[WARNING: This tutorial is outdated, read the introduction tutorials instead]


MEG auditory tutorial (CTF)

Authors: Francois Tadel, Elizabeth Bock.

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. It is the same dataset as the one used in the introduction tutorial, but at the full sampling rate (2400Hz).

Note that the operations used here are not detailed, the goal of this tutorial is not to introduce Brainstorm to new users. For in-depth explanations of the interface and theoretical foundations, please refer to the 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 with the right index finger
  • Auditory stimuli generated with the Matlab Psychophysics toolbox
  • The specifications of this dataset were discussed initially on the FieldTrip bug tracker:
    http://bugzilla.fcdonders.nl/show_bug.cgi?id=2300

MEG acquisition

  • Acquisition at 2400Hz, with a CTF 275 system, subject in sitting position

  • Recorded at the Montreal Neurological Institute in December 2013
  • Anti-aliasing low-pass filter at 600Hz, files saved with the 3rd order gradient
  • Recorded channels (340):
    • 1 Stim channel indicating the presentation times of the audio stimuli: UPPT001 (#1)
    • 1 Audio signal sent to the subject: UADC001 (#316)
    • 1 Response channel recordings the finger taps in response to the deviants: UDIO001 (#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)
    • 12 Head tracking channels: Nasion XYZ, Left XYZ, Right XYZ, Error N/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 + 40 deviants

    • S01_AEF_20131218_02.ds: Run #2, 360s, 200 standard + 40 deviants

    • S01_Noise_20131218_01.ds: Empty room recordings, 30s long

    • File name: S01=Subject01, AEF=Auditory evoked field, 20131218=date(Dec 18 2013), 01=run
  • Average reaction times for the button press after a deviant tone:
    • Run #1: 515ms +/- 108ms

    • Run #2: 596ms +/- 134ms

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

Stimulation delays

  • Delay #1: Production of the sound.
    Between the stim markers (channel UDIO001) and the moment when the sound card plays the sound (channel UADC001). This is mostly due to the software running on the computer (stimulation software, operating system, sound card drivers, sound card electronics). The delay can be measured from the recorded files by comparing the triggers in the two channels: Delay between 11.5ms and 12.8ms (std = 0.3ms) This delay is not constant, we will need to correct for it.

  • Delay #2: Transmission of the sound.
    Between when the sound card plays the sound and when the subject receives the sound in the ears. This is the time it takes for the transducer to convert the analog audio signal into a sound, plus the time it takes the sound to travel through the air tubes from the transducer to the subject's ears. This delay cannot be estimated from the recorded signals: before the acquisition, we placed a sound meter at the extremity of the tubes to record when the sound is delivered. Delay between 4.8ms and 5.0ms (std = 0.08ms). At a sampling rate of 2400Hz, this delay can be considered constant, we will not compensate for it.

  • Delay #3: Recording of the signals.
    The CTF MEG systems have a constant delay of 4 samples between the MEG/EEG channels and the analog channels (such as the audio signal UADC001), because of an anti-aliasing filter that is applied to the first and not the second. This translate here to a constant delay of 1.7ms.

  • Delay #4: Over-compensation of delay #1.
    When correcting of delay #1, the process we use to detect the beginning of the triggers on the audio signal (UADC001) sets the trigger in the middle of the ramp between silence and the beep. We "over-compensate" the delay #1 by 1.7ms. This can be considered as constant delay of about -1.7ms.

  • Uncorrected delays: We will correct for the delay #1, and keep the other delays (#2, #3 and #4). After we compensate for delay #1 our MEG signals will have a constant delay of about 4.9 + 1.7 - 1.7 = 4.9 ms. We decide not to compensate for these delays because they do not introduce any jitter in the responses and they are not going to change anything in the interpretation of the data.

delays_sketch.gif

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

Import the anatomy

Access the recordings

Convert to continuous

Stimulation triggers delay

Evaluation

Correction

Detect and remove artifacts

Spectral evaluation

Power line contamination

Bad segments

Saccades

Bad channels

Epoching and averaging

Import recordings

To import epochs from Run01:

Repeat the same operation for Run02:

Average responses

Visual exploration

Difference deviant-standard

Source estimation

Head model

Noise covariance matrix

Inverse model

Average in source space

Difference deviant-standard

Student's t-test

Regions of interest

Manual tracing

Influence of the number of trials

Scripting

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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Tutorials/Auditory (last edited 2016-08-01 22:39:30 by FrancoisTadel)