Brainstorm
  • Comments
  • Menu
    • Attachments
    • Versions
    • Raw Text
    • Print View
  • Login

Software

  • Introduction

  • Gallery

  • Download

  • Installation

Users

  • Tutorials

  • Forum

  • Courses

  • Community

  • Publications

Development

  • What's new

  • What's next

  • About us

  • Contact us

  • Contribute

Revision 14 as of 2021-02-04 06:37:44
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

[TUTORIAL UNDER CONSTRUCTION]

Will be completed soon

In this tutorial we describe the full FEM process as described in the SPIE paper

We may keep the previous tutorial as a basic and this tutorial is an advanced and complete version.

FEM tutorial: MEG/EEG Median nerve stimulation

Authors: Takfarinas Medani, Juan Garcia-Prieto, Wayne Mead.

This tutorial introduces the FEM modeling in the Brainstorm environment.

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.

Contents

  1. License
  2. Description of the experiment
  3. Export recordings from fif files?
  4. Download and installation
  5. Import the anatomy
  6. Access the recordings
    1. Link the recordings
    2. Prepare the channel file
    3. Refine the MRI registration
    4. Read the stimulation information
  7. Pre-processing
    1. Evaluate the recordings
    2. Frequency filters
  8. Review the recordings
    1. MEG: Default montages
    2. MEG: Bad channels
    3. EEG: Average reference
  9. Artifacts cleaning with ICA
    1. Detect heartbeats and blinks
    2. EEG: Heartbeats and eye movements
    3. MEG: Heartbeats and eye movements
  10. Epoching and averaging
    1. Import the recordings
    2. Averaging
  11. Source estimation
    1. Head model
    2. Noise covariance matrix
    3. Inverse model
    4. Regions of interest
  12. Scripting

License

This tutorial dataset (MEG/EEG and MRI data) remains proprietary of Yokogawa Electric Corporation, Japan. Its use and transfer outside the Brainstorm tutorial, e.g. for research purposes, is prohibited without written consent from Yokogawa Electric Corporation.

Description of the experiment

The experiment consists of two stimulation protocols being conducted during a single scanning session in a MEG laboratory with an Elekta Triux (Megin, Finland) scanner. The subject is a a right-handed 46 years old male. The two stimulation protocols consist of a unilateral median nerve stimulation and an eyes-close resting-state recording.

Median nerve stimulation:

  • The stimulation signal was a square-wave pulse with 2Hz frequency and a duration of 0.2ms.
  • An ISI (inter-stimulus interval) of 500ms with a variation of ±20ms in order to be able to average out time-locked noise to the stimulation, while remaining unnoticeable by the subject.
  • The stimulation was performed on both hands/wrists, independently, with a Digitimer DS7A stimulator. An electrode was placed on each urrent values were tuned to match the motor threshold of the subject on the stimulated hand, with a result of 10~12mA aproximately.
  • Initially, the left wrist was stimulated for aproximately 2 minutes (this corresponds with the file containing L1 in its name). After a 2-minute rest, while sitting in the MSR and with his head in the helmet, the stimulation was repeated (L2 file). After this stimulation the subject was asked to have a 10 minutes rest, during which he was allowed to sit calmly with his head out of the helmet, although remaining hooked to the scanner at all times. Finally, two subsequents runs of right wrist stimulation (R1 and R2 files) with an intermediate 2-minute rest were performed.
  • Recordings were performed with a 1kHz sampling rate. Continuous HPI was disabled during these recordings. And high-pass filters were set to DC for MEG channels and 0.03Hz for EEG channels.
  • All files underwent a MaxFilter (version 2.3.13) tsss post-processing.

  • All files were anonymized using MNE-Anonymize.

Resting-State protocol:

  • The subject was recorded for aproximately 80 minutes. Due to a 2GB maximum-size limitation for FIFF files, this translates into the recording being saved in files 'epi1' to 'epi4'.
  • The subject had his eyes closed.
  • Recordings were performed with a 1kHz sampling rate. Continuos HPI was enabled. High-pass filterse were set to DC for MEG channels and 0.03Hz for EEG channels.
  • All files underwent a MaxFilter (version 2.3.13) tsss post-processing, with motion compensation active.

  • All files were anonymized using MNE-Anonymize.

Export recordings from fif files?

Full head shape in the the digitizer file

In order to realize a precise MRI registration or for warping the default anatomy, you should collect 100 to 200 points describing the entire head shape in addition to the 8 Yokogawa/KIT standard stylus points. To import additional digitized points, follow the instruction below:

  • When digitizing head points:
    • Pick the 8 standard stylus points
    • Pick additional 100 to 200 head points, so that the selected points cover the entire head
  • Edit the digitizer label file (DigitizeLabel.txt) which is used in "Third-party export" so that it defines the 8 points and the additional points.

Download and installation

  • Requirements: You have already followed all the introduction tutorials and you have a working copy of Brainstorm installed on your computer.
  • Start Brainstorm (Matlab scripts or stand-alone version)
  • * "No, use individual anatomy",

    • "No, use one channel file per condition".

Import the anatomy

  • Right-click on the TutorialXXX 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"

    • Number of vertices of the cortex surface: 15000 (default value)
  • Click on the link "Click here to compute MNI transformation".

  • Set the 6 required fiducial points (indicated in MRI coordinates). This dataset is a good example
  • At the end of the process, make sure that the file "cortex_15000V" is selected (downsampled pial surface, which 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, the middle button in the toolbar above the database explorer.
  • Right-click on the subject folder > Review raw file:

Prepare the channel file

Refine the MRI registration

Read the stimulation information

Pre-processing

Evaluate the recordings

Frequency filters

Review the recordings

MEG: Default montages

MEG: Bad channels

EEG: Average reference

Artifacts cleaning with ICA

Detect heartbeats and blinks

EEG: Heartbeats and eye movements

MEG: Heartbeats and eye movements

Epoching and averaging

Import the recordings

Averaging

Source estimation

Head model

Noise covariance matrix

Inverse model

Regions of interest

  • Create two scouts S1 and S2 to represent the primary and secondary somatosensory cortex of the left hemisphere.

Scripting

  • MoinMoin Powered
  • Python Powered
  • GPL licensed
  • Valid HTML 4.01