. The green color indicates this is the default head model for the current folder (this can be changed by simply double clicking over the head model  nodes.)

 . {{attachment:tre_head_models.png||width="50%"}}

== Source estimation ==
Now that the '''noise covariance''' and '''head model(s)'''  are available, we will perform [[Tutorials/SourceEstimation|source estimation]], to find the sources that gave origin to the signals registered in the sensors. From the diverse [[Tutorials/SourceEstimation#Method|source estimation methods available in Brainstorm]], in this tutorial the '''minimum-norm imaging''' method is used. The minimum-norm method estimates the linear combination of the current at each point in the source grid that explains the recorded sensor signals favouring minimum energy (L2-norm) solutions. As result, a large matrix called the '''imaging kernel''' is obtained. By multiplying the imaging kernel with the sensor data, it is possible to obtain the estimates of brain sources time series. A different imaging kernel is derived for each of the head models we have produced above:  '''surface''' and '''volume'''. See the [[Tutorials/SourceEstimation|source estimation tutorial]] for more details.

{{{#!wiki note
'''Each dipole in the source grid may point arbitrarily in any direction in a 3D space.''' <<BR>><<BR>>
'''Only for surface grids''', the dipole orientation can be fixed to be normal to the cortical surface, this approach is based on anatomical observations of the brain cortex. The result is then in a smaller model that is faster to compute and display. <<BR>> A discussion on '''constrained''' vs '''unconstrained''' sources is presented [[Tutorials/SourceEstimation#Why_does_it_look_so_noisy.3F|here]].
}}}
=== Surface ===
Here we will estimate the sources in the surface space for '''constrained''' (normal to the cortex) and '''unconstrained''' dipole orientations.

 * Right-click on the '''Overlapping spheres (surface)''' head model and select '''Compute sources [2018]. '''Enter the following parameters:
+ . {{attachment:pro_head_model_vol.gif}}

 * The '''Overlapping spheres (volume)'''  head model is now added to the database explorer. The green color  indicates this is the default head model for the current folder: this can be changed by double clicking on a different head model.

 . {{attachment:tre_head_models.gif}}

=== Noise covariance ===
The [[Tutorials/NoiseCovariance#The_case_of_MEG|recommendation for MEG]] is to extract basic noise statistics from empty-room recordings. When not available, resting-state data can be used as proxies for MEG noise covariance. We will use a segment of the MEG recordings, away from the task or major artifacts: '''18s-29s'''.

 * Right-click on the clean continuous file > '''Noise covariance > Compute from recordings'''.<<BR>><<BR>> {{attachment:pro_noise_cov.gif}}

 * Right-click on the Noise covariance (<<Icon(iconNoiseCov.gif)>>) > '''Copy to other folders'''.<<BR>><<BR>> {{attachment:tre_covmat.gif}}

=== Inverse models ===
We will now compute three inverse models, with different source spaces: cortex surface with '''constrained''' (normal to the cortex) dipole orientations, cortex surface with '''unconstrained''' orientation, and MRI '''volume''' ([[Tutorials/SourceEstimation|more information]]).

==== Surface ====
 * Right-click on '''Overlapping spheres (surface)''' > '''Compute sources''':
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-The inversion kernels (<<Icon(iconResultKernel.gif)>>) '''MN: MEG (surface)(Constr) 2018''' and '''MN: MEG (surface)(Unconstr) 2018''' are now available in the database explorer.

 . {{attachment:tre_sources_srf.png||width="40%"}}
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-To compute the imaging kernel for the volume source space:

 * Right-click on the '''Overlapping spheres (volume)''' head model and select '''Compute sources [2018], '''with the following parameters:
  * '''Minimum norm imaging'''
+ * Right-click on the '''Overlapping spheres (volume)''' > '''Compute sources:'''
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+Line 344:
-At this point the imaging kernel (<<Icon(iconResultKernel.gif)>>) '''MN: MEG (volume)(Unconstr) 2018''' is now also available in the database explorer.

 . {{attachment:tre_sources_vol.png||width="40%"}}

-----
Note that each trial is associated with '''three''' source link (<<Icon(iconResultLink.gif)>>) nodes, that correspond to each of the imaging kernels obtained above.

 . {{attachment:gui_inverse_kernel.png||width="60%"}}

{{{#!wiki tip
The '''source link''' nodes are not files containing the sources time series, instead, the links indicate Brainstorm to: load the corresponding MEG recordings, load the respective inverse kernel, and multiply the two on the fly to generate the sources time series. This approach saves space on the hard drive.
}}}
== Coherence 1xN (source level) ==
Once we have computed the time series for the sources, it is time to compute coherence between the EMG signal and brain sources obtained with each of the imaging kernels.

Let's start with sources from the '''MN: MEG (surface)(Constr)''' kernel:

 * To select the source maps we want to include in the coherence estimation, click on the [[Tutorials/PipelineEditor#Search_Database|Search Database]] button (<<Icon(iconZoom.gif)>>), and select '''''New search'''''.

 * Set the search parameters as shown below, and click on '''Search'''.
+ * Three imaging kernels (<<Icon(iconResultKernel.gif)>>) are now available in the database explorer. Note that each trial is associated with three source links (<<Icon(iconResultLink.gif)>>).

 . {{attachment:tre_sources.gif}}

== Coherence: EMG x Sources ==
We can now compute the coherence between the EMG signal and the brain source time series, for each of the source models. Let's start with the '''surface/constrained''' model.

 * To select the source maps we want to include in the coherence estimation, click on the [[Tutorials/PipelineEditor#Search_Database|Search Database]] button (<<Icon(iconZoom.gif)>>), and select '''New search'''. Set the  parameters as shown below, and click on '''Search'''.
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+Line 355:
-This will create a new tab in the database explorer. This new tab contains '''only''' the files that match the search criteria.

 . {{attachment:tre_search_srf.png||width="40%"}}

 * Click the '''Process2''' tab at the bottom of the main Brainstorm window and drag-and-drop the '''Left (192 files)''' trial group into the '''Files A''' box and repeat for the '''Files B''' box. Select '''Process recordings''' (<<Icon(iconEegList.gif)>>) for Files A, and '''Process sources''' (<<Icon(iconResultList.gif)>>) for Files B. The logic is that we will extract from the same files the EMG signal (Files A side) and the sources time series (Files B side), and then compute coherence between these two sets. Note that blue labels over the '''Files A''' and the '''Files B''' boxes indicate that there are 185 "good trial" files per box.
+ * It creates a new tab in the database explorer, showing only the files that match the criteria.

 . {{attachment:tre_search_srf.gif}}

 * Click the '''Process2''' tab at the bottom of the main Brainstorm window.

  * '''Files A''': Drag-and-drop the '''Left (192 files)''' group, select '''Process recordings''' (<<Icon(iconEegList.gif)>>).

  * '''Files B''': Drag-and-drop the '''Left (192 files)''' group, select '''Process sources''' (<<Icon(iconResultList.gif)>>).

  * Objective: Extract from the same files the EMG recordings (Files A) and the sources time series (Files B), then compute coherence between these two sets. Note that the blue labels over the file lists indicate that there are 185 "good" files (7 bad epochs).
-Line 431:
+Line 369:
-Open the '''Pipeline editor''':

 * Add the process '''Connectivity > Coherence AxB [2021]''' with the following parameters:
  * '''Time window''' = `0 - 1000 ms` or check '''All file'''
+ * Select the process '''Connectivity > Coherence AxB [2021]''':
  * '''Time window''' = '''All file'''
-Line 438:
+Line 374:
-  * '''Magnitude squared coherence''', '''Window length''' = `0.5 s`
  * '''Overlap''' = `50%`
+  * '''Magnitude squared coherence'''
  * ''''''Window length''' = `0.5 s, `Overlap''' = `50%`
-Line 443:
+Line 379:
- * Add the process '''File > Add tag''' with the following parameters:
+ * Add the process '''File > Add tag''':
-Line 452:
+Line 388:
- * Once the processing is finish. Go to the '''Database''' tab of the database explorer and refresh it (with '''[F5]''') to show the resulting 1xN connectivity file (<<Icon(iconConnect1.gif)>>) '''mscohere(0.6Hz,555win): Left (#1) | (surface)(Constr)'''.

 * Repeat the steps above to compute the EMG-sources coherence for the sources from the kernels '''MN: MEG (surface)(Unconstr)''' and '''MN: MEG (volume)(Unconstr)'''. Do not forget to update the '''search criteria''' and the '''tag''' to be added to the result.

=== Results (Surface) ===
+ * Repeat the steps above to compute the EMG-sources coherence for the other source models: '''surface/unconstrained''' and '''volume''':

  * Edit the search criteria of an open serach tab: right-click on the tab > '''Edit search'''.

  * In Process2: Right-click on one of the file lists > '''Clear list'''.

  * Select again the Left group in FilesA (recrodings) and FilesB (sources).

  * Select the processes: Do not forget to update the '''tag''' to be added to the output file.

 * Go to the '''Database''' tab of the database explorer and refresh it (with '''[F5]''') to show the resulting 1xN connectivity file (<<Icon(iconConnect1.gif)>>)

=== Surface ===
-Line 461:
+Line 405:
- * Adjust the '''amplitude threshold''' to '''99%'''
+ * To compare the results, set manually the [[Tutorials/Colormaps|colormap]] range to `[0 - 0.07]`
 * In the '''Surface''' tab
  * Set the '''Smooth''' slider to `30%`
  * Adjust the '''amplitude threshold''' to `95%`
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+Line 411:
-The highest coherence value is located on the '''right primary motor cortex''' (precentral gyrus) at 14.65 Hz for the analysis using constrained and unconstrained sources. Set the '''amplitude threshold''' to '''0%''' to see the extension of the high coherence values.
+The highest coherence value is located on the '''right primary motor cortex''' (precentral gyrus) around 14.65 Hz for the analysis using constrained and unconstrained sources. Set the '''amplitude threshold''' to '''0%''' to see the extension of the high coherence values.
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+Line 418:
-We observe that results obtained with constrained and unconstrained sources agree  in the location and frequency of the peak coherence. The main difference between these results is that coherence values obtained with unconstrained sources appear smoother, this caused by the maximum aggregation performed across directions (explained at detail in the next section.

Finally, right-click on any of the cortex figures and select '''''Get coordinates'''''. Then click on the right motor cortex with the crosshair cursor that appears. The [[CoordinateSystems|SCS]] coordinates will be useful in the next section. <<BR>> SCS coordinates '''X:38.6''', '''Y:-21.3''' and '''Z: 115.5'''
+We observe that results obtained with constrained and unconstrained sources agree  in the location and frequency of the peak coherence. The main difference between these results is that coherence values obtained with unconstrained sources appear smoother, this caused by the maximum aggregation performed across directions, explained at detail in the next section.

Finally, right-click on any of the cortex figures and select '''Get coordinates'''. Then click on the right motor cortex with the crosshair cursor that appears. The [[CoordinateSystems|SCS]] coordinates will be useful in the next section. <<BR>> SCS coordinates '''X:39.2''', '''Y:-22.3''' and '''Z: 113.0'''
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-=== Results (Volume) ===
+=== Volume ===
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- * Set the SCS coordinates to '''X:38.6''', '''Y:-21.3''' and '''Z: 115.5'''
 * Set the '''Transparency''' of the coherence values (Data) in the '''''Surface''''' tab to '''30%'''
+ * Set the SCS coordinates to '''X:39.2''', '''Y:-22.3''' and '''Z: 113.0'''
 * Set the '''Transparency''' of the coherence values (Data) in the '''Surface''' tab to '''20%'''
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+Line 440:
-FIGURE (Diagram needed)
+[[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_constr.png|{{attachment:diagram_1xn_coh_constr.png|https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_constr.png|width="100%"}}]]
-Line 497:
+Line 444:
-FIGURE (Diagram needed)
+[[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_unconstr.png|{{attachment:diagram_1xn_coh_unconstr.png|https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_unconstr.png|width="100%"}}]]
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+Line 449:
+[[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_flattened.png|{{attachment:diagram_1xn_coh_flattened.png||width="100%"}}]]
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- * We have tested this flattening approach with simulations and the real data (from this tutorial) and we have found decremental effects on the expected results.
+ * We have tested this flattening approach with simulations and the real data (from this tutorial) and we have found decrimental effects on the expected results.
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+Line 455:
-== Coherence 1xN (scout level) ==
+== Coherence: EMG x Scouts ==
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+Line 461:
- * When the within-scout aggregation takes place. Either '''before''' or '''after''' the coherence estimation.
+ * When the within-scout aggregation takes place. Either '''before''' or '''after''' the coherence computation.
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+Line 464:
-  . FIGURE (Diagram needed)

  * '''After''': Coherence is computed between the reference signal and each direction of the vertices' source time series, [[#Coherence_1xN_.28source_level.29|as in the previous section]]. Then, the scout function is applied on the coherence spectra for each direction of the vertices within a scout, finally these spectra are aggregated across dimensions to obtain a coherence spectrum per scout.

  . FIGURE (Diagram needed)
+[[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_sct_bef.png|{{attachment:diagram_1xn_coh_sct_bef.png|https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_sct_bef.png|width="100%"}}]]

 * '''After''': Coherence is computed between the reference signal and each direction of the vertices' source time series, [[#Coherence_1xN_.28source_level.29|as in the previous section]]. Then, the scout function is applied on the coherence spectra for each direction of the vertices within a scout, finally these spectra are aggregated across dimensions to obtain a coherence spectrum per scout.

[[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_sct_aft.png|{{attachment:diagram_1xn_coh_sct_aft.png|https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=diagram_1xn_coh_sct_aft.png|width="100%"}}]]
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-Double-click on the resulting file. This time the coherence spectra are not displayed on the cortex, but they are plotted for each scout is show. Right click, and it can be also shown as image.
+Open the file: '''mscohere(0.6Hz,555win): EMGlft x 102 scouts, mean before | (surface)(Constr)''' by double-clicking on it. This time the coherence spectra are not displayed on the cortex, but they are plotted for each scout. Moreover, 1xN connectivity file can be shown as image.
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-Note that for 14.65 Hz, the higher peak corresponds to the '''SomMotA_2 R''' scout located over the right primary motor cortex.
+Note that for 14.65 Hz, the highest two peaks correspond to the '''SomMotA_4 R''' and '''SomMotA_2 R''' scouts, both located over the right primary motor cortex.
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-== Coherence NxN (scout level) ==
In previous sections, we have computed coherence between a reference signal (EMG) and sources (or scouts). However, depending on the experimental setup and hypotheses, we may want to study the brain as network, this is to say, to compute the NxN connectivity. Due to the large number of sources (often several thousands) while in theory it is possible to compute NxN connectivity, it is not practical, as such a common approach is to use of scouts to reflect the functional connections among its cell assemblies, resulting in a connectome.

{{{#!wiki note
Diverse studies have shown some overlap between connectomes derived from electrophysiological signals (MEG/EEG) and the ones derived from fMRI, which is reasonably expected as both are the result of the undergoing biological system. However, due to its nature, the '''electrophysiological connectomes provide unique insights on how functional communication is implemented in the brain''' ([[https://doi.org/10.1016/j.neuroimage.2021.118788|Sadaghiani et al., 2022]]).
}}}
Similar to the [[#Coherence_1xN_.28scout_level.29|coherence 1xN with scouts]], we need to define the '''scout function''' and '''when''' it is going to be applied. Note that computing coherence between two unconstrained leads to 9 (3x3) coherence spectra.

FIGURE (Diagram needed)

FIGURE (Diagram needed)

Let's here compute the NxN coherence, using '''mean''' as scout function alongside with the '''Before''' option, and the '''Schaefer 100''' parcellation atlas. All of this for the constrained sources results.

 * Use Search Database (<<Icon(iconZoom.gif)>>) to select all the '''(surface)(Constr)''' source maps
 . {{attachment:gui_search_srf_n.png||width="70%"}}

 * On the '''Process1''' tab drag-and-drop the '''Left (192 files)''' trial group into '''Files to process''', and select the '''Process sources''' (<<Icon(iconResultList.gif)>>). There should be '''185''' files.

 . {{attachment:process1_n.png||width="80%"}}

Open the '''Pipeline editor''':

 * Add the process '''Connectivity > Coherence NxN [2021]''' with the following parameters:
  * '''Time window''' = `0 - 1000 ms` or check '''All file'''
  * Check '''Use scouts (B)'''
  * From the menu at the right, select '''Schaefer_100_17net'''
  * Select all the scouts
  * '''Scout function''': `Mean`
  * '''When to apply the scout function''': `Before`
  * Do not '''Remove evoked responses from each trial'''
  * '''Magnitude squared coherence''', '''Window length''' = `0.5 s`
  * '''Overlap''' = `50%`
  * '''Highest frequency''' = `80 Hz`
  * '''Average cross-spectra'''.

 * Run the pipeline

{{attachment:pro_coh_n_srfc_bef_sct.png||width="60%"}}

The result is a NxN connectivity file (<<Icon(iconConnectN.gif)>>), It contains 10404 (102x102) coherence spectra. Such a visualization is not practical, thus the connectivity graph or the adjacent matrix are displayed for each frequency bin in the coherence spectra. For more details, see the [[Tutorials/ConnectivityGraph|connectivity graph tutorial]].

 * Right-click on the NxN connectivity file to see its display options.
 * Select both, the connectivity  '''graph''', and the '''image''' (adjacency matrix).
 * Set the frequency slider to 14.65 Hz.

{{attachment:res_coh_n_srfc_bef_sct.png}}

{{attachment:res_coh_n_srfc_bef_sct2.png}}
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-== Scripting ==
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-==== Forum discussions ====
{{{#!wiki important
[TODO] Find relevant Forum posts.
}}}
<<HTML(<!-- END-PAGE -->)>>
+== Scripting ==
The following script from the Brainstorm distribution reproduces the analysis presented in this tutorial page: [[https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/script/tutorial_coherence.m|brainstorm3/toolbox/script/tutorial_coherence.m]]

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Software

Users

Development

Corticomuscular coherence (CTF MEG)

Dataset description

Download and installation

Importing anatomy

MEG and EMG recordings

Link the recordings

MEG-MRI coregistration

Reviewing

Event markers

Pre-processing

Power line artifacts

EMG: Filter and rectify

MEG: Blink SSP and bad segments

Blink correction with SSP

Detection of "bad" data segments

Epoching

Comparison with FieldTrip

Coherence: EMG x MEG

Source estimation

MRI segmentation

Head models

Surface

Volume

Noise covariance

Inverse models

Surface

Volume

Coherence: EMG x Sources

Surface

Volume

Coherence with constrained and unconstrained sources

Coherence: EMG x Scouts

Additional documentation

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Scripting

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