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MEG visual tutorial: Single subject

Authors: Francois Tadel, Elizabeth Bock.

The aim of this tutorial is to reproduce in the Brainstorm environment the analysis described in the SPM tutorial "Multimodal, Multisubject data fusion". The data processed here consists in simulateneous MEG/EEG recordings of 16 subjects performing simple visual task on a large number of famous, unfamiliar and scrambled faces.

The analysis is split in two tutorial pages: the present tutorial describes the detailed analysis of one single subject and another one that the describes the batch processing and group analysis of the 16 subjects.

Note that the operations used here are not detailed, the goal of this tutorial is not to teach Brainstorm to a new inexperienced user. For in depth explanations of the interface and the theory, please refer to the introduction tutorials.

Contents

License
Presentation of the experiment
Download and installation
Import the anatomy
Access the recordings
Pre-processing
Artifact correction with SSP
Epoching and averaging
Empty room recordings
Source estimation
Scripting

License

These data are provided freely for research purposes only (as part of their Award of the BioMag2010 Data Competition). If you wish to publish any of these data, please acknowledge Daniel Wakeman and Richard Henson. The best single reference is: Wakeman DG, Henson RN, A multi-subject, multi-modal human neuroimaging dataset, Scientific Data (2015)

Any questions, please contact: rik.henson@mrc-cbu.cam.ac.uk

Presentation of the experiment

Experiment

16 subjects
6 runs (sessions) of approximately 10mins for each subject
Presentation of series of images: familiar faces, unfamiliar faces, phase-scrambled faces
The subject has to judge the left-right symmetry of each stimulus
Total of nearly 300 trials in total for each of the 3 conditions

MEG acquisition

Acquisition at 1100Hz with an Elekta-Neuromag VectorView system (simultaneous MEG+EEG).
Recorded channels (404):
- 102 magnetometers
- 204 planar gradiometers
- 70 EEG electrodes recorded with a nose reference.
MEG data have been "cleaned" using Signal-Space Separation as implemented in MaxFilter 2.1.
A Polhemus digitizer was used to digitise three fiducial points and a large number of other points across the scalp, which can be used to coregister the M/EEG data with the structural MRI image.
The distribution contains 3 sub-directories of empty-room recordings of 3-5mins acquired at roughly the same time of year (spring 2009) as the 16 subjects. The sub-directory names are Year (first 2 digits), Month (second 2 digits) and Day (third 2 digits). Inside each are 2 raw *.fif files: one for which basic SSS has been applied by maxfilter in a similar manner to the subject data above, and one (*-noSSS.fif) for which SSS has not been applied (though the data have been passed through maxfilter just to convert to float format).

Subject anatomy

MRI data acquired on a 3T Siemens TIM Trio: 1x1x1mm T1-weighted structural MRI
Processed with FreeSurfer 5.3

Download and installation

The data is hosted on this FTP site (use an FTP client such as FileZilla, not your web browser):
ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/
Download only the following folders (about 75Gb):
- EmptyRoom: MEG empty room measurements.
- SubXX/MEEG/*_sss.fif: MEG and EEG recordings in FIF format, corrected with SSS.
- SubXX/MEEG/Trials: Trials definition files.
- Publications: Reference publications related with this dataset.
- README.TXT: License and dataset description.
The FreeSurfer segmentations of the T1 images are not part of this package. You can either process them by yourself, or download the result of the segmentation from the Brainstorm website.
Go to the Download page, and download the file: sample_group_anat.zip
Unzip this file in the same folder where you downloaded all the datasets.
Reminder: Do not put the downloaded files in the Brainstorm folders (program or database folders).
Start Brainstorm (Matlab scripts or stand-alone version). For help, see the Installation page.
Select the menu File > Create new protocol. Name it "TutorialVisual" and select the options:
- "No, use individual anatomy",
- "No, use one channel file per condition".

Import the anatomy

Switch to the "anatomy" view.
Right-click on the TutorialAuditory folder > New subject > Sub01
- Leave the default options you set for the protocol
Right-click on the subject node > Import anatomy folder:
- Set the file format: "FreeSurfer folder"
- Select the folder: Anatomy/Sub01 (from sample_group_anat.zip)
- Number of vertices of the cortex surface: 15000 (default value)
The two sets of fiducials we usually have to define interactively are here automatically set.
- NAS/LPA/RPA: The file Anatomy/Sub01/fiducials.m contains the definition of the nasion, left and right ears. The anatomical points used by the authors are the same as the ones we recommend in the Brainstorm coordinates systems page.
- AC/PC/IH: Automatically identified using the SPM affine registration with an MNI template.
- If you want to double-check that all these points were correctly marked after importing the anatomy, right-click on the MRI > Edit MRI.
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. Do not worry about the big holes in the head surface, parts of MRI have been remove voluntarily for anonymization purposes.
All the anatomical atlases generated by FreeSurfer were automatically imported: the surface-based cortical atlases and the atlas of sub-cortical regions (ASEG).

Access the recordings

Link the recordings

Switch to the "functional data" view.
Right-click on the subject folder > Review raw file.
- Select the file format: "MEG/EEG: Neuromag FIFF (*.fif)"
- Select the first FIF files in: Sub01/MEEG
Events: Ignore
We will load the trial definition separately.
Refine registration now? NO
The head points that are available in the FIF files contain all the points that were digitized during the MEG acquisition, including the ones corresponding to the parts of the face that have been removed from the MRI. If we run the fitting algorithm, all the points around the nose will not match any close points on the head surface, leading to a wrong result. We will first remove the face points and then run the registration manually.

Channel classification

A few non-EEG channels are mixed in with the EEG channels, we need to change this before applying any operation on the EEG channels.

Right-click on the channel file > Edit channel file. Double-click on a cell to edit it.
Change the type of EEG062 to EOG (electrooculogram).
Change the type of EEG063 to ECG (electrocardiogram).
Change the type of EEG061 and EEG064 to NOSIG.

MRI registration

Right-click on the channel file > Digitized head points > Remove points below nasion.
Right-click on the channel file > MRI registration > Refine registration.
MEG/MRI registration, before (left) and after (right) this automatic registration procedure:
Right-click on the channel file > MRI registration > EEG: Edit...
Click on [Project electrodes on surface]
Close all the windows (use the [X] button at the top-right corner of the Brainstorm window).

Import triggers

Right-click on the "Link to raw file" > MEG (all) > Display time series.
In the Record tab, menu File > Add events from file:
- Select the file format: "FieldTrip trial definition (*.txt;*.mat)"
- Select the first text file: Sub01/MEEG/Trials/run_01_trldef.txt
There are two event categories created for each condition: the first one (eg. "Famous") represents when the stimulus was sent to the subject, the second (eg. "Famous_trial") is an extended event that represents the trial information that was present in the file.
We are going to use only the single events (triggers): Delete the 3 last categories ("_trial").
Close all the figures. YES, save the modifications.

Pre-processing

Spectral evaluation

Drag and drop the "Link to raw file" in Process1.
Run process "Frequency > Power spectrum density (Welch)" with the options illustrated below.
Right-click on the PSD file > Power spectrum.
Observations:
- Three groups of sensors, from top to bottom: EEG, MEG gradiometers, MEG magnetometers.
- Power lines: 50 Hz and harmonics
- Alpha peak around 10 Hz
- Artifacts due to Elekta electronics: 293Hz, 307Hz, 314Hz, 321Hz, 328Hz.
- Suspected bad EEG channels: EEG016
Close all the windows.

Remove line noise

Keep the "Link to raw file" in Process1.
Select process "Pre-process > Notch filter" to remove the line noise (50-200Hz).
Add immediately after the process "Frequency > Power spectrum density (Welch)"
Double-click on the PSD for the new continuous file to evaluate the quality of the correction.
Close all the windows (use the [X] button at the top-right corner of the Brainstorm window).

EEG reference and bad channels

Right-click on link to the processed file ("Raw | notch(50Hz ...") > EEG > Display time series.
Select channel EEG016 and mark it as bad (using the popup menu or pressing Delete key).
In the Record tab, menu Artifacts > Re-reference EEG > "AVERAGE".
At the end, the window "select active projectors" is open to show the new re-referencing projector. Just close this window. To get it back, use the menu Artifacts > Select active projectors.

Artifact correction with SSP

Artifact detection

In the record tab, run the following menus:

Artifacts > Detect heartbeats: Channel name=EEG063, All file, Event name=cardiac
Artifacts > Detect eye blinks: Channel name=EEG062, All file, Event name=blink_BAD
In many of the other tutorials, you will have detected the blink category and used the SSP process (below) to remove the blink artifact. In this experiment, we are particularly interested in the subject's response to seeing the stimulus. Therefore we will mark eyeblinks and other eye movements as BAD.
Close all the windows (using the [X] button).

Heartbeats

Empty the Process1 list (right-click > Clear list).
Drag and drop the continuous processed file ("Raw | notch(50Hz...)") to the Process1 list.
Select the following processes and run the pipeline:
- Artifacts > SSP: Heartbeats > Sensor type: MEG MAG
- Artifacts > SSP: Heartbeats > Sensor type: MEG GRAD
Double-click on the continuous file to show all the MEG sensors.
In the Record tab, select sensors "Left-temporal".
Menu Artifacts > Select active projectors.
- In category cardiac/MEG MAG: Select component #1.
- In category cardiac/MEG GRAD: Select component #1.
Make sure that selecting the two components removes the cardiac artifact. Then click [Save].

Additional bad segments

Process1>Run: Select the process "Events > Detect other artifacts".
Display the MEG and EEG sensors simultaneously, review the segments that were tagged as artifact and mark bad segments. This can be done by selecting the time window around the artifact, Right-click > Reject time segment.
SQUID SENSOR ARTIFACTS (SQUID JUMPS): Because of the different sensor technologies, one can expect that the different MEG systems may produce different types of artifact. With the Neuromag system, used for this experiment, one can see an artifact produced by the sensors which is exaggerated by the pre-processing completed with Max Filter, seen here in this example. The spacial filtering of Max Filter causes the artifact to be speard across most sensors and also spread a bit in time. Ideally, the offending should be marked bad before applying the Max Filter. In the case of this dataset, we have the artifacts - sometimes more frequent than others. The 1-7Hz artifact detection employed here will almost always catch them, and they should be marked as bad segments.

Epoching and averaging

Right-click on the continuous processed file > Import in database.
Drag and drop all the imported trials in Process1.
Run process "Average > Average files": By trial groups (subject average)
MEG evoked responses (famous, scrambled, unfamiliar):
EEG evoked response (famous, scrambled, unfamiliar):

Empty room recordings

Link the empty room recordings
Apply the same notch filter
Compute the noise covariance
Copy the noise covariance to the other conditions

Source estimation

Head model: OpenMEEG BEM (EEG) and Overlapping spheres (MEG)
Inverse model: ?

Scripting

Corresponding script in the Brainstorm distribution:
brainstorm3/toolbox/script/tutorial_visual_single.m

-  ⇤ ← Revision 25 as of 2015-07-24 23:27:03 → 
  Size: 9414
  Editor: FrancoisTadel
  Comment:
+   ← Revision 57 as of 2015-10-08 19:21:00 → ⇥
  Size: 15818
  Editor: ?ElizabethBock
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 43:
-  * '''SubXX/MEEG''': MEG and EEG recordings in FIF format and triggers definition files.
+  * '''SubXX/MEEG/*_sss.fif''': MEG and EEG recordings in FIF format, corrected with SSS.
  * '''SubXX/MEEG/Trials''': Trials definition files.
-Line 62:
+Line 63:
- * The two sets of fiducials we usually have to define interactively are here set automatically.
+ * The two sets of fiducials we usually have to define interactively are here automatically set.
-Line 64:
+Line 65:
-  * '''AC/PC/IH''': Identified automatically using the SPM affine registration with an MNI template.
+  * '''AC/PC/IH''': Automatically identified using the SPM affine registration with an MNI template.
-Line 69:
+Line 70:
- * All the anatomical atlases [[Tutorials/LabelFreeSurfer|generated by FreeSurfer]] were imported automatically: the surface-based cortical atlases and the atlas of sub-cortical regions (ASEG). <<BR>><<BR>> {{attachment:anatomy_atlas.gif||height="211",width="550"}}
+ * All the anatomical atlases [[Tutorials/LabelFreeSurfer|generated by FreeSurfer]] were automatically imported: the surface-based cortical atlases and the atlas of sub-cortical regions (ASEG). <<BR>><<BR>> {{attachment:anatomy_atlas.gif||height="211",width="550"}}
-Line 76:
+Line 77:
-  * Select the '''first''' FIF files in: '''Sub01/MEEG''' <<BR>><<BR>> {{attachment:review_raw.gif}}
 * Events:  '''SCREEN CAPTURES '''
 * Refine registration now? '''NO''' <<BR>>The head points that are available in the FIF files contain all the points that were digitized during the MEG acquisition, including the ones corresponding to the parts of the face that have been removed from the MRI. If we run the fitting algorithm, all the points around the nose will not match any close points on the head surface, leading to a wrong result.
 * Right-click on the channel file > '''Digitized head points > Remove points below nasion'''.<<BR>><<BR>>{{attachment:channel_remove.gif}}
 * Right-click on the channel file > '''MRI registration > Remove points below nasion'''.<<BR>><<BR>>{{attachment:channel_refine.gif}}
 * MEG/MRI registration, before (left) and after (right) this automatic registration procedure: {{attachment:registration.gif||height="222",width="251"}}
+  * Select the '''first''' FIF files in: '''Sub01/MEEG''' <<BR>><<BR>> {{attachment:review_raw.gif||height="181",width="446"}}
 * Events:''' Ignore'''<<BR>>We will load the trial definition separately.<<BR>><<BR>> {{attachment:review_ignore.gif||height="186",width="330"}}
 * Refine registration now? '''NO''' <<BR>>The head points that are available in the FIF files contain all the points that were digitized during the MEG acquisition, including the ones corresponding to the parts of the face that have been removed from the MRI. If we run the fitting algorithm, all the points around the nose will not match any close points on the head surface, leading to a wrong result. We will first remove the face points and then run the registration manually.
-Line 83:
+Line 81:
- *
+=== Channel classification ===
A few non-EEG channels are mixed in with the EEG channels, we need to change this before applying any operation on the EEG channels.
-Line 85:
+Line 84:
-=== Multiple runs and head position ===
 * The  two AEF runs 01 and 02 were acquired successively, the position of the  subject's head in the MEG helmet was estimated twice, once at the  beginning of each run. The subject might have moved between the two  runs. To evaluate visually the displacement between the two runs, select  at the same time all the channel files you want to compare (the ones  for run 01 and 02), right-click > Display sensors > MEG.
  . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=raw3.gif|raw3.gif|height="220",width="441",class="attachment"}}
 * Typically,  we would like to group the trials coming from multiple runs by  experimental conditions. However, because of the subject's movements  between runs, it's not possible to directly compare the sensor values  between runs because they probably do not capture the brain activity  coming from the same regions of the brain.
 * You have three options if you consider grouping information from multiple runs:
  * '''Method 1''':  Process all the runs separately and average between runs at the source  level: The more accurate option, but requires a lot more work,  computation time and storage.
  * '''Method 2''':  Ignore movements between runs: This can be acceptable for commodity if  the displacements are really minimal, less accurate but much faster to  process and easier to manipulate.
  * '''Method 3''':  Co-register properly the runs using the process Standardize >  Co-register MEG runs: Can be a good option for displacements under 2cm.
 Warning: This method has not be been fully evaluated on our side, to use  at your own risk. Also, it does not work correctly if you have  different SSP  projectors calculated for multiple runs.
 * In this tutorial, we will illustrate only method 1: runs are not co-registered.
+ * Right-click on the channel file > '''Edit channel file'''. Double-click on a cell to edit it.
 * Change the type of '''EEG062''' to '''EOG '''(electrooculogram).
 * Change the type of '''EEG063 '''to '''ECG '''(electrocardiogram).
 * Change the type of '''EEG061''' and '''EEG064''' to '''NOSIG'''. <<BR>><<BR>> {{attachment:channel_edit.gif||height="252",width="561"}}

=== MRI registration ===
 * Right-click on the channel file > '''Digitized head points > Remove points below nasion'''.<<BR>><<BR>> {{attachment:channel_remove.gif||height="194",width="329"}}
 * Right-click on the channel file > '''MRI registration > Refine registration'''.<<BR>><<BR>> {{attachment:channel_refine.gif||height="173",width="294"}}
 * MEG/MRI registration, before (left) and after (right) this automatic registration procedure:  <<BR>><<BR>> {{attachment:registration.gif||height="209",width="236"}} {{attachment:registration_final.gif||height="208",width="237"}}
 * Right-click on the channel file > MRI registration > EEG: Edit...<<BR>>Click on ['''Project electrodes on surface''']<<BR>><<BR>> {{attachment:channel_project.gif||height="207",width="477"}}
 * Close all the windows (use the [X] button at the top-right corner of the Brainstorm window).

=== Import triggers ===
 * Right-click on the  "Link to raw file" > '''MEG (all) > Display time series'''. <<BR>><<BR>> {{attachment:events_add.gif||height="183",width="537"}}
 * In the Record tab, menu '''File > Add events from file''':
  * Select the file format: "'''FieldTrip trial definition (*.txt''';'''*.mat)'''"
  * Select the '''first''' text file: '''Sub01/MEEG/Trials/run_01_trldef.txt'''
 * There  are two event categories created for each condition: the first  one (eg.  "Famous") represents when the stimulus was sent to the  subject, the  second (eg. "Famous_trial") is an extended event that  represents the  trial information that was present in the file. <<BR>><<BR>> {{attachment:events_file.gif||height="170",width="596"}}
 * We are going to use only the single events (triggers): '''Delete the 3 last categories''' ("_trial"). <<BR>><<BR>> {{attachment:events_select.gif}}
 * Close all the figures. YES, save the modifications.

== Pre-processing ==
=== Spectral evaluation ===
 * Drag and drop the "Link to raw file" in Process1. {{attachment:psd_select.gif||height="106",width="388"}}
 * Run process "'''Frequency > Power spectrum density (Welch)'''" with the options illustrated below. <<BR>><<BR>> {{attachment:psd_process.gif||height="321",width="543"}}
 * Right-click on the PSD file > Power spectrum.  <<BR>><<BR>> {{attachment:psd_plot.gif||height="315",width="546"}}
 * Observations:
  * Three groups of sensors, from top to bottom: EEG, MEG gradiometers, MEG magnetometers.
  * Power lines: '''50 '''Hz and harmonics
  * Alpha peak around 10 Hz
  * Artifacts due to Elekta electronics: '''293'''Hz, '''307'''Hz, '''314'''Hz, '''321'''Hz, '''328'''Hz.
  * Suspected bad EEG channels: '''EEG016'''
 * Close all the windows.

=== Remove line noise ===
 * Keep the "Link to raw file" in Process1.
 * Select process "'''Pre-process > Notch filter'''" to remove the line noise (50-200Hz).<<BR>>Add immediately after the process "'''Frequency > Power spectrum density (Welch)'''" <<BR>><<BR>> {{attachment:notch_process.gif||height="262",width="562"}}
 * Double-click on the PSD for the new continuous file to evaluate the quality of the correction. <<BR>><<BR>> {{attachment:notch_result.gif||height="215",width="566"}}
 * Close all the windows (use the [X] button at the top-right corner of the Brainstorm window).

=== EEG reference and bad channels ===
 * Right-click on link to the processed file ("Raw | notch(50Hz ...") > '''EEG > Display time series'''.
 * Select channel '''EEG016''' and mark it as '''bad''' (using the popup menu or pressing Delete key).<<BR>><<BR>> {{attachment:channel_bad.gif||height="214",width="587"}}
 * In the Record tab, menu '''Artifacts > Re-reference EEG''' > "AVERAGE". <<BR>><<BR>> {{attachment:channel_ref.gif||height="270",width="535"}}
 * At the end, the window "select active projectors" is open to show the new re-referencing projector. Just close this window. To get it back, use the menu Artifacts > Select active projectors.

== Artifact correction with SSP ==
=== Artifact detection ===
In the record tab, run the following menus:

 * '''Artifacts > Detect heartbeats''': Channel name='''EEG063''', All file, Event name=cardiac
 * '''Artifacts > Detect eye blinks''': Channel name='''EEG062''', All file, Event name='''blink_BAD'''<<BR>>In many of the other tutorials, you will have detected the blink category and used the SSP process (below) to remove the blink artifact. In this experiment, we are particularly interested in the subject's response to seeing the stimulus. Therefore we will mark eyeblinks and other eye movements as BAD. <<BR>> <<BR>> {{attachment:artifacts_detect.gif||height="186",width="545"}}

 * Close all the windows (using the [X] button).

=== Heartbeats ===
 * Empty the Process1 list (right-click > Clear list).
 * Drag and drop the continuous processed file ("Raw | notch(50Hz...)") to the Process1 list.
 * Select the following processes and run the pipeline:
  * Artifacts > '''SSP: Heartbeats''' > Sensor type: '''MEG MAG'''
  * Artifacts > '''SSP: Heartbeats''' > Sensor type: '''MEG GRAD '''<<BR>><<BR>> {{attachment:ssp_ecg_process.gif||height="225",width="293"}}
 * Double-click on the continuous file to show all the MEG sensors. <<BR>>In the Record tab, select sensors "Left-temporal".
 * Menu Artifacts > Select active projectors.
  * In category '''cardiac/MEG MAG''': Select '''component #1'''.
  * In category '''cardiac/MEG GRAD''': Select '''component #1'''.<<BR>><<BR>> {{attachment:ssp_ecg_topo.gif||height="175",width="604"}}
 * Make sure that selecting the two components removes the cardiac artifact. Then click '''[Save]'''.

=== Additional bad segments ===
 * Process1>Run: Select the process "'''Events > Detect other artifacts'''". <<BR>><<BR>> {{attachment:artifacts_other_process.gif||height="235",width="276"}}
 * Display the MEG and EEG sensors simultaneously, review the segments that were tagged as artifact and mark bad segments. This can be done by selecting the time window around the artifact, Right-click > '''Reject time segment'''.
 * __'''SQUID SENSOR ARTIFACTS'''__''' (SQUID JUMPS): '''Because of the different sensor technologies, one can expect that the different MEG systems may produce different types of artifact. With the Neuromag system, used for this experiment, one can see an artifact produced by the sensors which is exaggerated by the pre-processing completed with Max Filter, seen here in this example. The spacial filtering of Max Filter causes the artifact to be speard across most sensors and also spread a bit in time. Ideally, the offending should be marked bad before applying the Max Filter. In the case of this dataset, we have the artifacts - sometimes more frequent than others. The 1-7Hz artifact detection employed here will almost always catch them, and they should be marked as bad segments.

== Epoching and averaging ==
 * Right-click on the continuous processed file > '''Import in database'''. <<BR>><<BR>> {{attachment:import_epochs.gif||height="385",width="642"}}
 * Drag and drop all the imported trials in Process1.
 * Run process "'''Average > Average files'''": '''By trial groups (subject average)''' <<BR>><<BR>> {{attachment:average_process.gif||height="556",width="529"}}
 * MEG evoked responses (famous, scrambled, unfamiliar): <<BR>><<BR>> {{attachment:evoked_all.gif||height="401",width="401"}}
 * EEG evoked response (famous, scrambled, unfamiliar): <<BR>><<BR>> {{attachment:average_eeg.gif||height="403",width="402"}}

== Empty room recordings ==
 * Link the empty room recordings
 * Apply the same notch filter
 * Compute the noise covariance
 * Copy the noise covariance to the other conditions

== Source estimation ==
 * Head model: OpenMEEG BEM (EEG) and Overlapping spheres (MEG)
 * Inverse model: ?

== Scripting ==
Corresponding script in the Brainstorm distribution:<<BR>> '''brainstorm3/toolbox/script/tutorial_visual_single.m'''

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