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.

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 continuous recordings

• 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

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 pre-processing

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 pre-processing

Blink correction with SSP

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.

• 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.

Importing data epochs

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 1xN (sensor level)

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 = 0 - 1000 ms or check All file

  • 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 model: Surface

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.

• 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.

• 

Head model: 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.
  
  

Source estimation

Now that the noise covariance and head model(s) are available, we will perform source estimation, to find the sources that gave origin to the signals registered in the sensors. From the diverse 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 source estimation tutorial for more details.

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:

  • 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.

The inversion kernels () MN: MEG (surface)(Constr) 2018 and MN: MEG (surface)(Unconstr) 2018 are now available in the database explorer.

• 

Volume

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

  • Current density map

  • Unconstrained

  • Comment = MN: MEG (volume)

• 

At this point the imaging kernel () MN: MEG (volume)(Unconstr) 2018 is now also available in the database explorer.

• 

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Note that each trial is associated with three source link () nodes, that correspond to each of the imaging kernels obtained above.

• 

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 Search Database button (), and select New search.

• Set the search parameters as shown below, and click on Search.

• 

This will create a new tab in the database explorer. This new tab contains only the files that match the search criteria.

• 

• 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 () for Files A, and Process sources () 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.

• 

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

  • 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 with the following parameters:

  • Tag to add = (surface)(Constr)

  • Select Add to file name

• Run the pipeline

• 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 () mscohere(0.6Hz,555win): EMGlft | (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)

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.

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

Results (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%

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.

Coherence 1xN (scout level)

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.

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.

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.

Similar to the 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.

Scout function before:

Scout function after:

The result is a NxN connectivity file (), It contains (Scouts x Scouts) 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 connectivity graph tutorial.

A coherence NxN example can be found in the main connectivituy tutorial.

Scripting

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

• Tutorial: Functional connectivity

• Tutorial: Source estimation

• Tutorial: Volume source estimation

• Tutorial: Scouts

• Tutorial: Connectivity graphs

Forum discussions

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