MEG median nerve tutorial (CTF)

Authors: Francois Tadel, Elizabeth Bock, John C Mosher, Sylvain Baillet

The dataset presented in this tutorial was used in the previous generation of introduction tutorials. We kept it on the website as an additional illustration of data analysis, and as a comparison with median nerve experiments recorded with other MEG systems (Elekta-Neuromag, Yokogawa). No new concepts are presented in this tutorial. For in-depth explanations of the interface and theoretical foundations, please refer to the introduction tutorials.

Download and installation

• Requirements: You have already followed all the basic tutorials and you have a working copy of Brainstorm installed on your computer.

• Go to the Download page of this website, and download the file: sample_raw.zip

• Unzip it in a folder that is not in any of the Brainstorm folders (program folder or database folder).
• Start Brainstorm (Matlab scripts or stand-alone version).

• Select the menu File > Create new protocol. Name it "TutorialRaw" and select the options:

  • No, use individual anatomy,

  • No, use one channel file per run.

Import the anatomy

• Create a new subject Subject01.

• Right-click on the subject node > Import anatomy folder:

  • Set the file format: "FreeSurfer folder"

  • Select the folder: sample_raw/anatomy

  • Number of vertices of the cortex surface: 15000 (default value)

• Click on the link "Click here to compute MNI transformation".

• Set the 3 required fiducial points (indicated in MRI coordinates):
  • NAS: x=127, y=212, z=123
  • LPA: x=55, y=124, z=119
  • RPA: x=200, y=129, z=114

• At the end of the process, make sure that the file "cortex_15000V" is selected (downsampled pial surface, that will be used for the source estimation). If it is not, double-click on it to select it as the default cortex surface.
  
  

Link the recordings

• Switch to the "functional data" view (middle button in the toolbar above the database explorer).

• Right click on the Subject01 > Review raw file:

  • Select the file format: MEG/EEG: CTF

  • Select the folder: sample_raw/Data/subj001_somatosensory_20111109_01_AUX-f.ds

• Refine the registration with the head points: YES.
  
  

Pre-processing

Evaluate the recordings

• Drag and drop the "Link to raw file" into the Process1 list.

• Select the process "Frequency > Power spectrum density", configure it as follows:
  
  

• Double-click on the PSD file to display it. It shows the estimate of the power spectrum for the first 50 seconds of the continuous file, for all the sensors, with a logarithmic scale. You can identify four peaks at the following frequencies: 60Hz, 120Hz, 180Hz and 300Hz. The first three are related with the power lines (acquisition in Canada, where the electricity is delivered at 60Hz, plus the harmonics). The last one is an artifact of the low-pass filter at 300Hz that was applied on the recordings at the acquisition time.
  
  

Remove: 60Hz and harmonics

• In Process1, keep the "Link to raw file" selected.

• Run Pre-process > Notch filter: Frequencies to remove = 60, 120, 180 Hz.
  
  

• To evaluate the results of this process, select the new filtered file ("Raw | notch") and run again the process "Frequency > Power spectrum density".
  
  

• You should observe a significant decrease of the contributions of the removed frequencies (60Hz, 120Hz, 180Hz) compared with the original spectrum.
  
  

Heartbeats and blinks

Signal Space Projection (SSP) is a method for projecting the recordings away from stereotyped artifacts, such as eye blinks and heartbeats.

• Double-click on the filtered continuous file to display all the MEG recordings.

• Right-click on the link > ECG > Display time series, to look at the heartbeats.

• Right-click on the link > EOG > Display time series, to look at the eye movements.

• From the Artifacts menu in the Record tab, run the following detection processes:

• Artifacts > Detect heartbeats: Select channel EEG057, event name "cardiac".
  
  

• Artifacts > Detect eye blinks: Select channel EEG058, event name "blink".
  
  

• Artifacts > Remove simultaneous: To avoid capturing ocular artifacts in the cardiac SSP.
  
  

• Review the traces of ECG/EOG channels and make sure the events detected make sense.
  
  

• Artifacts > SSP: Heartbeats: Event "cardiac", sensors="MEG".

• Display the first three components: None of them can be clearly related to a cardiac component. This can have two interpretations: the cardiac artifact is not very strong for this subject and the influence of the heart activity over the MEG sensors is completely buried in the noise or the brain signals, or the characterization of the artifact that we did was not so good and we should refine it by selecting for instance smaller time windows around the cardiac peaks. Here, it's probably due to the subject's morphology. Some people generate strong artifacts in the MEG, others don't.

• In this case, it is safer to unselect this "cardiac" category, rather than removing randomly spatial components that we are not sure to identify.
  
  

• Artifacts > SSP: Eyeblinks: Event "blink", sensors="MEG ", use existing SSP. (select component #1)
  
  

Epoching and averaging

Import the recordings

• Right-click on the filtered file "Raw | notch" > Import in dabase.
  
  

• The following figure appears, and asks how to import these recordings in the Brainstorm database.
  
  

  • Time window: Time range of interest, keep all the time definition.

  • Split: Useful to import continuous recordings without events. We do not need this here.

  • Events selection: Check the "Use events" option, and select both Left and Right.

  • Epoch time: Time instants that will be extracted before an after each event, to create the epochs that will be saved in the database. Set it to [-100, +300] ms

  • Use Signal Space Projections: Use the active SSP projectors calculated during the previous pre-processing steps. Keep this option selected.

  • Remove DC Offset: Check this option, and select: Time range: [-100, 0] ms.

  For each epoch, this will: compute the average of each channel over the baseline (pre-stimulus interval: -100ms to 0ms), then subtract it from the channel at all the times in [-100,+300]ms.

  • Resample recordings: Keep this unchecked

  • Create a separate folder for each epoch type: If selected, a new folder is created for each event type (here, it would create two folders "left" and "right"). This option is mostly for EEG studies with channel files shared across runs. In a MEG study, we usually recommend to use one channel file per run, and to import all the epochs from one run in the same folder.

• Click on Import and wait. At the end, you are asked whether you want to ignore one epoch that is shorter than the others. This happens because the acquisition of the MEG signals was stopped less than 300ms after the last stimulus trigger was sent. Therefore, the last epoch cannot have the full [-100,300]ms time definition. This shorter epoch would prevent us from averaging all the trials easily. As we already have enough repetitions in this experiment, we can just ignore it. Answer Yes to this question to discard the last epoch.

• At this stage, you should review all the trials (press F3 to jump to the next file), separately for the magnetometers and the gradiometers, to make sure that you don't have any bad trial that have been imported. If you find a bad trial: right-click on the file or on the figure > Reject trial.
  
  

Averaging

Drag and drop the two lists of trials (or the two condition folders) in the Process1 list, and run the process "Average > Average files", with the option "Average by condition (subject average)". This will group the trials by conditions, and calculate one average per condition and subject. The option "By condition (grand average)" would average each condition for all the subjects together, but as there is only one subject in this case, the result would be same. The other options would generate one average file per subject ("by subject"), one average file per group of trials and per subject ("by trial group"), or only one average no matter what the input is ("everything").

The function to apply is the regular arithmetic average. The option "Keep all the events from the individual epochs" would group all the event markers present in all the epochs and save them in the new averaged file. It can be useful to check the relative position of the artifacts or the subject responses, or quickly detect some unwanted configuration such as a subject who would constantly blink right after a visual stimulus. We don't really need this here, leave this option unselected.

It creates two files: "Avg: left" and "Avg: right".

Double-click on the "Avg: Left" file to display the MEG recordings for this file (or right-click > MEG > display time series). It shows a very typical an clean evoked response, with a very high signal-to-noise ratio.

Just for the records, this is how it would look like if we had selected the option "Keep all the events from the individual epochs". The summary of the markers of all the epochs is: 37 heartbeats and 2 blinks.

Stimulation artifact

Now zoom around 4ms in time (mouse wheel, or two figures up/down on macbooks) and amplitude (control + zoom). Notice this very strong and sharp peak followed by fast oscillations. This is an artifact due to the electric stimulation device. In the stimulation setup: the stimulus trigger is initiated by the stimulation computer and sent to the electric stimulator. This stimulator generates an electric pulse that is sent to electrodes on the subject's wrists. This electric current flows in the wires and the in the body, so it also generates a small magnetic field that is captured by the MEG sensors. This is what we observe here at 4ms.

This means that whenever we decide to send an electric stimulus, there is a 4ms delay before the stimulus is actually received by the subject, due to all the electronics in between. Which means that everything is shifted by 4ms in the recordings. These hardware delays are unavoidable and should be quantified precisely before starting scanning subjects or patients.

Then you have two options: either you remember it and subtract the delays when you publish your results (there is a risk of forgetting about them), or you fix the files right now by fixing the time reference in all the files (there is a risk of forgetting to fix all the subjects/runs in the same way). Let's illustrate this second method now.

Close all the figures, clear the Process1 list (right-click > clear, or select+delete key), and drag and drop all the trials and all the averages (or simply the two left and right condition folders). Select the process "Pre-process > Add time offset". Set the time offset to -4.2 ms, to bring back this stimulation peak at 0ms. Select also the "Overwrite input files" option, to avoid too many duplications of unnecessary files in the database.

This fixes the individual trials and the averages. Double-click on the "Avg: left" file again to observe that the stimulation artifact is now occurring at 0ms exactly, which means that t=0s represents the time when the electric pulse is received by the subject.

Explore the average

Open the time series for the "Avg: left". Then press Control+T, to see on the side a spatial topography at the current time point. Then observe what is happening between 0ms and 100ms. Start at 0ms and play it like a movie using the arrow keys left and right, to follow the brain activity millisecond by millisecond:

• 16 ms: First response, the sensory information reaches the right somatosensory cortex (S1)

• 30 ms: Stronger and more focal activity in the same region, but with a source oriented in the opposite direction (S1)

• 60 ms: Activity appearing progressively in a more lateral region (S1 + S2)

• 70 ms: Activity in the same area in the left hemisphere (S2 left + S2 right)

Source analysis

Let's reproduce the same observations at the source level. The concepts related with the source estimation are not discussed here; for more information, refer to the introduction tutorials #6 to #8.

First, delete all the files related with the source estimation calculated in the previous tutorials, available in the (Common files) folder: the head model, the noise covariance and inverse model. We can now provide a better estimate of the noise (affects the inverse model), and we defined new SSP operators (affects the head model).

Head model

Right-click on any node that contains the channel file (including the channel file itself), and select: "Compute head model". Leave all the default options: cortex source space, and overlapping spheres. The lead field matrix is saved in file "Overlapping spheres" in (Common files).

Noise covariance matrix

To estimate the sources properly, we need an estimation of the noise level for each sensor. A good way to do this is to compute the covariance matrix of the concatenation of the baselines from all the trials in both conditions.

• Select at the same time the two groups of trials (right and left). To do this: hold the Control (or Cmd on Macs) key and click successively on the Right and the Left trial lists.

• Right-click on one of them and select: Noise covariance > Compute from recordings. Set the baseline to [-104,-5] ms, to consider as noise everything that happens before the beginning of the stimulation artifact. Leave the other options to the default values. Click on Ok.

• This operation computes the noise covariance matrix based on the baseline of all the good trials (199 files). The result is stored in a new file "Noise covariance" in the (Common files) folder.

Inverse model

Right-click on (Common files), on the head model or on the subject node, and select "Compute sources". A shared inversion kernel is created in (Common files); a link node is now visible for each recordings file, single trials and averages. For more information about what these links mean and the operations performed to display them, please refer to the tutorial #8 "Source estimation".

Explore the sources

Right-click on the sources for the left average > Cortical activations > Display on cortex, or simply double click on the file. Go to the main response peak at t = 30ms, and increase the amplitude threshold to 100%. You see a strong activity around the right primary somatosensory cortex, but there are still lots of brain areas that are shown in plain red (value >= 100% maximum of the colorbar ~= 280 pA.m).

The colorbar maximum is set to an estimation of the maximum source amplitude over the time. This estimation is done by finding the time instant with the highest global field power on the sensors (green trace GFP), estimating the sources for this time only, and then taking the maximum source value at this time point. It is a very fast estimate, but not very reliable; we use it because calculating the full source matrix (all the time points x all the sources) just for finding the maximum value would be too long.

In this case, the real maximum is probably higher than what is used by default. To redefine the colorbar maximum: right-click on the 3D figure > Colormap: sources > Set colorbar max value. Set the maximum to 480 pA.m, or any other value that would lead to see just one very focal point on the brain at 30ms.

Go back to the first small peak at t=16ms, and lower the threshold to 10%. Then do what you did with at the sensor level: follow the information processing in the brain until 100ms, millisecond by millisecond, adapting the threshold and the camera position when needed:

• 16 ms (top-left): First response, primary somatosensory cortex (S1 right)

• 30 ms (top-right): S1 right

• 60 ms (bottom-left): Secondary somatosensory cortex (S2 right)

• 70 ms (bottom-right): Activity ipsilateral to the stimulus (S2 left + S2 right)

Scripting

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