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= Corticomuscular coherence (MEG) =
'''[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE] '''

''Authors: Raymundo Cassani, Francois Tadel & Sylvain Baillet.''

[[https://en.wikipedia.org/wiki/Corticomuscular_coherence|Corticomuscular coherence]] measures a 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 "[[https://www.fieldtriptoolbox.org/tutorial/coherence/|Analysis of corticomuscular coherence]]" by FieldTrip.

<<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 cortico-muscular coherence effects in the 15–30 Hz range during maintained voluntary contractions.

IMAGE OF EXPERIMENT, SIGNALS and COHERENCE

== Dataset description ==
The dataset comprises recordings from MEG (151-channel CTF MEG system) and bipolar EMG (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!

== Download and installation ==
 * '''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.
 * '''Download the dataset''':
  * Download `SubjectCMC.zip` from FieldTrip's FTP server:<<BR>> ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectCMC.zip
  * Unzip the .zip in a folder not located in any of current Brainstorm's folders (the app per se  or its database folder).
 * '''Brainstorm''':
  * Launch Brainstorm (via Matlab's command line or use Brainstorm's 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'''.

The next sections describe how to import the participant's anatomical data, review raw data, manage event markers, pre-process EMG and MEG signals, epoch and import recordings for further analyzes, with a focus on computing coherence at the sensor (scalp) and brain map (sources) levels.

== Importing anatomy data ==
 * Right-click on the newly created '''TutorialCMC''' node in your Brainstorm data tree then '''''New subject > Subject01'''''.<<BR>>Keep the default options defined for the study (aka "protocol" in Brainstorm's jargon).

 * Switch to the '''Anatomy''' view of the study.
 * Right-click on the '''Subject01''' node then '''''Import MRI''''':
  * Select the adequate file format from the pull-down menu: '''All MRI file (subject space)'''
  * Select the file: `SubjectCMC/SubjectCMC.mri`

 * Register the individual anatomy to  MNI brain space, for standardization of coordinates: in the '''MRI viewer''' click on '''Click here to compute MNI normalization''', use the '''maff8''' method. When the normalization is complete, verify that the locations of the anatomical fiducials are adequate (essentially that they are indeed near the left/right ears and right above the nose) and click on '''Save'''.

{{{#!wiki comment
{{attachment:viewer_mni_norm.png}}
}}}
 . [[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=viewer_mni_norm.png|{{attachment:viewer_mni_norm.png|https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=viewer_mni_norm.png}}]]

We then need to segment the head tissues to obtain the surfaces required to derive a realistic MEG [[Tutorials/HeadModel|head model (aka "forward model")]].

 * Right-click on the '''SubjectCMC''' MRI node, then '''''MRI segmentation > FieldTrip: Tissues, BEM surfaces'''''.
 * Select all the tissues ('''scalp''', '''skull''', '''csf''', '''gray''' and '''white''').
 * Click '''OK'''.
 * For the option '''Generate surface meshes''' select '''No'''.
 * After the segmentation is complete, a '''tissues''' node will be shown in the tree.
 * Rick-click on the '''tissues''' node and select '''''Generate triangular meshes.'''''
 * Select the 5 layers to mesh.
 * Use the default parameters:
  * '''number of vertices''': `10,000`
  * '''erode factor''': `0`
  * '''fill holes factor''': `2`

A set of (head and brain) surface files are now available for further head modelling (see below).

 . {{attachment:import_result.png||width="40%"}}

You can display the surfaces by double-clicking on these new nodes. There are a couple of issues with the structural data available from this tutorial. Note how the '''cortex''' (shown in red) overlaps with the '''innerskull''' surface (shown in gray). For this reason, the [[Tutorials/TutBem|BEM  forward model cannot be derived with OpenMEEG]]. We will use an analytical approximationusing the [[Tutorials/HeadModel#Forward_model|overlapping-spheres method]], which in MEG has been shown to be adequately accurate for most studies. Note also how the '''cortex''' and '''white''' surfaces obtained do not register accurately with the cortical surface. We will therefore use a [[Tutorials/TutVolSource|volume-based source estimation]] approach based on a volumic grid of elementary MEG source across the cerebrum (not a surface-constrained source model). We encourage users to [[Tutorials/SegCAT12|CAT12]] or [[Tutorials/LabelFreeSurfer|FreeSurfer]] to obtain surface segmentations of higher quality.

 . {{attachment:over_innerskul_cortex.png||width="50%"}}

As the imported anatomy data is normalized in the MNI space, it is possible to apply use [[Tutorials/DefaultAnatomy#MNI_parcellations|MNI parcellation]] templates to define anatomical regions of the brain of the subject. These anatomical regions can be used to create [[Tutorials/TutVolSource#Volume_atlases|volume]] and [[Tutorials/Scouts|surface scouts]], which are  convenient when performing the coherence analysis in the source level. Let's add the [[https://www.gin.cnrs.fr/en/tools/aal/|AAL3]] parcellation to the imported data.

 * Right-click on Subject01 then go to the menu '''''Add MNI parcellation > AAL3'''''. The menu will appear as '''''Download: AAL3''''' if the atlas is not in your system. Once the MNI atlas is downloaded, an atlas node (ICON) appears in the database explorer and the atlas is displayed in the the MRI viewer.

 . {{attachment:gui_mni_aal3.png}} <<BR>>

== Review the MEG and EMG recordings ==
=== Link the recordings to Brainstorm's database ===
 * Switch now to the '''Functional data''' view (X button).
 * Right-click on the '''Subject01''' node then '''''Review raw file''''':
  * Select the file format of current data from the pulldown menu options: '''MEG/EEG: CTF(*.ds; *.meg4; *.res4)'''
  * Select the file: `SubjectCMC.ds`

A new folder is now created in Brainstorm's database explorer and contains:

 * '''SubjectCMC''': a folder that provides access to the MEG dataset. Note the "RAW" tag over the icon of the folder, 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 '''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's 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]]).

 . {{attachment:review_raw.png}} <<BR>>

=== 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:helmet_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'''''.

 . [[https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=timeseries_meg_emg.png|{{attachment:timeseries_meg_emg.png|https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence?action=AttachFile&do=get&target=timeseries_meg_emg.png}}]]

=== Event markers ===
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'''.

 . {{attachment:read_evnt_ch.png}}

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 selected all events with '''Ctrl+A''' and deselect the '''U1''' to '''U25''' events by clicking on them.
 * To make sure we reproduce FieldTrip's 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 ina new label ('''Left''') to describe this is 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}}

{{{#!wiki comment
=== Keep relevant recordings ===
As only data for the left wrist will be analyzed, we will import only the first '''330 s''' of the original file and rewrite that segment as a binary continuous file, a raw file. This will help to optimize computation times and memory usage.

 * In the Process1 box: Drag and drop the '''Link to raw file''' node inside '''SubjectCMC'''.
 * Run process '''Import > Import recordings > Import MEG/EEG: Time''':<<BR>>
  * '''Subject name'''=`Subject01`, '''Condition name'''= `Left`, '''Time window'''=`0.0 - 330.0 s`, '''Split recordings'''=`0`, and check the three remaining options.<<BR>>
 . {{attachment:import330_process.png||width="50%"}}

 * Right-click on the '''Raw(0.00s,330.00s)''' node inside the newly created '''Left''' condition and select '''Review as raw'''. This will crate the condition '''block001''' with the link to the created raw file.

 . {{attachment:review_as_raw.png||width="50%"}}

 * To avoid any confusion later, delete the conditions '''SubjectCMC''' (which is a link to the original file), and the condition '''Left'''. Select both folders containing and press Delete (or right-click '''File > Delete''').
}}}
== Pre-process ==
{{{#!wiki note
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.
}}}
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="50%"}}

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.
 * Run process '''Frequency > Power spectrum density (Welch)''':<<BR>>
  * '''Time window''': `0 - 330 s`
  * '''Window length='''`10 s`
  * '''Overlap'''=`50%`
  * '''Sensor types'''=`MEG, EMG

 . {{attachment:pro_psd.png||width="50%"}}

 * Double-click on the new '''PSD''' file to visualize the power spectrum density of the data.<<BR>>
 . {{attachment:psd_before_notch.png||width="70%"}}

 * The PSD plot shows two groups of sensors: EMG (highlighted in red above) and the MEG spectra below. Peaks at 50Hz and harmonics (150, 200Hz and above; European power line main and  harmonics) are clearly visible. We will use notch filters to attenuate power line contaminants at 50, 150 and 200 Hz.
 * 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`

 . {{attachment:pro_notch.png||width="50%"}}

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''' from 0 to 330 s only, in the options of the PSD process.

 . {{attachment:psd_after_notch.png||width="70%"}}

=== Pre-process EMG ===
Two of the 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)''' recordings 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

 . {{attachment:emg_processing.png||width="100%"}}

Once the pipeline ends, the new folders '''SubjectCMC_clean_notch_high''' and '''SubjectCMC_clean_notch_high_abs''' are added to the database explorer. To avoid any confusion later, we can delete folders that will not be needed.

 * Delete the conditions '''SubjectCMC_clean_notch''' and '''SubjectCMC_clean_notch_high'''. Select both folders containing and press Delete (or right-click '''''File > Delete''''').

=== Pre-process MEG ===
After applying the notch filter to the MEG signals, we still need to remove other type of artifacts, we will perform:

 1. '''Detection and removal of artifacts with SSP'''

 1. '''Detection of segments with other artifacts'''

==== Detection and removal of artifacts with SSP ====
In the case of stereotypical artifacts, as it is the case of the eye blinks and heartbeats, it is possible to identify their characteristic spatial distribution, and then remove it from MEG signals with methods such as Signal-Space Projection (SSP). For more details, consult the tutorials on [[Tutorials/ArtifactsDetect|detection]] and [[Tutorials/ArtifactsSsp|removal of artifacts with SSP]]. The dataset of this tutorial contains an EOG channel but not ECG signal, thus will perform only 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:
  * '''Channel name'''= `EOG`
  * '''Time window''' = `0 - 330 s`
  * '''Event name''' = `blink`

 . {{attachment:detect_blink_process.png||width="50%"}}

 * As result, there will be 3 blink event groups. Review the traces of EOG channels and the blink events to be sure the detected events make sense. Note that the '''blink''' group contains the real blinks, and blink2 and blink3 contain mostly saccades.

 . {{attachment:blinks.png||width="70%"}}

 * To [[Tutorials/ArtifactsSsp|remove blink artifacts with SSP]] go to '''''Artifacts > SSP: Eye blinks''''', and use the parameters:
  * '''Event name'''=`blink`
  * '''Sensors'''=`MEG`
  * Check '''Compute using existing SSP/ICA projectors'''

 . {{attachment:ssp_blink_process.png||width="50%"}}

 * Display the time series and topographies for the first two components. Only the first one is clearly related to blink artifacts. Select only component #1 for removal.

 . {{attachment:ssp_blink.png||width="100%"}}

 * Follow the same procedure for the other blink events ('''blink2''' and '''blink3'''). Note that none of first two components for the remaining blink events is clearly related to a ocular artifacts. This figure shows the first two components for the '''blink2''' group.

 . {{attachment:ssp_blink2.png||width="100%"}}

 . In this case, it is safer to unselect the '''blink2''' and '''blink3''' groups, rather than removing spatial components that we are not sure to identify.

 . {{attachment:ssp_active_projections.png||width="60%"}}

 * Close all the figures

==== Detection of segments with other artifacts ====
Here we will used [[Tutorials/BadSegments#Automatic_detection|automatic detection of artifacts]]. It aims to identify typical artifacts such as the ones related to eye movements, subject movement and muscle contractions.

 * Display the MEG and EOG time series. In the '''''Record''''' tab, select '''''Artifacts > Detect other artifacts''''', use the following parameters:
  * '''Time window''' = `0 - 330 s`
  * '''Sensor types'''=`MEG`
  * '''Sensitivity'''=`3`
  * Check both frequency bands '''1-7 Hz''' and '''40-240 Hz'''

 . {{attachment:detect_other.png||width="50%"}}

While this process can help identify segments with artifacts in the signals, it is still advised to review the selected segments. After a quick browse, it can be noticed that the selected segments indeed correspond to irregularities in the MEG signal. Then, we will label these events are bad.

 * Select the '''1-7Hz''' and '''40-240Hz''' event groups and use the menu '''''Events > Mark group as bad'''''. Alternatively, you can rename the events and add the tag '''bad_''' in their name, it would have the same effect.

 . {{attachment:bad_other.png||width="50%"}}

 * Close all the figures, and save the modifications.

== Importing the recordings ==
At this point we have finished with the pre-processing of our EMG and MEG recordings. Many operations operations can only be applied to short segments of recordings that have been imported in the database. We refer to these as '''epochs''' or '''trials'''. Thus, the next step is to import the data taking into account the '''Left''' 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%"}}

 * Set the following parameters:
  * '''Time window''' = `0 - 330 s`
  * 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%"}}

The new folder '''SubjectCMC_clean_notch_high_abs''' appears for '''Subject01'''. It contains a copy of the '''channel file''' in the continuous file, and the '''Left''' trial group. By expanding the trial group, we can notice that there are trials marked with an interrogation sign in a red circle (ICON). These '''bad''' trials are the ones that were overlapped with the '''bad''' segments identified in the previous section. All the bad trials are automatically ignored in the '''''Process1''''' and '''''Process2''''' tabs.

 . {{attachment:trials.png||width="40%"}}

== Coherence (sensor level) ==
Once we have imported the trials, we will compute the '''magnitude square coherence (MSC)''' between the '''left EMG''' signal and the signals from each of the MEG sensors.

 * 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 are ignored.
 . {{attachment:dragdrop_trialgroup.png||width="40%"}}

 * To compute the coherence between EMG and MEG signals. Run the process '''''Connectivity > Coherence 1xN [2021]''''' with the following parameters:
  * '''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]].

 . {{attachment:coh_meg_emgleft.png||width="40%"}}

 * 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 has its peak at 17.58 Hz. You can also use the frequency slider (below the '''''Time''''' panel) to explore the spectral representations.

*  Right-click on the spectrum and select '''2D Sensor cap''' for a spatial visualization of the coherence results, alternatively, the short cut `Ctrl-T` can be used. Once the '''2D Sensor cap''' is show, the sensor locations can be displayed with right-click then '''''Channels > Display sensors''''' or the shortcut `Ctrl-E`.

 . {{attachment:res_coh_meg_emgleft.png||width="80%"}}

The results above are based in the identification of single peak, as alternative we can average the MSC in a given frequency band (15 - 20 Hz), and observe its topographical distribution.

 * 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'''
  * Type `cmc_band / 15, 20 / mean` in the text box.

 . {{attachment:pro_group_freq.png||width="40%"}}

The resulting file '''mscohere(0.6Hz,555win): EMGlft | tfbands''' has only one MSC value for each sensor (the average in the 15-20 Hz band). Thus, it is more useful to display the result in a spatial representation. Brainstorm provides 3 spatial representations: '''2D Sensor cap''', '''2D Sensor cap''' and '''2D Disk''', which are accessible with right-click on the MSC node. Sensor '''MRC21''' is selected as reference.

 . {{attachment:res_coh_meg_emgleft1520.png||width="100%"}}

In agreement with the literature, we observe higher MSC values between the EMG signal and the MEG signal for MEG sensors over the contralateral primary motor cortex in the beta band range. In the next sections we will perform source estimation and compute coherence in the source level.

== Source analysis ==
In this tutorial we will perform source modelling using the [[Tutorials/HeadModel#Dipole_fitting_vs_distributed_models|distributed model]] approach for two sources spaces: '''cortex surface''' and '''MRI volume'''. In the first one the location of the sources is constrained to the cortical surface obtained when the subject anatomy was imported. For the second source space, the sources are uniformly distributed in the entire brain volume. Before estimating the brain sources, we need to compute  '''head model''' and the '''noise covariance'''. Note that a head model is required for each source space.

=== Head model ===
The head model describes how neural electric currents produce magnetic fields and differences in electrical potentials at external sensors, given the different head tissues. This model is independent of sensor recordings. See the [[Tutorials/HeadModel|head model tutorial]] for more details. Each source space, requires its own head model.

==== Cortex surface ====
 * In the '''SubjectCMC_clean_notch_high_abs''', right-click the '''CTF channels (191)''' node and select '''''Compute head model'''''. Keep the default options:
  * '''Comment''' = `Overlapping spheres (surface)`
  * '''Source space''' = `Cortex surface`
  * '''Forward model''' = `Overlapping spheres`.

Keep in mind that the number of sources (vertices) in this head model is '''10,000''', and was defined when when the subject anatomy was imported.

 . {{attachment:pro_head_model_srf.png||width="40%"}}

The (ICON) '''Overlapping spheres (surface)''' head model will appear in the database explorer.

==== MRI volume ====
 * In the '''SubjectCMC_clean_notch_high_abs''', right-click the '''CTF channels (191)''' node and select '''''Compute head model'''''. Keep the default options:
  * '''Comment''' = `Overlapping spheres (volume)`
  * '''Source space''' = `MRI volume`
  * '''Forward model''' = `Overlapping spheres`.

 . {{attachment:pro_head_model_vol.png||width="40%"}}

 * The '''Volume source grid''' window pop-up, to define the volume grid. Use  the following parameters, that will lead to an estimated number of '''12,200''' grid points.
  * Select '''Regular grid''' and '''Brain'''
  * '''Grid resolution''' = `5 mm`

 . {{attachment:pro_grid_vol.png||width="50%"}}

The '''Overlapping spheres (volume)''' node will be added to the database explorer. The green color indicates the default head model for the folder.

 . {{attachment:tre_head_models.png||width="50%"}}

=== Noise covariance ===
For MEG recordings it is [[Tutorials/NoiseCovariance#The_case_of_MEG|recommended]] to derive the noise covariance from empty room recordings. However, as we do not have those recordings in the dataset, we can compute the noise covariance from the MEG signals before the trials. See the [[Tutorials/NoiseCovariance|noise covariance tutorial]] for more details.

 * In the raw '''SubjectCMC_clean_notch_high_abs''', right-click the '''Raw | clean | notch(...''' node and select '''''Noise covariance > Compute from recordings'''''. As parameters select:
  * '''Baseline''' from `18 - 30 s`
  * Select the '''Block by block''' option.

 . {{attachment:pro_noise_cov.png||width="60%"}}

 * Lastly, copy the '''Noise covariance''' node to the '''SubjectCMC_clean_notch_high_abs''' folder with the head model. This can be done with the shortcuts `Ctrl-C` and `Ctrl-V`.

 . {{attachment:tre_covmat.png||width="40%"}}

=== Source estimation ===
Noe that the '''head model(s)''' and '''noise covariance''' have been computed, we can use the [[Tutorials/SourceEstimation#Method|minimum norm imaging]] method to solve the '''inverse problem'''. The result is a linear '''inversion kernel''', that  estimates the source brain activity that gives origin to the observed recordings in the sensors. Note that, an inversion kernel is obtained for each of the head models:  '''surface''' and '''volume'''. See the [[Tutorials/SourceEstimation|source estimation tutorial]] for more details.

==== Cortex surface ====
 * Compute the inversion kernel, right-click in the '''Overlapping spheres (surface)''' head model and select '''Compute sources [2018]'''. With the parameters:
  * '''Minimum norm imaging'''
  * '''Current density map'''
  * '''Unconstrained'''
  * '''Comment''' = `MN: MEG (surface)`

 . {{attachment:pro_sources_srf.png||width="40%"}}

The inversion kernel (ICON) '''MN: MEG (surface)(Unconstr) 2018''' is created, and added to the database explorer.

==== MRI volume ====
 * Compute the inversion kernel, right-click in the '''Overlapping spheres (volume)''' head model and select '''Compute sources [2018]'''. With the parameters:
  * '''Minimum norm imaging'''
  * '''Current density map'''
  * '''Unconstrained'''
  * '''Comment''' = `MN: MEG (volume)`

 . {{attachment:pro_sources_vol.png||width="40%"}}

The inversion kernel (ICON) '''MN: MEG (volume)(Unconstr) 2018''' is created, and added to the database explorer. The green color in the name indicates the current default head model. In addition, note that each trial has now '''two''' associated source link (ICON) nodes. One obtained with the '''MN: MEG (surface)(Unconstr) 2018''' kernel and the other obtained with the '''MN: MEG (volume)(Unconstr) 2018''' kernel.

 . {{attachment:gui_inverse_kernel.png||width="60%"}}

=== Scouts ===
From the [[#head_model|head model section]], we notice that the '''cortex''' and '''volume''' grid have around '''10,000''' vertices each, thus as many sources were estimated. As such, it is not practical to compute coherence between the left EMG signal and the signal of each source. A way to address this issue is with the use of regions of interest also known as '''scouts'''. Thus, there are [[Tutorials/Scouts|surface scouts]] and [[Tutorials/TutVolSource#Volume_scouts|volume scouts]]. Let's define scouts for the different source spaces.

==== Surface scouts ====
 * In the source link '''MN: MEG (surface)(Unconstr) 2018''' node for one of the trials, right-click and select '''''Cortical activations > Display on cortex'''''. In the '''''Surface''''' tab, set the '''''Amplitude''''' slider to `100%` to hide all the sources.

 . {{attachment:fig_sources_srf.png||width="100%"}}

 * In the '''''Scout''''' tab, select the menu '''''Atlas > From subject anatomy > AAL3 (MNI-linear)'''''. This will create the '''From volume: AAL3''' set of surface scouts. By clicking in the different scouts, at the bottom of the list, the number of vertices it contains and the approximate area in cm2 is shown. Activate the (ICON) '''Show only the selected scouts''' option to narrow down the shown scouts.

 . {{attachment:fig_scouts_srf.png||width="90%"}}

{{{#!wiki caution
[TODO] A note, that the definition of scouts is far from perfect, but can give us a good idea of the surface projections of the MNI parcellations (described in the importing anatomy section).
}}}
 * Close the figure.

==== Volume scouts ====
 * In the source link '''MN: MEG (volume)(Unconstr) 2018''' node for one of the trials, right-click and select '''''Cortical activations > Display on MRI (3D): Subject CMC'''''. In the '''''Surface''''' tab, set the '''''Amplitude''''' slider to `100%` to hide all the sources.

 . {{attachment:fig_sources_vol.png||width="100%"}}

 * In the '''''Scout''''' tab, select the menu '''''Atlas > From subject anatomy > AAL3 (MNI-linear)'''''. This will create the '''Volume 12203: AAL3''' set of volume scouts. By clicking in the different scouts, at the bottom of the list, the number of vertices it contains and the approximate volume in cm3 is shown. Activate the (ICON) '''Show only the selected scouts''' option to narrow down the shown scouts.

 . {{attachment:fig_scouts_vol.png||width="90%"}}

 * Close the figure.

== Coherence (source level) ==
Coherence in the source level is computed between a sensor signal (EMG) and source signals in the (surface or volume) scouts.

=== Coherence with surface scouts ===
To facilitate the selection of the indicated files to compute this coherence, let's [[Tutorials/PipelineEditor#Search_Database|search in the database]] the recordings and the source link files obtained wit the '''MN: MEG (surface)(Unconstr) 2018''' kernel.

 * Click on the magnifying glass (ICON) above the database explorer to open up the search dialog, and select '''''New search'''''.

 * Set the search query to look for files that '''are named (surface)''' or '''are named Left'''. This is done with the following configuration.

 . {{attachment:gui_search_srf.png||width="70%"}}

By performing the search, a new tab called '''''(surface)''''' appears in the database explorer. This new tab contains the recordings and ONLY the source link for the surface space.

 . {{attachment:tre_search_srf.png||width="40%"}}

 * Change to the '''Process2''' tab, and drag-and-drop the '''Left (192 files)''' trial group into the '''Files A''' and into the '''Files B''' boxes. And select '''Process recordings''' for Files A, and '''Process sources''' for Files B. Note that blue labels over the '''Files A''' and the '''Files B''' boxes indicate that there are 185 files per box.

 . {{attachment:process2.png||width="80%"}}

Open the '''Pipeline editor''' and add two process:

 * 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)'''
  * Select `From volume: AAL3` in the drop-down list (these are surface scouts)
  * Select all the scouts (shortcut `Ctrl-A`)
  * '''Scout function''' = `Mean`
  * '''When to apply''' = `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)`
  * Select '''Add to file name'''

 * Run the pipeline

 . {{attachment:pro_coh_srf.png||width="100%"}}

 * Double-click on the resulting node '''mscohere(0.6Hz,555win): Left (#1) | (surface)''' to display the coherence spectra. Also open the result node as image with '''Display image''' in its context menu.

 * To verify the location of the scouts on the cortex surface, double-click one of the (surface) source link for any of the trials. In the '''Surface''' tab, set the '''Amplitude''' threshold to `100%` to hide all the cortical activations. Lastly, in the '''Scouts''' tab, select the `From volume: AAL3` atlas in the drop-down list, select '''Show only the selected scouts''' and the '''Show/hide the scout labels'''. Note that the plots are linked by the scout selected in the '''image''' representation of the coherence results.

 . {{attachment:res_coh_srf.png||width="100%"}}

From the results we can see that the peak at 14.65 Hz corresponds to the '''Precentral R''' scout, which encompasses the right primary motor cortex, as expected. These results are inline with the ones in the literature.

=== Coherence with volume scouts ===
Similar to coherence with surface scouts, a search is needed to select the recordings and the source link files obtained wit the '''MN: MEG (volume)(Unconstr) 2018''' kernel.

 * Click on the magnifying glass (ICON) above the database explorer to open up the search dialog, and select '''''New search'''''. Set the search query to look for files that '''are named (volume)''' or '''are named Left'''. This is done with the following configuration.

 . {{attachment:gui_search_vol.png||width="70%"}}

 . {{attachment:tre_search_vol.png||width="40%"}}

 * Change to the '''Process2''' tab, and drag-and-drop the '''Left (192 files)''' trial group into the '''Files A''' and into the '''Files B''' boxes. And select '''Process recordings''' for Files A, and '''Process sources''' for Files B.

 . {{attachment:process2.png||width="80%"}}

Open the '''Pipeline editor''' and add two process:

 * 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)'''
  * Select `Volume 12203: AAL3` in the drop-down list (these are volume scouts)
  * Select all the scouts (shortcut `Ctrl-A`)
  * '''Scout function''' = `Mean`
  * '''When to apply''' = `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)`
  * Select '''Add to file name'''

 * Run the pipeline

 . {{attachment:pro_coh_vol.png||width="100%"}}

 * Double-click on the resulting node '''mscohere(0.6Hz,555win): Left (#1) | (volume)''' to display the coherence spectra. Also open the result node as image with '''Display image''' in its context menu.

 * To verify the location of the scouts on the cortex surface, open one of the (volume) source link for any of the trials with the as MRI (3D), in the context menu select '''''Display cortical activations > Display on MRI (3D): SubjectCMC '''''.  In the '''Surface''' tab, set the '''Amplitude''' threshold to `100%` to hide all the cortical activations. Lastly, in the '''Scouts''' tab, select the `Volume 12203: AAL3` atlas in the drop-down list, select the '''Show only the selected scouts''' and the '''Show/hide the scout labels'''. Note that the plots are linked by the scout selected in the '''image''' representation of the coherence results.

 . {{attachment:res_coh_vol.png||width="100%"}}

From the results we can see that the peak at 14.65 Hz corresponds to the '''Precentral R''' scout, which encompasses the right primary motor cortex, as expected. These results are inline with the ones in the literature.

<<TAG(Advanced)>>

== Coherence with all sources (no scouts) ==
{{{#!wiki caution
 * We could downsample the surface and  create a more sparse volume grid
 . OR
 * Refactor the coherence process to accumulate the auto- and cross-spectra outside of the function
 . OR
}}}
=== Comparison of cortex surface with FieldTrip and CAT ===
{{{#!wiki caution
'''[TO DISCUSS among authors]''' This image and GIF are just for reference. They were obtained with all the surface sources using FieldTrip and CAT derived  surfaces.

Comparison for 14.65 Hz

{{attachment:ft_vs_cat.png||width="100%"}}

Sweeping from 0 to 80 Hz

{{attachment:ft_vs_cat.gif||width="80%"}}
}}}
<<TAG(Advanced)>>

== Script ==
{{{#!wiki caution
'''[TO DO]''' Once we agree on all the steps above.
}}}
== Additional documentation ==
==== Articles ====
 * Conway BA, Halliday DM, Farmer SF, Shahani U, Maas P, Weir AI, et al. <<BR>> [[https://dx.doi.org/10.1113/jphysiol.1995.sp021104|Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man]]. <<BR>> The Journal of Physiology. 1995 Dec 15;489(3):917–24.

 * Kilner JM, Baker SN, Salenius S, Hari R, Lemon RN. <<BR>> [[https://doi.org/10.1523/JNEUROSCI.20-23-08838.2000|Human Cortical Muscle Coherence Is Directly Related to Specific Motor Parameters]]. <<BR>> J Neurosci. 2000 Dec 1;20(23):8838–45. '' ''

 * ''Liu J, Sheng Y, Liu H. <<BR>> [[https://doi.org/10.3389/fnhum.2019.00100Corticomuscular%20Coherence%20and%20Its%20Applications:%20A%20Review|https://doi.org/10.3389/fnhum.2019.00100Corticomuscular%20Coherence%20and%20Its%20Applications:%20A%20Review]]. Front Hum Neurosci. 2019 Mar 20;13:100.  ''

==== Tutorials ====
 * ''Tutorial: [[Tutorials/TutVolSource|Volume source estimation]]  ''
 * ''Tutorial: [[Tutorials/Connectivity|Functional connectivity]]  ''

==== Forum discussions ====
{{{#!wiki caution
'''[TO DO]''' Find relevant Forum posts.
}}}
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