Brainstorm-FieldTrip auditory tutorial

[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE]

Authors: Francois Tadel, Elizabeth Bock, Robert Oostenveld.

The aim of this tutorial is to provide high-quality recordings of a simple auditory stimulation and illustrate the best analysis paths possible with Brainstorm and FieldTrip. This page presents the workflow in the Brainstorm environment, the equivalent documentation for the FieldTrip environment will be available on the FieldTrip website.

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 12+3 introduction tutorials.

License

This tutorial dataset (MEG and MRI data) remains a property of the MEG Lab, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Canada. Its use and transfer outside the Brainstorm tutorial, e.g. for research purposes, is prohibited without written consent from the MEG Lab.

If you reference this dataset in your publications, please aknowledge its authors (Elizabeth Bock, Peter Donhauser, Francois Tadel and Sylvain Baillet) and cite Brainstorm as indicated on the website. For questions, please contact us through the forum.

Presentation of the experiment

Experiment

• One subject, two acquisition runs of 6 minutes each
• Subject stimulated binaurally with intra-aural earphones (air tubes+transducers)
• Each run contains:
  • 200 regular beeps (440Hz)
  • 40 easy deviant beeps (554.4Hz, 4 semitones higher)
• Random inter-stimulus interval: between 0.7s and 1.7s seconds, uniformly distributed
• The subject presses a button when detecting a deviant
• Auditory stimuli generated with the Matlab Psychophysics toolbox

MEG acquisition

• Acquisition at 2400Hz, with a CTF 275 system at the MNI, subject in seating position

• Online 600Hz low-pass filter, files saved with the 3rd order gradient
• Recorded channels (340):
  • 1 Stim channel indicating the presentation times of the audio stimuli: UDIO001 (#1)
  • 1 Response channel recordings the finger taps in resonse to the deviants: UPPT001 (#2)
  • 26 MEG reference sensors (#5-#30)
  • 274 MEG axial gradiometers (#31-#304)
  • 2 EEG electrodes: Cz, Pz (#305 and #306)
  • 1 ECG bipolar (#307)
  • 2 EOG bipolar (vertical #308, horizontal #309)
  • 1 Audio signal sent to the subject: UADC001 (#316)
  • 12 Head tracking channels: Nasion-X/Y/Z, Left-X/Y/Z, Right-X/Y/Z, Error-NAS/L/R (#317-#328)
  • 20 Unused channels (#3, #4, #310-#315, #329-340)
• 3 datasets:

  • S01_AEF_20131218_01.ds: Run #1, 360s, 200 standard + 41 deviants

  • S01_AEF_20131218_02.ds: Run #2, 360s, 202 standard + 40 deviants

  • S01_Noise_20131218_01.ds: Noise recordings, 30s long

  • File name: S01=Subject01, AEF=Auditory evoked field, 20131218=date(Dec 18 2013), 01=run

• Use of the .ds, not the AUX (standard at the MNI) because they are easier to manipulate in FieldTrip

Head shape and fiducial points

• 3D digitization using a Polhemus Fastrak device driven by Brainstorm (S01_20131218_*.pos)
  More information: Digitize EEG electrodes and head shape

• The output file is copied to each .ds folder and contains the following entries:
  • The position of the center of CTF coils

  • The position of the anatomical references we use in Brainstorm:
    Nasion and connections tragus/helix, as illustrated here.

• Around 150 head points distributed on the hard parts of the head (no soft tissues)

Subject anatomy

• Subject with 1.5T MRI
• Marker on the left cheek

• Processed with FreeSurfer 5.3

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_auditory.zip

• Unzip it in a folder that is not in any of the Brainstorm folders (program folder or database folder). This is really important that you always keep your original data files in a separate folder: the program folder can be deleted when updating the software, and the contents of the database folder is supposed to be manipulated only by the program itself.
• Start Brainstorm (Matlab scripts or stand-alone version)

• Select the menu File > Create new protocol. Name it "TutorialAuditory" 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 > Subject01

  • 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: sample_auditory/anatomy

  • Number of vertices of the cortex surface: 15000 (default value)
• Set the 6 required fiducial points (indicated in MRI coordinates):
  • NAS: x=127, y=213, z=139
  • LPA: x=52, y=113, z=96
  • RPA: x=202, y=113, z=91
  • AC: x=127, y=119, z=149
  • PC: x=128, y=93, z=141
  • IH: x=131, y=114, z=206 (anywhere on the midsagittal plane)
• 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.

  

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: CTF (*.ds...)"

  • Select all the .ds folders in: sample_auditory/data

• Refine registration now? YES
  
  

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.
  
  

• 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.

• In this tutorial, we will illustrate only method 1: runs are not co-registered.

Epoched vs. continuous

• The CTF MEG system can save two types of files: epoched (.ds) or continuous (_AUX.ds).
• Here we have an intermediate storage type: continuous recordings saved in an "epoched" file. The file is saved as small blocks of recordings of a constant time length (1 second in this case). All those time blocks are contiguous, there is no gap between them.
• Brainstorm can consider this file either as a continuous or an epoched file. By default it imports the regular .ds folders as epoched, but we can change this manually, to process it as a continuous file.

• Double-click on the "Link to raw file" for run 01 to view the MEG recordings. You can navigate in the file by blocks of 1s, and switch between blocks using the "Epoch" box in the Record tab. The events listed are relative to the current epoch.
  
  

• Right-click on the "Link to raw file" for run 01 > Switch epoched/continuous

• Double-click on the "Link to raw file" again. Now you can navigate in the file without interruptions. The box "Epoch" is disabled and all the events in the file are displayed at once.
  
  

• Repeat this operation twice to convert all the files to a continuous mode.

  • Run 02 > Switch epoched/continuous

  • Noise > Switch epoched/continuous

Detect and remove artifacts

Spectral evaluation

• One of the typical pre-processing steps consist in getting rid of the contamination due to the power lines (50 Hz or 60Hz). Let's start with the spectral evaluation of this file.

• Drag ALL the "Link to raw file" to the Process1 box, or easier, just drag the node "Subject01", it will select recursively all the files in it.

• Run the process "Frequency > Power spectrum density (Welch)":

  • Time window: [0 - 50]s

  • Window length: 4s

  • Overlap: 50%

  • Sensor types or names: MEG

  • Selected option "Save individual PSD values (for each trial)".
    
    

• Double-click on the new PSD files to display them.
  
  

• We can observe a series of peaks related with the power lines: 60Hz, 120Hz, 180Hz
  (240Hz and 300Hz could be observed as well depending on the window length used for the PSD)

• The drop after 600Hz corresponds to the low-pass filter applied at the acquisition time.

Power line contamination

• Put ALL the "Link to raw file" into the Process1 box (or directly the Subject01 folder)

• Run the process: Pre-process > Notch filter

  • Select the frequencies: 60, 120, 180 Hz

  • Sensor types or names: MEG

  • The higher harmonics are too high to bother us in this analysis, plus they are not clearly visible in all the recordings.

  • In output, this process creates new .ds folders in the same folder as the original files, and links the new files to the database.
    
    

• Run again the PSD process "Frequency > Power spectrum density (Welch)" on those new files, with the same parameters, to evaluate the quality of the correction.
  
  

• Double-click on the new PSD files to open them.
  
  

• You can zoom in with the mouse wheel to observer what is happening around 60Hz.
  
  

• To avoid the confusion later, delete the links to the original files: Select the folders containing the original unfiltered files and press the Delete key (or right-click > File > Delete).
  
  

Heartbeats and eye blinks

• Select the two AEF runs in the Process1 box.
• Select successively the following processes, then click on [Run]:

  • Events > Detect heartbeats: Select channel ECG, check "All file", event name "cardiac".

  • Events > Detect eye blinks: Select channel VEOG, check "All file", event name "blink".

  • Events > Remove simultaneous: Remove "cardiac", too close to "blink", delay 250ms.

  • Compute SSP: Heartbeats: Event name "cardiac", sensor types="MEG", use existing SSP.

  • Compute SSP: Eyeblinks: Event name "blink", sensor types="MEG", use existing SSP.
    
    

• Double-click on Run1 to open the MEG.

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

  • In the Record tab, menu SSP > Select active projectors.

  • Blink: The first component is selected and looks good.
  • Cardiac: By default the category is disabled because no component has a value superior to 12%.
  • Select the first component of the cardiac category and display its topography.

  • It looks exactly like a cardiac topography, keep it selected and click on [Save].
    
    

• Repeat the same operations for Run2:

  • Review the events.
  • Select the first cardiac component.

Saccades

Run2 contains a few saccades. The automatic detection is not working well.

To do:

• Provide an external file of marked saccades to be loaded with this dataset.
• Work on better algorithms for detecting saccades.

Bad segments

To do:

• Provide a list of bad segments
• Describe the methodology to define those bad segments

Epoching and averaging

import standard and deviant: [-100,500]ms, remove DC with [-100,0]ms baseline

do not make new condition - keep trials separate according to run average trial types see screen shot of MEG and EEG - P300 can be seen here even with only 40 epochs in the average

Source estimation

Head model

headmodel overlapping spheres, used cortex 15000 vertices

Noise covariance matrix

Noise recordings

Inverse model

sources min norm

z-score with baseline [-100,0] sec, abs of sources

Regions of interest

scouts over primary auditory cortex (A1)

see screen shots

Time-frequency

TF over scout 1, z-scored [-100,0] ms

see screen shots interesting gamma in the deviant

Coherence

• Run1, sensor magnitude-square coherence 1xN
  • UADC001 vs. other sensors
  • Time window: [0,150]ms
  • Sensor types
  • Remove evoked response: NOT SELECTED
  • Maximum frequency resolution: 1Hz
  • Highest frequency of interest: 80Hz
  • Metric significativity: 0.05
  • Concatenate input files: SELECTED

  • => Coh(0.6Hz,32win)

• Run1, source magnitude-square coherence 1xN
  • 50 standard trials
  • Highest frequency of interest: 40Hz
  • Same other inputs

Discussion

Discussion about the choice of the dataset on the FieldTrip bug tracker:
http://bugzilla.fcdonders.nl/show_bug.cgi?id=2300

Scripting

Process selection

Graphic edition

Generate Matlab script

The operations described in this tutorial can be reproduced from a Matlab script, available in the Brainstorm distribution: brainstorm3/toolbox/script/tutorial_auditory.m

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